US20040024720A1 - System and method for managing knowledge - Google Patents
System and method for managing knowledge Download PDFInfo
- Publication number
- US20040024720A1 US20040024720A1 US10/357,286 US35728603A US2004024720A1 US 20040024720 A1 US20040024720 A1 US 20040024720A1 US 35728603 A US35728603 A US 35728603A US 2004024720 A1 US2004024720 A1 US 2004024720A1
- Authority
- US
- United States
- Prior art keywords
- data
- ontology
- intelligence
- ucs
- environment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K13/00—Conveying record carriers from one station to another, e.g. from stack to punching mechanism
- G06K13/02—Conveying record carriers from one station to another, e.g. from stack to punching mechanism the record carrier having longitudinal dimension comparable with transverse dimension, e.g. punched card
- G06K13/08—Feeding or discharging cards
- G06K13/0806—Feeding or discharging cards using an arrangement for ejection of an inserted card
- G06K13/0825—Feeding or discharging cards using an arrangement for ejection of an inserted card the ejection arrangement being of the push-push kind
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/40—Transformation of program code
- G06F8/41—Compilation
- G06F8/42—Syntactic analysis
- G06F8/427—Parsing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/448—Execution paradigms, e.g. implementations of programming paradigms
- G06F9/4488—Object-oriented
- G06F9/4493—Object persistence
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/912—Applications of a database
- Y10S707/913—Multimedia
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/964—Database arrangement
- Y10S707/966—Distributed
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99942—Manipulating data structure, e.g. compression, compaction, compilation
Definitions
- UCS Unconstrained Systems
- the basic configuration of an intelligence system is that digital data of diverse types flows through the intake pipe and some small quantity is extracted, normalized, and transferred into the system environment and persistent storage. Once in the environment, the data is available for analysis and intelligence purposes. Any intercepted data that is not sampled as it passes the environment intake port, is lost.
- the information to be monitored is not just simple text, it is multimedia sounds, images, videos, compound documents etc. It is unstructured. It is multilingual. Most of what occurs in the world, does not do so in English. Information quality varies widely. Much of what is transmitted is garbage, wrong, or simply represents rumor or uninformed opinion. Knowledge of the source of the information must dictate its interpretation. The conventional assumption that the value of a field is exact and can be stored in a single box or cell simply does not apply. Even if the captured data can be regarded as absolute, its interpretation is a matter of opinion among those analysts using the system, and thus its value can be modified depending on the domain or perspective of the user of the data.
- Lexis/Nexus for example has thousands of high grade databases totaling more than 25 times the total data content of the web at this point, which can be accessed and searched (in a limited manner) only via a subscription account.
- News and reporting services all have different delivery formats, equipment, and media.
- An intelligence system must accommodate this diversity of sources as well as providing for custom, intercepted, and private feeds available only to a specific organization. Crawling the web, while enlightening, and certainly an important capability, is not a complete answer to intelligence, to in-depth research and analysis, or to the extraction of meaning. A datum coming from a given source must maintain a reference to that source since this will later determine the reliability placed on that datum should it contribute in any way to an analytical conclusion.
- the word ‘client’ may appear in a myriad of different contexts where it actually refers to completely different entities, we must extend the concept of a source to incorporate the concept of a ‘source domain’ identified either by the persons involved in the intercept, or by other means. Within this ‘domain’ the word ‘client’ is assumed to correspond to a given entity, possibly still unresolved. Outside this domain the word will have other connotations. The underlying architectural substrate must provide for and support this type of ambiguity
- each aspect of the data is best suited to analysis, search, storage, and distribution by different ‘containers.’
- large bodies of text are best handled and searched by inverted file type text engines whereas fixed numeric or descriptive fields rightly belong in a relational database.
- Image, video, maps, sounds, and other multimedia fields must be stored, distributed and searched using engines, processes, and hardware that are best suited to the needs of the particular type, and thus the system must support a variety of ‘containers’ targeted at different media types and processes.
- a fingerprint or face recognizer capability obviously belongs in a different container than relational fields relating to specific fingerprints or images. To attempt to force all such tools into the framework of a common container, presumably a relational database, would be cost-prohibitive and extraordinarily inefficient.
- the pool is simply an eddy in a rushing torrent where control of the torrent is out of the question.
- KM systems are in reality nothing more than thin veneers over relational databases, an approach that is wholly inadequate to the needs of an unconstrained intelligence architecture.
- an intelligence system The purpose of an intelligence system is to facilitate the analysis of captured data and allow the rapid and effective distribution of such analyses to the intelligence consumers (i.e., ‘clients’) of such a system.
- clients the intelligence consumers
- multimedia information the conventional solution of printing out a paper report and hand delivering it to the client becomes wholly inadequate.
- Multimedia information cannot be well represented on paper, and yet as the saying goes, a picture is worth a thousand words. What then is a video segment or sound recording worth?
- the truth of the matter is that multimedia data types are able to convey a much richer and more impactful presentation than words alone can. Thus, it is incumbent on such a system to design in the ability to easily create and electronically deliver full multimedia reports to its clients.
- the report must actually be a working ‘application’ capable of full interaction with the client, and when necessary retrieval and playback of any multimedia and other components from archival storage within the system. Creation of such reports must be a relatively trivial matter for the analyst(s) involved. Delivery of multimedia reports without the ability for those reports to access data from system storage would not be nearly as effective. Furthermore, by taking this approach, one opens the door to regarding the report as a custom portal for the information consumer client to examine the details of a particular issue, review the backup data that lead to the reports conclusions, and to draw additional conclusions regarding, or obtain additional details relating to, the subject matter as necessary.
- an intelligence architecture should be designed to be end-to-end; that is, it must handle every stage of the process from capture, storage, indexing, search, analysis and finally to presentation.
- Often decision makers or information consumers are unskilled in the use of computers, and so a simpler (possibly hands-off) kiosk or web-portal like end-user mode, in addition to the more extensive normal analytical mode, must be provided. This mode must anticipate the needs for projection on large screens and the likelihood that multiple individuals will be in the audience. Access security, possibly using biometrics is an issue.
- Multilingual requirements impact not only intake processing, but more obviously the user interface to the system, which must have the inherent ability to translate dynamically and on the fly between languages and appearances depending on the language or wishes of a particular user.
- the process of modifying a software program to appear and behave correctly in another language or script system is known as ‘localization,’ and is a multi-billion dollar industry and a major headache for all developers of software who wish to target foreign markets. Localization of a software product can take months, requires extensive source code changes or accommodations, and must be repeated (at vast expense) every time a new upgrade is released.
- One requirement of an unconstrained intelligence system is the ability reduce this localization process to an automatic and instantaneous behavior which is not in any way tied to the code that is generating or handling a particular aspect of the UI. If such a tie in did exist, the ability of the system to adapt globally (i.e., in a multilingual manner) to changes would be hampered by the rate at which localization could take place, and inevitably portions of the system would become inconsistent with other portions.
- the basic questions that are asked of an intelligence system can be summarized as “who”, “what”, “why”, “when”, and “where”.
- the answers to most of these questions cannot be expressed as a column of numbers or text since the answer itself may not be in the data but must instead be deduced or visualized by the analyst.
- An unconstrained environment must support the pervasive use of a large and ever expanding set of visualization tools. Certain visualizers should clearly be built into the environment and have commonly accepted appearances.
- the visualizer to answer the question “where” for example is generally a map and associated Geographic Information System (GIS).
- GIS Geographic Information System
- the standard visualizer for displaying the results of a database query is the list, though we may not normally think of this as a visualizer.
- the environment must provide a basic list capability including the ability to display arbitrary, possibly media rich columns, and to sort on those columns.
- the basic list must be capable of handling data organized in arbitrary hierarchies.
- Other environment (or underlying OS) supplied visualizers must exist for the common rich media types (i.e., images, sounds, and video).
- Complex graph and chart plotting is of course a basic visualization capability and must be built into the environment. The ability to define arbitrary exotic visualizers to aid in detecting patterns, trends, and anomalies must be supported.
- the analyst needs the ability to visualize relationships between data, not only along well defined axes (e.g., space and time), but also along arbitrary axes defined by the analyst himself. Examples of such axes might be “Adverse actions towards the US”, or “Activity relating to drugs”. Clearly, the analyst must be provided with a way to define new arbitrary axes, and to specify through some arbitrary computational means, how one should determine the intercepts for a given datum on each of these axes. Once this information is known for a given collection of data, it is relatively easy to see how graphical visualization tools can be used to good effect to look for patterns, trends, and anomalies appearing along or between a particular set of such axes.
- axes e.g., space and time
- arbitrary axes defined by the analyst himself. Examples of such axes might be “Adverse actions towards the US”, or “Activity relating to drugs”.
- the analyst must be provided with a way to define new arbitrary axes, and to specify through some
- the architecture must therefore support the ability to define such axes and rapidly determine coefficient vectors for any arbitrary set of data being visualized. Because such axis computation may be computationally expensive, doing it on the fly would drastically reduce visualizer responsiveness. For this reason, the architecture would preferably provide and support the concept of a “vector server” responsible for continuously maintaining and updating coefficients for all data in persistent storage along whatever axes are currently defined. As data is fetched for visualization, the required coefficients can also be rapidly fetched from such a vector server by the visualizer. These coefficients would also form a key part of the solution to maintaining, examining, and acting upon non-explicit relationships between different system datums.
- each axis may be in some way related to many others. This fact can be taken advantage of to address the basic intelligence problem of not knowing exactly what one is looking for. If we imagine two related axes, one known (A) and one unknown (B), then as part of un-related work, an analyst may see the ‘shadow’ of a trend or anomaly related to B on the A axis, and may then be motivated to examine the causes behind this shadow, thereby discovering the existence and significance of the hitherto unexplored B axis. By subsequently defining a B axis to the system and then re-examining data in this light, new insights and relationships may become clear. This is a key aspect of the intelligence process that is not well supported by existing systems.
- Certain specialized servers will have to interface directly to legacy or specialized external systems and will have to utilize the capabilities of those external systems while still providing behaviors and an interface to the rest of the environment that hides this fact.
- An example of such an external system that must be masked behind our modified definition of a server might be a face, voice, or fingerprint recognition system.
- the classic model of a big fat predefined server (a la Oracle etc.) that is purchased “as is” from a vendor, and wherein only the clients to that server can be changed by customer staff does not apply to a UCS.
- new servers may be brought on line to the system and must be able to be found and used by the rest of the system as they appear.
- Application code running within the system should remain unaware of the existence of such things as a relational database or servers in general if such code is to be of any general utility. What we need then is some kind of automatic environment mediated and abstracted tie-in between the definition of the data within the system, and the need to route and access all or part of that data from a distributed set of servers.
- the analyst workload will of course require the use of a number of other commercial off-the-shelf (COTS) packages. Things like word processors, spreadsheets, Internet browsers, e-mail, sound and video editors, image analysis tools etc.
- COTS commercial off-the-shelf
- Things like word processors, spreadsheets, Internet browsers, e-mail, sound and video editors, image analysis tools etc.
- the analyst needs all the same tools that a normal computer user does as well as, and in close conjunction with, the UCS environment.
- the choice of platform on which to build an architecture is thus limited to the two consumer level OS platforms available, namely Windows and Macintosh. Any useful UCS architecture must be capable of treating COTS software applications as building blocks in the creation of processes within the system, we do not want to re-invent everything that is provided by all the COTS applications.
- Security is obviously a major concern in most intelligence-related applications. Given the need to deliver reports and multimedia data to individuals, possibly beyond the confines of the system it is clear that reliance on security via access control alone (i.e., logging on to a Database) is not enough. Security must be built into the data itself. Given the nature of the intelligence cycle where the same item of data may be handled and annotated by many individuals, each of which may have different security privileges, we see that a sophisticated, data-centric approach to security must be supported by the environment.
- OOP systems generally introduce the concept of multiple inheritance to handle the fact that most real world objects are not exactly one kind of thing or another, but are rather mixtures of aspects of many classes.
- multiple inheritance only makes the scaling problem worse.
- the maintainer is forced to examine and internalize the operation of all inherited classes before being able to understand the code and being sure that his change is correct. Worse than this, the ‘right’ change generally involves changes to the assumptions and implementation of some ancestral class, and this in turn often has a ripple effect on other descendent classes.
- the present system and method meets each of these requirements and provides a robust and flexible system for storing, parsing, analyzing and typed data that is stored in a virtual ontological tree and is later available for retrieval from offline, nearline, or cache based storage and is viewed and processed in the language, interface and with the desired hyperlinks associated with the given User over a P2P or client-server architecture in a dynamic fashion and/or based on one or more user profiles.
- the issues presented herein are fully detailed in the patent application that have filed relating to the architecture described and attached hereto as appendices. This application details to the system level approach, in which each of these features are provided in a single UCS system.
- the present invention provides the following:
- [0047] A system for converting incoming unstructured data into a well described normalized form. Since the incoming data is multimedia and may represent some data type for which support is provided by the underlying OS platform, this normalized form include the ability to fully describe and manipulate arbitrarily complex native or non-native binary structures and collections. This support is provided by a dedicated ‘mining’ language tied intimately to the current system ontology (see appendices 6 and 7).
- a memory system tied to the ontology, which defines the structure of and access to any persistent storage containers that are required to contain the data.
- a memory management system for splitting incoming data into those portions to be directed to each container.
- a query system for querying each container to retrieve portions of such a composite object.
- all database tables and queries are auto-generated from the ontology, thereby eliminating the role of the conventional Database Administrator (DBA).
- DBA Database Administrator
- a UI to display and interact with data within the system is automatically generated and its behaviors automatically handled by the underlying substrate thus removing this programming burden from the developer (thereby largely eliminating the role of the GUI programmer).
- a memory system that forms collections of datums, and enables manipulation and exchange of these collections both within the local machine as well as across the network.
- collections support the ability to attach arbitrary tags or annotations to the binary data they contain without in any way altering the binary representation itself. Additionally, the system supports the concept of either null or dirty (i.e., has been changed locally) datum.
- the means (preferably implemented in software running on a processor) to specify, investigate and manipulate the inheritance of behaviors and fields from ancestral types described in the system ontology.
- the system must provide some kind of TV guide capability with the ability to request programs of interest. Additionally, a ‘snapshot’ view showing all currently captured channels at the client workstations is required with the means to click on such a snapshot image and immediately request live view and/or capture of the material involved. Video (live or captured) must be streamed across the network to client workstations where it can be viewed and/or edited. This represents not only a massive network load, but also due to the CPU intense nature of the capture, storage, and streaming process, it is clear that a video server cluster will require large numbers of machines to act in unison in order to support realistic client loads. Such a server architecture does not exist in the commercial space and thus must be developed and provided by the UCS architecture.
- Equipment item usage cost is determined by how much the available stream capture capacity will be degraded by the use of that item. For example, many older satellites ‘wobble’ so these and other satellites require active tracking using a moveable dish. Most commercial satellites can be captured by fixed dishes. Assuming that a smaller number of mobile dishes exist than fixed, it is obvious that allocating one such dish to a given capture reduces remaining capacity far more than does the use of a fixed dish with multiple feed-horns and a splitter.
- Capture equipment design and wiring needs to anticipate this problem and minimize this degradation effect. For example, use of a cable TV head-end to distribute captured video, removes the blocking implied by use of an analog switch to connect source to digitizer. This is a complex issue and must be closely coordinated with the system design and capabilities. Much equipment relating to video processing is not designed for computer control, and thus the system may have to provide the ability to control such equipment via IR links or whatever other means is provided. A generalized and fully programmable (from within the system) controller interface is required in this case. Massive storage capacity is needed to handle video.
- a key aspect of making use of video is to be able to determine what is being said during a given segment (e.g., a news report).
- a given segment e.g., a news report.
- There are a number of approaches to this problem firstly, at least of a large number of NTSC transmissions, closed captioned text is provided and equipment is available to capture this. Since we wish to maintain the correspondence between a particular portion of a video and what is being said (to aid in search, retrieval, and playback), we can see that this text ‘track’ must be stored in parallel with, and using the same time code as, the video itself.
- the QuickTimeTM architecture is ideal for this purpose, since it defines movies to be comprised of one or more tracks each of which can contain different media types.
- the present system creates as an output to the capture process a movie containing not only the video and sound tracks, but also a text track, and quite possibly later one or more voice-over tracks.
- Video capture Much of the video captured (especially in PAL and SECAM formats) will not have a text track and therefore a key aspect of video capture (and indeed any multimedia capture) is the ability to ‘tag’ the video with other related items (such as news stories) which are more easily associated.
- the environment must support arbitrary tagging of any datum with any other datum(s) in order to render it ‘computable’.
- a distributed video server and client(s), video snapshot server and client(s), equipment server and client(s), and various other video related technology have been fully implemented based on the technologies revealed in the referenced patents, particularly patent ref. 10. The details of these implementations and some of the unique features involved will be fully revealed in future patents.
- News stories and reports form one of the most useful, timely, and easily leveraged forms of open-source feed.
- News feeds are available in many languages and come in both localized (national) and global varieties. Examples are Reuters, API, BBC etc.
- Feeds are delivered in a variety of ways including satellite downlinks, analog land-lines, Internet sites, dial-up access, and CD-ROM based delivery.
- Archival news feeds are usually available for purchase from the publishers although delivery media can be archaic. There is little standardization in format between the feeds although an XML standard for Internet delivery is in its infancy. Multilingual issues abound and normalization can be quite a challenge. Many local feeds have poor quality control over syntactic structure.
- News feeds are characterized by a relatively low bandwidth with a high semantic content. Storage issues are minimal. For these reasons, the present system provides a news server based on the technologies revealed in appendix 7 and appendix 10 has been fully implemented under the system of this invention.
- Photowire feeds are available from many of the same global sources as are news feeds, and delivery platforms span a similar range. Images come in a huge variety of standard (and not so standard) formats and the system must natively handle all of these, or at a minimum convert losslessly to one of them. Images can be quite large and an associated mass storage subsystem is required. Unlike video, isochronous delivery to the client is not required. The concept of an image preview or ‘picon’ is key to ensuring that full image retrieval is only required for analysis or editing. Images from these sources can form a powerful part of any multimedia presentation. Many sources of photowires also provide graphics and illustrations which are intended for use in publications supported by the feed.
- Satellite Imagery is an important part of the intelligence process. Satellite images are essentially just high resolution images which contain additional semantic meaning by virtue of the fact that the ‘where’ for the image can be computed by knowledge of the satellite parameters and position involved. Thus it is clear that there is a close tie-in between satellite imagery, and the mapping and GIS facility that must be provided by the environment. The environment must be able to automatically project/overlay the image with respect to a map background so that the information it contains can be related back to other data in the system. Satellite images generally contain multiple ‘bands’ of data for different frequencies and sensors, and these bands can be used or combined to extract additional knowledge regarding the contents of the image. Tools for this purpose must be provided.
- Satellite imagery comes from a variety of sources including weather satellites, LandSat, SPOT etc. Delivery mechanisms for some (e.g., weather) involve the use of receiving dishes. For others, the imagery is delivered on a variety of media (often tape) or by FTP download. For the most part, satellite imagery is a non-real-time feed. Government agencies may have access to a number of other forms of satellite imagery whose nature and content is not discussed herein.
- Particular applications may require support for other specialized forms of imagery with additional semantic meaning. Examples include fingerprints, identification, x-ray images, astronomy, etc. Each of these types essentially requires its own server subsystem to provide extraction and support for the additional semantics.
- the environment provides for the easy creation of such servers. Most such sources will require a connection to some external equipment or system to provide capture and possibly storage and search of the imagery. In all other ways however, such subsystems are similar to the generic imagery subsystem.
- Text tracks are, in parallel, routed to the text subsystem to allow associative search.
- a Sound server based on the technology revealed in referenced patent 10 is the preferred embodiment of such a server.
- This source is perhaps the most widespread and the easiest to capture of any of the sources described. Unfortunately, with the exception of a few trusted sites, it is also one of the lowest grade and most misleading sources on which to base any automated calculations. Techniques to crawl or spider the web are widespread and readily available, often built into the underlying OS (e.g., the Macintosh ‘Sherlock’ facility), and because it is web data (i.e., HTML or even better tagged XML) it is designed to facilitate easy capture and use by digital systems.
- the web contains many invaluable trusted sources for real time data such as news, stock feeds, weather etc. and provided one sticks to these, it forms a key part of monitoring what is going on in the world.
- the system provides the ability to control a ‘drone’ insecure capture capability which then uploads its finds, via a secure path, to the system itself (which may not be physically connected to the web in any way).
- a ‘drone’ insecure capture capability which then uploads its finds, via a secure path, to the system itself (which may not be physically connected to the web in any way).
- Such an Internet server based is prefereable based on the technology disclosed in appendix 7 and appendix 10.
- published data also comprises the largest single source of any described. There are literally tens of thousands of different database and information publishers, each specializing in particular areas. The total amount of data available is immeasurably larger than the total content of the Internet. Few publishers post any high grade data on the web due to the lack of a business model to do so. Many that have done so have now gone out of business and this process is on-going. Because the livelihood of such sources is predicated on their continuing completeness and quality, published data provides some of the best supplies of background information necessary to populate a system's ‘lens’ of understanding. Published data sources come in many forms and tend to be expensive. CD-ROMs are now becoming the dominant distribution media although on-line databases such as Lexus/Nexus contain vast amounts of information that can be easily accessed and incorporated into the environment.
- UCS architecture also provides the means whereby plug-in modules, defined on a per application, per legacy system basis, can be registered within a standard UCS server.
- plug-in modules defined on a per application, per legacy system basis, can be registered within a standard UCS server.
- external containers may also be grouped by providing customized functionality specific to a given data type.
- a connection to a fingerprint recognition system would be treated as a legacy system requiring an encapsulating UCS server.
- Appendix 7 and Appendix 10 are sufficient to implement such a custom legacy interfaces.
- this may be the only practical means of capturing data, especially data that does not yet exist in the digital domain.
- the UCS environment also supports the ability to perform manual data entry based on a system ontology.
- One refinement of this is the provision of a programmable UI scripting capability to provide for the possibility that a process can be written to obtain the data somehow, and enter it not by ontology based mining, but rather by scripted data entry.
- Once any data (manually entered or otherwise) is in the system it is also possible to edit and change it and thus the auto-generated UI to the system supports data entry, complete with some level of validity checking, based directly on the system ontology definitions.
- the preferred ontological framework of the present invention is described in Appendix 6.
- Word processing documents are generally not just simply plain text, but rather contain embedded formatting and style information mixed in with the actual content. These formats are often proprietary. The final appearance of the document may have more information content to it than would be represented by the textual content alone, and for this reason a compliant system must have the ability to store and retrieve these documents in their original form, possibly for additional modification using the appropriate COTS application. Text held in these proprietary formats may not be directly useable for system functions. For these reasons, the system is able to strip the plain text content out of such documents and normalize it.
- scriptable COTS applications capable of import/export of a variety of text formats makes this practical by creating UCS wrapper servers that script such applications, extract the normalized information by scripting COTS applications (or by dedicated plug-in code), and store/retrieve the full document contents as required.
- Some of the more common formats include PDF, Word, RTF and others. See appendix 7 for further details of this aspect of the system.
- mapping data include such government agencies as NIMA, USGS, the US Census and others.
- Custom specialize maps are often created by dedicated COTS mapping environments. Such environments generally support import/export to/from a number of standard map interchange formats and the UCS map support also includes the ability to input and output from/to some number of such formats.
- the system provides the inherent ability to mine and normalize such data for system mapping purposes.
- NIMA maps can be obtained for the entire world on CD-ROM sets formatted according to MIL-STD-2407 (Vector map 0 and 1) and the ability to mine and interpret this format is basic to system operation.
- this identification phase is relatively simple. With non-covert feeds (other than the Internet), it is frequently the case that all or most incoming data is captured to persistent storage. With covert feeds, this is seldom the case. Much of the content of a covert feed may be irrelevant, thus the system provides an additional ‘phase’ in the capture process that is responsible for determining if the item should be kept or discarded. This determination is preferably under the control of the analysts using the system and the specific algorithm used will differ between analysts, data types, and over time. This ‘discriminator’ phase is closely tied with the concept of ‘Interest Profiles’ or alerts defined by the analysts and running autonomously in the system servers. See referenced appendix 7 and appendix 10 for details on the technology that is preferably used to implement this functionality.
- RDBMS storage is essentially based on the use of grids or matrices to store information. Because each cell in the matrix has a known size, efficient indexed access is possible. An RDBMS system is therefore best suited to the storage, search, and retrieval of small fixed sized fields, especially those that are numeric. For this reason in a UCS environment, RDBMS storage makes most sense when applied to these kinds of fields, not to large text fields or multimedia content.
- Variable sized text fields are best stored and searched via an inverted-file text engine.
- the inverted file approach for each significant word in the dictionary, the inverted file stores a list of all documents containing that word and the position(s) of that word within the document. Search and retrieval in this system therefore occurs via the inverted file list which is far more efficient than the corresponding brute force keyword scan in an RDBMS.
- statistical word relationships can be built up from the full set of data in the system and this allows powerful concept type searches which are poorly supported under RDBMS systems. Text stored in an inverted file container tends to be moderately large and may require a RAID array.
- the inverted file itself is generally best placed on a separate fast disk (array) preferably fronted by a large RAM disk/cache to increase search and query performance (see appendix 10 for additional details).
- Video information requires storage capacities many orders of magnitude larger than those described above. Terabyte or petabyte capacities are not uncommon.
- the nature of video is that it must be delivered to the client as an isochronous (i.e., constant data rate) stream at a relatively high bandwidth.
- the CPU load represented by the actual streaming process is considerable, and thus conventional desktop computers are capable of delivering only a small number of high quality video streams at a time.
- Another key aspect of video is that any given video segment contains a time axis and thus to find and view a relevant portion of the video the ability to tie searchable/indexed information to this time axis is required. For all these reasons, video probably represents the worst case scenario for any UCS storage, indexing and delivery architecture.
- the present system supports robotic autoloader mass storage using fast random-access media (to minimize wait time to start a play).
- Media types like CD-ROM and DVD are a natural match. Obviously because these media types have limited sustained data-rates by comparison with fast disk, but more importantly have a relatively long ‘seek’ period, it is not practical to sustain multiple streams from a single such disk. For this reason, the system also provides automatic disk caching during playback and supports large numbers of media drives into any given area of robotic storage and media duplication. Automated, unattended ‘burning’ of media and migration from capture cache is also provided and is preferably implemented.
- the video server is implemented as a large cluster of machines tightly integrated with the robotic storage so that the ‘master’ machine can select a ‘drone’ machine on the basis of current loading (or otherwise), load the media into a drive connected to that drone, and then commands the drone to perform playback. See patent appendix 10 for additional details. Indexing implications have been discussed previously under “Capture” above.
- Image data can be relatively large and generally requires a robotic autoloader component, however, unlike the video case, there is no isochronous requirement (since image files can be ‘downloaded’ entirely when accessed) and the need for a large image cluster is reduced.
- the image storage consists of a low resolution ‘picon’, accessible immediately from server disk storage. This is then combined with a high resolution full image which may require robotic access to retrieve. Many client uses of images can be handled using the picon alone thus avoiding excessive robotic accesses. Indexing in the case of images is straightforward since they are simply referenced via the common unique ID shared between all containers (see appendix 6 and appendix 10).
- Map indexing is totally different form all other forms above in that it is spatial, that is that the map is accessed mainly by spatial position.
- maps can be constructed on-the-fly from a map database, and thus the map container is capable of responding to map requests without the need for an ‘id’.
- Specialized maps can also be saved and then referenced, and in this case the unique ‘overlays’ that customize the ‘default’ base map overlays are probably best be stored either in the RDBMS container or in other ontology derived storage along with details of the map projection, scale, and other legend elements.
- the Internet presents another unique storage situation.
- indexing is via URL
- the storage device is the Internet itself. Nonetheless, this variant is transparently fitted into the same abstraction as all others described above.
- Other data types may imply yet more variants of the storage and indexing problem.
- the present system provides a two-layer approach to querying and query specification.
- the lower layer represents the registered search capabilities of each specific container.
- the ‘language’ supported by this lower layer is completely open ended in order to permit new media types and search engines to be easily added to the environment.
- the result of a search conducted at the lower layer is a list of ‘hits’ (i.e., unique ID, together with relevance and other details if appropriate) that is then passed to the upper query layer.
- This upper layer has a well defined and preferably limited language, the primary purpose of which is to specify logical combinations of the hit-list results returned by the lower layer modules.
- the language contains such Boolean operations as AND, OR and NOT.
- operators like AND THEN are also supported.
- the AND THEN operator implies that the query appearing before the operator is performed first and the resulting hit-list is then passed along with the query appearing after the operator. This allows efficient pruning of the search space in the container(s) implementing the second portion of the query.
- Other operators that would preferably be supported at the upper level include such things as MAX (limit # of hits returned), RELEVANCE (limit relevance returned), ORDER BY, GROUP BY etc. Further details of a system that can provided this functionality is set forth in Appendix 6.
- a querying GUI whose outermost aspect relates to the upper query layer, and within which specialized UI ‘pages’ can be displayed in order to specify container specific lower level queries is provided.
- the nature of these UI plug-in modules for well known querying engines such as SQL or inverted text files is fairly straightforward. When the list is broadened to sounds, videos, images, maps etc., however, the variety of UI components embedded within the querying interface in a unified manner becomes quite large. As such, querying and selection via visualizers is tied into the present invention.
- plug-in search engines accessed via corresponding GUI
- plug-in search engines include:
- Maps topological queries (within, next to, etc.), spatial relationships, terrain features, range, distances, routes, measured paths etc.
- the business of monitoring new inputs can be considerably more complicated because of the fact that not all algorithms to define a ‘match’ can be expressed directly to the querying layer. Often, to determine a match the analyst may need to combine a number of different functions. For this reason, the system provides ‘widgets’, each of which is capable of performing part of the analysis using whatever techniques are appropriate. This means that in addition to distributed queries in the querying language, widgets are preferably distributed that form part of the matching algorithm.
- the system of the present invention allows as large a range of widgets as possible to be used in defining these analyses. As such, the system provides a distributed framework whereby arbitrary algorithms expressed either as searches or via widget wiring can be placed into the input pipe of the UCS and can result in automated notification of the analyst when the desired match is found. See appendix 10 and 11 for additional details.
- Notification to the analyst may be as simple as beeping (or speaking) at his terminal and maintaining a list of pending hits to be viewed. Alternatively, notification could be handled via automated e-mail delivery.
- the present invention supports the ability to initiate execution of arbitrary widgets supplied by the user to perform whatever action in necessary when a match occurs. By using this facility, the system can now trigger automated but targeted responses to the occurrence of any given situation. Obviously the nature and scale of these responses is limited only by the imagination of those configuring a particular UCS system. See appendix 10 for details.
- the thrust of this invention is the infrastructure and architecture necessary to support any combination of analytical tools, and to allow those tools to interact between each other over a common substrate.
- analytical tools There are literally thousands of effective analytical tools out there, most of them operating in spectacular ‘stovepipe’ isolation, some small fraction of them available as COTS applications.
- Such tools can be integrated into a UCS and used in conjunction with others which, in combination with the other features provided by the present invention, can be used with devastating effect.
- the only ‘analytical tools’ that would preferably be built in to any UCS is a suite of visualizers, the basic querying tools, and the ability to “wire” these tools and others together into ever more elaborate domain specific algorithms.
- the UCS architecture preferably facilitates and captures this process using the system and method disclosed in Appendix 11.
- the final stage of the intelligence process is to deliver analyses to the intelligence consumer in a form that is multimedia rich, and which can allow that consumer to interact with the analysis in order to examine assumptions and determine if more information is needed.
- Reports must themselves be active and interactive custom portals relating to a given subject. The creation of such reports must be made easy enough that analysts themselves can accomplish this step. More importantly, reports are not static, that is, once an intelligence consumers needs are sufficiently well understood and algorithms designed to meet those needs have been expressed, it is essential that the system be able to deliver ‘today's report on . . . ’ to the consumer on an automated basis with no further analyst involvement. This trend is already being seen in web portals that allow limited customization on a per user basis.
- an intelligence system must take this approach to a whole new level.
- certain end users will require a simplified ‘executive’ interface and the present invention provides such an interface.
- a goal at least for some consumers, is to allow them to directly express their own interest profiles and to have these (as well as those from analyst initiated profiles) appear in their portals immediately any ‘hit’ occurs. This closes the intelligence OODA loop (see below) and allows the consumer to determine what additional analyses he needs in a much more timely manner.
- the system can manage the information overload problem that is experienced by the intelligence consumer himself, not just that of the intelligence professionals he tasks. See appendix 10 and 11 for details.
- the present system provides a data-flow system that is driven entirely off ontology, allowing almost instantaneous modification and adaptation to changes in the environment. No other approach currently offers this capability, and thus, no other current approach stands any chance of addressing today's critical need in the intelligence community.
- the process of ‘event reconstruction’ also occurs. That is, given the observations the system receives, knowledge of the actors involved and models of those actors motives and available action space, the system is able to perform a surface-tension type analysis looking for explanations of the event described that most closely match the motives of one or more of the initiating (i.e., subject, not object) actors involved. By postulating that this is in fact what occurred in the event, it becomes possible to define a pattern in the observations leading, up to the event that represent an indicator that a given entity, or entities, are attempting to cause a similar event to occur. Much of this process involves the analyst using the various visualization tools. Alternatively, however, the process can be automated as the analyst expresses the algorithms he believes imply a given motive vector is occurring.
- the architecture of the present invention is based on the concept of a distributed data-flow driven environment, rather than a conventional control-flow based solution.
- the form, content, and behavior of the data in the environment is described via an ontology that is specific to the given application.
- Control and/or data flow based programs (known as widgets) are caused to begin execution by virtue of a matching set of data objects or tokens appearing on the input data-flow pins of the widget. When they complete, they produce a set of resultant data tokens on their outputs that then become part of the environment (persistent or otherwise).
- a widget that is capable of processing images would specify at least one input pin of type image such that when an image passed through the intake pipe, it could appear at the widget's input pin and cause it to execute.
- conventional systems allocate execution time to a program without knowledge of what it is actually doing, and it is up to the program itself to seek out and acquire its required inputs. To do this, the program requires detailed knowledge of its environment, and the need for this knowledge reduces the generality of the program and increases the overall rigidity of the system thus making it resistive to change and more likely to develop a ‘stovepipe’ topology.
- the present invention provides an open-ended architecture on which intelligence and similar applications can be built.
Abstract
An intelligence system is provided that is comprised of the following basic components. First, a system for converting incoming unstructured data into a well described normalized form. Since the incoming data is multimedia and may represent some data type for which support is provided by the underlying OS platform, this normalized form include the ability to fully describe and manipulate arbitrarily complex native or non-native binary structures and collections. This support is preferably provided by a dedicated ‘mining’ language tied intimately to a system ontology. Second, a system for accessing and manipulating data held either in memory or in persistent storage in its normalized binary form so that small executables, or ‘widgets’, within the system can freely and effectively operate on data types they have never before encountered simply by knowledge of the ‘type’ of data involved. Third, an ‘ontology’ or world model that represents and contains the items and fields necessary for the target system to perform its function. The ontology would preferably fully specify the form of the normalized binary data. Fourth, a memory system, tied to the ontology, which defines the structure of and access to any persistent storage containers that are required to contain the data. Fifth, a memory management system for splitting incoming data into those portions to be directed to each container. Sixth, a query system for querying each container to retrieve portions of such a composite object. Preferably, all database tables and queries are auto-generated from the ontology, thereby eliminating the role of the conventional Database Administrator (DBA). Seventh, a UI to display and interact with data within the system. In the preferred embodiment, the UI is automatically generated and its behaviors automatically handled by the underlying substrate thus removing this programming burden from the developer (thereby largely eliminating the role of the GUI programmer). Finally, a memory system that forms collections of datums, and enables manipulation and exchange of these collections both within the local machine as well as across the network. In the preferred embodiment, such collections support the ability to attach arbitrary tags or annotations to the binary data they contain without in any way altering the binary representation itself. Additionally, the system supports the concept of either null or dirty (i.e., has been changed locally) datum.
Description
- Historically, a major problem with designing complex knowledge representation systems has been the difficulty of acquiring the necessary data in a structured form that algorithms representing the specific ‘application’ can process, and thus produce useful results. The traditional solution has been to restrict such systems to applications where the data is available within a database, normally relational and accessed using Structure Query Language (SQL). By applying these restrictions, the system design problem becomes tractable, and many useful but limited and localized calculations can be performed.
- In the overwhelming majority of cases, data gets into such a database by manual data entry. This requires a highly structured environment where an operator is led through the process of entering all the necessary fields of the database ‘tables’ by a user interface (UI) component that has been tailored to the particular application, and which thus embodies the know-how necessary to ensure correct data entry.
- In recent years, however, technologies such as B2B suites and XML have emerged to try to facilitate the exchange of information between disparate knowledge representation systems by use of common tags that may be used by the receiving end to identify the content of specific fields. If the receiving system does not understand the tag involved, the corresponding data may be discarded. These systems simply address the problem of converting from one ‘normalized’ representation to another, (i.e., how do I get it from my relational database into yours?) by use of a tagged, textual, intermediate form (e.g. XML). Such text-based approaches, while they work well for simple data objects, have major shortcomings when it comes to the interchange of complex multimedia and non-flat binary data. At a minimum, an interchange language designed to describe and manipulate binary data must be implemented, but current approaches fail to take this crucial step. Systems that operate in a domain where the source and destination have explicit or implicit knowledge of each other, or in which endpoints, to facilitate and enable interchange, comply with a standardized exchange format, we shall call ‘Constrained Systems’ (CS). The vast majority of systems in existence today are constrained systems. Despite the ‘buzz’ associated with the latest data-interchange techniques, such systems and approaches are totally inadequate for addressing the kinds of problems faced by a system, such as an intelligence system, which attempt to monitor and capture streams of unstructured or semi-structured inputs, from the outside world and derive knowledge, computability, and understanding from them.
- Once the purpose of a system is broadened to acquisition of unstructured, non-tagged, time-variant, multimedia information (much of which is designed specifically to prevent easy capture and normalization by non-recipient systems), a totally different approach is required. In this arena, many entrenched notions of information science and database methodology must be discarded to permit the problem to be addressed. We shall call systems that attempt to address this level of problem, ‘Unconstrained Systems’ (UCS). An unconstrained system is one in which the source(s) of data have no explicit or implicit knowledge of, or interest in, facilitating the capture and subsequent processing of that data by the system.
- Nowadays, the issue faced by any unconstrained system is not the lack of data but rather the flood of it. Digital information, mountains of it, is available everywhere. It floods the Internet (whose information contents by some estimates doubles every few months now), it fills the airwaves as phone calls, radio and video transmissions, e-mails, faxes, dedicated data feeds, databases, data streams, chat rooms, corporate networks, banking systems, peer-to-peer networks, bulletin boards, web pages, stock markets, telexes, etc. The problem now is that no system can handle the torrent of data that flows through the digital world we have created. The best that can be achieved is to sample some of the current as It washes by, and look for items of interest or significance within it. Even a small sample of such a stream represents a torrent that would overwhelm a conventional constrained system within seconds.
- The basic configuration of an intelligence system is that digital data of diverse types flows through the intake pipe and some small quantity is extracted, normalized, and transferred into the system environment and persistent storage. Once in the environment, the data is available for analysis and intelligence purposes. Any intercepted data that is not sampled as it passes the environment intake port, is lost.
- The information to be monitored is not just simple text, it is multimedia sounds, images, videos, compound documents etc. It is unstructured. It is multilingual. Most of what occurs in the world, does not do so in English. Information quality varies widely. Much of what is transmitted is garbage, wrong, or simply represents rumor or uninformed opinion. Knowledge of the source of the information must dictate its interpretation. The conventional assumption that the value of a field is exact and can be stored in a single box or cell simply does not apply. Even if the captured data can be regarded as absolute, its interpretation is a matter of opinion among those analysts using the system, and thus its value can be modified depending on the domain or perspective of the user of the data.
- Most of the information available on the web is low-grade, unreliable information placed there to further somebody's agenda, not to provide truth. Indeed, most ‘reliable’ or high grade open-source information comes from publishers of one sort of another, and these people have little or no incentive to place such information on the web given the lack of any workable business model for making money from information so posted. As a result, worthwhile information must be intercepted, or for open-source data ‘mined,’ from a multitude of other sources, many designed to make such extraction more difficult in order to preserve the publisher's intellectual property. Thus, Lexis/Nexus for example has thousands of high grade databases totaling more than 25 times the total data content of the web at this point, which can be accessed and searched (in a limited manner) only via a subscription account. News and reporting services all have different delivery formats, equipment, and media. An intelligence system must accommodate this diversity of sources as well as providing for custom, intercepted, and private feeds available only to a specific organization. Crawling the web, while enlightening, and certainly an important capability, is not a complete answer to intelligence, to in-depth research and analysis, or to the extraction of meaning. A datum coming from a given source must maintain a reference to that source since this will later determine the reliability placed on that datum should it contribute in any way to an analytical conclusion.
- To further complicate the issue of data sources, in intelligence applications, the identity and reliability of the persons involved in an intercept is frequently unknown or questionable. Additionally, the true identity and nature of entities referred to via key phrases or aliases in the intercept may be unknown, and may indeed be the subject of the analyst's investigation. Even known entities are frequently referred to via aliases. Thus, to perform analysis the system must support the concept of partially resolved references to data. That is, aliases to entities or things that have not yet been assigned to a known datum in the system. Thus, if the participants in an exchange refer to the ‘client,’ it becomes important to establish who that client is. However, since the word ‘client’ may appear in a myriad of different contexts where it actually refers to completely different entities, we must extend the concept of a source to incorporate the concept of a ‘source domain’ identified either by the persons involved in the intercept, or by other means. Within this ‘domain’ the word ‘client’ is assumed to correspond to a given entity, possibly still unresolved. Outside this domain the word will have other connotations. The underlying architectural substrate must provide for and support this type of ambiguity
- In a UCS, information is transitory. Once it has been transmitted, intercepted, and has flowed through the pipe, it is gone. It cannot be retrieved later from a web page or database engine. Because the information is transitory, it is essential that any monitoring system be able to identify it as important as it passes through the system intake pipe so that it can be selectively captured from the stream for subsequent analysis. Due to the huge volumes involved, not all data can be stored persistently and so reliable and automated sampling of the passing stream is a prerequisite. Moreover, the answer to any given question varies with time, and spotting these variations and the patterns they represent is the essence of intelligence. Again a conventional database is ill-suited to the demands of such time-variant data.
- Rich multimedia data is full of subtleties, contextual overtones, and fine detail that cannot be captured as ‘fields,’ thus it is essential that data captured for storage and analysis be preserved in its entirety. The integrity of the original data must not be compromised by the conventional process of shredding it into standardized relational fields. To do so may remove the most important ingredient of the data. On the other hand, without some kind of field-like partitioning, no useful computation can be done, so a system must do both. That is, the data may be stored multiple times in different forms and containers. Furthermore, in multimedia data, each aspect of the data is best suited to analysis, search, storage, and distribution by different ‘containers.’ For example large bodies of text are best handled and searched by inverted file type text engines whereas fixed numeric or descriptive fields rightly belong in a relational database. Image, video, maps, sounds, and other multimedia fields must be stored, distributed and searched using engines, processes, and hardware that are best suited to the needs of the particular type, and thus the system must support a variety of ‘containers’ targeted at different media types and processes. A fingerprint or face recognizer capability obviously belongs in a different container than relational fields relating to specific fingerprints or images. To attempt to force all such tools into the framework of a common container, presumably a relational database, would be cost-prohibitive and extraordinarily inefficient.
- Having taken the step of dispersing aspects of a given data item to the various containers that most effectively deal with those aspects, it becomes obvious that the system must now have the ability to seamlessly and transparently re-assemble those aspects back into the appearance of a unified whole for presentation to the user. Furthermore, the system must now provide a unified framework for querying the various aspects according to the querying concepts that make sense for the aspect involved, reassembling the results of various aspect specific portions of a query into a unified hit-list of results. Thus, for example, a fingerprint query would be specified and then routed to an entirely different container and engine than would other aspects of the same query such as the time period involved, or the physical region within which the search is to be constrained. These latter two aspects should be routed to relational and geographic container/query engines respectively. The need for a unified and extensible, distributed query language becomes readily apparent, as does the need for an auto-generated UI environment capable of smoothly stitching together the various components of whatever data is finally retrieved.
- The nature of the intelligence problem is that most of the time you do not know what you are looking for until you find it, often much later. However, when you have identified the significant aspect, it suddenly becomes necessary to do a detailed analysis of all past data to examine the newly significant aspect to see if there are similarities or trends. Thus, the ‘data-model’ for the system is subject to continuous change on-an analyst-by-analyst basis as they pursue divergent lines of inquiry into finding the key to some event of interest. What is needed, then, is a system designed for intelligence purposes that accommodates this behavior. Again, conventional systems fail to address this dynamic data-model issue.
- Supposing one could automate the capture of large quantities of the digital world's data stream and deliver it to many analysts whose task was to search the stream for significance and meaning; still the volume of data would overwhelm all but the largest installations. This is because human beings have evolved sensors and mental apparatus to deal with the unique characteristics of information as it is presented to us in the analog world in which we live. In this world, the relevance of information generally falls off exponentially with distance from the observer (both in space and time), and as a consequence all of our senses exhibit a similar falloff. We take advantage of this fact to limit the amount of data we need to process. Furthermore, the same is true of our minds; that is, we are able to apply ‘logical thought’ only to the one thing that is our current focus. Our senses compete to filter everything we observe (based for the most part on distance or apparent magnitude) so that the most important item is brought to our attention at any given time for processing. When asked to give a description of what has happened to us in the last few minutes, each observer will give a different answer, and that answer actually corresponds to a listing of the mental models that were triggered by the focus, and the order in which they occurred. This frequently yields a very different history to what occurred in actual reality, and accounts for the notorious unreliability of most witnesses.
- Unfortunately, in the digital domain, there is no exponential relevance decay phenomenon. Events occurring anywhere in the world may be as relevant to us as those occurring nearby. The analyst is forced to consider anything that may be potentially relevant regardless of spatial, temporal, or conceptual proximity. The result, given the volume of data, is information overload. Moreover, digital information environments such as the web are designed to capture and lead the focus of the person using them, primarily to garner advertising dollars. Thus, we have all experienced the problem of searching for the answer to something on the web, only to be forced into the focus of the web sites we look at, with the result that eventually, hours later we give up, having failed to find what we were looking for, or more likely, having forgotten entirely what it was in the first place. Again, this effect occurs because the digital domain is not constrained by the same falloff law that our analog world is. Each navigation step may be arbitrarily large, and our minds are poorly equipped to maintain focus, and thus search for meaning or relevance in this environment. Thus, a primary goal of any UCS must be to help the analyst maintain focus and empower him to direct his inquiries based on his analytical goals (see Patent ref. 8). To do this, the system must gather and pre-filter information to present only the most relevant portions while accentuating and visualizing the relationships between adjacent data (spatially, temporally, or conceptually) so that the sensors and mental models we all use can be applied to best advantage to analyze that data for patterns, trends, or anomalies. Such pre-filtering must be completely tailored on a per-analyst basis since the filters must be digital representations of the mental models that particular analyst has built up in order to categorize and thus process events.
- In effect, such a UCS must enable the analyst to construct or specify, over time, a digital alter ego which he empowers to be his representative in the torrent of information passing through such a system, and which is authorized to some level to filter and pre-process information, thus leaving the analyst free to make the non-linear leaps and connections that so uniquely characterize human thought. Many attempts have been made in the past to create such avatars, bots, or intelligent agents, mostly by the application of artificial intelligence techniques to specify a rule base that represents, in some way, the thought process of the analyst. Except in restricted domains, all such attempts have largely failed because human thought is not simply the repetitive application of a rule set. Indeed, we still have little idea how to model what we do when we solve a problem, and certainly the techniques we use are unique to each individual and more a result of experience, prejudices and judgment than they are the application of internal rule sets. This inevitably leads us to the conclusion that an architecture for a UCS must through some easy, presumably graphical means, allow each analyst to specify his personal analytical techniques out of whatever building blocks from whatever technical domain or technique he deems relevant. Some kind of visual wiring language where the information passing through the connecting flows represents data gleaned from the captured flow, and the blocks being connected represent limited and specialized processing blocks, is required. Once so specified, an analytical technique must be able to be launched on an automated basis into the intake stream in order to look for matching data to be brought to the attention of interested analysts.
- Central to the ability to analyze new information as it passes by us, is the fact that we are essentially the sum of our experiences. It is our ability to build mental models that allow categorization and processing of new information that constitutes what we call intelligence. A critical aspect of this ability is the need for a large and related experience base that can be used to mentally model and predict the outcome of potential actions in order to choose between alternatives. In the digital domain, if we are to analyze a deluge of data, the same is true, that is, only by building up a vast and encompassing history of past events and their consequences can we begin to understand the potential relevance and consequences of new events appearing in the intake pipe. For even a moderately sized UCS, this represents a storage requirement in the Terra-byte or Peta-byte range given the multimedia nature of the inputs. More important however is the fact that due to the diverse nature of the feeds, and because in any practical system for monitoring global events, feeds must be acquired globally, at the source. It becomes apparent that this storage must be distributed, and must be closely tied to the architecture of the acquisition intake. This acquisition server architecture must, of necessity, be distributed given the physical separation of feeds. Further, given the demanding storage and isochronous retrieval requirements of rich media types such as video, it is apparent that deep storage architecture and access must be tailored to exactly match such a distributed server architecture on a per data-type and per-feed basis.
- The concept of using the sum of our experiences as a kind of lens with which we view the world is key to understanding why systems claiming to provide such buzzword capabilities as “Asset Management” or “Knowledge Management” are only peripherally related to the intelligence problem itself. An asset or knowledge management (KM) system is engaged in the process of looking inwards into an organization to understand and control what is within. An intelligence system does this also, but then uses the knowledge gained by this experience and examination as a lens to allow interpretation of new information coming from the outside world. In effect, we use what we know and learn about ourselves to help us interpret what we see. In the KM case, the data pool is largely static, structured, and controllable. In the intelligence system case, the pool is simply an eddy in a rushing torrent where control of the torrent is out of the question. KM systems are in reality nothing more than thin veneers over relational databases, an approach that is wholly inadequate to the needs of an unconstrained intelligence architecture.
- The purpose of an intelligence system is to facilitate the analysis of captured data and allow the rapid and effective distribution of such analyses to the intelligence consumers (i.e., ‘clients’) of such a system. Once the system involves multimedia information, the conventional solution of printing out a paper report and hand delivering it to the client becomes wholly inadequate. Multimedia information cannot be well represented on paper, and yet as the saying goes, a picture is worth a thousand words. What then is a video segment or sound recording worth? The truth of the matter is that multimedia data types are able to convey a much richer and more impactful presentation than words alone can. Thus, it is incumbent on such a system to design in the ability to easily create and electronically deliver full multimedia reports to its clients. This means that the report must actually be a working ‘application’ capable of full interaction with the client, and when necessary retrieval and playback of any multimedia and other components from archival storage within the system. Creation of such reports must be a relatively trivial matter for the analyst(s) involved. Delivery of multimedia reports without the ability for those reports to access data from system storage would not be nearly as effective. Furthermore, by taking this approach, one opens the door to regarding the report as a custom portal for the information consumer client to examine the details of a particular issue, review the backup data that lead to the reports conclusions, and to draw additional conclusions regarding, or obtain additional details relating to, the subject matter as necessary. Thus, an intelligence architecture should be designed to be end-to-end; that is, it must handle every stage of the process from capture, storage, indexing, search, analysis and finally to presentation. Often decision makers or information consumers are unskilled in the use of computers, and so a simpler (possibly hands-off) kiosk or web-portal like end-user mode, in addition to the more extensive normal analytical mode, must be provided. This mode must anticipate the needs for projection on large screens and the likelihood that multiple individuals will be in the audience. Access security, possibly using biometrics is an issue.
- In adopting an architectural, rather than an application driven approach to solving the problem of unconstrained systems, a prerequisite is that the architecture provide a complete suite of tools to allow the end user to customize and extend the system by adding new tools and analyses as desired. Any approach to implementing a UCS that is not predicated on allowing the system staff to extend and modify the environment in arbitrary ways will not only be forced to severely constrain what is possible, but will also be so complex to define and subsequently implement that it may never work. Therefore, given that such customization is not only allowed, but encouraged, it is quickly apparent that a matching set of debugging tools must also be provided in order to make such customization practical. The system itself must expose a large and complete Applications Programming Interface (API) to allow development at the low level. Development however, must be possible on at least two levels. For the purposes of software engineers, whose goal is to integrate new capabilities seamlessly into the existing environment, code level support and APIs with detailed documentation is required. As much as possible of the detailed and housekeeping work must be handled automatically within the environment so that code level programmers can focus purely on the algorithm they wish to implement, not on such things as UT, communications, data access etc. For the purposes of analysts, who generally are not programmers, but who nonetheless need to express and specify analytical processes in terms of data flowing between a set of computational blocks, a visual programming language must be provided.
- The issue of multilingual data is also a key hurdle to be overcome in any practical intelligence and monitoring system. The reality is that most interesting ‘events’ first appear in some local, probably non-English source and only later after capture and refinement by others does the information appear in English from another secondary, tertiary, or more indirect source. At each step of this process, ‘integrity’ and nuances of the original source are degraded and lost. Any practical system must thus be capable of capture at the source and in the language/format of the original. Mechanisms must be developed to handle and process the information in a productive and speedy manner despite the fact that the associated text may not be in English. There may be no time for a full translation during the brief transit period of the data through the system intake pipe. Failure to address this issue would mean all data must be centralized for formal translation prior to processing, and this requirement would obviously clog the intakes of any installed system targeted at even a moderate sized multi-lingual stream.
- Non-English languages pose many problems that are trivially addressed in English. Foremost among these problems is the issue of ‘stemming’ or finding the root word or meaning of a given word. In English, stemming to extract the root word is trivial. One simply chops off common trailing modifiers to obtain the root word. Thus, in an English language search “Teachers” and “Teaching” are both trivially and automatically stemmed to yield the root word “Teach” and it is this that is actually searched (at least in non-trivial text search engines). In other languages, for example Arabic, each word may represent a mini-sentence. Thus, in Arabic “he taught them” or “they taught us” might be represented by single but very distinct words. The root word is not immediately apparent by examining the actual characters since even the characters involved in such mini-sentences are different. Meaningful search in many non-English languages is thus a subject of research since the Roman script derived language concept of a “key word” has little meaning in many other scripts. A key problem that must be addressed by a practical intelligence architecture is therefore how to stem foreign language inputs to allow meaningful word associations and “concept” queries to be made, while still allowing exact match searches where necessary or appropriate. Failure to address this problem makes the system virtually useless for many foreign script systems.
- Multilingual requirements impact not only intake processing, but more obviously the user interface to the system, which must have the inherent ability to translate dynamically and on the fly between languages and appearances depending on the language or wishes of a particular user. The process of modifying a software program to appear and behave correctly in another language or script system is known as ‘localization,’ and is a multi-billion dollar industry and a major headache for all developers of software who wish to target foreign markets. Localization of a software product can take months, requires extensive source code changes or accommodations, and must be repeated (at vast expense) every time a new upgrade is released. One requirement of an unconstrained intelligence system is the ability reduce this localization process to an automatic and instantaneous behavior which is not in any way tied to the code that is generating or handling a particular aspect of the UI. If such a tie in did exist, the ability of the system to adapt globally (i.e., in a multilingual manner) to changes would be hampered by the rate at which localization could take place, and inevitably portions of the system would become inconsistent with other portions.
- In any large collection of disparate data, the problem of how to navigate around it effectively becomes critical. We see that in the only successful example of a truly complex system, the Internet, the approach taken to navigation was to implement embedded hyperlinks which transition the users focus to the referenced URL. This works effectively, but is an incredibly manual, restrictive, and error prone business. The web-site designer must hand-insert the chosen hyperlink to the URL, thereby enforcing his perspective on the user rather than that of the user himself. Worse yet, URLs change continuously and the referencing link then becomes out of date and useless. What is needed in a UCS is the ability to define and enable/disable hyperlink domains on a per-user basis, and to have those hyperlinks automatically applied to every bit of textual data present in the system or displayed to the user. In other words, we need a dynamic hyperlinking architecture under the control of each user, not of the information source. This directly addresses the loss-of-focus issue discussed earlier by allowing the user to define and modify his own hyperlinking environment. The architecture and the UI it presents must provide and automate this facility. When a hyperlink is clicked, the architecture must be able to identify the nature and location of the datum to which that hyperlink refers, and to automatically launch the appropriate display behaviors to show the target datum to the user in the most appropriate manner.
- Given a distributed UCS through which large quantities of data will be passing, not only as it is ingested, but also as it is passed between various analytical processes, it is apparent that efficient representation of that data and its relationships in binary form must be supported by the environment. Most data is not ‘flat’, that is it comprises many chunks of variable sized memory which refer to each other via pointer or similar references. As it becomes necessary to pass such data from one process or machine to another, the data must be ‘flattened’ into a single contiguous chunk for transmission and then ‘unflattened’ at the other end into its original form. This process is known as serialization (and de-serialization). All present data interchange environments are forced to perform serialization and de-serialization every time data is exchanged between processes. As the amount of data involved increases, the processing overhead of the serialization/de-serialization cycle begins to dominate until one reaches a practical limit in the amount of data that can be exchanged and the rate of such exchange. Unfortunately, with present day machines this limit is far below what is required for even a moderate UCS. Any architecture for unconstrained systems must therefore find a way to eliminate the serialization problem in its entirety.
- The basic questions that are asked of an intelligence system can be summarized as “who”, “what”, “why”, “when”, and “where”. The answers to most of these questions cannot be expressed as a column of numbers or text since the answer itself may not be in the data but must instead be deduced or visualized by the analyst. An unconstrained environment must support the pervasive use of a large and ever expanding set of visualization tools. Certain visualizers should clearly be built into the environment and have commonly accepted appearances. The visualizer to answer the question “where” for example is generally a map and associated Geographic Information System (GIS). The environment must provide such a GIS built-in. Going back to basics, the standard visualizer for displaying the results of a database query is the list, though we may not normally think of this as a visualizer. The environment must provide a basic list capability including the ability to display arbitrary, possibly media rich columns, and to sort on those columns. The basic list must be capable of handling data organized in arbitrary hierarchies. Other environment (or underlying OS) supplied visualizers must exist for the common rich media types (i.e., images, sounds, and video). Complex graph and chart plotting is of course a basic visualization capability and must be built into the environment. The ability to define arbitrary exotic visualizers to aid in detecting patterns, trends, and anomalies must be supported. Since many such visualizers (including any truly useful GIS visualizer), require a 3-D world to express as many connections and nuances as possible, we are lead to the conclusion that the UI environment for the architecture should be based on (or support) a 3-D standard. Given the fact that gaming demands are pushing computer equipment manufacturers to incorporate faster and faster 3-D graphics chips, we must conclude that the UCS UI environment would preferably be based on a 3-D software standard such as OpenGL that, like gaming engines, can take advantage of this hardware.
- Focusing for a moment on the needs of a generalized GIS visualizer, consistent with our general UCS principals, it must permit the visualization of positional data in a variety of ways. Unfortunately, most, if not all, standard GIS systems suffer from a serious shortcoming in this regard. The problem is, that in order to be able to render maps in a reasonable time, GIS environments must eliminate the incredibly compute intensive process of performing the necessary projection calculations on every point in the map. These calculations involve 3-D transformations using transcendental functions that for a detailed large scale map are slow on present day commercial hardware. To overcome the problem, GIS systems pre-project their maps, and all map overlays, into a given projection (usually Mercator) so that the rendering of the maps to a client window does not involve the projection calculations. Unfortunately, there are large numbers of possible map projections and each of them has particular utility for visualizing different aspects of the information being projected. High end mapping systems may hold map data in multiple projections, but this requires storage many times that of the basic map data, and cannot in any case cover all possible projections or vantage points. This means for example that when one wishes to switch projections on the fly, or alternately to overlay data in one projection (a satellite image perhaps) on another (Mercator say), one is forced to go through a lengthy re-mapping process first. If multiple overlaid projections are involved the situation becomes untenable. The ideal UCS GIS system should find a way to store/render the data in its raw latitude/longitude format and do the projections on the fly.
- In intelligence, the analyst needs the ability to visualize relationships between data, not only along well defined axes (e.g., space and time), but also along arbitrary axes defined by the analyst himself. Examples of such axes might be “Adverse actions towards the US”, or “Activity relating to drugs”. Clearly, the analyst must be provided with a way to define new arbitrary axes, and to specify through some arbitrary computational means, how one should determine the intercepts for a given datum on each of these axes. Once this information is known for a given collection of data, it is relatively easy to see how graphical visualization tools can be used to good effect to look for patterns, trends, and anomalies appearing along or between a particular set of such axes. The architecture must therefore support the ability to define such axes and rapidly determine coefficient vectors for any arbitrary set of data being visualized. Because such axis computation may be computationally expensive, doing it on the fly would drastically reduce visualizer responsiveness. For this reason, the architecture would preferably provide and support the concept of a “vector server” responsible for continuously maintaining and updating coefficients for all data in persistent storage along whatever axes are currently defined. As data is fetched for visualization, the required coefficients can also be rapidly fetched from such a vector server by the visualizer. These coefficients would also form a key part of the solution to maintaining, examining, and acting upon non-explicit relationships between different system datums. It is important to understand that unlike conventional graphing axes, these arbitrary axes are non-orthogonal, each axis may be in some way related to many others. This fact can be taken advantage of to address the basic intelligence problem of not knowing exactly what one is looking for. If we imagine two related axes, one known (A) and one unknown (B), then as part of un-related work, an analyst may see the ‘shadow’ of a trend or anomaly related to B on the A axis, and may then be motivated to examine the causes behind this shadow, thereby discovering the existence and significance of the hitherto unexplored B axis. By subsequently defining a B axis to the system and then re-examining data in this light, new insights and relationships may become clear. This is a key aspect of the intelligence process that is not well supported by existing systems.
- It is essential that the system user interface provided to the analyst take the form of a multimedia ‘portal’ which can be reconfigured and changed on a per-analyst basis using a simple graphical metaphor. Each analyst may in fact use multiple portals depending on the nature of the task at hand. This capability must be supported by the environment. Portals can be assembled out of any of the building blocks registered with, or provided by, the environment. The images presented above and in other patents referenced by this one combined with the technology revealed in patent ref. 11 make it clear how this portal capability can be implemented. The image below is of an ‘executive mode’ variant of the same basic portal illustrated elsewhere in order to show that UI appearance can be drastically varied without any impact on the underlying implementation or building-blocks.
- Given the scale of the problem, it is clear that we are talking about a highly distributed architecture, even individual servers must clearly be implemented as distributed clusters. Equipment changes (and breaks), the environment changes, users move and change, as do the preferences of each user over time. It is clear then that the environment must provide extensive support for the reconfiguration of any system parameter that might change. Such preferences span the range from the numbers and location of machines making up a given server cluster and the equipment to which they are connected, to the font a user prefers or the color he likes to see buttons displayed in the UI. APIs and interfaces to access, distribute, and manipulate these preferences must also be provided. The goal of an environment should be to support dynamic and on-going reconfiguration of any target installation all the way from a single machine portable demo (if practical), to a worldwide distributed system and all its connected equipment, without the need to change a single line of compiled architectural code. Obviously, this goal is unattainable with most conventional approaches.
- Having determined that we need an architecture that supports distributed server clusters, we should further ask ourselves what do we mean by a sever, and what is a client, in such a system. In conventional client/server architectures a server is essentially a huge repository for storing, searching, and retrieving data. Clients tend to be applications or veneers that access or supply server data in order to implement the required system functionality. In an unconstrained intelligence architecture, servers must sample from the torrent of data going though the (virtual) intake pipe. Thus it is clear that unlike the standard model, we will require our servers to automatically and in an unattended manner create and source new normalized data gleaned from the intake pipe and then examine that data to see if it may be of interest to one or more users. We need every server to have a built in client capable of sampling data in the pipe and instantiating it into the server and the rest of persistent storage as necessary. Thus we have little use for a standard ‘server’ but instead our minimum useful block is a server-client pair. As to the nature of the server portion itself, since each server will specialize in a different kind of multimedia data, and because the handling of each and every multimedia type cannot be defined beforehand, we see that we need a server architecture where the basic behaviors of a server (e.g., talking to a client, access to storage, etc.) are provided by the architecture but at any point where customization to server behaviors may be required, the server must call back to a plug-in API that allows system programmers to define these behaviors. Certain specialized servers will have to interface directly to legacy or specialized external systems and will have to utilize the capabilities of those external systems while still providing behaviors and an interface to the rest of the environment that hides this fact. An example of such an external system that must be masked behind our modified definition of a server might be a face, voice, or fingerprint recognition system. Thus the classic model of a big fat predefined server (a la Oracle etc.) that is purchased “as is” from a vendor, and wherein only the clients to that server can be changed by customer staff, does not apply to a UCS. Furthermore, at any time new servers may be brought on line to the system and must be able to be found and used by the rest of the system as they appear. This requirement combined with our server-client building block starts to blur the line between what is a server and what is a client. Why shouldn't any ‘client’ machine be able to declare its intent to ‘serve’ data into the environment, indeed in a large community of analysts, over time this ability is essential if analysts are to be able to build on and reference the work of others. Thus every client must also potentially be a server. The only real distinction we can draw between a mostly-server and a mostly-client is that a server tends to source a lot more data on an on-going basis than does a client. An unconstrained network architecture must therefore be more like a peer-to-peer network than it is a classic client/server model. Application code running within the system should remain unaware of the existence of such things as a relational database or servers in general if such code is to be of any general utility. What we need then is some kind of automatic environment mediated and abstracted tie-in between the definition of the data within the system, and the need to route and access all or part of that data from a distributed set of servers.
- Given the intense computational and processing requirements represented by a UCS, it is clear that we cannot afford the overhead or limitations of such cross-platform interpreted languages as Java. The system must therefore be based on one or more underlying OS platforms which are accessed from the environment via direct, efficient, compiled code. Since platforms may change, and differ from each other, the architecture must provide, wherever possible, a platform independent abstraction layer to which API level application programmers can write. The UCS architecture in effect becomes its own operating system (OS), layered on top of a conventional operating system and targeted specifically at providing OS type features related to the requirements of unconstrained systems. Since we must break computation up into large numbers of smaller, autonomous, computing blocks, which exchange data (and messages) through the substrate, it is clear that a highly threaded environment is required. This cannot be a monolithic deterministic application (see Patent ref. 11). Because we must pick a given OS architecture, the system should support the ability to deliver to, and interact with, its UI on a variety of client platforms perhaps via a less extensive UI set (such as a web page) or alternatively by interacting through a cross-platform GUT layer.
- The analyst workload will of course require the use of a number of other commercial off-the-shelf (COTS) packages. Things like word processors, spreadsheets, Internet browsers, e-mail, sound and video editors, image analysis tools etc. The analyst needs all the same tools that a normal computer user does as well as, and in close conjunction with, the UCS environment. As a practical matter, it is clear then that the choice of platform on which to build an architecture is thus limited to the two consumer level OS platforms available, namely Windows and Macintosh. Any useful UCS architecture must be capable of treating COTS software applications as building blocks in the creation of processes within the system, we do not want to re-invent everything that is provided by all the COTS applications. Thus it must be possible in the architecture to ‘wrap’ a COTS application in a proxy process that exists within the environment so that the functionality that application provides can be utilized in an automated and scripted manner within the environment. Ease of such application scripting is a consideration in choosing the underlying OS. Given the multimedia nature of the information in an intelligence UCS, excellent and pervasive multimedia capability in the underlying OS platform is obviously crucial. Another consideration is the level and pervasiveness of that OS's (and its COTS applications) support for foreign languages and scripting systems. OS level security is another key factor. Finally, we must consider the range of COTS solutions available on the platform. In the preferred embodiment of the system of this invention, the Macintosh platform is considered to be the most appropriate.
- While the ability to utilize COTS packages is essential, there are often severe limitations caused by the narrow scripting interface available between distinct applications. For this reason, it is far more desirable to incorporate functionality from existing object libraries providing a rich and complete API. Such commercial object libraries (as well as open-source code) are available to cover a wide range of techniques and capabilities. The need to integrate object-code libraries implies several constraints on the approach taken by the UCS environment as far as encapsulating blocks of compiled functionality (widgets). In particular, because such libraries are built on the underlying OS Toolbox, it is essential that the UCS threaded environment appear to such code as if it were within a stand-alone application. The principal impact of this requirement is on the need for a toolbox abstraction and patching layer, as well as the approach taken to providing a UI windowing environment. Since object libraries involving UI are unaware of the UCS and yet must be integrated into UCS windows, a number of otherwise viable approaches to providing a GUI environment will not work. Given that changes to object libraries are not possible, the UCS GUI environment must take all steps necessary to ensure that non-UCS aware UI code, works un-modified within the UCS windowing environment. This UI sharing environment would preferably be implemented by associating dynamic and overlapping UI ‘regions’ with small executables such that the scheduling environment switches all UI parameters necessary whenever a given UI-related widget is running.
- Security is obviously a major concern in most intelligence-related applications. Given the need to deliver reports and multimedia data to individuals, possibly beyond the confines of the system it is clear that reliance on security via access control alone (i.e., logging on to a Database) is not enough. Security must be built into the data itself. Given the nature of the intelligence cycle where the same item of data may be handled and annotated by many individuals, each of which may have different security privileges, we see that a sophisticated, data-centric approach to security must be supported by the environment.
- The analytical process is frequently collaborative, that is it involves the need for multiple analysts to review each others work and interact with a given visualizer or display in order to discuss possible meanings for patterns found. For this reason, it is highly desirable that the UI for the UCS architecture inherently support collaboration such that users of the system residing on different machines can view and interact with a single display/portal in a coordinated manner, perhaps marking it up in a whiteboard-like manner as part of their discussions. Additionally, the ability to perform video-conferences during such sessions greatly enhances the utility of the environment. A system wherein an intelligence consumer can contact the analyst responsible for a given report and interact with both that analyst and the report is obviously far more useful than one that does not. This close interaction is critical to closing the intelligence system OODA loop (see below). Network level support for such conferencing and collaboration will be necessary.
- On the subject of change, it is obvious that in any UCS connected to the external world, change is the norm, not the exception. The outside world does not stay still just to make it convenient for us to monitor it. Moreover, in any system involving multiple analysts with divergent requirements, even the data models and requirements of the system itself will be subject to continuous and pervasive change. By most estimates, more than 90% of the cost and time spent on software is devoted to maintenance and upgrade of the installed system to handle the inevitability of change.
- Over and above the Bermuda Triangle effect, another software paradigm related phenomenon contributes to our inability to implement complex unconstrained systems. In object oriented programming (OOP) systems (the current wisdom), key emphasis is placed on the advantages of inheriting behaviors from ancestral classes. This removes the need for derived classes to implement basic methods of the class, allowing them to simply modify the methods as appropriate. This technique yields significant productivity improvements in small to medium sized systems, and is ideally suited to addressing some problem domains, notably the problem of constructing user interfaces. However, as size, complexity, and rate of environmental change are scaled beyond these limits, the OOP technique, rather than helping the situation, serves only to aggravate it. Because the implementation of an object becomes a non-localized phenomenon, tendrils of dependency are created between classes, and the ability of others to rapidly examine a piece of code during the maintenance and upgrade portion of the development (the bulk of the actual effort) is made more difficult. OOP systems generally introduce the concept of multiple inheritance to handle the fact that most real world objects are not exactly one kind of thing or another, but are rather mixtures of aspects of many classes. Unfortunately, multiple inheritance only makes the scaling problem worse. The maintainer is forced to examine and internalize the operation of all inherited classes before being able to understand the code and being sure that his change is correct. Worse than this, the ‘right’ change generally involves changes to the assumptions and implementation of some ancestral class, and this in turn often has a ripple effect on other descendent classes. Eventually, such systems max out at a level of complexity represented roughly by what can fit into a single programmer's brain. While this may be large, it is not large enough to address the complexity of a system for understanding world events, and thus an object oriented approach to attacking such a massive problem is essentially doomed to failure. OOP techniques still rely on the notion of one controlling top-down design. No such design exists in a complex UCS. Since we have said that change is fundamental to the nature of an unconstrained intelligence system, it is obvious that in addition to all the problems detailed above, we must also move to a totally new software paradigm and methodology if we are to succeed in this endeavor.
- To summarize the principal issues that lead one to seek a new paradigm to address unconstrained systems, they are as follows:
- a) Change is the norm. The incoming data formats and content will change. The needs and requirements of the analysts using the data will change, and this will be reflected not only in their demands of the UI to the system, but also in the data model and field set that is to be captured and stored by the system.
- b) An unconstrained system can only sample from the flow going through the pipe that is our digital world. It is neither the source nor the destination for that flow, but simply a monitoring station attached to the pipe capable of selectively extracting data from the pipe as it passes by.
- c) The system cannot ‘control’ the data that impinges on it. Indeed we must give up any idea that it is possible to ‘control’ the system that the data represents. All we can do is monitor and react to it. This step of giving up the idea of control is one of the hardest for most people, especially software engineers, to take. After all, we have all grown up to learn that software consists of a ‘controlling’ program which takes in inputs, performs certain predefined computations, and produces outputs. Every installed system we see out there complies with this world view, and yet it is obvious from the discussion above that this model can only hold true on a very localized level in a UCS. The flow of data through the system is really in control. It must trigger execution of code as appropriate depending on the nature of the data itself. That code must be localized and autonomous. It cannot cause or rely upon tendrils of dependency without eventually clogging up the pipe. The concept of data initiating control (or program) execution rather than the other way is alien to most programmers, and yet it becomes fundamental to addressing unconstrained systems. See patent ref. 11 for details.
- d) We cannot in general predict what algorithms or approaches are appropriate to solving the problem of ‘understanding the world’, the problem is simply too complex. Once again we are thus forced away from our conventional approach of defining processing and interface requirements, and then breaking down the problem into successively smaller and smaller sub-problems. Again, it appears that this uncertainly forces us away from any idea of a ‘control’ based system and into a model where we must create a substrate through which data can flow and within which localized areas of control flow can be triggered by the presence of certain data. The only practical approach to addressing such a system is to focus on the requirements and design of the substrate and trust that by facilitating the easy incorporation of new plug-in control flow based ‘widgets’ and their interface to data flowing through the substrate, it will be possible for those using the system to develop and ‘evolve’ it towards their needs. In essence, the users, knowingly or otherwise, must teach the system how they do what they do as a side effect of expressing their needs to it. Any more direct attempt to extract knowledge from analysts to achieve computability, has in the experience of the author been difficult, imprecise, and in the end contradictory and unworkable. No two analysts will agree completely on the meaning of a set of data, nor will they concur on the correct approach to extracting meaning from data in the first place. Because all such perspectives and techniques may have merit, the system must allow all to co-exist side by side, and to contribute, through a formalized substrate and protocol, to the meta-analysis that is the eventual system output. It is illustrative to note that the only successful example of a truly massive software environment is the Internet itself. This success was achieved by defining a rigid set of protocols (IP, HTML etc.) and then allowing Darwinian-like and unplanned development of autonomous but compliant systems to develop on top of the substrate. A similar approach is required in the design of unconstrained systems.
- Any data substrate that is intended to model and understand the real world must, of necessity, imitate it in order to represent it. Just as for our own mental models, simulation must be an integral part of analysis in order to evaluate potentials. This immediately implies that some data can be artificial or predictive while other data may be ‘real.’ Both must be represented and behave identically within the environment. Furthermore, all data objects within the system must have the potential to have a spatial and temporal position. Many patterns evolve along the time axis and most ‘events’ involve, or are precipitated by, physical proximity in both space and time between the actors involved. This means that it must be possible to reconstruct the state of a captured datum at any point in time. Failure to embody this concept at the datum level would prevent the substrate from faithfully representing reality, and thus would involve the need to re-introduce complex control programs to supply this aspect. These control based edifices would naturally tend to diverge and thus leach and/or dissipate utility out of the environment rendering it non-uniform and less useful as an interchange medium. A simulation in an unconstrained environment should just be an evolving set of data in which some portion (but not by any means all) is predictive or program generated. Once such artificial data outlives its utility, it must be easily purged from the environment to make way for a new simulation run. It is this failure to treat simulations as an integral part of a UCS that makes them so difficult to develop, and once developed, makes their results out of date, irrelevant and difficult to apply back to the real world. A well designed UCS architecture, in addition to all its other benefits, provides a means whereby simulations can become useful, relevant, and pervasive parts of the intelligence cycle (or indeed any application). This is a radical departure from current day simulation practice.
- The present system and method meets each of these requirements and provides a robust and flexible system for storing, parsing, analyzing and typed data that is stored in a virtual ontological tree and is later available for retrieval from offline, nearline, or cache based storage and is viewed and processed in the language, interface and with the desired hyperlinks associated with the given User over a P2P or client-server architecture in a dynamic fashion and/or based on one or more user profiles. The issues presented herein are fully detailed in the patent application that have filed relating to the architecture described and attached hereto as appendices. This application details to the system level approach, in which each of these features are provided in a single UCS system.
- The present invention provides the following:
- 1. A system for converting incoming unstructured data into a well described normalized form. Since the incoming data is multimedia and may represent some data type for which support is provided by the underlying OS platform, this normalized form include the ability to fully describe and manipulate arbitrarily complex native or non-native binary structures and collections. This support is provided by a dedicated ‘mining’ language tied intimately to the current system ontology (see appendices 6 and 7).
- 2. A system for accessing and manipulating data held either in memory or in persistent storage in its normalized binary form so that small executables, or ‘widgets’, within the system can freely and effectively operate on data types they have never before encountered simply by knowledge of the ‘type’ of data involved (see appendix 4).
- 3. An ‘ontology’ or world model that represents and contains the items and fields necessary for the target system to perform its function. The ontology would preferably fully specify the form of the normalized binary data.
- 4. A memory system, tied to the ontology, which defines the structure of and access to any persistent storage containers that are required to contain the data.
- 5. A memory management system for splitting incoming data into those portions to be directed to each container.
- 6. A query system for querying each container to retrieve portions of such a composite object. Preferably, all database tables and queries are auto-generated from the ontology, thereby eliminating the role of the conventional Database Administrator (DBA).
- 7. A UI to display and interact with data within the system. In the preferred embodiment, the UI is automatically generated and its behaviors automatically handled by the underlying substrate thus removing this programming burden from the developer (thereby largely eliminating the role of the GUI programmer).
- 8. A memory system that forms collections of datums, and enables manipulation and exchange of these collections both within the local machine as well as across the network. In the preferred embodiment, such collections support the ability to attach arbitrary tags or annotations to the binary data they contain without in any way altering the binary representation itself. Additionally, the system supports the concept of either null or dirty (i.e., has been changed locally) datum.
- 9. The means (preferably implemented in software running on a processor) to specify, investigate and manipulate the inheritance of behaviors and fields from ancestral types described in the system ontology.
- 10. Support for incremental changes to the ontology and automated handling of the implementation and impact of those changes both on persistent storage as well as the UI and other dependant areas.
- 11. Inherent and pervasive support for the concept of units and their interchangeability. In other words, this system does not leave unit handling to the application logic. Such an approach would make it very difficult to meaningfully and easily exchange data.
- For the purposes of this discussion, various appendices will be referenced and are fully incorporated herein. Each of these appendixes describe in detail one embodiment for the various pieces of the UCS system. As will be appreciated, various other functions and approaches could also be used.
- The reader is referred to these lower level building-block patent applications as follows:
- 1) Appendix 1—Flat Memory Model
- 2) Appendix 2—Lexical Analyzer
- 3) Appendix 3—Parser
- 4) Appendix 4—Run-time type system
- 5) Appendix 5—Collections
- 6) Appendix 6—Ontology
- 7) Appendix 7—MitoMine
- 8) Appendix 8—User-centric Hyperlinks
- 9) Appendix 9—User Interface Localization
- 10) Appendix 10—Client/Server and MSS Architecture
- 11) Appendix 11—Data-Flow
- Process Flow and Related Issues
- It is important to understand the intelligence process in more detail before attempting to describe the software architecture to address the problem. A conventional description of the intelligence process would lead one to define a system as a linear flow from inputs (feeds) to outputs (reports) having the following basic stages:
- 1) Capture
- 2) Storage, Retrieval & Indexing
- 3) Search & Monitoring
- 4) Analysis
- 5) Presentation
- While this is a wholly inappropriate way to design a system, and does not reflect the reality of the intelligence process, nonetheless this breakdown gives us a useful framework in which to further examine some of the issues.
- Capture
- The main issue here is the large number of sources and types of data, each with its own unique requirements. Some of these sources and the associated issues are discussed below:
- Video
- The robust capture and use of video information presents one of the biggest challenges to a multimedia intelligence architecture. High quality video digitization, storage, and playback places the ultimate test on the server architecture and its associated mass storage subsystem. A great deal of external capture equipment is required including (but not limited to) satellite dishes, tuners, receivers (PAL, SECAM and NTSC—all variants), format converters, video switches, VCRs (multi-format), digitizers, CODECs, satellite tracking systems, de-scramblers, cable feeds etc. It is clear that the system must provide a framework for the definition, reconfiguration, and statusing of all the equipment connected to it. All equipment must be under automatic and transparent control of the system based on capture requests from the users. To this end, the system must provide some kind of TV guide capability with the ability to request programs of interest. Additionally, a ‘snapshot’ view showing all currently captured channels at the client workstations is required with the means to click on such a snapshot image and immediately request live view and/or capture of the material involved. Video (live or captured) must be streamed across the network to client workstations where it can be viewed and/or edited. This represents not only a massive network load, but also due to the CPU intense nature of the capture, storage, and streaming process, it is clear that a video server cluster will require large numbers of machines to act in unison in order to support realistic client loads. Such a server architecture does not exist in the commercial space and thus must be developed and provided by the UCS architecture. Given a limited pool of equipment available for the capture process, and the differing costs of using a given equipment item to satisfy a user request, it is clear that the environment must provide some form of equipment scheduling capability which attempts to map present and future requests onto the available capture equipment by means of some kind of weighted graph. Equipment item usage cost is determined by how much the available stream capture capacity will be degraded by the use of that item. For example, many older satellites ‘wobble’ so these and other satellites require active tracking using a moveable dish. Most commercial satellites can be captured by fixed dishes. Assuming that a smaller number of mobile dishes exist than fixed, it is obvious that allocating one such dish to a given capture reduces remaining capacity far more than does the use of a fixed dish with multiple feed-horns and a splitter. The same effect is repeated through the equipment chain that must be created (e.g., format converters, switches etc.) in order to meet any given request. Capture equipment design and wiring needs to anticipate this problem and minimize this degradation effect. For example, use of a cable TV head-end to distribute captured video, removes the blocking implied by use of an analog switch to connect source to digitizer. This is a complex issue and must be closely coordinated with the system design and capabilities. Much equipment relating to video processing is not designed for computer control, and thus the system may have to provide the ability to control such equipment via IR links or whatever other means is provided. A generalized and fully programmable (from within the system) controller interface is required in this case. Massive storage capacity is needed to handle video. A key aspect of making use of video is to be able to determine what is being said during a given segment (e.g., a news report). There are a number of approaches to this problem, firstly, at least of a large number of NTSC transmissions, closed captioned text is provided and equipment is available to capture this. Since we wish to maintain the correspondence between a particular portion of a video and what is being said (to aid in search, retrieval, and playback), we can see that this text ‘track’ must be stored in parallel with, and using the same time code as, the video itself. The QuickTime™ architecture is ideal for this purpose, since it defines movies to be comprised of one or more tracks each of which can contain different media types. Thus the present system creates as an output to the capture process a movie containing not only the video and sound tracks, but also a text track, and quite possibly later one or more voice-over tracks.
- Text to speech, although in its infancy is another approach although this applies less well to foreign languages. The choice of video CODEC is determined by the quality required as well as by the need for real-time symmetric capture and playback, preferably using CPU resources alone, not dedicated cards (which rapidly become obsolete). Storage of multiple video resolutions can significantly reduce the required server resources. Video sources, especially those derived from terrestrial transmissions, must be captured locally, thus it is clear that a ‘logical’ video subsystem is likely to be physically distributed, possibly globally. Given the streaming nature of video, this implies a number of other challenges relating to streaming, load balancing, and storage. The UCS architecture must support mechanisms whereby all these requirements can be tailored and handled. Much of the video captured (especially in PAL and SECAM formats) will not have a text track and therefore a key aspect of video capture (and indeed any multimedia capture) is the ability to ‘tag’ the video with other related items (such as news stories) which are more easily associated. The environment must support arbitrary tagging of any datum with any other datum(s) in order to render it ‘computable’. A distributed video server and client(s), video snapshot server and client(s), equipment server and client(s), and various other video related technology have been fully implemented based on the technologies revealed in the referenced patents, particularly patent ref. 10. The details of these implementations and some of the unique features involved will be fully revealed in future patents.
- News Feeds
- News stories and reports form one of the most useful, timely, and easily leveraged forms of open-source feed. News feeds are available in many languages and come in both localized (national) and global varieties. Examples are Reuters, API, BBC etc. Feeds are delivered in a variety of ways including satellite downlinks, analog land-lines, Internet sites, dial-up access, and CD-ROM based delivery. Archival news feeds are usually available for purchase from the publishers although delivery media can be archaic. There is little standardization in format between the feeds although an XML standard for Internet delivery is in its infancy. Multilingual issues abound and normalization can be quite a challenge. Many local feeds have poor quality control over syntactic structure. News feeds are characterized by a relatively low bandwidth with a high semantic content. Storage issues are minimal. For these reasons, the present system provides a news server based on the technologies revealed in appendix 7 and appendix 10 has been fully implemented under the system of this invention.
- PhotoWire Feeds
- Photowire feeds are available from many of the same global sources as are news feeds, and delivery platforms span a similar range. Images come in a huge variety of standard (and not so standard) formats and the system must natively handle all of these, or at a minimum convert losslessly to one of them. Images can be quite large and an associated mass storage subsystem is required. Unlike video, isochronous delivery to the client is not required. The concept of an image preview or ‘picon’ is key to ensuring that full image retrieval is only required for analysis or editing. Images from these sources can form a powerful part of any multimedia presentation. Many sources of photowires also provide graphics and illustrations which are intended for use in publications supported by the feed. These graphics (e.g., stock charts, topical maps, etc.) can be very helpful in understanding issues and in presenting conclusions. Support for the capture, storage, and retrieval/use of these graphics must also be provided by the environment. Graphic formats are generally different from image formats since they are intended to allow editing of the graphic for incorporation into page-layout and similar applications. The Adobe Illustrator™ format appears to be the most widespread. An Image server based on the technology revealed in patent reference 10 and which is capable of handling all image types discussed herein, has been fully implemented under the system of this invention.
- Satellite Imagery
- Satellite Imagery is an important part of the intelligence process. Satellite images are essentially just high resolution images which contain additional semantic meaning by virtue of the fact that the ‘where’ for the image can be computed by knowledge of the satellite parameters and position involved. Thus it is clear that there is a close tie-in between satellite imagery, and the mapping and GIS facility that must be provided by the environment. The environment must be able to automatically project/overlay the image with respect to a map background so that the information it contains can be related back to other data in the system. Satellite images generally contain multiple ‘bands’ of data for different frequencies and sensors, and these bands can be used or combined to extract additional knowledge regarding the contents of the image. Tools for this purpose must be provided. Commercial satellite imagery comes from a variety of sources including weather satellites, LandSat, SPOT etc. Delivery mechanisms for some (e.g., weather) involve the use of receiving dishes. For others, the imagery is delivered on a variety of media (often tape) or by FTP download. For the most part, satellite imagery is a non-real-time feed. Government agencies may have access to a number of other forms of satellite imagery whose nature and content is not discussed herein.
- Specialized Imagery
- Particular applications may require support for other specialized forms of imagery with additional semantic meaning. Examples include fingerprints, identification, x-ray images, astronomy, etc. Each of these types essentially requires its own server subsystem to provide extraction and support for the additional semantics. The environment provides for the easy creation of such servers. Most such sources will require a connection to some external equipment or system to provide capture and possibly storage and search of the imagery. In all other ways however, such subsystems are similar to the generic imagery subsystem.
- Sounds
- Like video, recorded sound can convey a richness and subtlety far beyond that possible with other media types. Because video often includes sound, there is an obvious overlap between the two data types. Sounds come in a number of formats and have widely varying quality levels. Like video, sound must be delivered isochronously to the client, however, data rates are significantly lower though still high enough to require a clustered server and associated mass storage subsystem. Sound sources include phone recordings, covert intercepts, and published media. Like video, a key consideration with sound in order to attain computability, is the ability to convert it into one or more associated text tracks. For this reason, the sound architecture of the present system, like video, uses a time based media framework such as QuickTime™. As with video, voice-overs (or translations) are supported as distinct tracks. Text tracks are, in parallel, routed to the text subsystem to allow associative search. A Sound server based on the technology revealed in referenced patent 10 is the preferred embodiment of such a server.
- Internet
- This source is perhaps the most widespread and the easiest to capture of any of the sources described. Unfortunately, with the exception of a few trusted sites, it is also one of the lowest grade and most misleading sources on which to base any automated calculations. Techniques to crawl or spider the web are widespread and readily available, often built into the underlying OS (e.g., the Macintosh ‘Sherlock’ facility), and because it is web data (i.e., HTML or even better tagged XML) it is designed to facilitate easy capture and use by digital systems. The web contains many invaluable trusted sources for real time data such as news, stock feeds, weather etc. and provided one sticks to these, it forms a key part of monitoring what is going on in the world. The rest of the web data, i.e., the un-trusted bulk of it, must be treated with skepticism much in the manner needed for a covert intercept. That is a ‘discriminator’ phase is required to determine usefulness and relevance. This having been said, much valuable insight can be obtained from such data, especially if one includes e-mail capture into the equation. Storage requirements for web capture are relatively manageable, and like news feeds it is characterized by high semantic content (once filtered). The key issue for any secure installation, is that mining the web on an automated basis implies a connection between the system and the web itself. This is dangerous and often totally unacceptable, especially in government installations. For this reason, the system provides the ability to control a ‘drone’ insecure capture capability which then uploads its finds, via a secure path, to the system itself (which may not be physically connected to the web in any way). Such an Internet server based is prefereable based on the technology disclosed in appendix 7 and appendix 10.
- Published Data Sources
- Perhaps the highest grade and most reliable of all non-covert sources, published data also comprises the largest single source of any described. There are literally tens of thousands of different database and information publishers, each specializing in particular areas. The total amount of data available is immeasurably larger than the total content of the Internet. Few publishers post any high grade data on the web due to the lack of a business model to do so. Many that have done so have now gone out of business and this process is on-going. Because the livelihood of such sources is predicated on their continuing completeness and quality, published data provides some of the best supplies of background information necessary to populate a system's ‘lens’ of understanding. Published data sources come in many forms and tend to be expensive. CD-ROMs are now becoming the dominant distribution media although on-line databases such as Lexus/Nexus contain vast amounts of information that can be easily accessed and incorporated into the environment.
- The extraction of information from these sources tends to be a non-real-time batch process and requires a parsing process that can parse data on a per-source basis. Because publishers have no interest in facilitating the automated extraction of their intellectual property, this data tends to be in semi-structured formats with all kinds of inconsistent usage, even within the same data source. On-line sources tend to have built-in defenses against automated mining. To extract useful normalized data from these sources therefore, the present invention provides a very powerful, generalized, and robust data mining framework tied to the system data models. The ability to rapidly absorb a new published source and seamlessly integrate it into the system enables the system to react in a focused and informed manner to on-going events. When a particular new issue suddenly becomes critical, as they always do, it is likely that very little information exists in the system on the subject. To empower the analysts to rapidly come up to speed on the issue and make analyses relating to it, the system provides a turnaround time measured in hours or at the most days, to acquire and integrate new published sources. Classic mining techniques and system architectures cannot meet this requirement. The preferred technology for enabling this aspect of the system is described in Appendix 7.
- Legacy Systems
- All large organizations utilize as part of their operations a number of ‘legacy’ information processing environments both internal and external. Much of what an organization is, has, and knows is encapsulated in these systems. Such legacy systems do not go away, and often tend to be based on old or antiquated equipment. The present system makes use of the information contained within these systems as part of it's operation. Generally such legacy systems present themselves as databases, usually relational. The ability to access, mine, and source/sink data to/from these legacy systems is often essential to system operation. More specifically, the architecture provides a generalized framework for interfacing to and using such systems through the specification of ‘scripts’ utilized via an encapsulating UCS server. Ideally, the implementation of a connection to such a legacy system would involve little more than definition of the necessary logical scripts. The SQL language makes this relatively easy although it is often the case that custom code is required in order to implement such a connection. As the, UCS architecture also provides the means whereby plug-in modules, defined on a per application, per legacy system basis, can be registered within a standard UCS server. In legacy systems, external containers may also be grouped by providing customized functionality specific to a given data type. Thus for example, a connection to a fingerprint recognition system would be treated as a legacy system requiring an encapsulating UCS server. The system and methods disclosed in Appendix 7 and Appendix 10 are sufficient to implement such a custom legacy interfaces.
- Manual Data Entry
- In certain cases, this may be the only practical means of capturing data, especially data that does not yet exist in the digital domain. The UCS environment also supports the ability to perform manual data entry based on a system ontology. One refinement of this is the provision of a programmable UI scripting capability to provide for the possibility that a process can be written to obtain the data somehow, and enter it not by ontology based mining, but rather by scripted data entry. Once any data (manually entered or otherwise) is in the system, it is also possible to edit and change it and thus the auto-generated UI to the system supports data entry, complete with some level of validity checking, based directly on the system ontology definitions. The preferred ontological framework of the present invention is described in Appendix 6.
- Documents
- Much textual data exists in the form of word processing documents and this is a legitimate source of data for the system. Word processing documents are generally not just simply plain text, but rather contain embedded formatting and style information mixed in with the actual content. These formats are often proprietary. The final appearance of the document may have more information content to it than would be represented by the textual content alone, and for this reason a compliant system must have the ability to store and retrieve these documents in their original form, possibly for additional modification using the appropriate COTS application. Text held in these proprietary formats may not be directly useable for system functions. For these reasons, the system is able to strip the plain text content out of such documents and normalize it. The existence of scriptable COTS applications, capable of import/export of a variety of text formats makes this practical by creating UCS wrapper servers that script such applications, extract the normalized information by scripting COTS applications (or by dedicated plug-in code), and store/retrieve the full document contents as required. Some of the more common formats include PDF, Word, RTF and others. See appendix 7 for further details of this aspect of the system.
- Maps
- Full support for the capture, visualization, and creation of maps is also provided by the system. Sources of such mapping data include such government agencies as NIMA, USGS, the US Census and others. Custom specialize maps are often created by dedicated COTS mapping environments. Such environments generally support import/export to/from a number of standard map interchange formats and the UCS map support also includes the ability to input and output from/to some number of such formats. In the case of more global and extensive data such as that from government agencies, the system provides the inherent ability to mine and normalize such data for system mapping purposes. NIMA maps can be obtained for the entire world on CD-ROM sets formatted according to MIL-STD-2407 (Vector map 0 and 1) and the ability to mine and interpret this format is basic to system operation. Targa and similar data are also be natively supported. Detailed world maps require significant amounts of storage at the map server(s) but not more than can be accommodated on the large disks (or raid arrays) available today. Speed of random access to the data stored on these disks is absolutely critical to map server rendering performance and in the most demanding situations, budget permitting, massive fronting RAM disks and preferably also large amounts of system RAM at the server (to allow data internalization) will be required. A compliant map and GIS server is preferably based upon the technology described in Appendix 5 and Appendix 10.
- Covert Digital Intercepts
- Few organizations outside government intelligence agencies have the resources or legal rights to engage in this kind of activity. For this reason, let us assume the existence of equipment and systems capable of taking a digital stream off a satellite or ‘tapped’ communications path, de-multiplexing it into its constituent parts, and delivering those parts to the intelligence system either as text or standard multimedia data. A number of significant issues occur once the source of data is an intercept, and these need to be anticipated by the architecture. Firstly, the syntactic and semantic quality of the data is likely to be much lower than for other forms of capture. This is partly because the data was not intended for capture, but also because the de-multiplexing and re-assembly processes will be less than perfect and so some of the data may be partial, corrupt, or unusable. This implies a far greater burden on the robustness of the process used to convert data into its normalized form. If the approach taken is to ‘parse’ the input in some manner, it now becomes essential that the parser have error recovery and fallback strategies, rather than simply aborting following a syntax error. In this manner, it remains possible to extract and possibly use those portions of the item that are valid while retaining corrupt portions for possible subsequent interpretation by human beings or other processes in the environmnent. The variety of forms that are likely to be encountered in covert intercepts is significantly greater than for most other feeds and as a result the present invention provides a robust mechanism to decide ‘what’ a given item represents prior to invoking a parser or parsers to attempt to normalize it. Generally with other feeds, this identification phase is relatively simple. With non-covert feeds (other than the Internet), it is frequently the case that all or most incoming data is captured to persistent storage. With covert feeds, this is seldom the case. Much of the content of a covert feed may be irrelevant, thus the system provides an additional ‘phase’ in the capture process that is responsible for determining if the item should be kept or discarded. This determination is preferably under the control of the analysts using the system and the specific algorithm used will differ between analysts, data types, and over time. This ‘discriminator’ phase is closely tied with the concept of ‘Interest Profiles’ or alerts defined by the analysts and running autonomously in the system servers. See referenced appendix 7 and appendix 10 for details on the technology that is preferably used to implement this functionality.
- Others
- There are of course an almost infinite number of other possible media types and sources. Examples might include seismic data, monitoring systems of all kinds, stock feeds, scientific experiments etc. The intrinsic ability to add these data types to the ontology and rapidly implement an encapsulating server(s) for acquisition, search and retrieval, is fundamental to the present invention.
- Storage, Retrieval & Indexing
- The issue of storage and the strategies necessary to effectively index items in storage for rapid retrieval takes on a whole new level of complexity. The main problem is that each different multimedia type implies a different storage and indexing requirement. This means that the conventional approach, i.e., store everything in a relational database system (RDBMS), does not work well.
- RDBMS storage is essentially based on the use of grids or matrices to store information. Because each cell in the matrix has a known size, efficient indexed access is possible. An RDBMS system is therefore best suited to the storage, search, and retrieval of small fixed sized fields, especially those that are numeric. For this reason in a UCS environment, RDBMS storage makes most sense when applied to these kinds of fields, not to large text fields or multimedia content. More specifically, because storage is distributed across a number of dissimilar ‘containers’ of which a RDBMS/SQL container is just one, it is clear that in order to re-assemble a complete multimedia item for display, we need a common unique ID number that can the applied to all containers to retrieve content for an item (see patent ref. 6). The RDBMS system is ideal for defining these ID numbers and retrieving the basic fixed sized fields of an item. In the preferred embodiment, RDBMS data tends to be relatively small, and generally fits easily onto a single large disk.
- Variable sized text fields are best stored and searched via an inverted-file text engine. In the inverted file approach, for each significant word in the dictionary, the inverted file stores a list of all documents containing that word and the position(s) of that word within the document. Search and retrieval in this system therefore occurs via the inverted file list which is far more efficient than the corresponding brute force keyword scan in an RDBMS. Additionally, because of the inverted file organization, statistical word relationships can be built up from the full set of data in the system and this allows powerful concept type searches which are poorly supported under RDBMS systems. Text stored in an inverted file container tends to be moderately large and may require a RAID array. Furthermore, the inverted file itself is generally best placed on a separate fast disk (array) preferably fronted by a large RAM disk/cache to increase search and query performance (see appendix 10 for additional details).
- Video information requires storage capacities many orders of magnitude larger than those described above. Terabyte or petabyte capacities are not uncommon. In addition, the nature of video is that it must be delivered to the client as an isochronous (i.e., constant data rate) stream at a relatively high bandwidth. Furthermore, the CPU load represented by the actual streaming process is considerable, and thus conventional desktop computers are capable of delivering only a small number of high quality video streams at a time. Another key aspect of video is that any given video segment contains a time axis and thus to find and view a relevant portion of the video the ability to tie searchable/indexed information to this time axis is required. For all these reasons, video probably represents the worst case scenario for any UCS storage, indexing and delivery architecture. To address the storage capacity, the present system supports robotic autoloader mass storage using fast random-access media (to minimize wait time to start a play). Media types like CD-ROM and DVD are a natural match. Obviously because these media types have limited sustained data-rates by comparison with fast disk, but more importantly have a relatively long ‘seek’ period, it is not practical to sustain multiple streams from a single such disk. For this reason, the system also provides automatic disk caching during playback and supports large numbers of media drives into any given area of robotic storage and media duplication. Automated, unattended ‘burning’ of media and migration from capture cache is also provided and is preferably implemented. Finally, because of the CPU load and the need for isochronous playback, the video server is implemented as a large cluster of machines tightly integrated with the robotic storage so that the ‘master’ machine can select a ‘drone’ machine on the basis of current loading (or otherwise), load the media into a drive connected to that drone, and then commands the drone to perform playback. See patent appendix 10 for additional details. Indexing implications have been discussed previously under “Capture” above.
- Image data can be relatively large and generally requires a robotic autoloader component, however, unlike the video case, there is no isochronous requirement (since image files can be ‘downloaded’ entirely when accessed) and the need for a large image cluster is reduced. As a result, in the preferred embodiment, the image storage consists of a low resolution ‘picon’, accessible immediately from server disk storage. This is then combined with a high resolution full image which may require robotic access to retrieve. Many client uses of images can be handled using the picon alone thus avoiding excessive robotic accesses. Indexing in the case of images is straightforward since they are simply referenced via the common unique ID shared between all containers (see appendix 6 and appendix 10).
- The storage requirements for Maps have been discussed previously under “Capture”. Map indexing is totally different form all other forms above in that it is spatial, that is that the map is accessed mainly by spatial position. Unlike other data types described above, maps can be constructed on-the-fly from a map database, and thus the map container is capable of responding to map requests without the need for an ‘id’. Specialized maps can also be saved and then referenced, and in this case the unique ‘overlays’ that customize the ‘default’ base map overlays are probably best be stored either in the RDBMS container or in other ontology derived storage along with details of the map projection, scale, and other legend elements.
- The Internet presents another unique storage situation. In the case of the Internet, indexing is via URL, and the storage device is the Internet itself. Nonetheless, this variant is transparently fitted into the same abstraction as all others described above. Other data types may imply yet more variants of the storage and indexing problem.
- It should be noted that the product of many feeds to the system is not a single type as discussed above, but rather some combination of multimedia parts each of which must be routed to the appropriate container but tied back to each other by use of a common unique ID. This dispersal aspect is further discussed in Appendix 6.
- Search & Monitoring
- One of the primary issues with searching over multiple dissimilar ‘containers’ is the need to create a framework within which the necessary search plug-ins can be registered with the environment and the corresponding GUI necessary to easily specify such a search can be tied-in to match. As described above, each container presents a different set of search capabilities varying from standard SQL and text searches to such things as voice and image recognition.
- The present system provides a two-layer approach to querying and query specification. The lower layer represents the registered search capabilities of each specific container. The ‘language’ supported by this lower layer is completely open ended in order to permit new media types and search engines to be easily added to the environment. The result of a search conducted at the lower layer is a list of ‘hits’ (i.e., unique ID, together with relevance and other details if appropriate) that is then passed to the upper query layer. This upper layer has a well defined and preferably limited language, the primary purpose of which is to specify logical combinations of the hit-list results returned by the lower layer modules. Thus the language contains such Boolean operations as AND, OR and NOT. In addition, to support query optimization based on knowledge of the query domain, operators like AND THEN are also supported. The AND THEN operator implies that the query appearing before the operator is performed first and the resulting hit-list is then passed along with the query appearing after the operator. This allows efficient pruning of the search space in the container(s) implementing the second portion of the query. Other operators that would preferably be supported at the upper level include such things as MAX (limit # of hits returned), RELEVANCE (limit relevance returned), ORDER BY, GROUP BY etc. Further details of a system that can provided this functionality is set forth in Appendix 6.
- In the preferred embodiment, a querying GUI whose outermost aspect relates to the upper query layer, and within which specialized UI ‘pages’ can be displayed in order to specify container specific lower level queries is provided. The nature of these UI plug-in modules for well known querying engines such as SQL or inverted text files is fairly straightforward. When the list is broadened to sounds, videos, images, maps etc., however, the variety of UI components embedded within the querying interface in a unified manner becomes quite large. As such, querying and selection via visualizers is tied into the present invention.
- Examples of plug-in search engines (accessed via corresponding GUI) include:
- a) SQL—basic numerical, date, range, keyword, Boolean etc. search criteria.
- b) Text—statistical relatedness, stemming, proximity, multilingual, fuzzy and concept searches.
- c) Images—Face recognition, pattern recognition, fingerprints, clustered and similar searches.
- d) Video—Searches based on text track, voice recognition, scene analysis, close caption etc.
- e) Maps—topological queries (within, next to, etc.), spatial relationships, terrain features, range, distances, routes, measured paths etc.
- As to the issue of monitoring new inputs to the system for compliance with certain criteria, this can be treated as simply an automated query applied to new input. For example, a multi-container query can be defined that returns only those hits that meet our desired criteria and then launches this query into the system to be automatically applied to all new input. This type of automated query will be referred to as an “Interest Profile” (see Appendix 10). The benefits of the two layered query approach now becomes clear because this same38>mechanism may be applied by combining the ‘hits’ from parts of an interest profile in order to determine if a globally compliant ‘hit’ has occurred.
- Unfortunately, the business of monitoring new inputs can be considerably more complicated because of the fact that not all algorithms to define a ‘match’ can be expressed directly to the querying layer. Often, to determine a match the analyst may need to combine a number of different functions. For this reason, the system provides ‘widgets’, each of which is capable of performing part of the analysis using whatever techniques are appropriate. This means that in addition to distributed queries in the querying language, widgets are preferably distributed that form part of the matching algorithm. The system of the present invention allows as large a range of widgets as possible to be used in defining these analyses. As such, the system provides a distributed framework whereby arbitrary algorithms expressed either as searches or via widget wiring can be placed into the input pipe of the UCS and can result in automated notification of the analyst when the desired match is found. See appendix 10 and 11 for additional details.
- Notification to the analyst may be as simple as beeping (or speaking) at his terminal and maintaining a list of pending hits to be viewed. Alternatively, notification could be handled via automated e-mail delivery. Finally, the present invention supports the ability to initiate execution of arbitrary widgets supplied by the user to perform whatever action in necessary when a match occurs. By using this facility, the system can now trigger automated but targeted responses to the occurrence of any given situation. Obviously the nature and scale of these responses is limited only by the imagination of those configuring a particular UCS system. See appendix 10 for details.
- Analysis
- The thrust of this invention is the infrastructure and architecture necessary to support any combination of analytical tools, and to allow those tools to interact between each other over a common substrate. There are literally thousands of effective analytical tools out there, most of them operating in splendid ‘stovepipe’ isolation, some small fraction of them available as COTS applications. Such tools can be integrated into a UCS and used in conjunction with others which, in combination with the other features provided by the present invention, can be used with devastating effect. The only ‘analytical tools’ that would preferably be built in to any UCS is a suite of visualizers, the basic querying tools, and the ability to “wire” these tools and others together into ever more elaborate domain specific algorithms. The UCS architecture preferably facilitates and captures this process using the system and method disclosed in Appendix 11.
- Presentation
- As discussed previously, the final stage of the intelligence process is to deliver analyses to the intelligence consumer in a form that is multimedia rich, and which can allow that consumer to interact with the analysis in order to examine assumptions and determine if more information is needed. Reports must themselves be active and interactive custom portals relating to a given subject. The creation of such reports must be made easy enough that analysts themselves can accomplish this step. More importantly, reports are not static, that is, once an intelligence consumers needs are sufficiently well understood and algorithms designed to meet those needs have been expressed, it is essential that the system be able to deliver ‘today's report on . . . ’ to the consumer on an automated basis with no further analyst involvement. This trend is already being seen in web portals that allow limited customization on a per user basis. Obviously, an intelligence system must take this approach to a whole new level. As mentioned previously certain end users will require a simplified ‘executive’ interface and the present invention provides such an interface. A goal, at least for some consumers, is to allow them to directly express their own interest profiles and to have these (as well as those from analyst initiated profiles) appear in their portals immediately any ‘hit’ occurs. This closes the intelligence OODA loop (see below) and allows the consumer to determine what additional analyses he needs in a much more timely manner. Through this approach the system can manage the information overload problem that is experienced by the intelligence consumer himself, not just that of the intelligence professionals he tasks. See appendix 10 and 11 for details.
- The Intelligence Cycle
- In the traditional intelligence cycle, the intelligence consumers make known their needs for information via requests that are passed to the organization that assigns priorities to information requirements. Determination of priorities leads to tasking which results in the various collection mechanisms or agencies taking steps to gather the raw information necessary to pass on to the analysts. After performing whatever analyses best fit the problem domain, the analysts prepare reports, which are then reviewed and coordinated and finally disseminated back to the original intelligence consumer.
- The cycle described above represents the best thinking on how intelligence should work from the 1940's and 1950's. The cycle is still utilized today by the government intelligence community. In today's fast moving and information rich environment, such a cycle is unfortunately inadequate to the task of tracking the complexities of unfolding world events. A full description of the problems with such a cycle is beyond the scope of this document, however, the basic problems can be summarized as follows:
- a) The cycle is too slow. Indeed it is not clear that it is a cycle at all, since most requests result in just one iteration. The existence of various organizations bureaucracies in the cycle combined with the time taken for information to pass through the bureaucratic interfaces in the loop mean that the cycle cannot keep up with evolving events.
- b) Because it is essentially command driven, the cycle only allows looking into questions that the intelligence consumer already ‘knows’ to ask. As discussed previously, the reality is that the cycle must support the discovery of things you didn't even know were important. The September 11th attacks provide a perfect example. This top-down approach may have suited a situation where the enemy was known and stable (i.e., USSR), but it does not deal well with today's world where enemies are small, distributed, loosely coupled, change constantly, and can have impacts disproportionate to their size. The intelligence consumer cannot anticipate all possible threats and task the complete cycle to investigate each.
- c) The lack of feedback in the cycle between the consumer and the analyst, combined with the inability of the consumer to directly access and examine the backup material leading to analytical conclusions, tends to create a situation where the final product may not meet the consumer's requirements and thus redundant iterations through the cycle with corresponding increases in time and cost are required.
- Modern competitive and business intelligence cycles are now based on some derivative of the Boyd cycle (or OODA loop). This cycle was developed by Colonel John Boyd as a result of his studies (and experience) of air-to-air combat in the Korean war. What Boyd discovered was that the main factors that enabled US pilots to consistently win dogfights, were firstly that their F-86 fighter aircraft's canopy was larger than that of the opposing Mig-15's, thus giving a greater field of vision, and secondly, that although the F-86 aircraft was larger and slower, it was more maneuverable (higher roll-rate) thus allowing US pilots to make more frequent adjustments. Boyd was later largely responsible for the design of the F-15 canopy and perhaps more than anyone else, contributed to development and deployment of the F-16. The result of formalizing and abstracting Boyd's insight became a fundamental part of air-force tactics and later of military tactics in general.
- The central idea behind the OODA loop is that all thinking entities are executing OODA loops of their own (consciously or otherwise), the key to success in any conflict or competition is therefore either:
- a) Being able to cycle around the loop faster than your opponent.
- b) Disrupt the opponents OODA loop to cause him to slow down or make mistakes.
- c) Alter the tempo and rhythms of your own loop so that the opponent cannot keep up with you.
- For a full description of the OODA loop and how it ties in with the intelligence problem, as well as a complete bibliography in this area, see the paper “Avoiding Information Overload Through the Understanding of OODA Loops, A Cognitive Hierarchy and Object-Oriented Analysis and Design” by Dr. R. J. Curts, CDR, USN (Ret.), and Dr. D. E. Campbell, LCDR, USNR-R(Ret.). This paper can be downloaded from www.belisarius.com. This site deals with business intelligence and is heavily focused on the work of Boyd. While this author is not in complete agreement with the paper's assertion that object oriented (OO) techniques provide a practical approach to addressing the issue, the paper does effectively describe the need for a ground-up approach, and a consistent method for representing and storing data.
- For this reason, the intelligence cycle itself needs to become a Boyd cycle. The speed with which it is possible to iterate through the loop is critical to success. Moreover, this same OODA loop would preferably be practiced at all levels of the intelligence hierarchy. This need for rapid iteration and recursive loop cycling is a key driver for the end-to-end UCS approach described in this document. By using the present system, the barriers between intelligence consumers and those involved in the intelligence process itself can be broken down, and the rapid feedback loop required can be implemented. Most importantly however, the key lesson of Boyd's teachings is that the ability to rapidly adapt to change is the single most important determinant in any competitive situation. The present system provides a data-flow system that is driven entirely off ontology, allowing almost instantaneous modification and adaptation to changes in the environment. No other approach currently offers this capability, and thus, no other current approach stands any chance of addressing today's critical need in the intelligence community.
- The ontology presented above is an example high-level ontology targeted at intelligence. This is an example and in no way should such an ontology be mandated by the system architecture. A full discussion of this example ontology is given in Appendix 6. For the purpose of deriving some level of meaning from incoming observations, the application of such an ontology can be summarized as follows:
- 1) Over time, or by pre-loading from published or legacy sources, the system builds up a set of known actors that can be identified by name (or alias) in new input. In addition, the ontology for actions must be populated. At the same time, system input sources are identified and the necessary scripts to convert the contents of those sources into the normalized system ontology (primarily as observations) are developed.
- 2) Once the stream of observations from feeds is underway, the dictionary of actors and actions can be used to identify which data in the system an observation relates to (i.e., the actors involved), and the kinds of interactions that are occurring between those data (actions). Over time, the system builds up statistics on the relations between various elements of the ontology.
- 3) Analysts define conceptual axes to the system together with the algorithms necessary to compute axis intercepts. These conceptual axes can now be used to re-cast the data in the system in a new light, looking for trends, relationships and anomalies.
- 4) Analysts build models for the motives of various entities and to define algorithms for mapping between motives and the actions available to those entities. This allows modeling and prediction to be used as part of the matching process in the input stream. More importantly, system data can now be re-cast and visualized in light of the motive-action models in order to look for patterns in the data that significantly correlate with meeting the motives of specific entities of interest. Since entities rarely announce their intentions beforehand, this ability to interpret incoming data in terms of how it maps to entity motive models is key to finding insights to answer the ‘who’ and ‘why’ questions.
- 5) The process of ‘event reconstruction’ also occurs. That is, given the observations the system receives, knowledge of the actors involved and models of those actors motives and available action space, the system is able to perform a surface-tension type analysis looking for explanations of the event described that most closely match the motives of one or more of the initiating (i.e., subject, not object) actors involved. By postulating that this is in fact what occurred in the event, it becomes possible to define a pattern in the observations leading, up to the event that represent an indicator that a given entity, or entities, are attempting to cause a similar event to occur. Much of this process involves the analyst using the various visualization tools. Alternatively, however, the process can be automated as the analyst expresses the algorithms he believes imply a given motive vector is occurring.
- 6) Examination/visualization of ‘instrumented’ events occurring over a period of time against entity-motive models allow the system to reveal trends, patterns, and anomalies in those events. This in turn yields the possibility of identifying hidden entity involvement, known entity ‘meta-intent’, and ultimately in using that knowledge to predict future behavior. Once future behavior can be predicted to some level of accuracy, the system can allow the intelligence consumer to move from a reactive to a proactive role in order to influence the occurrence (or non-occurrence) of that behavior. Once this point has been reached, the system allows the Boyd-cycle described in the previous section to be iterated over more quickly and thus gives the intelligence consumer a significant advantage over others, this is of course the ultimate goal of any intelligence system.
- To present these ontology ideas in a more graphical and perhaps more intuitive way, think of the problem as though it were a particle-physics experiment occurring within an accelerator. In this example, suppose the experiment consists of a target into which is fired a particle beam. The collisions between the beam and the target produce events which emit a set of secondary particles which may be observed using different sensor devices each designed to detect a particular particle type. The data streams resulting from each sensor are fed into a computer for recording and subsequent analysis. Since it is likely that not all particles resulting from the collision are detected, the purpose of the analysis is to use the data gathered to infer exactly what type of event must have occurred during the collision and from that to deduce the nature and behavior of the particles involved. The next stage is then to use this model to predict other events and then search for the signatures of those events in order to confirm the model.
- In an intelligence system the situation is very similar although the terminology changes. A number of sensors and other data capture devices capture aspects of an event (or future event). The goal of the system is still to reconstruct what event has occurred by analysis of the observation data streams coming from the various feeds. The variety of feed and sensor types is infinitely larger than in the particle physics case, however, as for the particle physics case, many effects of the event are not observed. The major difference between the two systems is simply the fact that in the intelligence system, the concept of an event is distributed over time and detectable particles are emitted a long time before what is considered “the event”. This is simply because the interacting ‘particles’ are intelligent entities, for which a characteristic is forward planning, and which as a result give off ‘signals’ that can be analyzed via a UCS in order to determine intent. In the recent September 11th attacks, for example, there were a number of prior indicators (e.g., flight training school attendance) that were consistent with the fact that such an event was likely to happen in the future. The intelligence community failed to recognize the emerging pattern, however, due to the magnitude of the search, correlation, and analysis task. This is exactly the issue addressed using the UCS of the present invention combined with a domain specific ontology and the other capabilities.
- From the discussion above, it is clear that a radically different approach is needed to solving the problem of unconstrained systems. The architecture of the present invention is based on the concept of a distributed data-flow driven environment, rather than a conventional control-flow based solution. The form, content, and behavior of the data in the environment is described via an ontology that is specific to the given application. Control and/or data flow based programs (known as widgets) are caused to begin execution by virtue of a matching set of data objects or tokens appearing on the input data-flow pins of the widget. When they complete, they produce a set of resultant data tokens on their outputs that then become part of the environment (persistent or otherwise). Thus, a widget that is capable of processing images would specify at least one input pin of type image such that when an image passed through the intake pipe, it could appear at the widget's input pin and cause it to execute. By contrast, conventional systems allocate execution time to a program without knowledge of what it is actually doing, and it is up to the program itself to seek out and acquire its required inputs. To do this, the program requires detailed knowledge of its environment, and the need for this knowledge reduces the generality of the program and increases the overall rigidity of the system thus making it resistive to change and more likely to develop a ‘stovepipe’ topology. By adopting the radical approach to attacking the problem, the present invention provides an open-ended architecture on which intelligence and similar applications can be built.
Claims (1)
1. A system for managing knowledge represented by an incoming data stream, comprising:
a. a system for converting incoming unstructured data into a well described normalized form;
b. a types system for accessing and manipulating data held either in memory or in persistent storage in its normalized binary form;
c. one or more ‘widgets’ within the system that can freely and effectively operate on data types they have never before encountered simply by knowledge of the ‘type’ of data involved as determined by the types system;
d. an ‘ontology’ or world model that represents and contains the items and fields necessary for the target system, wherein the ontology fully specifies the form of the normalized binary data;
e. a memory management system, tied to the ontology, wherein such system splits any incoming data into one or more portions directed to one or more data containers and which defines the structure of and access to any persistent storage containers that are required to store the data;
f. a query system, wherein such system may be used to query each container to retrieve portions of such a composite object
g. a software creation system, wherein all database tables and queries are autogenerated from the ontology;
h. a user interface (UI) to display and interact with data within the system;
i. a memory collection system that forms collections of datums, and enables manipulation and exchange of these collections both within the local machine as well as across the network; and
j. an automated storage system, wherein such automated storage system is capable of storing data in offline, near line, or cache based storage for automated retrieval.
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US10/357,286 US20040024720A1 (en) | 2002-02-01 | 2003-02-03 | System and method for managing knowledge |
US11/484,220 US7685083B2 (en) | 2002-02-01 | 2006-07-10 | System and method for managing knowledge |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US35348702P | 2002-02-01 | 2002-02-01 | |
US10/357,286 US20040024720A1 (en) | 2002-02-01 | 2003-02-03 | System and method for managing knowledge |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/484,220 Continuation US7685083B2 (en) | 2002-02-01 | 2006-07-10 | System and method for managing knowledge |
Publications (1)
Publication Number | Publication Date |
---|---|
US20040024720A1 true US20040024720A1 (en) | 2004-02-05 |
Family
ID=27663215
Family Applications (14)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/357,304 Active 2024-08-09 US7308449B2 (en) | 2002-02-01 | 2003-02-03 | System and method for managing collections of data on a network |
US10/357,326 Active 2024-10-15 US7328430B2 (en) | 2002-02-01 | 2003-02-03 | Method for analyzing data and performing lexical analysis |
US10/357,290 Abandoned US20030172053A1 (en) | 2002-02-01 | 2003-02-03 | System and method for mining data |
US10/357,325 Expired - Lifetime US7158984B2 (en) | 2002-02-01 | 2003-02-03 | System for exchanging binary data |
US10/357,324 Active 2025-01-09 US7210130B2 (en) | 2002-02-01 | 2003-02-03 | System and method for parsing data |
US10/357,284 Active 2026-05-11 US7555755B2 (en) | 2002-02-01 | 2003-02-03 | System and method for navigating data |
US10/357,259 Active 2024-11-25 US7143087B2 (en) | 2002-02-01 | 2003-02-03 | System and method for creating a distributed network architecture |
US10/357,289 Active 2025-05-02 US7369984B2 (en) | 2002-02-01 | 2003-02-03 | Platform-independent real-time interface translation by token mapping without modification of application code |
US10/357,286 Abandoned US20040024720A1 (en) | 2002-02-01 | 2003-02-03 | System and method for managing knowledge |
US10/357,283 Active 2024-08-23 US7240330B2 (en) | 2002-02-01 | 2003-02-03 | Use of ontologies for auto-generating and handling applications, their persistent storage, and user interfaces |
US10/357,288 Active 2024-04-10 US7103749B2 (en) | 2002-02-01 | 2003-02-03 | System and method for managing memory |
US11/455,304 Expired - Lifetime US7533069B2 (en) | 2002-02-01 | 2006-06-16 | System and method for mining data |
US11/484,220 Active - Reinstated 2025-05-17 US7685083B2 (en) | 2002-02-01 | 2006-07-10 | System and method for managing knowledge |
US11/776,299 Active 2026-05-09 US8099722B2 (en) | 2002-02-01 | 2007-07-11 | Method for analyzing data and performing lexical analysis |
Family Applications Before (8)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/357,304 Active 2024-08-09 US7308449B2 (en) | 2002-02-01 | 2003-02-03 | System and method for managing collections of data on a network |
US10/357,326 Active 2024-10-15 US7328430B2 (en) | 2002-02-01 | 2003-02-03 | Method for analyzing data and performing lexical analysis |
US10/357,290 Abandoned US20030172053A1 (en) | 2002-02-01 | 2003-02-03 | System and method for mining data |
US10/357,325 Expired - Lifetime US7158984B2 (en) | 2002-02-01 | 2003-02-03 | System for exchanging binary data |
US10/357,324 Active 2025-01-09 US7210130B2 (en) | 2002-02-01 | 2003-02-03 | System and method for parsing data |
US10/357,284 Active 2026-05-11 US7555755B2 (en) | 2002-02-01 | 2003-02-03 | System and method for navigating data |
US10/357,259 Active 2024-11-25 US7143087B2 (en) | 2002-02-01 | 2003-02-03 | System and method for creating a distributed network architecture |
US10/357,289 Active 2025-05-02 US7369984B2 (en) | 2002-02-01 | 2003-02-03 | Platform-independent real-time interface translation by token mapping without modification of application code |
Family Applications After (5)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/357,283 Active 2024-08-23 US7240330B2 (en) | 2002-02-01 | 2003-02-03 | Use of ontologies for auto-generating and handling applications, their persistent storage, and user interfaces |
US10/357,288 Active 2024-04-10 US7103749B2 (en) | 2002-02-01 | 2003-02-03 | System and method for managing memory |
US11/455,304 Expired - Lifetime US7533069B2 (en) | 2002-02-01 | 2006-06-16 | System and method for mining data |
US11/484,220 Active - Reinstated 2025-05-17 US7685083B2 (en) | 2002-02-01 | 2006-07-10 | System and method for managing knowledge |
US11/776,299 Active 2026-05-09 US8099722B2 (en) | 2002-02-01 | 2007-07-11 | Method for analyzing data and performing lexical analysis |
Country Status (4)
Country | Link |
---|---|
US (14) | US7308449B2 (en) |
EP (1) | EP1527414A2 (en) |
AU (8) | AU2003210789A1 (en) |
WO (12) | WO2004002044A2 (en) |
Cited By (133)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20050015340A1 (en) * | 2003-06-27 | 2005-01-20 | Oracle International Corporation | Method and apparatus for supporting service enablers via service request handholding |
US20050021670A1 (en) * | 2003-06-27 | 2005-01-27 | Oracle International Corporation | Method and apparatus for supporting service enablers via service request composition |
US20060020501A1 (en) * | 2004-07-22 | 2006-01-26 | Leicht Howard J | Benefit plans |
US20060053173A1 (en) * | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for support of chemical data within multi-relational ontologies |
US20060053382A1 (en) * | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for facilitating user interaction with multi-relational ontologies |
US20060053172A1 (en) * | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for creating, editing, and using multi-relational ontologies |
US20060053175A1 (en) * | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for creating, editing, and utilizing one or more rules for multi-relational ontology creation and maintenance |
US20060053171A1 (en) * | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for curating one or more multi-relational ontologies |
US20060053174A1 (en) * | 2004-09-03 | 2006-03-09 | Bio Wisdom Limited | System and method for data extraction and management in multi-relational ontology creation |
US20060074836A1 (en) * | 2004-09-03 | 2006-04-06 | Biowisdom Limited | System and method for graphically displaying ontology data |
US20060074833A1 (en) * | 2004-09-03 | 2006-04-06 | Biowisdom Limited | System and method for notifying users of changes in multi-relational ontologies |
US20060116912A1 (en) * | 2004-12-01 | 2006-06-01 | Oracle International Corporation | Managing account-holder information using policies |
US20060143686A1 (en) * | 2004-12-27 | 2006-06-29 | Oracle International Corporation | Policies as workflows |
US20060212574A1 (en) * | 2005-03-01 | 2006-09-21 | Oracle International Corporation | Policy interface description framework |
US20060288329A1 (en) * | 2005-06-21 | 2006-12-21 | Microsoft Corporation | Content syndication platform |
US20060288011A1 (en) * | 2005-06-21 | 2006-12-21 | Microsoft Corporation | Finding and consuming web subscriptions in a web browser |
US20070011145A1 (en) * | 2005-07-08 | 2007-01-11 | Matthew Snyder | System and method for operation control functionality |
US20070088556A1 (en) * | 2005-10-17 | 2007-04-19 | Microsoft Corporation | Flexible speech-activated command and control |
US20070150821A1 (en) * | 2005-12-22 | 2007-06-28 | Thunemann Paul Z | GUI-maker (data-centric automated GUI-generation) |
US20070179826A1 (en) * | 2006-02-01 | 2007-08-02 | International Business Machines Corporation | Creating a modified ontological model of a business machine |
WO2007085304A1 (en) * | 2006-01-27 | 2007-08-02 | Swiss Reinsurance Company | System for automated generation of database structures and/or databases and a corresponding method |
US20070204017A1 (en) * | 2006-02-16 | 2007-08-30 | Oracle International Corporation | Factorization of concerns to build a SDP (Service delivery platform) |
US20070208759A1 (en) * | 2006-03-03 | 2007-09-06 | Microsoft Corporation | RSS Data-Processing Object |
US20070214164A1 (en) * | 2006-03-10 | 2007-09-13 | Microsoft Corporation | Unstructured data in a mining model language |
US20070299823A1 (en) * | 2006-06-26 | 2007-12-27 | Microsoft Corporation | Customizable parameter user interface |
US20080065678A1 (en) * | 2006-09-12 | 2008-03-13 | Petri John E | Dynamic schema assembly to accommodate application-specific metadata |
US20080098289A1 (en) * | 2006-10-23 | 2008-04-24 | Carnet Williams | Method and system for providing a widget for displaying multimedia content |
US20080098325A1 (en) * | 2006-10-23 | 2008-04-24 | Carnet Williams | Method and system for facilitating social payment or commercial transactions |
US20080097906A1 (en) * | 2006-10-23 | 2008-04-24 | Carnet Williams | Method and system for providing a widget usable in financial transactions |
US20080098290A1 (en) * | 2006-10-23 | 2008-04-24 | Carnet Williams | Method and system for providing a widget for displaying multimedia content |
US20080097871A1 (en) * | 2006-10-23 | 2008-04-24 | Carnet Williams | Method and system for providing a widget usable in affiliate marketing |
US20080104496A1 (en) * | 2006-10-23 | 2008-05-01 | Carnet Williams | Method and system for facilitating social payment or commercial transactions |
US20080140387A1 (en) * | 2006-12-07 | 2008-06-12 | Linker Sheldon O | Method and system for machine understanding, knowledge, and conversation |
US20080147757A1 (en) * | 2006-12-14 | 2008-06-19 | Microsoft Corporation | Finalizable object usage in software transactions |
US20080222514A1 (en) * | 2004-02-17 | 2008-09-11 | Microsoft Corporation | Systems and Methods for Editing XML Documents |
US20080228748A1 (en) * | 2007-03-16 | 2008-09-18 | John Fairweather | Language independent stemming |
US20080232567A1 (en) * | 2007-03-23 | 2008-09-25 | Oracle International Corporation | Abstract application dispatcher |
US20080263103A1 (en) * | 2007-03-02 | 2008-10-23 | Mcgregor Lucas | Digital asset management system (DAMS) |
US7493333B2 (en) | 2004-09-03 | 2009-02-17 | Biowisdom Limited | System and method for parsing and/or exporting data from one or more multi-relational ontologies |
US7496593B2 (en) | 2004-09-03 | 2009-02-24 | Biowisdom Limited | Creating a multi-relational ontology having a predetermined structure |
WO2009025681A2 (en) * | 2007-08-20 | 2009-02-26 | James Heidenreich | System to customize the facilitation of development and documentation of user thinking about an arbitrary problem |
US7505989B2 (en) | 2004-09-03 | 2009-03-17 | Biowisdom Limited | System and method for creating customized ontologies |
US20090112875A1 (en) * | 2007-10-29 | 2009-04-30 | Oracle International Corporation | Shared view of customers across business support systems (bss) and a service delivery platform (sdp) |
US20090125595A1 (en) * | 2007-11-14 | 2009-05-14 | Oracle International Corporation | Intelligent message processing |
US20090132717A1 (en) * | 2007-11-20 | 2009-05-21 | Oracle International Corporation | Session initiation protocol-based internet protocol television |
US20090177961A1 (en) * | 2003-03-24 | 2009-07-09 | Microsoft Corporation | Designing Electronic Forms |
US20090182728A1 (en) * | 2008-01-16 | 2009-07-16 | Arlen Anderson | Managing an Archive for Approximate String Matching |
US20090193433A1 (en) * | 2008-01-24 | 2009-07-30 | Oracle International Corporation | Integrating operational and business support systems with a service delivery platform |
US20090193057A1 (en) * | 2008-01-24 | 2009-07-30 | Oracle International Corporation | Service-oriented architecture (soa) management of data repository |
US7571151B1 (en) * | 2005-12-15 | 2009-08-04 | Gneiss Software, Inc. | Data analysis tool for analyzing data stored in multiple text files |
US20090201917A1 (en) * | 2008-02-08 | 2009-08-13 | Oracle International Corporation | Pragmatic approaches to ims |
US20090228584A1 (en) * | 2008-03-10 | 2009-09-10 | Oracle International Corporation | Presence-based event driven architecture |
US7596549B1 (en) | 2006-04-03 | 2009-09-29 | Qurio Holdings, Inc. | Methods, systems, and products for analyzing annotations for related content |
US20090307671A1 (en) * | 2008-06-06 | 2009-12-10 | Cornell University | System and method for scaling simulations and games |
US20100031147A1 (en) * | 2008-07-31 | 2010-02-04 | Chipln Inc. | Method and system for mixing of multimedia content |
US20100049826A1 (en) * | 2008-08-21 | 2010-02-25 | Oracle International Corporation | In-vehicle multimedia real-time communications |
US7719971B1 (en) | 2004-09-15 | 2010-05-18 | Qurio Holdings, Inc. | Peer proxy binding |
US7730216B1 (en) | 2006-12-14 | 2010-06-01 | Qurio Holdings, Inc. | System and method of sharing content among multiple social network nodes using an aggregation node |
US7764701B1 (en) | 2006-02-22 | 2010-07-27 | Qurio Holdings, Inc. | Methods, systems, and products for classifying peer systems |
US7779004B1 (en) | 2006-02-22 | 2010-08-17 | Qurio Holdings, Inc. | Methods, systems, and products for characterizing target systems |
US7782866B1 (en) | 2006-09-29 | 2010-08-24 | Qurio Holdings, Inc. | Virtual peer in a peer-to-peer network |
US7801971B1 (en) | 2006-09-26 | 2010-09-21 | Qurio Holdings, Inc. | Systems and methods for discovering, creating, using, and managing social network circuits |
US7840903B1 (en) | 2007-02-26 | 2010-11-23 | Qurio Holdings, Inc. | Group content representations |
US7873541B1 (en) * | 2004-02-11 | 2011-01-18 | SQAD, Inc. | System and method for aggregating advertising pricing data |
US7873988B1 (en) | 2006-09-06 | 2011-01-18 | Qurio Holdings, Inc. | System and method for rights propagation and license management in conjunction with distribution of digital content in a social network |
US7925592B1 (en) | 2006-09-27 | 2011-04-12 | Qurio Holdings, Inc. | System and method of using a proxy server to manage lazy content distribution in a social network |
US7925621B2 (en) | 2003-03-24 | 2011-04-12 | Microsoft Corporation | Installing a solution |
US20110119404A1 (en) * | 2009-11-19 | 2011-05-19 | Oracle International Corporation | Inter-working with a walled garden floor-controlled system |
US20110125913A1 (en) * | 2009-11-20 | 2011-05-26 | Oracle International Corporation | Interface for Communication Session Continuation |
US20110125909A1 (en) * | 2009-11-20 | 2011-05-26 | Oracle International Corporation | In-Session Continuation of a Streaming Media Session |
US20110126261A1 (en) * | 2009-11-20 | 2011-05-26 | Oracle International Corporation | Methods and systems for implementing service level consolidated user information management |
US20110134804A1 (en) * | 2009-06-02 | 2011-06-09 | Oracle International Corporation | Telephony application services |
US20110145347A1 (en) * | 2009-12-16 | 2011-06-16 | Oracle International Corporation | Global presence |
US20110145278A1 (en) * | 2009-11-20 | 2011-06-16 | Oracle International Corporation | Methods and systems for generating metadata describing dependencies for composable elements |
US7966340B2 (en) | 2009-03-18 | 2011-06-21 | Aster Data Systems, Inc. | System and method of massively parallel data processing |
US7979803B2 (en) | 2006-03-06 | 2011-07-12 | Microsoft Corporation | RSS hostable control |
US7979856B2 (en) | 2000-06-21 | 2011-07-12 | Microsoft Corporation | Network-based software extensions |
US8005841B1 (en) | 2006-04-28 | 2011-08-23 | Qurio Holdings, Inc. | Methods, systems, and products for classifying content segments |
US20110270829A1 (en) * | 2005-12-20 | 2011-11-03 | Araicom Research Llc | System, method and computer program product for information sorting and retrieval using a language-modeling kernal function |
US8135800B1 (en) | 2006-12-27 | 2012-03-13 | Qurio Holdings, Inc. | System and method for user classification based on social network aware content analysis |
US8271369B2 (en) * | 2003-03-12 | 2012-09-18 | Norman Gilmore | Financial modeling and forecasting system |
US8276207B2 (en) | 2006-12-11 | 2012-09-25 | Qurio Holdings, Inc. | System and method for social network trust assessment |
US20130060803A1 (en) * | 2010-05-17 | 2013-03-07 | Green Sql Ltd | Database translation system and method |
US20130066883A1 (en) * | 2011-09-12 | 2013-03-14 | Fujitsu Limited | Data management apparatus and system |
US8429522B2 (en) | 2003-08-06 | 2013-04-23 | Microsoft Corporation | Correlation, association, or correspondence of electronic forms |
US20130124524A1 (en) * | 2011-11-15 | 2013-05-16 | Arlen Anderson | Data clustering based on variant token networks |
US20130132402A1 (en) * | 2011-11-21 | 2013-05-23 | Nec Laboratories America, Inc. | Query specific fusion for image retrieval |
US8458703B2 (en) | 2008-06-26 | 2013-06-04 | Oracle International Corporation | Application requesting management function based on metadata for managing enabler or dependency |
US20130152057A1 (en) * | 2011-12-13 | 2013-06-13 | Microsoft Corporation | Optimizing data partitioning for data-parallel computing |
US20130159116A1 (en) * | 2007-08-14 | 2013-06-20 | John Nicholas Gross | Method for predicting news content |
US20130262429A1 (en) * | 2009-12-24 | 2013-10-03 | At&T Intellectual Property I, L.P. | Method and apparatus for automated end to end content tracking in peer to peer environments |
US20130262382A1 (en) * | 2012-03-29 | 2013-10-03 | Empire Technology Development, Llc | Determining user key-value storage needs from example queries |
US8554827B2 (en) | 2006-09-29 | 2013-10-08 | Qurio Holdings, Inc. | Virtual peer for a content sharing system |
US20130290354A1 (en) * | 2012-04-26 | 2013-10-31 | Sap Ag | Calculation Models Using Annotations For Filter Optimization |
US20130290326A1 (en) * | 2012-04-25 | 2013-10-31 | Yevgeniy Lebedev | System for dynamically linking tags with a virtual repository of a registered user |
US20130290369A1 (en) * | 2012-04-30 | 2013-10-31 | Craig Peter Sayers | Contextual application recommendations |
US8615573B1 (en) | 2006-06-30 | 2013-12-24 | Quiro Holdings, Inc. | System and method for networked PVR storage and content capture |
US20140040211A1 (en) * | 2012-08-03 | 2014-02-06 | International Business Machines Corporation | System for on-line archiving of content in an object store |
US20140052735A1 (en) * | 2006-03-31 | 2014-02-20 | Daniel Egnor | Propagating Information Among Web Pages |
US20140074838A1 (en) * | 2011-12-12 | 2014-03-13 | International Business Machines Corporation | Anomaly, association and clustering detection |
US8862585B2 (en) * | 2012-10-10 | 2014-10-14 | Polytechnic Institute Of New York University | Encoding non-derministic finite automation states efficiently in a manner that permits simple and fast union operations |
US8874617B2 (en) * | 2012-11-14 | 2014-10-28 | International Business Machines Corporation | Determining potential enterprise partnerships |
US8892993B2 (en) | 2003-08-01 | 2014-11-18 | Microsoft Corporation | Translation file |
US8898172B2 (en) * | 2011-05-11 | 2014-11-25 | Google Inc. | Parallel generation of topics from documents |
US20140358975A1 (en) * | 2013-05-30 | 2014-12-04 | ClearStory Data Inc. | Apparatus and Method for Ingesting and Augmenting Data |
US8930328B2 (en) * | 2012-11-13 | 2015-01-06 | Hitachi, Ltd. | Storage system, storage system control method, and storage control device |
US8943110B2 (en) * | 2012-10-25 | 2015-01-27 | Blackberry Limited | Method and system for managing data storage and access on a client device |
US8954188B2 (en) | 2011-09-09 | 2015-02-10 | Symbotic, LLC | Storage and retrieval system case unit detection |
US8996551B2 (en) * | 2012-10-01 | 2015-03-31 | Longsand Limited | Managing geographic region information |
US9008884B2 (en) | 2010-12-15 | 2015-04-14 | Symbotic Llc | Bot position sensing |
US9038082B2 (en) | 2004-05-28 | 2015-05-19 | Oracle International Corporation | Resource abstraction via enabler and metadata |
WO2015080567A1 (en) | 2013-11-27 | 2015-06-04 | Mimos Berhad | A method for converting a knowledge base to binary form |
US9111285B2 (en) | 2007-08-27 | 2015-08-18 | Qurio Holdings, Inc. | System and method for representing content, user presence and interaction within virtual world advertising environments |
US9165006B2 (en) | 2012-10-25 | 2015-10-20 | Blackberry Limited | Method and system for managing data storage and access on a client device |
US9191434B2 (en) | 2008-10-31 | 2015-11-17 | Disney Enterprises, Inc. | System and method for managing digital media content |
US9210234B2 (en) | 2005-12-05 | 2015-12-08 | Microsoft Technology Licensing, Llc | Enabling electronic documents for limited-capability computing devices |
US9229917B2 (en) | 2003-03-28 | 2016-01-05 | Microsoft Technology Licensing, Llc | Electronic form user interfaces |
US9235572B2 (en) * | 2008-10-31 | 2016-01-12 | Disney Enterprises, Inc. | System and method for updating digital media content |
US9286571B2 (en) | 2012-04-01 | 2016-03-15 | Empire Technology Development Llc | Machine learning for database migration source |
US9369770B2 (en) | 1999-11-04 | 2016-06-14 | Xdrive, Llc | Network personal digital video recorder system (NPDVR) |
US9378212B2 (en) | 1999-11-04 | 2016-06-28 | Xdrive, Llc | Methods and systems for providing file data and metadata |
US20160267268A1 (en) * | 2015-03-13 | 2016-09-15 | Microsoft Technology Licensing, Llc | Implicit process detection and automation from unstructured activity |
US9503407B2 (en) | 2009-12-16 | 2016-11-22 | Oracle International Corporation | Message forwarding |
US9529829B1 (en) * | 2011-11-18 | 2016-12-27 | Veritas Technologies Llc | System and method to facilitate the use of processed data from a storage system to perform tasks |
US9565297B2 (en) | 2004-05-28 | 2017-02-07 | Oracle International Corporation | True convergence with end to end identity management |
US9607103B2 (en) | 2008-10-23 | 2017-03-28 | Ab Initio Technology Llc | Fuzzy data operations |
US9654515B2 (en) | 2008-01-23 | 2017-05-16 | Oracle International Corporation | Service oriented architecture-based SCIM platform |
US9684712B1 (en) * | 2010-09-28 | 2017-06-20 | EMC IP Holding Company LLC | Analyzing tenant-specific data |
US9906367B2 (en) * | 2014-08-05 | 2018-02-27 | Sap Se | End-to-end tamper protection in presence of cloud integration |
US11062142B2 (en) | 2017-06-29 | 2021-07-13 | Accenture Gobal Solutions Limited | Natural language unification based robotic agent control |
US11095673B2 (en) | 2018-06-06 | 2021-08-17 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11222166B2 (en) * | 2019-11-19 | 2022-01-11 | International Business Machines Corporation | Iteratively expanding concepts |
US11709946B2 (en) | 2018-06-06 | 2023-07-25 | Reliaquest Holdings, Llc | Threat mitigation system and method |
Families Citing this family (944)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5867153A (en) | 1996-10-30 | 1999-02-02 | Transaction Technology, Inc. | Method and system for automatically harmonizing access to a software application program via different access devices |
US7249344B1 (en) | 1996-10-31 | 2007-07-24 | Citicorp Development Center, Inc. | Delivery of financial services to remote devices |
US7668781B2 (en) | 1996-10-31 | 2010-02-23 | Citicorp Development Center, Inc. | Global method and system for providing enhanced transactional functionality through a customer terminal |
US6493698B1 (en) * | 1999-07-26 | 2002-12-10 | Intel Corporation | String search scheme in a distributed architecture |
US20060116865A1 (en) | 1999-09-17 | 2006-06-01 | Www.Uniscape.Com | E-services translation utilizing machine translation and translation memory |
US20010048448A1 (en) | 2000-04-06 | 2001-12-06 | Raiz Gregory L. | Focus state themeing |
US6753885B2 (en) | 2000-04-06 | 2004-06-22 | Microsoft Corporation | System and theme file format for creating visual styles |
US7313692B2 (en) | 2000-05-19 | 2007-12-25 | Intertrust Technologies Corp. | Trust management systems and methods |
US8402068B2 (en) | 2000-12-07 | 2013-03-19 | Half.Com, Inc. | System and method for collecting, associating, normalizing and presenting product and vendor information on a distributed network |
US7904595B2 (en) | 2001-01-18 | 2011-03-08 | Sdl International America Incorporated | Globalization management system and method therefor |
US7406432B1 (en) | 2001-06-13 | 2008-07-29 | Ricoh Company, Ltd. | Project management over a network with automated task schedule update |
US7191141B2 (en) * | 2001-06-13 | 2007-03-13 | Ricoh Company, Ltd. | Automated management of development project files over a network |
JP3773426B2 (en) * | 2001-07-18 | 2006-05-10 | 株式会社日立製作所 | Preprocessing method and preprocessing system in data mining |
US20030035582A1 (en) * | 2001-08-14 | 2003-02-20 | Christian Linhart | Dynamic scanner |
US7010779B2 (en) * | 2001-08-16 | 2006-03-07 | Knowledge Dynamics, Inc. | Parser, code generator, and data calculation and transformation engine for spreadsheet calculations |
US9189501B2 (en) * | 2001-08-31 | 2015-11-17 | Margaret Runchey | Semantic model of everything recorded with UR-URL combination identity-identifier-addressing-indexing method, means, and apparatus |
US10489364B2 (en) * | 2001-08-31 | 2019-11-26 | Margaret Runchey | Semantic model of everything recorded with UR-URL combination identity-identifier-addressing-indexing method, means and apparatus |
US7308449B2 (en) * | 2002-02-01 | 2007-12-11 | John Fairweather | System and method for managing collections of data on a network |
US8527495B2 (en) * | 2002-02-19 | 2013-09-03 | International Business Machines Corporation | Plug-in parsers for configuring search engine crawler |
JP4047053B2 (en) * | 2002-04-16 | 2008-02-13 | 富士通株式会社 | Retrieval apparatus and method using sequence pattern including repetition |
US6938239B2 (en) * | 2002-04-18 | 2005-08-30 | Wind River Systems, Inc. | Automatic gopher program generator |
US7359861B2 (en) * | 2002-04-24 | 2008-04-15 | Polyglot Systems, Inc. | Inter-language translation device |
US7210136B2 (en) * | 2002-05-24 | 2007-04-24 | Avaya Inc. | Parser generation based on example document |
US6996798B2 (en) * | 2002-05-29 | 2006-02-07 | Sun Microsystems, Inc. | Automatically deriving an application specification from a web-based application |
US7127520B2 (en) | 2002-06-28 | 2006-10-24 | Streamserve | Method and system for transforming input data streams |
US7840550B2 (en) * | 2002-08-13 | 2010-11-23 | International Business Machines Corporation | System and method for monitoring database queries |
US7376696B2 (en) * | 2002-08-27 | 2008-05-20 | Intel Corporation | User interface to facilitate exchanging files among processor-based devices |
US20080313282A1 (en) | 2002-09-10 | 2008-12-18 | Warila Bruce W | User interface, operating system and architecture |
JP4369708B2 (en) * | 2002-09-27 | 2009-11-25 | パナソニック株式会社 | Data processing device |
EP1406183A3 (en) * | 2002-10-01 | 2004-04-14 | Sap Ag | Method and system for refreshing browser pages |
US7913183B2 (en) * | 2002-10-08 | 2011-03-22 | Microsoft Corporation | System and method for managing software applications in a graphical user interface |
US7171652B2 (en) * | 2002-12-06 | 2007-01-30 | Ricoh Company, Ltd. | Software development environment with design specification verification tool |
JP4284497B2 (en) * | 2003-01-29 | 2009-06-24 | 日本電気株式会社 | Information sharing method, apparatus, and program |
US9412141B2 (en) * | 2003-02-04 | 2016-08-09 | Lexisnexis Risk Solutions Fl Inc | Systems and methods for identifying entities using geographical and social mapping |
US7305391B2 (en) * | 2003-02-07 | 2007-12-04 | Safenet, Inc. | System and method for determining the start of a match of a regular expression |
US7451144B1 (en) * | 2003-02-25 | 2008-11-11 | At&T Corp. | Method of pattern searching |
US7350191B1 (en) | 2003-04-22 | 2008-03-25 | Noetix, Inc. | Computer implemented system and method for the generation of data access applications |
US7295852B1 (en) * | 2003-05-01 | 2007-11-13 | Palm, Inc. | Automated telephone conferencing method and system |
US7415484B1 (en) | 2003-05-09 | 2008-08-19 | Vignette Corporation | Method and system for modeling of system content for businesses |
US7660817B2 (en) * | 2003-05-22 | 2010-02-09 | Microsoft Corporation | System and method for representing content in a file system |
US7676486B1 (en) | 2003-05-23 | 2010-03-09 | Vignette Software Llc | Method and system for migration of legacy data into a content management system |
US7404186B2 (en) * | 2003-05-28 | 2008-07-22 | Microsoft Corporation | Signature serialization |
JP2004362331A (en) * | 2003-06-05 | 2004-12-24 | Sony Corp | Information processor and program |
US7197746B1 (en) * | 2003-06-12 | 2007-03-27 | Sun Microsystems, Inc. | Multipurpose lexical analyzer |
US8095500B2 (en) | 2003-06-13 | 2012-01-10 | Brilliant Digital Entertainment, Inc. | Methods and systems for searching content in distributed computing networks |
GB0314593D0 (en) * | 2003-06-23 | 2003-07-30 | Symbian Ltd | A method of enabling an application to access files stored on a storage medium |
WO2005008440A2 (en) * | 2003-07-11 | 2005-01-27 | Computer Associates Think, Inc. | System and method for common storage object model |
US8938595B2 (en) * | 2003-08-05 | 2015-01-20 | Sepaton, Inc. | Emulated storage system |
US7237224B1 (en) * | 2003-08-28 | 2007-06-26 | Ricoh Company Ltd. | Data structure used for skeleton function of a class in a skeleton code creation tool |
US7308675B2 (en) * | 2003-08-28 | 2007-12-11 | Ricoh Company, Ltd. | Data structure used for directory structure navigation in a skeleton code creation tool |
US7793257B2 (en) * | 2003-08-28 | 2010-09-07 | Ricoh Company, Ltd. | Technique for automating code generation in developing software systems |
US7721254B2 (en) | 2003-10-24 | 2010-05-18 | Microsoft Corporation | Programming interface for a computer platform |
EP1725922A4 (en) * | 2003-10-30 | 2008-11-12 | Lavastorm Technologies Inc | Methods and systems for automated data processing |
US7664727B2 (en) * | 2003-11-28 | 2010-02-16 | Canon Kabushiki Kaisha | Method of constructing preferred views of hierarchical data |
WO2005057362A2 (en) * | 2003-12-08 | 2005-06-23 | Notable Solutions, Inc. | Systems and methods for data interchange among autonomous processing entities |
US8548170B2 (en) | 2003-12-10 | 2013-10-01 | Mcafee, Inc. | Document de-registration |
US7984175B2 (en) | 2003-12-10 | 2011-07-19 | Mcafee, Inc. | Method and apparatus for data capture and analysis system |
US8656039B2 (en) | 2003-12-10 | 2014-02-18 | Mcafee, Inc. | Rule parser |
US20050192944A1 (en) * | 2004-02-27 | 2005-09-01 | Melodeo, Inc. | A method and apparatus for searching large databases via limited query symbol sets |
US7983896B2 (en) | 2004-03-05 | 2011-07-19 | SDL Language Technology | In-context exact (ICE) matching |
US8260764B1 (en) * | 2004-03-05 | 2012-09-04 | Open Text S.A. | System and method to search and generate reports from semi-structured data |
US7966658B2 (en) * | 2004-04-08 | 2011-06-21 | The Regents Of The University Of California | Detecting public network attacks using signatures and fast content analysis |
US7627567B2 (en) * | 2004-04-14 | 2009-12-01 | Microsoft Corporation | Segmentation of strings into structured records |
US7398274B2 (en) * | 2004-04-27 | 2008-07-08 | International Business Machines Corporation | Mention-synchronous entity tracking system and method for chaining mentions |
US7539982B2 (en) * | 2004-05-07 | 2009-05-26 | International Business Machines Corporation | XML based scripting language |
US7797333B1 (en) * | 2004-06-11 | 2010-09-14 | Seisint, Inc. | System and method for returning results of a query from one or more slave nodes to one or more master nodes of a database system |
US8266234B1 (en) | 2004-06-11 | 2012-09-11 | Seisint, Inc. | System and method for enhancing system reliability using multiple channels and multicast |
WO2006002084A1 (en) * | 2004-06-15 | 2006-01-05 | Wms Gaming Inc. | Gaming software providing operating system independence |
US20060010122A1 (en) * | 2004-07-07 | 2006-01-12 | International Business Machines Corporation | System and method for improved database table record insertion and reporting |
US20060036451A1 (en) | 2004-08-10 | 2006-02-16 | Lundberg Steven W | Patent mapping |
US20060026174A1 (en) * | 2004-07-27 | 2006-02-02 | Lundberg Steven W | Patent mapping |
TWI272530B (en) * | 2004-07-30 | 2007-02-01 | Mediatek Inc | Method for accessing file in file system, machine readable medium thereof, and related file system |
US8560534B2 (en) | 2004-08-23 | 2013-10-15 | Mcafee, Inc. | Database for a capture system |
US7949849B2 (en) | 2004-08-24 | 2011-05-24 | Mcafee, Inc. | File system for a capture system |
US7440888B2 (en) * | 2004-09-02 | 2008-10-21 | International Business Machines Corporation | Methods, systems and computer program products for national language support using a multi-language property file |
US8056008B2 (en) * | 2004-09-14 | 2011-11-08 | Adobe Systems Incorporated | Interactive object property region for graphical user interface |
US20060059424A1 (en) * | 2004-09-15 | 2006-03-16 | Petri Jonah W | Real-time data localization |
EP1638336A1 (en) * | 2004-09-17 | 2006-03-22 | Korea Electronics Technology Institute | Method for providing requested fields by get-data operation in TV-Anytime metadata service |
US7406592B1 (en) * | 2004-09-23 | 2008-07-29 | American Megatrends, Inc. | Method, system, and apparatus for efficient evaluation of boolean expressions |
US7809536B1 (en) * | 2004-09-30 | 2010-10-05 | Motive, Inc. | Model-building interface |
US20060095480A1 (en) * | 2004-10-29 | 2006-05-04 | Microsoft Corporation | Method and subsystem for performing subset computation for replication topologies |
US7933868B2 (en) * | 2004-11-04 | 2011-04-26 | Microsoft Corporation | Method and system for partition level cleanup of replication conflict metadata |
US8010685B2 (en) * | 2004-11-09 | 2011-08-30 | Cisco Technology, Inc. | Method and apparatus for content classification |
US7936682B2 (en) * | 2004-11-09 | 2011-05-03 | Cisco Technology, Inc. | Detecting malicious attacks using network behavior and header analysis |
US20060106895A1 (en) * | 2004-11-12 | 2006-05-18 | Microsoft Corporation | Method and subsystem for performing metadata cleanup for replication topologies |
US20060117304A1 (en) * | 2004-11-23 | 2006-06-01 | Microsoft Corporation | Method and system for localizing a package |
US7395269B2 (en) * | 2004-12-20 | 2008-07-01 | Microsoft Corporation | Systems and methods for changing items in a computer file |
US7383278B2 (en) * | 2004-12-20 | 2008-06-03 | Microsoft Corporation | Systems and methods for changing items in a computer file |
US7552137B2 (en) * | 2004-12-22 | 2009-06-23 | International Business Machines Corporation | Method for generating a choose tree for a range partitioned database table |
US7869989B1 (en) * | 2005-01-28 | 2011-01-11 | Artificial Cognition Inc. | Methods and apparatus for understanding machine vocabulary |
WO2006086508A2 (en) | 2005-02-08 | 2006-08-17 | Oblong Industries, Inc. | System and method for genture based control system |
US7765219B2 (en) * | 2005-02-24 | 2010-07-27 | Microsoft Corporation | Sort digits as number collation in server |
US8103640B2 (en) * | 2005-03-02 | 2012-01-24 | International Business Machines Corporation | Method and apparatus for role mapping methodology for user registry migration |
US7643687B2 (en) * | 2005-03-18 | 2010-01-05 | Microsoft Corporation | Analysis hints |
CN1842081B (en) * | 2005-03-30 | 2010-06-02 | 华为技术有限公司 | ABNF character string mode matching and analyzing method and device |
US20060224571A1 (en) * | 2005-03-30 | 2006-10-05 | Jean-Michel Leon | Methods and systems to facilitate searching a data resource |
US20060235820A1 (en) * | 2005-04-14 | 2006-10-19 | International Business Machines Corporation | Relational query of a hierarchical database |
US20060241932A1 (en) * | 2005-04-20 | 2006-10-26 | Carman Ron C | Translation previewer and validator |
US7574578B2 (en) * | 2005-05-02 | 2009-08-11 | Elliptic Semiconductor Inc. | System and method of adaptive memory structure for data pre-fragmentation or pre-segmentation |
US7882116B2 (en) * | 2005-05-18 | 2011-02-01 | International Business Machines Corporation | Method for localization of programming modeling resources |
US20060271920A1 (en) * | 2005-05-24 | 2006-11-30 | Wael Abouelsaadat | Multilingual compiler system and method |
WO2006126679A1 (en) * | 2005-05-27 | 2006-11-30 | Sanyo Electric Co., Ltd. | Data recording device and data file transmission method in the data recording device |
WO2006128183A2 (en) | 2005-05-27 | 2006-11-30 | Schwegman, Lundberg, Woessner & Kluth, P.A. | Method and apparatus for cross-referencing important ip relationships |
US7885979B2 (en) * | 2005-05-31 | 2011-02-08 | Sorenson Media, Inc. | Method, graphical interface and computer-readable medium for forming a batch job |
US8296649B2 (en) * | 2005-05-31 | 2012-10-23 | Sorenson Media, Inc. | Method, graphical interface and computer-readable medium for generating a preview of a reformatted preview segment |
US7975219B2 (en) * | 2005-05-31 | 2011-07-05 | Sorenson Media, Inc. | Method, graphical interface and computer-readable medium for reformatting data |
US8311091B1 (en) * | 2005-06-03 | 2012-11-13 | Visualon, Inc. | Cache optimization for video codecs and video filters or color converters |
CN100447743C (en) * | 2005-06-24 | 2008-12-31 | 国际商业机器公司 | System and method for localizing JAVA GUI application without modifying source code |
GB0514192D0 (en) * | 2005-07-12 | 2005-08-17 | Ibm | Methods, apparatus and computer programs for differential deserialization |
US7467155B2 (en) * | 2005-07-12 | 2008-12-16 | Sand Technology Systems International, Inc. | Method and apparatus for representation of unstructured data |
TW200705271A (en) * | 2005-07-22 | 2007-02-01 | Mitac Technology Corp | Method using a data disk with a built-in operating system to promptly boot computer device |
EP1910923A2 (en) * | 2005-07-25 | 2008-04-16 | Hercules Software, LLC | Direct execution virtual machine |
EP1913465A4 (en) | 2005-07-27 | 2010-09-22 | Schwegman Lundberg & Woessner | Patent mapping |
US20070038981A1 (en) * | 2005-07-29 | 2007-02-15 | Timothy Hanson | System and method for multi-threaded resolver with deadlock detection |
US7907608B2 (en) | 2005-08-12 | 2011-03-15 | Mcafee, Inc. | High speed packet capture |
US8161548B1 (en) | 2005-08-15 | 2012-04-17 | Trend Micro, Inc. | Malware detection using pattern classification |
US7818326B2 (en) | 2005-08-31 | 2010-10-19 | Mcafee, Inc. | System and method for word indexing in a capture system and querying thereof |
WO2007028226A1 (en) * | 2005-09-09 | 2007-03-15 | Ibm Canada Limited - Ibm Canada Limitee | Method and system for state machine translation |
US7779472B1 (en) * | 2005-10-11 | 2010-08-17 | Trend Micro, Inc. | Application behavior based malware detection |
US7730011B1 (en) | 2005-10-19 | 2010-06-01 | Mcafee, Inc. | Attributes of captured objects in a capture system |
US7827373B2 (en) * | 2005-10-31 | 2010-11-02 | Honeywell International Inc. | System and method for managing a short-term heap memory |
US7818181B2 (en) | 2005-10-31 | 2010-10-19 | Focused Medical Analytics Llc | Medical practice pattern tool |
US10319252B2 (en) | 2005-11-09 | 2019-06-11 | Sdl Inc. | Language capability assessment and training apparatus and techniques |
US9075630B1 (en) * | 2005-11-14 | 2015-07-07 | The Mathworks, Inc. | Code evaluation of fixed-point math in the presence of customizable fixed-point typing rules |
US7665015B2 (en) * | 2005-11-14 | 2010-02-16 | Sun Microsystems, Inc. | Hardware unit for parsing an XML document |
US20090125892A1 (en) * | 2005-11-18 | 2009-05-14 | Robert Arthur Crewdson | Computer Software Development System and Method |
JP2007150785A (en) * | 2005-11-29 | 2007-06-14 | Sony Corp | Transmission/reception system, transmission apparatus and transmission method, receiving apparatus and receiving method, and program |
US20070136746A1 (en) * | 2005-12-08 | 2007-06-14 | Electronics And Telecommunications Research Institute | User context based dynamic service combination system and method |
WO2007084790A2 (en) | 2006-01-20 | 2007-07-26 | Glenbrook Associates, Inc. | System and method for context-rich database optimized for processing of concepts |
US7640247B2 (en) * | 2006-02-06 | 2009-12-29 | Microsoft Corporation | Distributed namespace aggregation |
US9910497B2 (en) | 2006-02-08 | 2018-03-06 | Oblong Industries, Inc. | Gestural control of autonomous and semi-autonomous systems |
US8537111B2 (en) | 2006-02-08 | 2013-09-17 | Oblong Industries, Inc. | Control system for navigating a principal dimension of a data space |
US8531396B2 (en) | 2006-02-08 | 2013-09-10 | Oblong Industries, Inc. | Control system for navigating a principal dimension of a data space |
US9823747B2 (en) | 2006-02-08 | 2017-11-21 | Oblong Industries, Inc. | Spatial, multi-modal control device for use with spatial operating system |
US8370383B2 (en) | 2006-02-08 | 2013-02-05 | Oblong Industries, Inc. | Multi-process interactive systems and methods |
US7675854B2 (en) | 2006-02-21 | 2010-03-09 | A10 Networks, Inc. | System and method for an adaptive TCP SYN cookie with time validation |
US20070208582A1 (en) * | 2006-03-02 | 2007-09-06 | International Business Machines Corporation | Method, system, and program product for providing an aggregated view |
US7752596B2 (en) * | 2006-03-17 | 2010-07-06 | Microsoft Corporation | Connecting alternative development environment to interpretive runtime engine |
US8504537B2 (en) | 2006-03-24 | 2013-08-06 | Mcafee, Inc. | Signature distribution in a document registration system |
US20070239505A1 (en) * | 2006-03-30 | 2007-10-11 | Microsoft Corporation | Abstract execution model for a continuation-based meta-runtime |
US8838536B2 (en) * | 2006-04-18 | 2014-09-16 | Sandeep Bhanote | Method and apparatus for mobile data collection and management |
US7958227B2 (en) | 2006-05-22 | 2011-06-07 | Mcafee, Inc. | Attributes of captured objects in a capture system |
US8799043B2 (en) | 2006-06-07 | 2014-08-05 | Ricoh Company, Ltd. | Consolidation of member schedules with a project schedule in a network-based management system |
US8050953B2 (en) * | 2006-06-07 | 2011-11-01 | Ricoh Company, Ltd. | Use of a database in a network-based project schedule management system |
US20070288288A1 (en) * | 2006-06-07 | 2007-12-13 | Tetsuro Motoyama | Use of schedule editors in a network-based project schedule management system |
US20070294500A1 (en) * | 2006-06-16 | 2007-12-20 | Falco Michael A | Methods and system to provide references associated with data streams |
US7600088B1 (en) | 2006-06-26 | 2009-10-06 | Emc Corporation | Techniques for providing storage array services to a cluster of nodes using portal devices |
US8046749B1 (en) * | 2006-06-27 | 2011-10-25 | The Mathworks, Inc. | Analysis of a sequence of data in object-oriented environments |
US8904299B1 (en) | 2006-07-17 | 2014-12-02 | The Mathworks, Inc. | Graphical user interface for analysis of a sequence of data in object-oriented environment |
US7583262B2 (en) * | 2006-08-01 | 2009-09-01 | Thomas Yeh | Optimization of time-critical software components for real-time interactive applications |
US8060514B2 (en) | 2006-08-04 | 2011-11-15 | Apple Inc. | Methods and systems for managing composite data files |
US7747562B2 (en) * | 2006-08-15 | 2010-06-29 | International Business Machines Corporation | Virtual multidimensional datasets for enterprise software systems |
CN101127101A (en) * | 2006-08-18 | 2008-02-20 | 鸿富锦精密工业(深圳)有限公司 | Label information supervision system and method |
US8295459B2 (en) * | 2006-08-24 | 2012-10-23 | Verisign, Inc. | System and method for dynamically partitioning context servers |
US7973954B2 (en) * | 2006-08-28 | 2011-07-05 | Sharp Laboratories Of America, Inc. | Method and apparatus for automatic language switching for an imaging device |
US7793211B2 (en) * | 2006-08-28 | 2010-09-07 | Walter Brenner | Method for delivering targeted web advertisements and user annotations to a web page |
US7895150B2 (en) * | 2006-09-07 | 2011-02-22 | International Business Machines Corporation | Enterprise planning and performance management system providing double dispatch retrieval of multidimensional data |
US9202184B2 (en) | 2006-09-07 | 2015-12-01 | International Business Machines Corporation | Optimizing the selection, verification, and deployment of expert resources in a time of chaos |
US8255790B2 (en) * | 2006-09-08 | 2012-08-28 | Microsoft Corporation | XML based form modification with import/export capability |
US8271429B2 (en) | 2006-09-11 | 2012-09-18 | Wiredset Llc | System and method for collecting and processing data |
US7953713B2 (en) * | 2006-09-14 | 2011-05-31 | International Business Machines Corporation | System and method for representing and using tagged data in a management system |
US20080077384A1 (en) * | 2006-09-22 | 2008-03-27 | International Business Machines Corporation | Dynamically translating a software application to a user selected target language that is not natively provided by the software application |
US11170879B1 (en) | 2006-09-26 | 2021-11-09 | Centrifyhealth, Llc | Individual health record system and apparatus |
BRPI0717323A2 (en) | 2006-09-26 | 2014-12-23 | Ralph Korpman | SYSTEM AND APPARATUS FOR INDIVIDUAL HEALTH RECORD |
US7693900B2 (en) * | 2006-09-27 | 2010-04-06 | The Boeing Company | Querying of distributed databases using neutral ontology model for query front end |
US8055603B2 (en) | 2006-10-03 | 2011-11-08 | International Business Machines Corporation | Automatic generation of new rules for processing synthetic events using computer-based learning processes |
US20080294459A1 (en) * | 2006-10-03 | 2008-11-27 | International Business Machines Corporation | Health Care Derivatives as a Result of Real Time Patient Analytics |
US8145582B2 (en) * | 2006-10-03 | 2012-03-27 | International Business Machines Corporation | Synthetic events for real time patient analysis |
US8555247B2 (en) | 2006-10-13 | 2013-10-08 | International Business Machines Corporation | Systems and methods for expressing temporal relationships spanning lifecycle representations |
US8918755B2 (en) * | 2006-10-17 | 2014-12-23 | International Business Machines Corporation | Enterprise performance management software system having dynamic code generation |
US8312507B2 (en) | 2006-10-17 | 2012-11-13 | A10 Networks, Inc. | System and method to apply network traffic policy to an application session |
US8584199B1 (en) | 2006-10-17 | 2013-11-12 | A10 Networks, Inc. | System and method to apply a packet routing policy to an application session |
US8515912B2 (en) | 2010-07-15 | 2013-08-20 | Palantir Technologies, Inc. | Sharing and deconflicting data changes in a multimaster database system |
US7962495B2 (en) * | 2006-11-20 | 2011-06-14 | Palantir Technologies, Inc. | Creating data in a data store using a dynamic ontology |
US7634454B2 (en) * | 2006-11-21 | 2009-12-15 | Microsoft Corporation | Concept keywords colorization in program identifiers |
US20080120317A1 (en) * | 2006-11-21 | 2008-05-22 | Gile Bradley P | Language processing system |
EP2097861A4 (en) * | 2006-11-27 | 2012-01-04 | Creative Tech Ltd | A communication system, a media player used in the system and a method thereof |
US7974993B2 (en) * | 2006-12-04 | 2011-07-05 | Microsoft Corporation | Application loader for support of version management |
US8438535B2 (en) * | 2006-12-04 | 2013-05-07 | Sap Ag | Method and apparatus for persistent object tool |
US20080141230A1 (en) * | 2006-12-06 | 2008-06-12 | Microsoft Corporation | Scope-Constrained Specification Of Features In A Programming Language |
US7934207B2 (en) * | 2006-12-19 | 2011-04-26 | Microsoft Corporation | Data schemata in programming language contracts |
US8799448B2 (en) * | 2006-12-20 | 2014-08-05 | Microsoft Corporation | Generating rule packs for monitoring computer systems |
US20220414775A1 (en) * | 2006-12-21 | 2022-12-29 | Ice Data, Lp | Method and system for collecting and using market data from various sources |
US7680765B2 (en) * | 2006-12-27 | 2010-03-16 | Microsoft Corporation | Iterate-aggregate query parallelization |
US20080168049A1 (en) * | 2007-01-08 | 2008-07-10 | Microsoft Corporation | Automatic acquisition of a parallel corpus from a network |
AU2008206570A1 (en) * | 2007-01-16 | 2008-07-24 | Timmins Software Corporation | Systems and methods for analyzing information technology systems using collaborative intelligence |
US7675527B2 (en) * | 2007-01-26 | 2010-03-09 | Microsoft Corp. | Multisource composable projection of text |
US20080183725A1 (en) * | 2007-01-31 | 2008-07-31 | Microsoft Corporation | Metadata service employing common data model |
US8850414B2 (en) * | 2007-02-02 | 2014-09-30 | Microsoft Corporation | Direct access of language metadata |
US8560654B2 (en) * | 2007-02-02 | 2013-10-15 | Hewlett-Packard Development Company | Change management |
US7917507B2 (en) * | 2007-02-12 | 2011-03-29 | Microsoft Corporation | Web data usage platform |
US8429185B2 (en) | 2007-02-12 | 2013-04-23 | Microsoft Corporation | Using structured data for online research |
US8166056B2 (en) * | 2007-02-16 | 2012-04-24 | Palo Alto Research Center Incorporated | System and method for searching annotated document collections |
US8276060B2 (en) * | 2007-02-16 | 2012-09-25 | Palo Alto Research Center Incorporated | System and method for annotating documents using a viewer |
US8615404B2 (en) * | 2007-02-23 | 2013-12-24 | Microsoft Corporation | Self-describing data framework |
US7783586B2 (en) * | 2007-02-26 | 2010-08-24 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for analysis of biological systems |
US7788203B2 (en) * | 2007-02-26 | 2010-08-31 | International Business Machines Corporation | System and method of accident investigation for complex situations involving numerous known and unknown factors along with their probabilistic weightings |
US7805390B2 (en) * | 2007-02-26 | 2010-09-28 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for analysis of complex accidents |
US7970759B2 (en) | 2007-02-26 | 2011-06-28 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for pharmaceutical analysis |
US7853611B2 (en) | 2007-02-26 | 2010-12-14 | International Business Machines Corporation | System and method for deriving a hierarchical event based database having action triggers based on inferred probabilities |
US7792774B2 (en) | 2007-02-26 | 2010-09-07 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for analysis of chaotic events |
US7788202B2 (en) * | 2007-02-26 | 2010-08-31 | International Business Machines Corporation | System and method for deriving a hierarchical event based database optimized for clinical applications |
US7882153B1 (en) * | 2007-02-28 | 2011-02-01 | Intuit Inc. | Method and system for electronic messaging of trade data |
US20110106720A1 (en) * | 2009-11-05 | 2011-05-05 | Jerome Dale Johnson | Expert system for gap analysis |
US7958104B2 (en) * | 2007-03-08 | 2011-06-07 | O'donnell Shawn C | Context based data searching |
US8204856B2 (en) * | 2007-03-15 | 2012-06-19 | Google Inc. | Database replication |
US20100121839A1 (en) * | 2007-03-15 | 2010-05-13 | Scott Meyer | Query optimization |
US20090024590A1 (en) * | 2007-03-15 | 2009-01-22 | Sturge Timothy | User contributed knowledge database |
US7870499B2 (en) * | 2007-03-16 | 2011-01-11 | Sap Ag | System for composing software appliances using user task models |
US9729843B1 (en) | 2007-03-16 | 2017-08-08 | The Mathworks, Inc. | Enriched video for a technical computing environment |
US8005812B1 (en) | 2007-03-16 | 2011-08-23 | The Mathworks, Inc. | Collaborative modeling environment |
US20080235066A1 (en) * | 2007-03-19 | 2008-09-25 | Hiroko Mano | Task management device, task management method, and task management program |
US8095630B1 (en) * | 2007-03-20 | 2012-01-10 | Hewlett-Packard Development Company, L.P. | Network booting |
US8065667B2 (en) * | 2007-03-20 | 2011-11-22 | Yahoo! Inc. | Injecting content into third party documents for document processing |
US9558184B1 (en) * | 2007-03-21 | 2017-01-31 | Jean-Michel Vanhalle | System and method for knowledge modeling |
US20080244511A1 (en) * | 2007-03-30 | 2008-10-02 | Microsoft Corporation | Developing a writing system analyzer using syntax-directed translation |
US20100031342A1 (en) * | 2007-04-12 | 2010-02-04 | Honeywell International, Inc | Method and system for providing secure video data transmission and processing |
US8290967B2 (en) * | 2007-04-19 | 2012-10-16 | Barnesandnoble.Com Llc | Indexing and search query processing |
WO2009009192A2 (en) * | 2007-04-18 | 2009-01-15 | Aumni Data, Inc. | Adaptive archive data management |
US8332209B2 (en) * | 2007-04-24 | 2012-12-11 | Zinovy D. Grinblat | Method and system for text compression and decompression |
JP5905662B2 (en) * | 2007-04-24 | 2016-04-20 | オブロング・インダストリーズ・インコーポレーテッド | Protein, pool, and slows processing environment |
US7987446B2 (en) * | 2007-04-24 | 2011-07-26 | International Business Machines Corporation | Method for automating variables in end-user programming system |
EG25474A (en) * | 2007-05-21 | 2012-01-11 | Sherikat Link Letatweer Elbarmaguey At Sae | Method for translitering and suggesting arabic replacement for a given user input |
US7797309B2 (en) * | 2007-06-07 | 2010-09-14 | Datamaxx Applied Technologies, Inc. | System and method for search parameter data entry and result access in a law enforcement multiple domain security environment |
US20080306948A1 (en) * | 2007-06-08 | 2008-12-11 | Yahoo! Inc. | String and binary data sorting |
US9015279B2 (en) * | 2007-06-15 | 2015-04-21 | Bryte Computer Technologies | Methods, systems, and computer program products for tokenized domain name resolution |
US8200644B2 (en) * | 2007-06-15 | 2012-06-12 | Bryte Computer Technologies, Inc. | Methods, systems, and computer program products for search result driven charitable donations |
WO2008156809A1 (en) * | 2007-06-19 | 2008-12-24 | Wms Gaming Inc. | Plug-in architecture for a wagering game network |
US8086597B2 (en) * | 2007-06-28 | 2011-12-27 | International Business Machines Corporation | Between matching |
US7895189B2 (en) * | 2007-06-28 | 2011-02-22 | International Business Machines Corporation | Index exploitation |
US8494911B2 (en) * | 2007-06-29 | 2013-07-23 | Verizon Patent And Licensing Inc. | Dashboard maintenance/outage correlation |
US10007739B1 (en) * | 2007-07-03 | 2018-06-26 | Valassis Direct Mail, Inc. | Address database reconciliation |
US20120229473A1 (en) * | 2007-07-17 | 2012-09-13 | Airgini Group, Inc. | Dynamic Animation in a Mobile Device |
US20090024366A1 (en) * | 2007-07-18 | 2009-01-22 | Microsoft Corporation | Computerized progressive parsing of mathematical expressions |
US20090055433A1 (en) * | 2007-07-25 | 2009-02-26 | Gerard Group International Llc | System, Apparatus and Method for Organizing Forecasting Event Data |
US10795949B2 (en) * | 2007-07-26 | 2020-10-06 | Hamid Hatami-Hanza | Methods and systems for investigation of compositions of ontological subjects and intelligent systems therefrom |
WO2009021044A1 (en) * | 2007-08-07 | 2009-02-12 | The Research Foundation Of Suny | Referent tracking of portions of reality |
CN101369249B (en) * | 2007-08-14 | 2011-08-17 | 国际商业机器公司 | Method and apparatus for marking GUI component of software |
US7970943B2 (en) * | 2007-08-14 | 2011-06-28 | Oracle International Corporation | Providing interoperability in software identifier standards |
US20090055806A1 (en) * | 2007-08-22 | 2009-02-26 | Jian Tang | Techniques for Employing Aspect Advice Based on an Object State |
US8943432B2 (en) * | 2007-08-29 | 2015-01-27 | International Business Machines Corporation | Dynamically configurable portlet |
US8386630B1 (en) | 2007-09-09 | 2013-02-26 | Arris Solutions, Inc. | Video-aware P2P streaming and download with support for real-time content alteration |
US9135340B2 (en) * | 2007-09-12 | 2015-09-15 | Datalaw, Inc. | Research system and method with record builder |
US8522195B2 (en) * | 2007-09-14 | 2013-08-27 | Exigen Properties, Inc. | Systems and methods to generate a software framework based on semantic modeling and business rules |
US8494941B2 (en) * | 2007-09-25 | 2013-07-23 | Palantir Technologies, Inc. | Feature-based similarity measure for market instruments |
US7765204B2 (en) * | 2007-09-27 | 2010-07-27 | Microsoft Corporation | Method of finding candidate sub-queries from longer queries |
US8484115B2 (en) | 2007-10-03 | 2013-07-09 | Palantir Technologies, Inc. | Object-oriented time series generator |
US8239342B2 (en) * | 2007-10-05 | 2012-08-07 | International Business Machines Corporation | Method and apparatus for providing on-demand ontology creation and extension |
US8171029B2 (en) * | 2007-10-05 | 2012-05-01 | Fujitsu Limited | Automatic generation of ontologies using word affinities |
US7930262B2 (en) * | 2007-10-18 | 2011-04-19 | International Business Machines Corporation | System and method for the longitudinal analysis of education outcomes using cohort life cycles, cluster analytics-based cohort analysis, and probabilistic data schemas |
US20090106319A1 (en) * | 2007-10-22 | 2009-04-23 | Kabushiki Kaisha Toshiba | Data management apparatus and data management method |
WO2009090498A2 (en) * | 2007-10-30 | 2009-07-23 | Transformer Software, Ltd. | Key semantic relations for text processing |
US8055497B2 (en) * | 2007-11-02 | 2011-11-08 | International Business Machines Corporation | Method and system to parse addresses using a processing system |
US20110138319A1 (en) * | 2007-11-08 | 2011-06-09 | David Sidman | Apparatuses, Methods and Systems for Hierarchical Multidimensional Information Interfaces |
US20090144318A1 (en) * | 2007-12-03 | 2009-06-04 | Chartsource, Inc., A Delaware Corporation | System for searching research data |
US20090144317A1 (en) * | 2007-12-03 | 2009-06-04 | Chartsource, Inc., A Delaware Corporation | Data search markup language for searching research data |
US20090144222A1 (en) * | 2007-12-03 | 2009-06-04 | Chartsource, Inc., A Delaware Corporation | Chart generator for searching research data |
US20090144243A1 (en) * | 2007-12-03 | 2009-06-04 | Chartsource, Inc., A Delaware Corporation | User interface for searching research data |
US20090144265A1 (en) * | 2007-12-03 | 2009-06-04 | Chartsource, Inc., A Delaware Corporation | Search engine for searching research data |
US20090144241A1 (en) * | 2007-12-03 | 2009-06-04 | Chartsource, Inc., A Delaware Corporation | Search term parser for searching research data |
US20090144242A1 (en) * | 2007-12-03 | 2009-06-04 | Chartsource, Inc., A Delaware Corporation | Indexer for searching research data |
US8140584B2 (en) * | 2007-12-10 | 2012-03-20 | Aloke Guha | Adaptive data classification for data mining |
US10002189B2 (en) | 2007-12-20 | 2018-06-19 | Apple Inc. | Method and apparatus for searching using an active ontology |
US7779051B2 (en) | 2008-01-02 | 2010-08-17 | International Business Machines Corporation | System and method for optimizing federated and ETL'd databases with considerations of specialized data structures within an environment having multidimensional constraints |
US9330720B2 (en) | 2008-01-03 | 2016-05-03 | Apple Inc. | Methods and apparatus for altering audio output signals |
US20090178104A1 (en) * | 2008-01-08 | 2009-07-09 | Hemal Shah | Method and system for a multi-level security association lookup scheme for internet protocol security |
US20090177646A1 (en) * | 2008-01-09 | 2009-07-09 | Microsoft Corporation | Plug-In for Health Monitoring System |
US8099267B2 (en) * | 2008-01-11 | 2012-01-17 | Schlumberger Technology Corporation | Input deck migrator for simulators |
US8103660B2 (en) * | 2008-01-22 | 2012-01-24 | International Business Machines Corporation | Computer method and system for contextual management and awareness of persistent queries and results |
US7877367B2 (en) * | 2008-01-22 | 2011-01-25 | International Business Machines Corporation | Computer method and apparatus for graphical inquiry specification with progressive summary |
US8005788B2 (en) * | 2008-01-28 | 2011-08-23 | International Business Machines Corporation | System and method for legacy system component incremental migration |
US8225288B2 (en) * | 2008-01-29 | 2012-07-17 | Intuit Inc. | Model-based testing using branches, decisions, and options |
US9817822B2 (en) | 2008-02-07 | 2017-11-14 | International Business Machines Corporation | Managing white space in a portal web page |
US10540712B2 (en) | 2008-02-08 | 2020-01-21 | The Pnc Financial Services Group, Inc. | User interface with controller for selectively redistributing funds between accounts |
US9076342B2 (en) * | 2008-02-19 | 2015-07-07 | Architecture Technology Corporation | Automated execution and evaluation of network-based training exercises |
US7885973B2 (en) * | 2008-02-22 | 2011-02-08 | International Business Machines Corporation | Computer method and apparatus for parameterized semantic inquiry templates with type annotations |
US7949679B2 (en) * | 2008-03-05 | 2011-05-24 | International Business Machines Corporation | Efficient storage for finite state machines |
EP2105847A1 (en) * | 2008-03-27 | 2009-09-30 | Alcatel Lucent | Device and method for automatically generating ontologies from term definitions contained into a dictionary |
US8620889B2 (en) * | 2008-03-27 | 2013-12-31 | Microsoft Corporation | Managing data transfer between endpoints in a distributed computing environment |
US9070095B2 (en) * | 2008-04-01 | 2015-06-30 | Siemens Aktiengesellschaft | Ensuring referential integrity of medical image data |
US8650228B2 (en) * | 2008-04-14 | 2014-02-11 | Roderick B. Wideman | Methods and systems for space management in data de-duplication |
WO2009130606A2 (en) * | 2008-04-21 | 2009-10-29 | Vaka Corporation | Methods and systems for shareable virtual devices |
US9740293B2 (en) | 2009-04-02 | 2017-08-22 | Oblong Industries, Inc. | Operating environment with gestural control and multiple client devices, displays, and users |
US9740922B2 (en) | 2008-04-24 | 2017-08-22 | Oblong Industries, Inc. | Adaptive tracking system for spatial input devices |
US10642364B2 (en) | 2009-04-02 | 2020-05-05 | Oblong Industries, Inc. | Processing tracking and recognition data in gestural recognition systems |
US9684380B2 (en) | 2009-04-02 | 2017-06-20 | Oblong Industries, Inc. | Operating environment with gestural control and multiple client devices, displays, and users |
US8723795B2 (en) | 2008-04-24 | 2014-05-13 | Oblong Industries, Inc. | Detecting, representing, and interpreting three-space input: gestural continuum subsuming freespace, proximal, and surface-contact modes |
US9952673B2 (en) | 2009-04-02 | 2018-04-24 | Oblong Industries, Inc. | Operating environment comprising multiple client devices, multiple displays, multiple users, and gestural control |
US9495013B2 (en) | 2008-04-24 | 2016-11-15 | Oblong Industries, Inc. | Multi-modal gestural interface |
US8521512B2 (en) * | 2008-04-30 | 2013-08-27 | Deep Sky Concepts, Inc | Systems and methods for natural language communication with a computer |
US8401938B1 (en) | 2008-05-12 | 2013-03-19 | The Pnc Financial Services Group, Inc. | Transferring funds between parties' financial accounts |
US8751385B1 (en) | 2008-05-15 | 2014-06-10 | The Pnc Financial Services Group, Inc. | Financial email |
US8001329B2 (en) * | 2008-05-19 | 2011-08-16 | International Business Machines Corporation | Speculative stream scanning |
US8738360B2 (en) | 2008-06-06 | 2014-05-27 | Apple Inc. | Data detection of a character sequence having multiple possible data types |
JP5258400B2 (en) * | 2008-06-06 | 2013-08-07 | キヤノン株式会社 | Document management system, document management method, and computer program |
US8311806B2 (en) | 2008-06-06 | 2012-11-13 | Apple Inc. | Data detection in a sequence of tokens using decision tree reductions |
US7917547B2 (en) * | 2008-06-10 | 2011-03-29 | Microsoft Corporation | Virtualizing objects within queries |
US8032768B2 (en) * | 2008-06-20 | 2011-10-04 | Dell Products, Lp | System and method for smoothing power reclamation of blade servers |
US8176149B2 (en) * | 2008-06-30 | 2012-05-08 | International Business Machines Corporation | Ejection of storage drives in a computing network |
US7982764B2 (en) * | 2008-07-08 | 2011-07-19 | United Parcel Service Of America, Inc. | Apparatus for monitoring a package handling system |
US8205242B2 (en) | 2008-07-10 | 2012-06-19 | Mcafee, Inc. | System and method for data mining and security policy management |
WO2010009178A1 (en) * | 2008-07-14 | 2010-01-21 | Borland Software Corporation | Open application lifecycle management framework domain model |
US20100023924A1 (en) * | 2008-07-23 | 2010-01-28 | Microsoft Corporation | Non-constant data encoding for table-driven systems |
US8301437B2 (en) | 2008-07-24 | 2012-10-30 | Yahoo! Inc. | Tokenization platform |
US9032390B2 (en) * | 2008-07-29 | 2015-05-12 | Qualcomm Incorporated | Framework versioning |
US8171045B2 (en) * | 2008-07-31 | 2012-05-01 | Xsevo Systems, Inc. | Record based code structure |
US20100030549A1 (en) | 2008-07-31 | 2010-02-04 | Lee Michael M | Mobile device having human language translation capability with positional feedback |
US20100031235A1 (en) * | 2008-08-01 | 2010-02-04 | Modular Mining Systems, Inc. | Resource Double Lookup Framework |
WO2010017250A1 (en) * | 2008-08-05 | 2010-02-11 | Wms Gaming, Inc. | Wagering game digital representative |
US8762969B2 (en) * | 2008-08-07 | 2014-06-24 | Microsoft Corporation | Immutable parsing |
US7984311B2 (en) | 2008-08-08 | 2011-07-19 | Dell Products L.P. | Demand based power allocation |
US9253154B2 (en) | 2008-08-12 | 2016-02-02 | Mcafee, Inc. | Configuration management for a capture/registration system |
US8959053B2 (en) * | 2008-08-13 | 2015-02-17 | Alcatel Lucent | Configuration file framework to support high availability schema based upon asynchronous checkpointing |
US8429194B2 (en) | 2008-09-15 | 2013-04-23 | Palantir Technologies, Inc. | Document-based workflows |
US20110167121A1 (en) * | 2008-09-15 | 2011-07-07 | Ben Matzkel | System, apparatus and method for encryption and decryption of data transmitted over a network |
US20100070426A1 (en) | 2008-09-15 | 2010-03-18 | Palantir Technologies, Inc. | Object modeling for exploring large data sets |
GB2463669A (en) * | 2008-09-19 | 2010-03-24 | Motorola Inc | Using a semantic graph to expand characterising terms of a content item and achieve targeted selection of associated content items |
US8768892B2 (en) * | 2008-09-29 | 2014-07-01 | Microsoft Corporation | Analyzing data and providing recommendations |
US8166077B2 (en) * | 2008-09-30 | 2012-04-24 | International Business Machines Corporation | Mapping a class, method, package, and/or pattern to a component |
US8676904B2 (en) | 2008-10-02 | 2014-03-18 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US8266148B2 (en) * | 2008-10-07 | 2012-09-11 | Aumni Data, Inc. | Method and system for business intelligence analytics on unstructured data |
US20100131513A1 (en) | 2008-10-23 | 2010-05-27 | Lundberg Steven W | Patent mapping |
US8548797B2 (en) * | 2008-10-30 | 2013-10-01 | Yahoo! Inc. | Short text language detection using geographic information |
KR101574603B1 (en) | 2008-10-31 | 2015-12-04 | 삼성전자주식회사 | A method for conditional processing and an apparatus thereof |
US20100115438A1 (en) * | 2008-11-05 | 2010-05-06 | Yu-Chung Chu | Method for creating multi-level widgets and system thereof |
US9542700B2 (en) * | 2008-11-05 | 2017-01-10 | Yu-Hua Chu | Business model based on multi-level application widgets and system thereof |
TW201020992A (en) * | 2008-11-19 | 2010-06-01 | Univ Chung Yuan Christian | User interface for interactive teaching, and method for operating the same |
KR101301243B1 (en) | 2008-12-02 | 2013-08-28 | 한국전자통신연구원 | Method for controlling restriction to viewing multimedia contents and system thereof |
US20100138854A1 (en) * | 2008-12-02 | 2010-06-03 | Electronics And Telecommunications Research Institute | Method and system for controlling restriction on viewing multimedia contents |
US8762963B2 (en) * | 2008-12-04 | 2014-06-24 | Beck Fund B.V. L.L.C. | Translation of programming code |
US8397222B2 (en) * | 2008-12-05 | 2013-03-12 | Peter D. Warren | Any-to-any system for doing computing |
US8805861B2 (en) * | 2008-12-09 | 2014-08-12 | Google Inc. | Methods and systems to train models to extract and integrate information from data sources |
CN101459619B (en) * | 2009-01-05 | 2011-01-05 | 杭州华三通信技术有限公司 | Method and apparatus for packet transmission processing in network |
US8850591B2 (en) | 2009-01-13 | 2014-09-30 | Mcafee, Inc. | System and method for concept building |
US8706709B2 (en) | 2009-01-15 | 2014-04-22 | Mcafee, Inc. | System and method for intelligent term grouping |
US20110093500A1 (en) * | 2009-01-21 | 2011-04-21 | Google Inc. | Query Optimization |
US20100192053A1 (en) * | 2009-01-26 | 2010-07-29 | Kabushiki Kaisha Toshiba | Workflow system and method of designing entry form used for workflow |
US10891037B1 (en) | 2009-01-30 | 2021-01-12 | The Pnc Financial Services Group, Inc. | User interfaces and system including same |
US8965798B1 (en) | 2009-01-30 | 2015-02-24 | The Pnc Financial Services Group, Inc. | Requesting reimbursement for transactions |
US8458105B2 (en) * | 2009-02-12 | 2013-06-04 | Decisive Analytics Corporation | Method and apparatus for analyzing and interrelating data |
US20100235314A1 (en) * | 2009-02-12 | 2010-09-16 | Decisive Analytics Corporation | Method and apparatus for analyzing and interrelating video data |
US8180824B2 (en) | 2009-02-23 | 2012-05-15 | Trane International, Inc. | Log collection data harvester for use in a building automation system |
US8239842B2 (en) * | 2009-02-24 | 2012-08-07 | Microsoft Corporation | Implicit line continuation |
US8473442B1 (en) | 2009-02-25 | 2013-06-25 | Mcafee, Inc. | System and method for intelligent state management |
US8782025B2 (en) * | 2009-03-10 | 2014-07-15 | Ims Software Services Ltd. | Systems and methods for address intelligence |
US20100241755A1 (en) * | 2009-03-18 | 2010-09-23 | Microsoft Corporation | Permission model for feed content |
US20100241579A1 (en) * | 2009-03-19 | 2010-09-23 | Microsoft Corporation | Feed Content Presentation |
US9342508B2 (en) * | 2009-03-19 | 2016-05-17 | Microsoft Technology Licensing, Llc | Data localization templates and parsing |
US8077050B2 (en) * | 2009-03-24 | 2011-12-13 | United Parcel Service Of America, Inc. | Transport system evaluator |
US8667121B2 (en) | 2009-03-25 | 2014-03-04 | Mcafee, Inc. | System and method for managing data and policies |
US8447722B1 (en) | 2009-03-25 | 2013-05-21 | Mcafee, Inc. | System and method for data mining and security policy management |
US8799877B2 (en) * | 2009-03-27 | 2014-08-05 | Optumsoft, Inc. | Interpreter-based program language translator using embedded interpreter types and variables |
US20100250613A1 (en) * | 2009-03-30 | 2010-09-30 | Microsoft Corporation | Query processing using arrays |
CA2660748C (en) * | 2009-03-31 | 2016-08-09 | Trapeze Software Inc. | System for aggregating data and a method for providing the same |
US9317128B2 (en) | 2009-04-02 | 2016-04-19 | Oblong Industries, Inc. | Remote devices used in a markerless installation of a spatial operating environment incorporating gestural control |
US10824238B2 (en) | 2009-04-02 | 2020-11-03 | Oblong Industries, Inc. | Operating environment with gestural control and multiple client devices, displays, and users |
US8364644B1 (en) * | 2009-04-22 | 2013-01-29 | Network Appliance, Inc. | Exclusion of data from a persistent point-in-time image |
US9805020B2 (en) | 2009-04-23 | 2017-10-31 | Deep Sky Concepts, Inc. | In-context access of stored declarative knowledge using natural language expression |
US8972445B2 (en) | 2009-04-23 | 2015-03-03 | Deep Sky Concepts, Inc. | Systems and methods for storage of declarative knowledge accessible by natural language in a computer capable of appropriately responding |
US8275788B2 (en) | 2009-11-17 | 2012-09-25 | Glace Holding Llc | System and methods for accessing web pages using natural language |
US20100281025A1 (en) * | 2009-05-04 | 2010-11-04 | Motorola, Inc. | Method and system for recommendation of content items |
US8204900B2 (en) * | 2009-05-21 | 2012-06-19 | Bank Of America Corporation | Metrics library |
US8311961B2 (en) * | 2009-05-29 | 2012-11-13 | International Business Machines Corporation | Effort estimation using text analysis |
US8429395B2 (en) | 2009-06-12 | 2013-04-23 | Microsoft Corporation | Controlling access to software component state |
US9594759B2 (en) * | 2009-06-16 | 2017-03-14 | Microsoft Technology Licensing, Llc | Backup and archival of selected items as a composite object |
US20100325214A1 (en) * | 2009-06-18 | 2010-12-23 | Microsoft Corporation | Predictive Collaboration |
WO2010149986A2 (en) | 2009-06-23 | 2010-12-29 | Secerno Limited | A method, a computer program and apparatus for analysing symbols in a computer |
US9933914B2 (en) * | 2009-07-06 | 2018-04-03 | Nokia Technologies Oy | Method and apparatus of associating application state information with content and actions |
JP4892626B2 (en) * | 2009-07-08 | 2012-03-07 | 東芝テック株式会社 | Printer and message data management program |
JP5471106B2 (en) * | 2009-07-16 | 2014-04-16 | 独立行政法人情報通信研究機構 | Speech translation system, dictionary server device, and program |
JP5375413B2 (en) | 2009-07-30 | 2013-12-25 | 富士通株式会社 | Data conversion apparatus, data conversion method, and data conversion program |
US20110029904A1 (en) * | 2009-07-30 | 2011-02-03 | Adam Miles Smith | Behavior and Appearance of Touch-Optimized User Interface Elements for Controlling Computer Function |
US8386498B2 (en) * | 2009-08-05 | 2013-02-26 | Loglogic, Inc. | Message descriptions |
US9123006B2 (en) * | 2009-08-11 | 2015-09-01 | Novell, Inc. | Techniques for parallel business intelligence evaluation and management |
US9542408B2 (en) | 2010-08-27 | 2017-01-10 | Pneuron Corp. | Method and process for enabling distributing cache data sources for query processing and distributed disk caching of large data and analysis requests |
CA3129946A1 (en) * | 2009-08-28 | 2011-03-03 | Ust Global (Singapore) Pte. Limited | System and method for employing the use of neural networks for the purpose of real-time business intelligence and automation control |
US8505813B2 (en) | 2009-09-04 | 2013-08-13 | Bank Of America Corporation | Customer benefit offer program enrollment |
JP4992945B2 (en) * | 2009-09-10 | 2012-08-08 | 株式会社日立製作所 | Stream data generation method, stream data generation device, and stream data generation program |
FR2950170B1 (en) * | 2009-09-16 | 2011-10-14 | Airbus Operations Sas | METHOD FOR GENERATING INTERFACE CONFIGURATION FILES FOR CALCULATORS OF AN AVIONIC PLATFORM |
US8364463B2 (en) | 2009-09-25 | 2013-01-29 | International Business Machines Corporation | Optimizing a language/media translation map |
US9031243B2 (en) * | 2009-09-28 | 2015-05-12 | iZotope, Inc. | Automatic labeling and control of audio algorithms by audio recognition |
US8832676B2 (en) * | 2009-09-30 | 2014-09-09 | Zynga Inc. | Apparatuses, methods and systems for a social networking application updater |
US8266125B2 (en) * | 2009-10-01 | 2012-09-11 | Starcounter Ab | Systems and methods for managing databases |
US9971807B2 (en) | 2009-10-14 | 2018-05-15 | Oblong Industries, Inc. | Multi-process interactive systems and methods |
US9933852B2 (en) | 2009-10-14 | 2018-04-03 | Oblong Industries, Inc. | Multi-process interactive systems and methods |
US9960967B2 (en) | 2009-10-21 | 2018-05-01 | A10 Networks, Inc. | Determining an application delivery server based on geo-location information |
US8341154B2 (en) * | 2009-10-28 | 2012-12-25 | Microsoft Corporation | Extending types hosted in database to other platforms |
US20110106776A1 (en) * | 2009-11-03 | 2011-05-05 | Schlumberger Technology Corporation | Incremental implementation of undo/redo support in legacy applications |
US20110107246A1 (en) * | 2009-11-03 | 2011-05-05 | Schlumberger Technology Corporation | Undo/redo operations for multi-object data |
TWI480746B (en) * | 2009-11-09 | 2015-04-11 | Hewlett Packard Development Co | Enabling faster full-text searching using a structured data store |
KR101767262B1 (en) | 2009-11-09 | 2017-08-11 | 삼성전자주식회사 | Method and apparatus for changing input format in input system using universal plug and play |
US9137206B2 (en) * | 2009-11-20 | 2015-09-15 | International Business Machines Corporation | Service registry for saving and restoring a faceted selection |
KR20110072847A (en) * | 2009-12-23 | 2011-06-29 | 삼성전자주식회사 | Dialog management system or method for processing information seeking dialog |
US8495312B2 (en) * | 2010-01-25 | 2013-07-23 | Sepaton, Inc. | System and method for identifying locations within data |
US8140533B1 (en) | 2010-01-26 | 2012-03-20 | Google Inc. | Harvesting relational tables from lists on the web |
US8682667B2 (en) | 2010-02-25 | 2014-03-25 | Apple Inc. | User profiling for selecting user specific voice input processing information |
US20110219016A1 (en) * | 2010-03-04 | 2011-09-08 | Src, Inc. | Stream Mining via State Machine and High Dimensionality Database |
US10417646B2 (en) | 2010-03-09 | 2019-09-17 | Sdl Inc. | Predicting the cost associated with translating textual content |
US8874526B2 (en) * | 2010-03-31 | 2014-10-28 | Cloudera, Inc. | Dynamically processing an event using an extensible data model |
US8780115B1 (en) | 2010-04-06 | 2014-07-15 | The Pnc Financial Services Group, Inc. | Investment management marketing tool |
US8791949B1 (en) | 2010-04-06 | 2014-07-29 | The Pnc Financial Services Group, Inc. | Investment management marketing tool |
US8954413B2 (en) * | 2010-04-12 | 2015-02-10 | Thermopylae Sciences and Technology | Methods and apparatus for adaptively harvesting pertinent data |
CN102236681A (en) * | 2010-04-20 | 2011-11-09 | 中兴通讯股份有限公司 | System and method for storing and obtaining data |
US8412510B2 (en) * | 2010-04-21 | 2013-04-02 | Fisher-Rosemount Systems, Inc. | Methods and apparatus to display localized resources in process control applications |
US8490056B2 (en) * | 2010-04-28 | 2013-07-16 | International Business Machines Corporation | Automatic identification of subroutines from test scripts |
US9880860B2 (en) * | 2010-05-05 | 2018-01-30 | Microsoft Technology Licensing, Llc | Automatic return to synchronization context for asynchronous computations |
AU2011254219A1 (en) | 2010-05-21 | 2012-12-13 | Vaultive Ltd. | System and method for controlling and monitoring access to data processing applications |
US8850354B1 (en) * | 2010-05-21 | 2014-09-30 | Google Inc. | Multi-window web-based application structure |
US8266102B2 (en) * | 2010-05-26 | 2012-09-11 | International Business Machines Corporation | Synchronization of sequential access storage components with backup catalog |
GB2494337A (en) * | 2010-05-28 | 2013-03-06 | Securitymetrics Inc | Systems and methods for determining whether data includes strings that correspond to sensitive information |
WO2011156593A1 (en) * | 2010-06-09 | 2011-12-15 | Decernis, Llc | System and method for analysis and visualization of emerging issues in manufacturing and supply chain management |
US8423444B1 (en) | 2010-07-02 | 2013-04-16 | The Pnc Financial Services Group, Inc. | Investor personality tool |
US11475523B1 (en) | 2010-07-02 | 2022-10-18 | The Pnc Financial Services Group, Inc. | Investor retirement lifestyle planning tool |
US11475524B1 (en) | 2010-07-02 | 2022-10-18 | The Pnc Financial Services Group, Inc. | Investor retirement lifestyle planning tool |
US8417614B1 (en) | 2010-07-02 | 2013-04-09 | The Pnc Financial Services Group, Inc. | Investor personality tool |
US9043296B2 (en) | 2010-07-30 | 2015-05-26 | Microsoft Technology Licensing, Llc | System of providing suggestions based on accessible and contextual information |
US8468391B2 (en) * | 2010-08-04 | 2013-06-18 | International Business Machines Corporation | Utilizing log event ontology to deliver user role specific solutions for problem determination |
JP5124001B2 (en) * | 2010-09-08 | 2013-01-23 | シャープ株式会社 | Translation apparatus, translation method, computer program, and recording medium |
CN105760782B (en) | 2010-09-22 | 2019-01-15 | 尼尔森(美国)有限公司 | Monitor the method being exposed by the media and server |
US10089390B2 (en) | 2010-09-24 | 2018-10-02 | International Business Machines Corporation | System and method to extract models from semi-structured documents |
US9177017B2 (en) * | 2010-09-27 | 2015-11-03 | Microsoft Technology Licensing, Llc | Query constraint encoding with type-based state machine |
US9215275B2 (en) | 2010-09-30 | 2015-12-15 | A10 Networks, Inc. | System and method to balance servers based on server load status |
FR2965952B1 (en) * | 2010-10-06 | 2013-06-21 | Commissariat Energie Atomique | METHOD FOR UPDATING A REVERSE INDEX AND SERVER IMPLEMENTING SAID METHOD |
US10318877B2 (en) | 2010-10-19 | 2019-06-11 | International Business Machines Corporation | Cohort-based prediction of a future event |
US8818963B2 (en) | 2010-10-29 | 2014-08-26 | Microsoft Corporation | Halloween protection in a multi-version database system |
US8965751B2 (en) * | 2010-11-01 | 2015-02-24 | Microsoft Corporation | Providing multi-lingual translation for third party content feed applications |
US8806615B2 (en) * | 2010-11-04 | 2014-08-12 | Mcafee, Inc. | System and method for protecting specified data combinations |
TWI415427B (en) | 2010-11-04 | 2013-11-11 | Ind Tech Res Inst | System and method for peer-to-peer live streaming |
US9710429B1 (en) * | 2010-11-12 | 2017-07-18 | Google Inc. | Providing text resources updated with translation input from multiple users |
US9609052B2 (en) | 2010-12-02 | 2017-03-28 | A10 Networks, Inc. | Distributing application traffic to servers based on dynamic service response time |
CN102486798A (en) * | 2010-12-03 | 2012-06-06 | 腾讯科技(深圳)有限公司 | Data loading method and device |
US9304672B2 (en) | 2010-12-17 | 2016-04-05 | Microsoft Technology Licensing, Llc | Representation of an interactive document as a graph of entities |
US9110957B2 (en) | 2010-12-17 | 2015-08-18 | Microsoft Technology Licensing, Llc | Data mining in a business intelligence document |
US9336184B2 (en) | 2010-12-17 | 2016-05-10 | Microsoft Technology Licensing, Llc | Representation of an interactive document as a graph of entities |
US9069557B2 (en) | 2010-12-17 | 2015-06-30 | Microsoft Technology Licensing, LLP | Business intelligence document |
US9104992B2 (en) | 2010-12-17 | 2015-08-11 | Microsoft Technology Licensing, Llc | Business application publication |
US9171272B2 (en) | 2010-12-17 | 2015-10-27 | Microsoft Technology Licensing, LLP | Automated generation of analytic and visual behavior |
US9111238B2 (en) * | 2010-12-17 | 2015-08-18 | Microsoft Technology Licensing, Llc | Data feed having customizable analytic and visual behavior |
US9024952B2 (en) | 2010-12-17 | 2015-05-05 | Microsoft Technology Licensing, Inc. | Discovering and configuring representations of data via an insight taxonomy |
US9864966B2 (en) | 2010-12-17 | 2018-01-09 | Microsoft Technology Licensing, Llc | Data mining in a business intelligence document |
US9122639B2 (en) | 2011-01-25 | 2015-09-01 | Sepaton, Inc. | Detection and deduplication of backup sets exhibiting poor locality |
WO2012101701A1 (en) * | 2011-01-27 | 2012-08-02 | 日本電気株式会社 | Ui (user interface) creation support device, ui creation support method, and program |
US9171079B2 (en) * | 2011-01-28 | 2015-10-27 | Cisco Technology, Inc. | Searching sensor data |
US9225793B2 (en) * | 2011-01-28 | 2015-12-29 | Cisco Technology, Inc. | Aggregating sensor data |
US9275093B2 (en) * | 2011-01-28 | 2016-03-01 | Cisco Technology, Inc. | Indexing sensor data |
US9547626B2 (en) | 2011-01-29 | 2017-01-17 | Sdl Plc | Systems, methods, and media for managing ambient adaptability of web applications and web services |
US10657540B2 (en) | 2011-01-29 | 2020-05-19 | Sdl Netherlands B.V. | Systems, methods, and media for web content management |
US9058560B2 (en) | 2011-02-17 | 2015-06-16 | Superior Edge, Inc. | Methods, apparatus and systems for generating, updating and executing an invasive species control plan |
US10580015B2 (en) | 2011-02-25 | 2020-03-03 | Sdl Netherlands B.V. | Systems, methods, and media for executing and optimizing online marketing initiatives |
US10140320B2 (en) | 2011-02-28 | 2018-11-27 | Sdl Inc. | Systems, methods, and media for generating analytical data |
US9665908B1 (en) | 2011-02-28 | 2017-05-30 | The Pnc Financial Services Group, Inc. | Net worth analysis tools |
US8321316B1 (en) | 2011-02-28 | 2012-11-27 | The Pnc Financial Services Group, Inc. | Income analysis tools for wealth management |
US8374940B1 (en) | 2011-02-28 | 2013-02-12 | The Pnc Financial Services Group, Inc. | Wealth allocation analysis tools |
US9852470B1 (en) | 2011-02-28 | 2017-12-26 | The Pnc Financial Services Group, Inc. | Time period analysis tools for wealth management transactions |
WO2012122516A1 (en) * | 2011-03-10 | 2012-09-13 | Redoak Logic, Inc. | System and method for converting large data sets to other information to observations for analysis to reveal complex relationship |
US9104663B1 (en) * | 2011-03-18 | 2015-08-11 | Emc Corporation | Dynamic allocation of memory for memory intensive operators |
CN106156363B (en) | 2011-03-18 | 2019-08-09 | 尼尔森(美国)有限公司 | The method and apparatus for determining media impression |
US9262612B2 (en) | 2011-03-21 | 2016-02-16 | Apple Inc. | Device access using voice authentication |
WO2012139098A1 (en) | 2011-04-07 | 2012-10-11 | Pneuron Corp. | Legacy application migration to real time, parallel performance cloud |
US10749887B2 (en) | 2011-04-08 | 2020-08-18 | Proofpoint, Inc. | Assessing security risks of users in a computing network |
US9558677B2 (en) * | 2011-04-08 | 2017-01-31 | Wombat Security Technologies, Inc. | Mock attack cybersecurity training system and methods |
WO2012139127A1 (en) * | 2011-04-08 | 2012-10-11 | Wombat Security Technologies, Inc. | Context-aware training systems, apparatuses, and methods |
US9824609B2 (en) | 2011-04-08 | 2017-11-21 | Wombat Security Technologies, Inc. | Mock attack cybersecurity training system and methods |
US9373267B2 (en) * | 2011-04-08 | 2016-06-21 | Wombat Security Technologies, Inc. | Method and system for controlling context-aware cybersecurity training |
US10733570B1 (en) | 2011-04-19 | 2020-08-04 | The Pnc Financial Services Group, Inc. | Facilitating employee career development |
US9904726B2 (en) | 2011-05-04 | 2018-02-27 | Black Hills IP Holdings, LLC. | Apparatus and method for automated and assisted patent claim mapping and expense planning |
US8751298B1 (en) | 2011-05-09 | 2014-06-10 | Bank Of America Corporation | Event-driven coupon processor alert |
US9892419B1 (en) | 2011-05-09 | 2018-02-13 | Bank Of America Corporation | Coupon deposit account fraud protection system |
US20120291011A1 (en) * | 2011-05-12 | 2012-11-15 | Google Inc. | User Interfaces to Assist in Creating Application Scripts |
US20120296910A1 (en) * | 2011-05-16 | 2012-11-22 | Michal Skubacz | Method and system for retrieving information |
US8856452B2 (en) | 2011-05-31 | 2014-10-07 | Illinois Institute Of Technology | Timing-aware data prefetching for microprocessors |
US10057736B2 (en) | 2011-06-03 | 2018-08-21 | Apple Inc. | Active transport based notifications |
US10331658B2 (en) * | 2011-06-03 | 2019-06-25 | Gdial Inc. | Systems and methods for atomizing and individuating data as data quanta |
US9146133B2 (en) * | 2011-06-06 | 2015-09-29 | Honeywell International Inc. | Methods and systems for displaying procedure information on an aircraft display |
US8924974B1 (en) * | 2011-06-08 | 2014-12-30 | Workday, Inc. | System for error checking of process definitions for batch processes |
US9626458B2 (en) * | 2011-06-14 | 2017-04-18 | Nec Corporation | Evaluation model generation device, evaluation model generation method, and evaluation model generation program |
US8538949B2 (en) | 2011-06-17 | 2013-09-17 | Microsoft Corporation | Interactive web crawler |
US9092482B2 (en) | 2013-03-14 | 2015-07-28 | Palantir Technologies, Inc. | Fair scheduling for mixed-query loads |
US9378138B2 (en) * | 2011-06-29 | 2016-06-28 | International Business Machines Corporation | Conservative garbage collection and access protection |
US10536508B2 (en) * | 2011-06-30 | 2020-01-14 | Telefonaktiebolaget Lm Ericsson (Publ) | Flexible data communication |
US9946991B2 (en) | 2011-06-30 | 2018-04-17 | 3M Innovative Properties Company | Methods using multi-dimensional representations of medical codes |
US8935676B2 (en) * | 2011-08-07 | 2015-01-13 | Hewlett-Packard Development Company, L.P. | Automated test failure troubleshooter |
US8510320B2 (en) * | 2011-08-10 | 2013-08-13 | Sap Ag | Silent migration of business process binaries |
US20130042235A1 (en) * | 2011-08-10 | 2013-02-14 | International Business Machines Corporation | Dynamic bootstrap literal processing within a managed runtime environment |
CA2759516C (en) | 2011-11-24 | 2019-12-31 | Ibm Canada Limited - Ibm Canada Limitee | Serialization of pre-initialized objects |
US8688499B1 (en) * | 2011-08-11 | 2014-04-01 | Google Inc. | System and method for generating business process models from mapped time sequenced operational and transaction data |
US9984054B2 (en) | 2011-08-24 | 2018-05-29 | Sdl Inc. | Web interface including the review and manipulation of a web document and utilizing permission based control |
US20130055078A1 (en) * | 2011-08-24 | 2013-02-28 | Salesforce.Com, Inc. | Systems and methods for improved navigation of a multi-page display |
US8732574B2 (en) | 2011-08-25 | 2014-05-20 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US9053394B2 (en) * | 2011-08-30 | 2015-06-09 | 5D Robotics, Inc. | Vehicle management system |
US8694462B2 (en) | 2011-09-12 | 2014-04-08 | Microsoft Corporation | Scale-out system to acquire event data |
US9208476B2 (en) | 2011-09-12 | 2015-12-08 | Microsoft Technology Licensing, Llc | Counting and resetting broadcast system badge counters |
US8595322B2 (en) * | 2011-09-12 | 2013-11-26 | Microsoft Corporation | Target subscription for a notification distribution system |
US8898628B2 (en) | 2011-09-23 | 2014-11-25 | Ahmad RAZA | Method and an apparatus for developing software |
US10630559B2 (en) | 2011-09-27 | 2020-04-21 | UST Global (Singapore) Pte. Ltd. | Virtual machine (VM) realm integration and management |
JP5594269B2 (en) * | 2011-09-29 | 2014-09-24 | コニカミノルタ株式会社 | File name creation device, image forming device, and file name creation program |
US8972385B2 (en) | 2011-10-03 | 2015-03-03 | Black Hills Ip Holdings, Llc | System and method for tracking patent ownership change |
US9940363B2 (en) | 2011-10-03 | 2018-04-10 | Black Hills Ip Holdings, Llc | Systems, methods and user interfaces in a patent management system |
US8897154B2 (en) | 2011-10-24 | 2014-11-25 | A10 Networks, Inc. | Combining stateless and stateful server load balancing |
US8181254B1 (en) * | 2011-10-28 | 2012-05-15 | Google Inc. | Setting default security features for use with web applications and extensions |
CA2756102A1 (en) * | 2011-11-01 | 2012-01-03 | Cit Global Mobile Division | Method and system for localizing an application on a computing device |
US9203805B2 (en) | 2011-11-23 | 2015-12-01 | Cavium, Inc. | Reverse NFA generation and processing |
US10423515B2 (en) * | 2011-11-29 | 2019-09-24 | Microsoft Technology Licensing, Llc | Recording touch information |
US9386088B2 (en) | 2011-11-29 | 2016-07-05 | A10 Networks, Inc. | Accelerating service processing using fast path TCP |
KR101277145B1 (en) | 2011-12-07 | 2013-06-20 | 한국과학기술연구원 | Method For Transforming Intermediate Language by Using Common Representation, System And Computer-Readable Recording Medium with Program Therefor |
KR101349628B1 (en) | 2011-12-07 | 2014-01-09 | 한국과학기술연구원 | Method For Transforming Intermediate Language by Using Operator, System And Computer-Readable Recording Medium with Program Therefor |
WO2013090555A1 (en) | 2011-12-13 | 2013-06-20 | Pneuron Corp. | Pneuron distributed analytics |
US9094364B2 (en) | 2011-12-23 | 2015-07-28 | A10 Networks, Inc. | Methods to manage services over a service gateway |
US20130246334A1 (en) | 2011-12-27 | 2013-09-19 | Mcafee, Inc. | System and method for providing data protection workflows in a network environment |
TWI480730B (en) | 2011-12-30 | 2015-04-11 | Ibm | Method and apparatus for measuring performance of an appliance |
US20140358625A1 (en) * | 2012-01-11 | 2014-12-04 | Hitachi, Ltd. | Operating Support System, Operating Support Method and Operating Support Program |
US10169812B1 (en) | 2012-01-20 | 2019-01-01 | The Pnc Financial Services Group, Inc. | Providing financial account information to users |
US10044582B2 (en) | 2012-01-28 | 2018-08-07 | A10 Networks, Inc. | Generating secure name records |
US8762315B2 (en) | 2012-02-07 | 2014-06-24 | Alan A. Yelsey | Interactive portal for facilitating the representation and exploration of complexity |
US9015255B2 (en) | 2012-02-14 | 2015-04-21 | The Nielsen Company (Us), Llc | Methods and apparatus to identify session users with cookie information |
WO2013128238A1 (en) * | 2012-02-29 | 2013-09-06 | Freescale Semiconductor, Inc. | Debugging method and computer program product |
US10134385B2 (en) | 2012-03-02 | 2018-11-20 | Apple Inc. | Systems and methods for name pronunciation |
US9760380B2 (en) * | 2012-03-14 | 2017-09-12 | Microsoft Technology Licensing, Llc | Using grammar to serialize and de-serialize objects |
US20130254139A1 (en) * | 2012-03-21 | 2013-09-26 | Xiaoguang Lei | Systems and methods for building a universal intelligent assistant with learning capabilities |
US8813046B2 (en) * | 2012-03-23 | 2014-08-19 | Infosys Limited | System and method for internationalization encoding |
US9418083B2 (en) | 2012-04-20 | 2016-08-16 | Patterson Thuente Pedersen, P.A. | System for computerized evaluation of patent-related information |
US8914809B1 (en) | 2012-04-24 | 2014-12-16 | Open Text S.A. | Message broker system and method |
US9773270B2 (en) | 2012-05-11 | 2017-09-26 | Fredhopper B.V. | Method and system for recommending products based on a ranking cocktail |
US9141290B2 (en) * | 2012-05-13 | 2015-09-22 | Emc Corporation | Snapshot mechanism |
US10261994B2 (en) | 2012-05-25 | 2019-04-16 | Sdl Inc. | Method and system for automatic management of reputation of translators |
US8694508B2 (en) * | 2012-06-04 | 2014-04-08 | Sap Ag | Columnwise storage of point data |
US8830714B2 (en) | 2012-06-07 | 2014-09-09 | International Business Machines Corporation | High speed large scale dictionary matching |
US9721563B2 (en) | 2012-06-08 | 2017-08-01 | Apple Inc. | Name recognition system |
AU2013204865B2 (en) | 2012-06-11 | 2015-07-09 | The Nielsen Company (Us), Llc | Methods and apparatus to share online media impressions data |
US9489649B2 (en) * | 2012-06-18 | 2016-11-08 | Sap Se | Message payload editor |
US9672209B2 (en) * | 2012-06-21 | 2017-06-06 | International Business Machines Corporation | Dynamic translation substitution |
US9465835B2 (en) | 2012-06-25 | 2016-10-11 | Sap Se | Columnwise spatial aggregation |
US8782221B2 (en) | 2012-07-05 | 2014-07-15 | A10 Networks, Inc. | Method to allocate buffer for TCP proxy session based on dynamic network conditions |
CA2918472C (en) * | 2012-07-16 | 2018-05-29 | Pneuron Corp. | A method and process for enabling distributing cache data sources for query processing and distributed disk caching of large data and analysis requests |
US9727350B2 (en) * | 2012-07-26 | 2017-08-08 | Entit Software Llc | Localizing computer program code |
FR2994296B1 (en) * | 2012-08-01 | 2015-06-19 | Netwave | DATA PROCESSING METHOD FOR SITUATIONAL ANALYSIS |
US9113590B2 (en) | 2012-08-06 | 2015-08-25 | Superior Edge, Inc. | Methods, apparatus, and systems for determining in-season crop status in an agricultural crop and alerting users |
US11461862B2 (en) | 2012-08-20 | 2022-10-04 | Black Hills Ip Holdings, Llc | Analytics generation for patent portfolio management |
US9461876B2 (en) * | 2012-08-29 | 2016-10-04 | Loci | System and method for fuzzy concept mapping, voting ontology crowd sourcing, and technology prediction |
AU2013204953B2 (en) | 2012-08-30 | 2016-09-08 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
US9375582B2 (en) | 2012-08-31 | 2016-06-28 | Nuvectra Corporation | Touch screen safety controls for clinician programmer |
US8812125B2 (en) | 2012-08-31 | 2014-08-19 | Greatbatch Ltd. | Systems and methods for the identification and association of medical devices |
US8903496B2 (en) | 2012-08-31 | 2014-12-02 | Greatbatch Ltd. | Clinician programming system and method |
US9180302B2 (en) | 2012-08-31 | 2015-11-10 | Greatbatch Ltd. | Touch screen finger position indicator for a spinal cord stimulation programming device |
US9259577B2 (en) | 2012-08-31 | 2016-02-16 | Greatbatch Ltd. | Method and system of quick neurostimulation electrode configuration and positioning |
US9615788B2 (en) | 2012-08-31 | 2017-04-11 | Nuvectra Corporation | Method and system of producing 2D representations of 3D pain and stimulation maps and implant models on a clinician programmer |
US9507912B2 (en) | 2012-08-31 | 2016-11-29 | Nuvectra Corporation | Method and system of simulating a pulse generator on a clinician programmer |
US9594877B2 (en) | 2012-08-31 | 2017-03-14 | Nuvectra Corporation | Virtual reality representation of medical devices |
US8868199B2 (en) | 2012-08-31 | 2014-10-21 | Greatbatch Ltd. | System and method of compressing medical maps for pulse generator or database storage |
US8761897B2 (en) | 2012-08-31 | 2014-06-24 | Greatbatch Ltd. | Method and system of graphical representation of lead connector block and implantable pulse generators on a clinician programmer |
US10668276B2 (en) | 2012-08-31 | 2020-06-02 | Cirtec Medical Corp. | Method and system of bracketing stimulation parameters on clinician programmers |
US8983616B2 (en) | 2012-09-05 | 2015-03-17 | Greatbatch Ltd. | Method and system for associating patient records with pulse generators |
US9471753B2 (en) | 2012-08-31 | 2016-10-18 | Nuvectra Corporation | Programming and virtual reality representation of stimulation parameter Groups |
US8757485B2 (en) | 2012-09-05 | 2014-06-24 | Greatbatch Ltd. | System and method for using clinician programmer and clinician programming data for inventory and manufacturing prediction and control |
US9767255B2 (en) | 2012-09-05 | 2017-09-19 | Nuvectra Corporation | Predefined input for clinician programmer data entry |
US11308528B2 (en) | 2012-09-14 | 2022-04-19 | Sdl Netherlands B.V. | Blueprinting of multimedia assets |
US10452740B2 (en) | 2012-09-14 | 2019-10-22 | Sdl Netherlands B.V. | External content libraries |
US11386186B2 (en) | 2012-09-14 | 2022-07-12 | Sdl Netherlands B.V. | External content library connector systems and methods |
CN103685399B (en) * | 2012-09-17 | 2018-03-23 | 腾讯科技(深圳)有限公司 | A kind of methods, devices and systems for logging in class Unix virtual containers |
US10002141B2 (en) | 2012-09-25 | 2018-06-19 | A10 Networks, Inc. | Distributed database in software driven networks |
CN108027805B (en) | 2012-09-25 | 2021-12-21 | A10网络股份有限公司 | Load distribution in a data network |
US9843484B2 (en) | 2012-09-25 | 2017-12-12 | A10 Networks, Inc. | Graceful scaling in software driven networks |
US10021174B2 (en) | 2012-09-25 | 2018-07-10 | A10 Networks, Inc. | Distributing service sessions |
WO2014055772A1 (en) | 2012-10-03 | 2014-04-10 | Globesherpa, Inc. | Mobile ticketing |
US9213707B2 (en) * | 2012-10-12 | 2015-12-15 | Watson Manwaring Conner | Ordered access of interrelated data files |
US8954940B2 (en) * | 2012-10-12 | 2015-02-10 | International Business Machines Corporation | Integrating preprocessor behavior into parsing |
US9081900B2 (en) | 2012-10-15 | 2015-07-14 | Toyota Motor Engineering & Manufacturing North America, Inc. | Systems and methods for mining temporal requirements from block diagram models of control systems |
US9916306B2 (en) | 2012-10-19 | 2018-03-13 | Sdl Inc. | Statistical linguistic analysis of source content |
US9348677B2 (en) | 2012-10-22 | 2016-05-24 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US9081975B2 (en) | 2012-10-22 | 2015-07-14 | Palantir Technologies, Inc. | Sharing information between nexuses that use different classification schemes for information access control |
JP2016505912A (en) * | 2012-11-02 | 2016-02-25 | ジーイー・インテリジェント・プラットフォームズ・インコーポレイテッド | Content storage apparatus and method |
US9501761B2 (en) | 2012-11-05 | 2016-11-22 | Palantir Technologies, Inc. | System and method for sharing investigation results |
US9338225B2 (en) | 2012-12-06 | 2016-05-10 | A10 Networks, Inc. | Forwarding policies on a virtual service network |
US20140201629A1 (en) * | 2013-01-17 | 2014-07-17 | Microsoft Corporation | Collaborative learning through user generated knowledge |
US9531846B2 (en) | 2013-01-23 | 2016-12-27 | A10 Networks, Inc. | Reducing buffer usage for TCP proxy session based on delayed acknowledgement |
US9330659B2 (en) | 2013-02-25 | 2016-05-03 | Microsoft Technology Licensing, Llc | Facilitating development of a spoken natural language interface |
US9658999B2 (en) | 2013-03-01 | 2017-05-23 | Sony Corporation | Language processing method and electronic device |
US9900252B2 (en) | 2013-03-08 | 2018-02-20 | A10 Networks, Inc. | Application delivery controller and global server load balancer |
US9524273B2 (en) | 2013-03-11 | 2016-12-20 | Oracle International Corporation | Method and system for generating a web page layout using nested drop zone widgets having different software functionalities |
US11205036B2 (en) * | 2013-03-11 | 2021-12-21 | Oracle International Corporation | Method and system for implementing contextual widgets |
US9195712B2 (en) | 2013-03-12 | 2015-11-24 | Microsoft Technology Licensing, Llc | Method of converting query plans to native code |
US9152466B2 (en) * | 2013-03-13 | 2015-10-06 | Barracuda Networks, Inc. | Organizing file events by their hierarchical paths for multi-threaded synch and parallel access system, apparatus, and method of operation |
US9262555B2 (en) * | 2013-03-15 | 2016-02-16 | Yahoo! Inc. | Machine for recognizing or generating Jabba-type sequences |
US9996502B2 (en) * | 2013-03-15 | 2018-06-12 | Locus Lp | High-dimensional systems databases for real-time prediction of interactions in a functional system |
US8903717B2 (en) | 2013-03-15 | 2014-12-02 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
WO2014144837A1 (en) | 2013-03-15 | 2014-09-18 | A10 Networks, Inc. | Processing data packets using a policy based network path |
US9245299B2 (en) | 2013-03-15 | 2016-01-26 | Locus Lp | Segmentation and stratification of composite portfolios of investment securities |
US10599623B2 (en) | 2013-03-15 | 2020-03-24 | Locus Lp | Matching multidimensional projections of functional space |
US8909656B2 (en) | 2013-03-15 | 2014-12-09 | Palantir Technologies Inc. | Filter chains with associated multipath views for exploring large data sets |
US8868486B2 (en) | 2013-03-15 | 2014-10-21 | Palantir Technologies Inc. | Time-sensitive cube |
CA2906232C (en) * | 2013-03-15 | 2023-09-19 | Locus Analytics, Llc | Domain-specific syntax tagging in a functional information system |
US8855999B1 (en) | 2013-03-15 | 2014-10-07 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
US9098878B2 (en) * | 2013-03-15 | 2015-08-04 | Locus, LP | Stratified composite portfolios of investment securities |
US10515123B2 (en) | 2013-03-15 | 2019-12-24 | Locus Lp | Weighted analysis of stratified data entities in a database system |
US9990380B2 (en) | 2013-03-15 | 2018-06-05 | Locus Lp | Proximity search and navigation for functional information systems |
US8930897B2 (en) | 2013-03-15 | 2015-01-06 | Palantir Technologies Inc. | Data integration tool |
US9766832B2 (en) | 2013-03-15 | 2017-09-19 | Hitachi Data Systems Corporation | Systems and methods of locating redundant data using patterns of matching fingerprints |
US9171207B1 (en) * | 2013-03-15 | 2015-10-27 | Peter L Olcott | Method and system for recognizing machine generated character glyphs in graphic images |
US9740369B2 (en) | 2013-03-15 | 2017-08-22 | Palantir Technologies Inc. | Systems and methods for providing a tagging interface for external content |
US10268639B2 (en) * | 2013-03-15 | 2019-04-23 | Inpixon | Joining large database tables |
US9898167B2 (en) | 2013-03-15 | 2018-02-20 | Palantir Technologies Inc. | Systems and methods for providing a tagging interface for external content |
US9530094B2 (en) | 2013-03-15 | 2016-12-27 | Yahoo! Inc. | Jabba-type contextual tagger |
US10235649B1 (en) | 2014-03-14 | 2019-03-19 | Walmart Apollo, Llc | Customer analytics data model |
US9767190B2 (en) | 2013-04-23 | 2017-09-19 | Black Hills Ip Holdings, Llc | Patent claim scope evaluator |
US9519914B2 (en) | 2013-04-30 | 2016-12-13 | The Nielsen Company (Us), Llc | Methods and apparatus to determine ratings information for online media presentations |
WO2014179753A2 (en) | 2013-05-03 | 2014-11-06 | A10 Networks, Inc. | Facilitating secure network traffic by an application delivery controller |
US10027761B2 (en) | 2013-05-03 | 2018-07-17 | A10 Networks, Inc. | Facilitating a secure 3 party network session by a network device |
US10223637B1 (en) | 2013-05-30 | 2019-03-05 | Google Llc | Predicting accuracy of submitted data |
US20140358616A1 (en) * | 2013-06-03 | 2014-12-04 | International Business Machines Corporation | Asset management for a computer-based system using aggregated weights of changed assets |
US9256611B2 (en) | 2013-06-06 | 2016-02-09 | Sepaton, Inc. | System and method for multi-scale navigation of data |
US9779182B2 (en) * | 2013-06-07 | 2017-10-03 | Microsoft Technology Licensing, Llc | Semantic grouping in search |
WO2014197335A1 (en) | 2013-06-08 | 2014-12-11 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
US10176167B2 (en) | 2013-06-09 | 2019-01-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
EP3937002A1 (en) | 2013-06-09 | 2022-01-12 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US10068246B2 (en) | 2013-07-12 | 2018-09-04 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
US9588956B2 (en) * | 2013-07-12 | 2017-03-07 | Ab Initio Technology Llc | Parser generation |
US9223773B2 (en) | 2013-08-08 | 2015-12-29 | Palatir Technologies Inc. | Template system for custom document generation |
IN2013MU02617A (en) * | 2013-08-08 | 2015-06-12 | Subramanian JAYAKUMAR | |
US9313294B2 (en) | 2013-08-12 | 2016-04-12 | The Nielsen Company (Us), Llc | Methods and apparatus to de-duplicate impression information |
US10223401B2 (en) | 2013-08-15 | 2019-03-05 | International Business Machines Corporation | Incrementally retrieving data for objects to provide a desired level of detail |
US9563399B2 (en) * | 2013-08-30 | 2017-02-07 | Cavium, Inc. | Generating a non-deterministic finite automata (NFA) graph for regular expression patterns with advanced features |
US9367449B2 (en) * | 2013-09-11 | 2016-06-14 | Owtware Holdings Limited, BVI | Hierarchical garbage collection in an object relational database system |
JP2015060423A (en) * | 2013-09-19 | 2015-03-30 | 株式会社東芝 | Voice translation system, method of voice translation and program |
US9767222B2 (en) | 2013-09-27 | 2017-09-19 | International Business Machines Corporation | Information sets for data management |
US8938686B1 (en) | 2013-10-03 | 2015-01-20 | Palantir Technologies Inc. | Systems and methods for analyzing performance of an entity |
JP6465372B2 (en) * | 2013-10-09 | 2019-02-06 | 株式会社インタラクティブソリューションズ | Mobile terminal device, slide information management system, and mobile terminal control method |
US11790154B2 (en) | 2013-10-09 | 2023-10-17 | Interactive Solutions Corp. | Mobile terminal device, slide information managing system, and a control method of mobile terminal |
US9678973B2 (en) | 2013-10-15 | 2017-06-13 | Hitachi Data Systems Corporation | Multi-node hybrid deduplication |
US20150112708A1 (en) * | 2013-10-23 | 2015-04-23 | The Charlotte-Mecklenburg Hospital Authority D/B/A Carolinas Healthcare System | Methods and systems for merging and analyzing healthcare data |
US20150120224A1 (en) | 2013-10-29 | 2015-04-30 | C3 Energy, Inc. | Systems and methods for processing data relating to energy usage |
US9262136B2 (en) * | 2013-11-07 | 2016-02-16 | Netronome Systems, Inc. | Allocate instruction and API call that contain a sybmol for a non-memory resource |
US10230770B2 (en) | 2013-12-02 | 2019-03-12 | A10 Networks, Inc. | Network proxy layer for policy-based application proxies |
US10296160B2 (en) | 2013-12-06 | 2019-05-21 | Apple Inc. | Method for extracting salient dialog usage from live data |
EP2881899B1 (en) | 2013-12-09 | 2018-09-12 | Deutsche Telekom AG | System and method for automated aggregation of descriptions of individual object variants |
US9105000B1 (en) | 2013-12-10 | 2015-08-11 | Palantir Technologies Inc. | Aggregating data from a plurality of data sources |
US10956947B2 (en) | 2013-12-23 | 2021-03-23 | The Nielsen Company (Us), Llc | Methods and apparatus to measure media using media object characteristics |
US9852163B2 (en) | 2013-12-30 | 2017-12-26 | The Nielsen Company (Us), Llc | Methods and apparatus to de-duplicate impression information |
US9237138B2 (en) | 2013-12-31 | 2016-01-12 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
US10147114B2 (en) | 2014-01-06 | 2018-12-04 | The Nielsen Company (Us), Llc | Methods and apparatus to correct audience measurement data |
US20150193816A1 (en) | 2014-01-06 | 2015-07-09 | The Nielsen Company (Us), Llc | Methods and apparatus to correct misattributions of media impressions |
US9729353B2 (en) * | 2014-01-09 | 2017-08-08 | Netronome Systems, Inc. | Command-driven NFA hardware engine that encodes multiple automatons |
US9602532B2 (en) | 2014-01-31 | 2017-03-21 | Cavium, Inc. | Method and apparatus for optimizing finite automata processing |
US9904630B2 (en) | 2014-01-31 | 2018-02-27 | Cavium, Inc. | Finite automata processing based on a top of stack (TOS) memory |
US11720599B1 (en) * | 2014-02-13 | 2023-08-08 | Pivotal Software, Inc. | Clustering and visualizing alerts and incidents |
US9842152B2 (en) * | 2014-02-19 | 2017-12-12 | Snowflake Computing, Inc. | Transparent discovery of semi-structured data schema |
US9009827B1 (en) | 2014-02-20 | 2015-04-14 | Palantir Technologies Inc. | Security sharing system |
US10474645B2 (en) | 2014-02-24 | 2019-11-12 | Microsoft Technology Licensing, Llc | Automatically retrying transactions with split procedure execution |
US9718558B2 (en) | 2014-02-26 | 2017-08-01 | Honeywell International Inc. | Pilot centered system and method for decluttering aircraft displays |
US10346769B1 (en) * | 2014-03-14 | 2019-07-09 | Walmart Apollo, Llc | System and method for dynamic attribute table |
US10565538B1 (en) | 2014-03-14 | 2020-02-18 | Walmart Apollo, Llc | Customer attribute exemption |
US10235687B1 (en) * | 2014-03-14 | 2019-03-19 | Walmart Apollo, Llc | Shortest distance to store |
US10733555B1 (en) | 2014-03-14 | 2020-08-04 | Walmart Apollo, Llc | Workflow coordinator |
US9990046B2 (en) | 2014-03-17 | 2018-06-05 | Oblong Industries, Inc. | Visual collaboration interface |
US8924429B1 (en) | 2014-03-18 | 2014-12-30 | Palantir Technologies Inc. | Determining and extracting changed data from a data source |
US9942152B2 (en) | 2014-03-25 | 2018-04-10 | A10 Networks, Inc. | Forwarding data packets using a service-based forwarding policy |
US10020979B1 (en) | 2014-03-25 | 2018-07-10 | A10 Networks, Inc. | Allocating resources in multi-core computing environments |
US9489576B2 (en) | 2014-03-26 | 2016-11-08 | F12 Solutions, LLC. | Crop stand analysis |
US9942162B2 (en) | 2014-03-31 | 2018-04-10 | A10 Networks, Inc. | Active application response delay time |
US10896421B2 (en) | 2014-04-02 | 2021-01-19 | Brighterion, Inc. | Smart retail analytics and commercial messaging |
US20150287336A1 (en) * | 2014-04-04 | 2015-10-08 | Bank Of America Corporation | Automated phishing-email training |
US10002326B2 (en) | 2014-04-14 | 2018-06-19 | Cavium, Inc. | Compilation of finite automata based on memory hierarchy |
US10110558B2 (en) | 2014-04-14 | 2018-10-23 | Cavium, Inc. | Processing of finite automata based on memory hierarchy |
US9535664B1 (en) | 2014-04-23 | 2017-01-03 | William Knight Foster | Computerized software development process and management environment |
US11294665B1 (en) | 2014-04-23 | 2022-04-05 | William Knight Foster | Computerized software version control with a software database and a human database |
US9806943B2 (en) | 2014-04-24 | 2017-10-31 | A10 Networks, Inc. | Enabling planned upgrade/downgrade of network devices without impacting network sessions |
US9600599B2 (en) * | 2014-05-13 | 2017-03-21 | Spiral Genetics, Inc. | Prefix burrows-wheeler transformation with fast operations on compressed data |
US9906422B2 (en) | 2014-05-16 | 2018-02-27 | A10 Networks, Inc. | Distributed system to determine a server's health |
US10170123B2 (en) | 2014-05-30 | 2019-01-01 | Apple Inc. | Intelligent assistant for home automation |
EP3480811A1 (en) | 2014-05-30 | 2019-05-08 | Apple Inc. | Multi-command single utterance input method |
US9633004B2 (en) | 2014-05-30 | 2017-04-25 | Apple Inc. | Better resolution when referencing to concepts |
US9430463B2 (en) | 2014-05-30 | 2016-08-30 | Apple Inc. | Exemplar-based natural language processing |
US9992229B2 (en) | 2014-06-03 | 2018-06-05 | A10 Networks, Inc. | Programming a data network device using user defined scripts with licenses |
US10129122B2 (en) | 2014-06-03 | 2018-11-13 | A10 Networks, Inc. | User defined objects for network devices |
US9986061B2 (en) | 2014-06-03 | 2018-05-29 | A10 Networks, Inc. | Programming a data network device using user defined scripts |
US10397371B2 (en) * | 2014-06-09 | 2019-08-27 | International Business Machines Corporation | Saving and restoring a state of a web application |
US9338493B2 (en) | 2014-06-30 | 2016-05-10 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US10572496B1 (en) | 2014-07-03 | 2020-02-25 | Palantir Technologies Inc. | Distributed workflow system and database with access controls for city resiliency |
WO2016007923A1 (en) * | 2014-07-11 | 2016-01-14 | Craymer Loring G Iii | Method and system for linear generalized ll recognition and context-aware parsing |
US10311464B2 (en) | 2014-07-17 | 2019-06-04 | The Nielsen Company (Us), Llc | Methods and apparatus to determine impressions corresponding to market segments |
JP6594950B2 (en) | 2014-07-24 | 2019-10-23 | アビニシオ テクノロジー エルエルシー | Summary of data lineage |
US9398029B2 (en) | 2014-08-01 | 2016-07-19 | Wombat Security Technologies, Inc. | Cybersecurity training system with automated application of branded content |
US20150066771A1 (en) | 2014-08-08 | 2015-03-05 | Brighterion, Inc. | Fast access vectors in real-time behavioral profiling |
US20160055427A1 (en) | 2014-10-15 | 2016-02-25 | Brighterion, Inc. | Method for providing data science, artificial intelligence and machine learning as-a-service |
US20150032589A1 (en) | 2014-08-08 | 2015-01-29 | Brighterion, Inc. | Artificial intelligence fraud management solution |
US10275458B2 (en) | 2014-08-14 | 2019-04-30 | International Business Machines Corporation | Systematic tuning of text analytic annotators with specialized information |
US20160063539A1 (en) | 2014-08-29 | 2016-03-03 | The Nielsen Company (Us), Llc | Methods and apparatus to associate transactions with media impressions |
US9818400B2 (en) | 2014-09-11 | 2017-11-14 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US10516980B2 (en) * | 2015-10-24 | 2019-12-24 | Oracle International Corporation | Automatic redisplay of a user interface including a visualization |
US10108931B2 (en) * | 2014-09-26 | 2018-10-23 | Oracle International Corporation | Lock-based updating of a document |
US10127911B2 (en) | 2014-09-30 | 2018-11-13 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US10613755B1 (en) | 2014-09-30 | 2020-04-07 | EMC IP Holding Company LLC | Efficient repurposing of application data in storage environments |
US10628379B1 (en) | 2014-09-30 | 2020-04-21 | EMC IP Holding Company LLC | Efficient local data protection of application data in storage environments |
US9668121B2 (en) | 2014-09-30 | 2017-05-30 | Apple Inc. | Social reminders |
US10074360B2 (en) | 2014-09-30 | 2018-09-11 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US11080709B2 (en) | 2014-10-15 | 2021-08-03 | Brighterion, Inc. | Method of reducing financial losses in multiple payment channels upon a recognition of fraud first appearing in any one payment channel |
US10546099B2 (en) | 2014-10-15 | 2020-01-28 | Brighterion, Inc. | Method of personalizing, individualizing, and automating the management of healthcare fraud-waste-abuse to unique individual healthcare providers |
US20160063502A1 (en) | 2014-10-15 | 2016-03-03 | Brighterion, Inc. | Method for improving operating profits with better automated decision making with artificial intelligence |
US20160078367A1 (en) | 2014-10-15 | 2016-03-17 | Brighterion, Inc. | Data clean-up method for improving predictive model training |
US10290001B2 (en) | 2014-10-28 | 2019-05-14 | Brighterion, Inc. | Data breach detection |
US9229952B1 (en) | 2014-11-05 | 2016-01-05 | Palantir Technologies, Inc. | History preserving data pipeline system and method |
US10373062B2 (en) * | 2014-12-12 | 2019-08-06 | Omni Ai, Inc. | Mapper component for a neuro-linguistic behavior recognition system |
US9792604B2 (en) | 2014-12-19 | 2017-10-17 | moovel North Americ, LLC | Method and system for dynamically interactive visually validated mobile ticketing |
EP3241310B1 (en) | 2015-01-02 | 2019-07-31 | Systech Corporation | Control infrastructure |
US9417850B2 (en) * | 2015-01-10 | 2016-08-16 | Logics Research Centre | Grace˜operator for changing order and scope of implicit parameters |
US11106871B2 (en) * | 2015-01-23 | 2021-08-31 | Conversica, Inc. | Systems and methods for configurable messaging response-action engine |
TWI567679B (en) * | 2015-01-23 | 2017-01-21 | 羅瑞 里奇士 | A computer-implemented method and system for constructing a representation of investment securities in a database |
US9922037B2 (en) | 2015-01-30 | 2018-03-20 | Splunk Inc. | Index time, delimiter based extractions and previewing for use in indexing |
US9454907B2 (en) | 2015-02-07 | 2016-09-27 | Usman Hafeez | System and method for placement of sensors through use of unmanned aerial vehicles |
US9454157B1 (en) | 2015-02-07 | 2016-09-27 | Usman Hafeez | System and method for controlling flight operations of an unmanned aerial vehicle |
KR102054568B1 (en) * | 2015-02-11 | 2020-01-22 | 아브 이니티오 테크놀로지 엘엘시 | Filtering Data Schematic Diagram |
CA2975530C (en) * | 2015-02-11 | 2020-01-28 | Ab Initio Technology Llc | Filtering data lineage diagrams |
US10489463B2 (en) * | 2015-02-12 | 2019-11-26 | Microsoft Technology Licensing, Llc | Finding documents describing solutions to computing issues |
US10803106B1 (en) | 2015-02-24 | 2020-10-13 | Palantir Technologies Inc. | System with methodology for dynamic modular ontology |
US9727560B2 (en) | 2015-02-25 | 2017-08-08 | Palantir Technologies Inc. | Systems and methods for organizing and identifying documents via hierarchies and dimensions of tags |
CN104599623B (en) * | 2015-02-27 | 2017-07-04 | 京东方科技集团股份有限公司 | A kind of method for displaying image, device and electronic equipment |
US10152299B2 (en) | 2015-03-06 | 2018-12-11 | Apple Inc. | Reducing response latency of intelligent automated assistants |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US9886953B2 (en) | 2015-03-08 | 2018-02-06 | Apple Inc. | Virtual assistant activation |
US9721566B2 (en) | 2015-03-08 | 2017-08-01 | Apple Inc. | Competing devices responding to voice triggers |
US9830603B2 (en) | 2015-03-20 | 2017-11-28 | Microsoft Technology Licensing, Llc | Digital identity and authorization for machines with replaceable parts |
US20180130006A1 (en) | 2015-03-31 | 2018-05-10 | Brighterion, Inc. | Addrressable smart agent data technology to detect unauthorized transaction activity |
WO2016178655A1 (en) | 2015-05-01 | 2016-11-10 | Hewlett Packard Enterprise Development Lp | Secure multi-party information retrieval |
US11416216B2 (en) | 2015-05-22 | 2022-08-16 | Micro Focus Llc | Semantic consolidation of data |
US10083688B2 (en) | 2015-05-27 | 2018-09-25 | Apple Inc. | Device voice control for selecting a displayed affordance |
CA3128629A1 (en) | 2015-06-05 | 2016-07-28 | C3.Ai, Inc. | Systems and methods for data processing and enterprise ai applications |
US9578173B2 (en) | 2015-06-05 | 2017-02-21 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US9891933B2 (en) * | 2015-06-24 | 2018-02-13 | International Business Machines Corporation | Automated testing of GUI mirroring |
US10380633B2 (en) | 2015-07-02 | 2019-08-13 | The Nielsen Company (Us), Llc | Methods and apparatus to generate corrected online audience measurement data |
US10045082B2 (en) | 2015-07-02 | 2018-08-07 | The Nielsen Company (Us), Llc | Methods and apparatus to correct errors in audience measurements for media accessed using over-the-top devices |
US10083624B2 (en) | 2015-07-28 | 2018-09-25 | Architecture Technology Corporation | Real-time monitoring of network-based training exercises |
US10803766B1 (en) | 2015-07-28 | 2020-10-13 | Architecture Technology Corporation | Modular training of network-based training exercises |
US9996595B2 (en) | 2015-08-03 | 2018-06-12 | Palantir Technologies, Inc. | Providing full data provenance visualization for versioned datasets |
US10089687B2 (en) * | 2015-08-04 | 2018-10-02 | Fidelity National Information Services, Inc. | System and associated methodology of creating order lifecycles via daisy chain linkage |
US10581976B2 (en) | 2015-08-12 | 2020-03-03 | A10 Networks, Inc. | Transmission control of protocol state exchange for dynamic stateful service insertion |
US10243791B2 (en) | 2015-08-13 | 2019-03-26 | A10 Networks, Inc. | Automated adjustment of subscriber policies |
CN106470360B (en) * | 2015-08-20 | 2019-12-10 | 腾讯科技(深圳)有限公司 | Video player calling method and device |
US10853378B1 (en) | 2015-08-25 | 2020-12-01 | Palantir Technologies Inc. | Electronic note management via a connected entity graph |
US10102280B2 (en) * | 2015-08-31 | 2018-10-16 | International Business Machines Corporation | Determination of expertness level for a target keyword |
US9576015B1 (en) | 2015-09-09 | 2017-02-21 | Palantir Technologies, Inc. | Domain-specific language for dataset transformations |
CN106557531B (en) | 2015-09-30 | 2020-07-03 | 伊姆西Ip控股有限责任公司 | Method, apparatus and storage medium for converting complex structured objects into flattened data |
US10586042B2 (en) * | 2015-10-01 | 2020-03-10 | Twistlock, Ltd. | Profiling of container images and enforcing security policies respective thereof |
US10664590B2 (en) * | 2015-10-01 | 2020-05-26 | Twistlock, Ltd. | Filesystem action profiling of containers and security enforcement |
US10943014B2 (en) | 2015-10-01 | 2021-03-09 | Twistlock, Ltd | Profiling of spawned processes in container images and enforcing security policies respective thereof |
RU2611257C1 (en) * | 2015-10-01 | 2017-02-21 | Акционерное общество "Калужский научно-исследовательский институт телемеханических устройств" | Method of preparation, storage and transfer of operational and command information in telecode control complexes |
US10223534B2 (en) | 2015-10-15 | 2019-03-05 | Twistlock, Ltd. | Static detection of vulnerabilities in base images of software containers |
US10922418B2 (en) | 2015-10-01 | 2021-02-16 | Twistlock, Ltd. | Runtime detection and mitigation of vulnerabilities in application software containers |
US10706145B2 (en) | 2015-10-01 | 2020-07-07 | Twistlock, Ltd. | Runtime detection of vulnerabilities in software containers |
US10567411B2 (en) | 2015-10-01 | 2020-02-18 | Twistlock, Ltd. | Dynamically adapted traffic inspection and filtering in containerized environments |
US10599833B2 (en) | 2015-10-01 | 2020-03-24 | Twistlock, Ltd. | Networking-based profiling of containers and security enforcement |
US10693899B2 (en) * | 2015-10-01 | 2020-06-23 | Twistlock, Ltd. | Traffic enforcement in containerized environments |
US10599718B2 (en) * | 2015-10-09 | 2020-03-24 | Software Ag | Systems and/or methods for graph based declarative mapping |
US10778446B2 (en) | 2015-10-15 | 2020-09-15 | Twistlock, Ltd. | Detection of vulnerable root certificates in software containers |
US10430587B2 (en) * | 2015-10-28 | 2019-10-01 | Hrl Laboratories, Llc | System and method for maintaining security tags and reference counts for objects in computer memory |
US10614167B2 (en) | 2015-10-30 | 2020-04-07 | Sdl Plc | Translation review workflow systems and methods |
US10346446B2 (en) | 2015-11-02 | 2019-07-09 | Radiant Geospatial Solutions Llc | System and method for aggregating multi-source data and identifying geographic areas for data acquisition |
US10282376B2 (en) * | 2015-11-10 | 2019-05-07 | The United States Of America, As Represented By The Secretary Of The Navy | Semi-structured spatial data conversion |
WO2017082875A1 (en) | 2015-11-10 | 2017-05-18 | Hewlett Packard Enterprise Development Lp | Data allocation based on secure information retrieval |
US9767011B2 (en) | 2015-12-01 | 2017-09-19 | International Business Machines Corporation | Globalization testing management using a set of globalization testing operations |
US9740601B2 (en) * | 2015-12-01 | 2017-08-22 | International Business Machines Corporation | Globalization testing management service configuration |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10205994B2 (en) | 2015-12-17 | 2019-02-12 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
WO2017116259A1 (en) * | 2015-12-28 | 2017-07-06 | Limited Liability Company Mail.Ru | Dynamic contextual re-ordering of suggested query hints |
US10318288B2 (en) | 2016-01-13 | 2019-06-11 | A10 Networks, Inc. | System and method to process a chain of network applications |
US9715375B1 (en) * | 2016-01-27 | 2017-07-25 | International Business Machines Corporation | Parallel compilation of software application |
US10270673B1 (en) | 2016-01-27 | 2019-04-23 | The Nielsen Company (Us), Llc | Methods and apparatus for estimating total unique audiences |
CN105511890B (en) * | 2016-01-29 | 2018-02-23 | 腾讯科技(深圳)有限公司 | A kind of graphical interfaces update method and device |
US10248722B2 (en) | 2016-02-22 | 2019-04-02 | Palantir Technologies Inc. | Multi-language support for dynamic ontology |
US10698938B2 (en) | 2016-03-18 | 2020-06-30 | Palantir Technologies Inc. | Systems and methods for organizing and identifying documents via hierarchies and dimensions of tags |
US11263650B2 (en) * | 2016-04-25 | 2022-03-01 | [24]7.ai, Inc. | Process and system to categorize, evaluate and optimize a customer experience |
US10394552B2 (en) * | 2016-05-17 | 2019-08-27 | Dropbox, Inc. | Interface description language for application programming interfaces |
US10606921B2 (en) | 2016-05-27 | 2020-03-31 | Open Text Sa Ulc | Document architecture with fragment-driven role-based access controls |
US10621370B2 (en) * | 2016-05-27 | 2020-04-14 | Intel Corporation | Methods and apparatus to provide group-based row-level security for big data platforms |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
US10567460B2 (en) * | 2016-06-09 | 2020-02-18 | Apple Inc. | Managing data using a time-based directory structure |
US10586535B2 (en) | 2016-06-10 | 2020-03-10 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
DK179415B1 (en) | 2016-06-11 | 2018-06-14 | Apple Inc | Intelligent device arbitration and control |
DK201670540A1 (en) | 2016-06-11 | 2018-01-08 | Apple Inc | Application integration with a digital assistant |
US10007674B2 (en) | 2016-06-13 | 2018-06-26 | Palantir Technologies Inc. | Data revision control in large-scale data analytic systems |
TWI579718B (en) * | 2016-06-15 | 2017-04-21 | 陳兆煒 | System and Methods for Graphical Resources Management Application for Graphical Resources Management |
CN107545008B (en) * | 2016-06-27 | 2021-02-19 | 五八同城信息技术有限公司 | Data format requirement storage method and device |
US20180011910A1 (en) * | 2016-07-06 | 2018-01-11 | Facebook, Inc. | Systems and methods for performing operations with data acquired from multiple sources |
US10529302B2 (en) | 2016-07-07 | 2020-01-07 | Oblong Industries, Inc. | Spatially mediated augmentations of and interactions among distinct devices and applications via extended pixel manifold |
US10417283B2 (en) | 2016-07-14 | 2019-09-17 | Securitymetrics, Inc. | Identification of potentially sensitive information in data strings |
US11049190B2 (en) | 2016-07-15 | 2021-06-29 | Intuit Inc. | System and method for automatically generating calculations for fields in compliance forms |
US11222266B2 (en) | 2016-07-15 | 2022-01-11 | Intuit Inc. | System and method for automatic learning of functions |
US10579721B2 (en) | 2016-07-15 | 2020-03-03 | Intuit Inc. | Lean parsing: a natural language processing system and method for parsing domain-specific languages |
US10140260B2 (en) * | 2016-07-15 | 2018-11-27 | Sap Se | Intelligent text reduction for graphical interface elements |
US10503808B2 (en) | 2016-07-15 | 2019-12-10 | Sap Se | Time user interface with intelligent text reduction |
US10725896B2 (en) | 2016-07-15 | 2020-07-28 | Intuit Inc. | System and method for identifying a subset of total historical users of a document preparation system to represent a full set of test scenarios based on code coverage |
US20180018322A1 (en) * | 2016-07-15 | 2018-01-18 | Intuit Inc. | System and method for automatically understanding lines of compliance forms through natural language patterns |
US20180145701A1 (en) * | 2016-09-01 | 2018-05-24 | Anthony Ben Benavides | Sonic Boom: System For Reducing The Digital Footprint Of Data Streams Through Lossless Scalable Binary Substitution |
US10474753B2 (en) | 2016-09-07 | 2019-11-12 | Apple Inc. | Language identification using recurrent neural networks |
US10043516B2 (en) | 2016-09-23 | 2018-08-07 | Apple Inc. | Intelligent automated assistant |
US9830345B1 (en) * | 2016-09-26 | 2017-11-28 | Semmle Limited | Content-addressable data storage |
US11080301B2 (en) | 2016-09-28 | 2021-08-03 | Hewlett Packard Enterprise Development Lp | Storage allocation based on secure data comparisons via multiple intermediaries |
JP6705506B2 (en) * | 2016-10-04 | 2020-06-03 | 富士通株式会社 | Learning program, information processing apparatus, and learning method |
US11727288B2 (en) | 2016-10-05 | 2023-08-15 | Kyndryl, Inc. | Database-management system with artificially intelligent virtual database administration |
US10102229B2 (en) | 2016-11-09 | 2018-10-16 | Palantir Technologies Inc. | Validating data integrations using a secondary data store |
US10268345B2 (en) * | 2016-11-17 | 2019-04-23 | General Electric Company | Mehtod and system for multi-modal lineage tracing and impact assessment in a concept lineage data flow network |
US11281993B2 (en) | 2016-12-05 | 2022-03-22 | Apple Inc. | Model and ensemble compression for metric learning |
US10783158B2 (en) * | 2016-12-19 | 2020-09-22 | Datalogic IP Tech, S.r.l. | Method and algorithms for auto-identification data mining through dynamic hyperlink search analysis |
US9946777B1 (en) | 2016-12-19 | 2018-04-17 | Palantir Technologies Inc. | Systems and methods for facilitating data transformation |
US20220277304A1 (en) * | 2017-01-04 | 2022-09-01 | Jpmorgan Chase Bank, N.A. | Systems and Methods for Sanction Management |
US9922108B1 (en) | 2017-01-05 | 2018-03-20 | Palantir Technologies Inc. | Systems and methods for facilitating data transformation |
US11204787B2 (en) | 2017-01-09 | 2021-12-21 | Apple Inc. | Application integration with a digital assistant |
US10389835B2 (en) | 2017-01-10 | 2019-08-20 | A10 Networks, Inc. | Application aware systems and methods to process user loadable network applications |
US10528415B2 (en) | 2017-02-28 | 2020-01-07 | International Business Machines Corporation | Guided troubleshooting with autofilters |
US11163616B2 (en) | 2017-03-07 | 2021-11-02 | Polyjuice Ab | Systems and methods for enabling interoperation of independent software applications |
US10534640B2 (en) * | 2017-03-24 | 2020-01-14 | Oracle International Corporation | System and method for providing a native job control language execution engine in a rehosting platform |
WO2018176356A1 (en) * | 2017-03-31 | 2018-10-04 | Oracle International Corporation | System and method for determining the success of a cross-platform application migration |
US11592817B2 (en) * | 2017-04-28 | 2023-02-28 | Intel Corporation | Storage management for machine learning at autonomous machines |
DK201770383A1 (en) | 2017-05-09 | 2018-12-14 | Apple Inc. | User interface for correcting recognition errors |
US10417266B2 (en) | 2017-05-09 | 2019-09-17 | Apple Inc. | Context-aware ranking of intelligent response suggestions |
US10395654B2 (en) | 2017-05-11 | 2019-08-27 | Apple Inc. | Text normalization based on a data-driven learning network |
US10726832B2 (en) | 2017-05-11 | 2020-07-28 | Apple Inc. | Maintaining privacy of personal information |
DK201770439A1 (en) | 2017-05-11 | 2018-12-13 | Apple Inc. | Offline personal assistant |
DK179745B1 (en) | 2017-05-12 | 2019-05-01 | Apple Inc. | SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT |
DK179496B1 (en) | 2017-05-12 | 2019-01-15 | Apple Inc. | USER-SPECIFIC Acoustic Models |
US11301477B2 (en) | 2017-05-12 | 2022-04-12 | Apple Inc. | Feedback analysis of a digital assistant |
DK201770428A1 (en) | 2017-05-12 | 2019-02-18 | Apple Inc. | Low-latency intelligent automated assistant |
DK201770432A1 (en) | 2017-05-15 | 2018-12-21 | Apple Inc. | Hierarchical belief states for digital assistants |
DK201770431A1 (en) | 2017-05-15 | 2018-12-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
DK179560B1 (en) | 2017-05-16 | 2019-02-18 | Apple Inc. | Far-field extension for digital assistant services |
US20180336275A1 (en) | 2017-05-16 | 2018-11-22 | Apple Inc. | Intelligent automated assistant for media exploration |
US10403278B2 (en) | 2017-05-16 | 2019-09-03 | Apple Inc. | Methods and systems for phonetic matching in digital assistant services |
US10311144B2 (en) | 2017-05-16 | 2019-06-04 | Apple Inc. | Emoji word sense disambiguation |
US10379825B2 (en) | 2017-05-22 | 2019-08-13 | Ab Initio Technology Llc | Automated dependency analyzer for heterogeneously programmed data processing system |
US10243904B1 (en) | 2017-05-26 | 2019-03-26 | Wombat Security Technologies, Inc. | Determining authenticity of reported user action in cybersecurity risk assessment |
KR101926977B1 (en) * | 2017-05-29 | 2019-03-07 | 연세대학교 산학협력단 | Method for Creating Automata for determination of Nested-duplication |
US11222076B2 (en) * | 2017-05-31 | 2022-01-11 | Microsoft Technology Licensing, Llc | Data set state visualization comparison lock |
US10657328B2 (en) | 2017-06-02 | 2020-05-19 | Apple Inc. | Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling |
US10956406B2 (en) | 2017-06-12 | 2021-03-23 | Palantir Technologies Inc. | Propagated deletion of database records and derived data |
US10524165B2 (en) | 2017-06-22 | 2019-12-31 | Bank Of America Corporation | Dynamic utilization of alternative resources based on token association |
US10481881B2 (en) * | 2017-06-22 | 2019-11-19 | Archeo Futurus, Inc. | Mapping a computer code to wires and gates |
US9996328B1 (en) * | 2017-06-22 | 2018-06-12 | Archeo Futurus, Inc. | Compiling and optimizing a computer code by minimizing a number of states in a finite machine corresponding to the computer code |
US10313480B2 (en) | 2017-06-22 | 2019-06-04 | Bank Of America Corporation | Data transmission between networked resources |
US10511692B2 (en) | 2017-06-22 | 2019-12-17 | Bank Of America Corporation | Data transmission to a networked resource based on contextual information |
US10691729B2 (en) | 2017-07-07 | 2020-06-23 | Palantir Technologies Inc. | Systems and methods for providing an object platform for a relational database |
CN110019350B (en) * | 2017-07-28 | 2021-06-29 | 北京京东尚科信息技术有限公司 | Data query method and device based on configuration information |
US10599129B2 (en) * | 2017-08-04 | 2020-03-24 | Duro Labs, Inc. | Method for data normalization |
CN107391890B (en) * | 2017-09-01 | 2020-10-09 | 山东永利精工石油装备有限公司 | Prediction and optimal control method for oil casing threaded joint machining chatter defect |
US10545742B2 (en) * | 2017-09-06 | 2020-01-28 | Nicira, Inc. | Annotation-driven framework for generating state machine updates |
US10445429B2 (en) | 2017-09-21 | 2019-10-15 | Apple Inc. | Natural language understanding using vocabularies with compressed serialized tries |
US10755051B2 (en) | 2017-09-29 | 2020-08-25 | Apple Inc. | Rule-based natural language processing |
RU2658147C1 (en) * | 2017-10-05 | 2018-06-19 | федеральное государственное автономное образовательное учреждение высшего образования "Национальный исследовательский ядерный университет "МИФИ" (НИЯУ МИФИ) | Data decompression device |
US11295232B2 (en) * | 2017-10-30 | 2022-04-05 | Microsoft Technology Licensing, Llc | Learning the structure of hierarchical extraction models |
US10635863B2 (en) | 2017-10-30 | 2020-04-28 | Sdl Inc. | Fragment recall and adaptive automated translation |
US20190138623A1 (en) * | 2017-11-03 | 2019-05-09 | Drishti Technologies, Inc. | Automated birth certificate systems and methods |
US10956508B2 (en) | 2017-11-10 | 2021-03-23 | Palantir Technologies Inc. | Systems and methods for creating and managing a data integration workspace containing automatically updated data models |
EP3622444A1 (en) | 2017-11-21 | 2020-03-18 | Google LLC | Improved onboarding of entity data |
US10636424B2 (en) | 2017-11-30 | 2020-04-28 | Apple Inc. | Multi-turn canned dialog |
CN107948181A (en) * | 2017-12-06 | 2018-04-20 | 吉旗(成都)科技有限公司 | A kind of expansible data word description scheme method |
US10599766B2 (en) | 2017-12-15 | 2020-03-24 | International Business Machines Corporation | Symbolic regression embedding dimensionality analysis |
US10817676B2 (en) | 2017-12-27 | 2020-10-27 | Sdl Inc. | Intelligent routing services and systems |
JP2019117571A (en) * | 2017-12-27 | 2019-07-18 | シャープ株式会社 | Information processing apparatus, information processing system, information processing method and program |
US10733982B2 (en) | 2018-01-08 | 2020-08-04 | Apple Inc. | Multi-directional dialog |
US10733375B2 (en) | 2018-01-31 | 2020-08-04 | Apple Inc. | Knowledge-based framework for improving natural language understanding |
EP3521948A1 (en) | 2018-02-06 | 2019-08-07 | Tata Consultancy Services Limited | Systems and methods for auto-generating a control and monitoring solution for smart and robotics environments |
CN108471401A (en) * | 2018-02-07 | 2018-08-31 | 山东省科学院自动化研究所 | A kind of encapsulation of CAN signal, analysis method and device |
US10606954B2 (en) | 2018-02-15 | 2020-03-31 | International Business Machines Corporation | Topic kernelization for real-time conversation data |
US11182565B2 (en) * | 2018-02-23 | 2021-11-23 | Samsung Electronics Co., Ltd. | Method to learn personalized intents |
US10789959B2 (en) | 2018-03-02 | 2020-09-29 | Apple Inc. | Training speaker recognition models for digital assistants |
US10592604B2 (en) | 2018-03-12 | 2020-03-17 | Apple Inc. | Inverse text normalization for automatic speech recognition |
US10818288B2 (en) | 2018-03-26 | 2020-10-27 | Apple Inc. | Natural assistant interaction |
US20190294735A1 (en) * | 2018-03-26 | 2019-09-26 | Apple Inc. | Search functions for spreadsheets |
US11327993B2 (en) * | 2018-03-26 | 2022-05-10 | Verizon Patent And Licensing Inc. | Systems and methods for managing and delivering digital content |
US10909331B2 (en) | 2018-03-30 | 2021-02-02 | Apple Inc. | Implicit identification of translation payload with neural machine translation |
US10754822B1 (en) | 2018-04-18 | 2020-08-25 | Palantir Technologies Inc. | Systems and methods for ontology migration |
US20190342297A1 (en) * | 2018-05-01 | 2019-11-07 | Brighterion, Inc. | Securing internet-of-things with smart-agent technology |
US10928918B2 (en) | 2018-05-07 | 2021-02-23 | Apple Inc. | Raise to speak |
US11145294B2 (en) | 2018-05-07 | 2021-10-12 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US11461355B1 (en) | 2018-05-15 | 2022-10-04 | Palantir Technologies Inc. | Ontological mapping of data |
US10984780B2 (en) | 2018-05-21 | 2021-04-20 | Apple Inc. | Global semantic word embeddings using bi-directional recurrent neural networks |
US11314940B2 (en) | 2018-05-22 | 2022-04-26 | Samsung Electronics Co., Ltd. | Cross domain personalized vocabulary learning in intelligent assistants |
DK180639B1 (en) | 2018-06-01 | 2021-11-04 | Apple Inc | DISABILITY OF ATTENTION-ATTENTIVE VIRTUAL ASSISTANT |
US11386266B2 (en) | 2018-06-01 | 2022-07-12 | Apple Inc. | Text correction |
DK179822B1 (en) | 2018-06-01 | 2019-07-12 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US10892996B2 (en) | 2018-06-01 | 2021-01-12 | Apple Inc. | Variable latency device coordination |
DK201870355A1 (en) | 2018-06-01 | 2019-12-16 | Apple Inc. | Virtual assistant operation in multi-device environments |
US11076039B2 (en) | 2018-06-03 | 2021-07-27 | Apple Inc. | Accelerated task performance |
US11568142B2 (en) | 2018-06-04 | 2023-01-31 | Infosys Limited | Extraction of tokens and relationship between tokens from documents to form an entity relationship map |
US10749890B1 (en) | 2018-06-19 | 2020-08-18 | Architecture Technology Corporation | Systems and methods for improving the ranking and prioritization of attack-related events |
US10817604B1 (en) | 2018-06-19 | 2020-10-27 | Architecture Technology Corporation | Systems and methods for processing source codes to detect non-malicious faults |
US11308038B2 (en) * | 2018-06-22 | 2022-04-19 | Red Hat, Inc. | Copying container images |
US10893008B2 (en) * | 2018-08-30 | 2021-01-12 | Koopid, Inc | System and method for generating and communicating communication components over a messaging channel |
US11256867B2 (en) | 2018-10-09 | 2022-02-22 | Sdl Inc. | Systems and methods of machine learning for digital assets and message creation |
US10699069B2 (en) * | 2018-10-11 | 2020-06-30 | International Business Machines Corporation | Populating spreadsheets using relational information from documents |
US10691304B1 (en) | 2018-10-22 | 2020-06-23 | Tableau Software, Inc. | Data preparation user interface with conglomerate heterogeneous process flow elements |
US10691428B2 (en) * | 2018-10-24 | 2020-06-23 | Sap Se | Digital compliance platform |
RU2697618C1 (en) * | 2018-10-30 | 2019-08-15 | федеральное государственное автономное образовательное учреждение высшего образования "Национальный исследовательский ядерный университет МИФИ" (НИЯУ МИФИ) | Device for decompression of data |
CN109299131B (en) * | 2018-11-14 | 2020-05-29 | 百度在线网络技术(北京)有限公司 | Spark query method and system supporting trusted computing |
US10903977B2 (en) | 2018-12-19 | 2021-01-26 | Rankin Labs, Llc | Hidden electronic file systems |
WO2020154223A1 (en) | 2019-01-21 | 2020-07-30 | John Rankin | Systems and methods for processing network traffic using dynamic memory |
WO2020154219A1 (en) * | 2019-01-21 | 2020-07-30 | John Rankin | Systems and methods for controlling machine operations |
US11526357B2 (en) | 2019-01-21 | 2022-12-13 | Rankin Labs, Llc | Systems and methods for controlling machine operations within a multi-dimensional memory space |
US11429713B1 (en) | 2019-01-24 | 2022-08-30 | Architecture Technology Corporation | Artificial intelligence modeling for cyber-attack simulation protocols |
US11128654B1 (en) | 2019-02-04 | 2021-09-21 | Architecture Technology Corporation | Systems and methods for unified hierarchical cybersecurity |
US11669514B2 (en) | 2019-04-03 | 2023-06-06 | Unitedhealth Group Incorporated | Managing data objects for graph-based data structures |
US11487674B2 (en) | 2019-04-17 | 2022-11-01 | Rankin Labs, Llc | Virtual memory pool within a network which is accessible from multiple platforms |
US11887505B1 (en) | 2019-04-24 | 2024-01-30 | Architecture Technology Corporation | System for deploying and monitoring network-based training exercises |
WO2020227434A1 (en) | 2019-05-07 | 2020-11-12 | Cerebri AI Inc. | Predictive, machine-learning, locale-aware computer models suitable for location- and trajectory-aware training sets |
US20200356866A1 (en) * | 2019-05-08 | 2020-11-12 | International Business Machines Corporation | Operative enterprise application recommendation generated by cognitive services from unstructured requirements |
US11163956B1 (en) | 2019-05-23 | 2021-11-02 | Intuit Inc. | System and method for recognizing domain specific named entities using domain specific word embeddings |
US11372773B2 (en) | 2019-05-28 | 2022-06-28 | Rankin Labs, Llc | Supporting a virtual memory area at a remote computing machine |
CN110222143B (en) * | 2019-05-31 | 2022-11-04 | 北京小米移动软件有限公司 | Character string matching method, device, storage medium and electronic equipment |
US10977268B2 (en) * | 2019-05-31 | 2021-04-13 | Snowflake Inc. | Data exchange |
CN110188106B (en) * | 2019-05-31 | 2021-04-16 | 北京明朝万达科技股份有限公司 | Data management method and device |
US11620389B2 (en) | 2019-06-24 | 2023-04-04 | University Of Maryland Baltimore County | Method and system for reducing false positives in static source code analysis reports using machine learning and classification techniques |
US11403405B1 (en) | 2019-06-27 | 2022-08-02 | Architecture Technology Corporation | Portable vulnerability identification tool for embedded non-IP devices |
US10489454B1 (en) * | 2019-06-28 | 2019-11-26 | Capital One Services, Llc | Indexing a dataset based on dataset tags and an ontology |
US11531703B2 (en) | 2019-06-28 | 2022-12-20 | Capital One Services, Llc | Determining data categorizations based on an ontology and a machine-learning model |
CN112230909B (en) * | 2019-07-15 | 2023-05-23 | 腾讯科技(深圳)有限公司 | Method, device, equipment and storage medium for binding data of applet |
US11868744B2 (en) * | 2019-08-08 | 2024-01-09 | Nec Corporation | Estimation of features corresponding to extracted commands used to divide code of software |
US20220342879A1 (en) * | 2019-10-08 | 2022-10-27 | Nec Corporation | Data searching system, device, method and program |
US11269942B2 (en) * | 2019-10-10 | 2022-03-08 | International Business Machines Corporation | Automatic keyphrase extraction from text using the cross-entropy method |
US11194840B2 (en) | 2019-10-14 | 2021-12-07 | Microsoft Technology Licensing, Llc | Incremental clustering for enterprise knowledge graph |
US11709878B2 (en) | 2019-10-14 | 2023-07-25 | Microsoft Technology Licensing, Llc | Enterprise knowledge graph |
US11444974B1 (en) | 2019-10-23 | 2022-09-13 | Architecture Technology Corporation | Systems and methods for cyber-physical threat modeling |
US11216492B2 (en) * | 2019-10-31 | 2022-01-04 | Microsoft Technology Licensing, Llc | Document annotation based on enterprise knowledge graph |
CN110853327B (en) * | 2019-11-02 | 2021-04-02 | 杭州雅格纳科技有限公司 | Ship cabin equipment data field debugging and collecting method and device based on single chip microcomputer |
WO2021113626A1 (en) | 2019-12-06 | 2021-06-10 | John Rankin | High-level programming language which utilizes virtual memory |
US11503075B1 (en) | 2020-01-14 | 2022-11-15 | Architecture Technology Corporation | Systems and methods for continuous compliance of nodes |
US10841251B1 (en) * | 2020-02-11 | 2020-11-17 | Moveworks, Inc. | Multi-domain chatbot |
US11783128B2 (en) | 2020-02-19 | 2023-10-10 | Intuit Inc. | Financial document text conversion to computer readable operations |
US10814489B1 (en) * | 2020-02-28 | 2020-10-27 | Nimble Robotics, Inc. | System and method of integrating robot into warehouse management software |
US11763083B2 (en) | 2020-05-18 | 2023-09-19 | Google Llc | Inference methods for word or wordpiece tokenization |
EP4154108A1 (en) | 2020-05-24 | 2023-03-29 | Quixotic Labs Inc. | Domain-specific language interpreter and interactive visual interface for rapid screening |
US11734590B2 (en) | 2020-06-16 | 2023-08-22 | Northrop Grumman Systems Corporation | System and method for automating observe-orient-decide-act (OODA) loop enabling cognitive autonomous agent systems |
WO2021262180A1 (en) * | 2020-06-25 | 2021-12-30 | Hints Inc. | System and method for detecting misinformation and fake news via network analysis |
US11620280B2 (en) * | 2020-08-19 | 2023-04-04 | Palantir Technologies Inc. | Projections for big database systems |
CN112073521B (en) * | 2020-09-10 | 2022-09-02 | 成都中科大旗软件股份有限公司 | Sharing scheduling method and system for scattered data |
US11861039B1 (en) | 2020-09-28 | 2024-01-02 | Amazon Technologies, Inc. | Hierarchical system and method for identifying sensitive content in data |
US11461103B2 (en) * | 2020-10-23 | 2022-10-04 | Centaur Technology, Inc. | Dual branch execute and table update with single port |
US11556558B2 (en) | 2021-01-11 | 2023-01-17 | International Business Machines Corporation | Insight expansion in smart data retention systems |
US11494418B2 (en) * | 2021-01-28 | 2022-11-08 | The Florida International University Board Of Trustees | Systems and methods for determining document section types |
CN113505127A (en) * | 2021-06-22 | 2021-10-15 | 侍意(厦门)网络信息技术有限公司 | Storage structure and method for data of related objects, retrieval and visual display method |
CN113535813B (en) * | 2021-06-30 | 2023-07-28 | 北京百度网讯科技有限公司 | Data mining method and device, electronic equipment and storage medium |
US11411805B1 (en) | 2021-07-12 | 2022-08-09 | Bank Of America Corporation | System and method for detecting root cause of an exception error in a task flow in a distributed network |
US20230229998A1 (en) * | 2022-01-20 | 2023-07-20 | Copperleaf Technologies Inc. | Methods and systems for asset management using customized calculation module |
US11888793B2 (en) | 2022-02-22 | 2024-01-30 | Open Text Holdings, Inc. | Systems and methods for intelligent delivery of communications |
US11892937B2 (en) | 2022-02-28 | 2024-02-06 | Bank Of America Corporation | Developer test environment with containerization of tightly coupled systems |
US11438251B1 (en) | 2022-02-28 | 2022-09-06 | Bank Of America Corporation | System and method for automatic self-resolution of an exception error in a distributed network |
US11868344B1 (en) | 2022-09-09 | 2024-01-09 | Tencent America LLC | System, method, and computer program for cross-lingual text-to-SQL semantic parsing with representation mixup |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6732090B2 (en) * | 2001-08-13 | 2004-05-04 | Xerox Corporation | Meta-document management system with user definable personalities |
US6778979B2 (en) * | 2001-08-13 | 2004-08-17 | Xerox Corporation | System for automatically generating queries |
US6820075B2 (en) * | 2001-08-13 | 2004-11-16 | Xerox Corporation | Document-centric system with auto-completion |
Family Cites Families (174)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4041462A (en) * | 1976-04-30 | 1977-08-09 | International Business Machines Corporation | Data processing system featuring subroutine linkage operations using hardware controlled stacks |
US4905138A (en) * | 1985-10-17 | 1990-02-27 | Westinghouse Electric Corp. | Meta-interpreter |
US5610828A (en) * | 1986-04-14 | 1997-03-11 | National Instruments Corporation | Graphical system for modelling a process and associated method |
US4918526A (en) * | 1987-03-20 | 1990-04-17 | Digital Equipment Corporation | Apparatus and method for video signal image processing under control of a data processing system |
US4870610A (en) * | 1987-08-25 | 1989-09-26 | Bell Communications Research, Inc. | Method of operating a computer system to provide customed I/O information including language translation |
US5105353A (en) * | 1987-10-30 | 1992-04-14 | International Business Machines Corporation | Compressed LR parsing table and method of compressing LR parsing tables |
WO1991003791A1 (en) * | 1989-09-01 | 1991-03-21 | Amdahl Corporation | Operating system and data base |
CA2066724C (en) * | 1989-09-01 | 2000-12-05 | Helge Knudsen | Operating system and data base |
US5214785A (en) * | 1989-09-27 | 1993-05-25 | Third Point Systems, Inc. | Controller with keyboard emulation capability for control of host computer operation |
US5276880A (en) * | 1989-12-15 | 1994-01-04 | Siemens Corporate Research, Inc. | Method for parsing and representing multi-versioned computer programs, for simultaneous and synchronous processing of the plural parses |
US5313575A (en) * | 1990-06-13 | 1994-05-17 | Hewlett-Packard Company | Processing method for an iconic programming system |
US5787432A (en) * | 1990-12-06 | 1998-07-28 | Prime Arithmethics, Inc. | Method and apparatus for the generation, manipulation and display of data structures |
US5369577A (en) * | 1991-02-01 | 1994-11-29 | Wang Laboratories, Inc. | Text searching system |
US5430836A (en) * | 1991-03-01 | 1995-07-04 | Ast Research, Inc. | Application control module for common user access interface |
US5507030A (en) * | 1991-03-07 | 1996-04-09 | Digitial Equipment Corporation | Successive translation, execution and interpretation of computer program having code at unknown locations due to execution transfer instructions having computed destination addresses |
US5487147A (en) * | 1991-09-05 | 1996-01-23 | International Business Machines Corporation | Generation of error messages and error recovery for an LL(1) parser |
US5410701A (en) * | 1992-01-29 | 1995-04-25 | Devonrue Ltd. | System and method for analyzing programmed equations |
US6104836A (en) * | 1992-02-19 | 2000-08-15 | 8×8, Inc. | Computer architecture for video data processing and method thereof |
US5303392A (en) | 1992-02-27 | 1994-04-12 | Sun Microsystems, Inc. | Accessing current symbol definitions in a dynamically configurable operating system |
US5339406A (en) | 1992-04-03 | 1994-08-16 | Sun Microsystems, Inc. | Reconstructing symbol definitions of a dynamically configurable operating system defined at the time of a system crash |
US5625554A (en) * | 1992-07-20 | 1997-04-29 | Xerox Corporation | Finite-state transduction of related word forms for text indexing and retrieval |
ATE190156T1 (en) * | 1992-09-04 | 2000-03-15 | Caterpillar Inc | INTEGRATED DESIGN AND TRANSLATION SYSTEM |
US5375241A (en) * | 1992-12-21 | 1994-12-20 | Microsoft Corporation | Method and system for dynamic-link library |
US6219830B1 (en) * | 1993-03-23 | 2001-04-17 | Apple Computer, Inc. | Relocatable object code format and method for loading same into a computer system |
US5819083A (en) * | 1993-09-02 | 1998-10-06 | International Business Machines Corporation | Minimal sufficient buffer space for data redistribution in a parallel database system |
US5701482A (en) * | 1993-09-03 | 1997-12-23 | Hughes Aircraft Company | Modular array processor architecture having a plurality of interconnected load-balanced parallel processing nodes |
US6279029B1 (en) * | 1993-10-12 | 2001-08-21 | Intel Corporation | Server/client architecture and method for multicasting on a computer network |
US5583761A (en) * | 1993-10-13 | 1996-12-10 | Kt International, Inc. | Method for automatic displaying program presentations in different languages |
US5499358A (en) * | 1993-12-10 | 1996-03-12 | Novell, Inc. | Method for storing a database in extended attributes of a file system |
CA2138830A1 (en) * | 1994-03-03 | 1995-09-04 | Jamie Joanne Marschner | Real-time administration-translation arrangement |
US5467472A (en) * | 1994-04-15 | 1995-11-14 | Microsoft Corporation | Method and system for generating and maintaining property sets with unique format identifiers |
US5655148A (en) * | 1994-05-27 | 1997-08-05 | Microsoft Corporation | Method for automatically configuring devices including a network adapter without manual intervention and without prior configuration information |
AU2767295A (en) * | 1994-06-03 | 1996-01-04 | Synopsys, Inc. | Method and apparatus for context sensitive text displays |
US5778371A (en) * | 1994-09-13 | 1998-07-07 | Kabushiki Kaisha Toshiba | Code string processing system and method using intervals |
US6083282A (en) * | 1994-10-21 | 2000-07-04 | Microsoft Corporation | Cross-project namespace compiler and method |
US5850518A (en) * | 1994-12-12 | 1998-12-15 | Northrup; Charles J. | Access-method-independent exchange |
US6139201A (en) * | 1994-12-22 | 2000-10-31 | Caterpillar Inc. | Integrated authoring and translation system |
US5794050A (en) * | 1995-01-04 | 1998-08-11 | Intelligent Text Processing, Inc. | Natural language understanding system |
US6324558B1 (en) * | 1995-02-14 | 2001-11-27 | Scott A. Wilber | Random number generator and generation method |
US6061675A (en) * | 1995-05-31 | 2000-05-09 | Oracle Corporation | Methods and apparatus for classifying terminology utilizing a knowledge catalog |
US5694523A (en) * | 1995-05-31 | 1997-12-02 | Oracle Corporation | Content processing system for discourse |
US5887120A (en) * | 1995-05-31 | 1999-03-23 | Oracle Corporation | Method and apparatus for determining theme for discourse |
US5768580A (en) * | 1995-05-31 | 1998-06-16 | Oracle Corporation | Methods and apparatus for dynamic classification of discourse |
US5748975A (en) * | 1995-07-06 | 1998-05-05 | Sun Microsystems, Inc. | System and method for textual editing of structurally-represented computer programs with on-the-fly typographical display |
US5721939A (en) * | 1995-08-03 | 1998-02-24 | Xerox Corporation | Method and apparatus for tokenizing text |
US5826087A (en) * | 1995-10-02 | 1998-10-20 | Lohmann; William C. | Method and apparatus for cross calling programs of different lexical scoping methodology |
RU2115159C1 (en) * | 1995-10-24 | 1998-07-10 | Владимир Олегович Сафонов | Method and device for checking use of record fields during compilation |
US6366933B1 (en) * | 1995-10-27 | 2002-04-02 | At&T Corp. | Method and apparatus for tracking and viewing changes on the web |
US5797004A (en) * | 1995-12-08 | 1998-08-18 | Sun Microsystems, Inc. | System and method for caching and allocating thread synchronization constructs |
US5822580A (en) * | 1996-01-19 | 1998-10-13 | Object Technology Licensing Corp. | Object oriented programming based global registry system, method, and article of manufacture |
US6076088A (en) * | 1996-02-09 | 2000-06-13 | Paik; Woojin | Information extraction system and method using concept relation concept (CRC) triples |
US5974372A (en) * | 1996-02-12 | 1999-10-26 | Dst Systems, Inc. | Graphical user interface (GUI) language translator |
CA2175711A1 (en) * | 1996-05-01 | 1997-11-02 | Lee Richard Nackman | Incremental compilation of c++ programs |
US5832484A (en) * | 1996-07-02 | 1998-11-03 | Sybase, Inc. | Database system with methods for parallel lock management |
IL118959A (en) * | 1996-07-26 | 1999-07-14 | Ori Software Dev Ltd | Database apparatus |
US6044367A (en) * | 1996-08-02 | 2000-03-28 | Hewlett-Packard Company | Distributed I/O store |
US6085186A (en) * | 1996-09-20 | 2000-07-04 | Netbot, Inc. | Method and system using information written in a wrapper description language to execute query on a network |
US5961594A (en) * | 1996-09-26 | 1999-10-05 | International Business Machines Corporation | Remote node maintenance and management method and system in communication networks using multiprotocol agents |
US5787425A (en) * | 1996-10-01 | 1998-07-28 | International Business Machines Corporation | Object-oriented data mining framework mechanism |
US6055561A (en) * | 1996-10-02 | 2000-04-25 | International Business Machines Corporation | Mapping of routing traffic to switching networks |
US5903756A (en) * | 1996-10-11 | 1999-05-11 | Sun Microsystems, Incorporated | Variable lookahead parser generator |
US5916305A (en) * | 1996-11-05 | 1999-06-29 | Shomiti Systems, Inc. | Pattern recognition in data communications using predictive parsers |
US6065039A (en) * | 1996-11-14 | 2000-05-16 | Mitsubishi Electric Information Technology Center America, Inc. (Ita) | Dynamic synchronous collaboration framework for mobile agents |
US6460058B2 (en) * | 1996-12-06 | 2002-10-01 | Microsoft Corporation | Object-oriented framework for hyperlink navigation |
US6286093B1 (en) * | 1996-12-10 | 2001-09-04 | Logic Express Systems, Inc. | Multi-bus programmable interconnect architecture |
JP3008872B2 (en) * | 1997-01-08 | 2000-02-14 | 日本電気株式会社 | GUI system automatic operation device and operation macro execution device |
US5951653A (en) * | 1997-01-29 | 1999-09-14 | Microsoft Corporation | Method and system for coordinating access to objects of different thread types in a shared memory space |
US5900871A (en) * | 1997-03-10 | 1999-05-04 | International Business Machines Corporation | System and method for managing multiple cultural profiles in an information handling system |
US6470389B1 (en) * | 1997-03-14 | 2002-10-22 | Lucent Technologies Inc. | Hosting a network service on a cluster of servers using a single-address image |
US6108754A (en) * | 1997-04-03 | 2000-08-22 | Sun Microsystems, Inc. | Thread-local synchronization construct cache |
US6138170A (en) * | 1997-04-07 | 2000-10-24 | Novell, Inc. | Method and system for integrating external functions into an application environment |
US6115782A (en) * | 1997-04-23 | 2000-09-05 | Sun Micosystems, Inc. | Method and apparatus for locating nodes in a carded heap using a card marking structure and a node advance value |
US5915255A (en) * | 1997-04-23 | 1999-06-22 | Sun Microsystems, Inc. | Method and apparatus for referencing nodes using links |
US6104715A (en) * | 1997-04-28 | 2000-08-15 | International Business Machines Corporation | Merging of data cells in an ATM network |
US6389379B1 (en) * | 1997-05-02 | 2002-05-14 | Axis Systems, Inc. | Converification system and method |
US5960382A (en) * | 1997-07-07 | 1999-09-28 | Lucent Technologies Inc. | Translation of an initially-unknown message |
US5897642A (en) * | 1997-07-14 | 1999-04-27 | Microsoft Corporation | Method and system for integrating an object-based application with a version control system |
EP0996886B1 (en) * | 1997-07-25 | 2002-10-09 | BRITISH TELECOMMUNICATIONS public limited company | Software system generation |
US6101508A (en) * | 1997-08-01 | 2000-08-08 | Hewlett-Packard Company | Clustered file management for network resources |
US6003066A (en) * | 1997-08-14 | 1999-12-14 | International Business Machines Corporation | System for distributing a plurality of threads associated with a process initiating by one data processing station among data processing stations |
US5963742A (en) * | 1997-09-08 | 1999-10-05 | Lucent Technologies, Inc. | Using speculative parsing to process complex input data |
US5991539A (en) * | 1997-09-08 | 1999-11-23 | Lucent Technologies, Inc. | Use of re-entrant subparsing to facilitate processing of complicated input data |
DE19741475A1 (en) * | 1997-09-19 | 1999-03-25 | Siemens Ag | Message translation method for in communication system |
US6094650A (en) * | 1997-12-15 | 2000-07-25 | Manning & Napier Information Services | Database analysis using a probabilistic ontology |
US6098093A (en) * | 1998-03-19 | 2000-08-01 | International Business Machines Corp. | Maintaining sessions in a clustered server environment |
US6393386B1 (en) * | 1998-03-26 | 2002-05-21 | Visual Networks Technologies, Inc. | Dynamic modeling of complex networks and prediction of impacts of faults therein |
US6173316B1 (en) * | 1998-04-08 | 2001-01-09 | Geoworks Corporation | Wireless communication device with markup language based man-machine interface |
US6189004B1 (en) * | 1998-05-06 | 2001-02-13 | E. Piphany, Inc. | Method and apparatus for creating a datamart and for creating a query structure for the datamart |
US6161103A (en) * | 1998-05-06 | 2000-12-12 | Epiphany, Inc. | Method and apparatus for creating aggregates for use in a datamart |
US6092036A (en) * | 1998-06-02 | 2000-07-18 | Davox Corporation | Multi-lingual data processing system and system and method for translating text used in computer software utilizing an embedded translator |
US6237005B1 (en) * | 1998-06-29 | 2001-05-22 | Compaq Computer Corporation | Web server mechanism for processing multiple transactions in an interpreted language execution environment |
US6226630B1 (en) * | 1998-07-22 | 2001-05-01 | Compaq Computer Corporation | Method and apparatus for filtering incoming information using a search engine and stored queries defining user folders |
US6378126B2 (en) * | 1998-09-29 | 2002-04-23 | International Business Machines Corporation | Compilation of embedded language statements in a source code program |
US6564368B1 (en) * | 1998-10-01 | 2003-05-13 | Call Center Technology, Inc. | System and method for visual application development without programming |
US6327587B1 (en) * | 1998-10-05 | 2001-12-04 | Digital Archaeology, Inc. | Caching optimization with disk and/or memory cache management |
US6654953B1 (en) * | 1998-10-09 | 2003-11-25 | Microsoft Corporation | Extending program languages with source-program attribute tags |
US6564263B1 (en) * | 1998-12-04 | 2003-05-13 | International Business Machines Corporation | Multimedia content description framework |
US6269189B1 (en) * | 1998-12-29 | 2001-07-31 | Xerox Corporation | Finding selected character strings in text and providing information relating to the selected character strings |
US6671273B1 (en) * | 1998-12-31 | 2003-12-30 | Compaq Information Technologies Group L.P. | Method for using outgoing TCP/IP sequence number fields to provide a desired cluster node |
US6453321B1 (en) * | 1999-02-11 | 2002-09-17 | Ibm Corporation | Structured cache for persistent objects |
US6324581B1 (en) * | 1999-03-03 | 2001-11-27 | Emc Corporation | File server system using file system storage, data movers, and an exchange of meta data among data movers for file locking and direct access to shared file systems |
US6748481B1 (en) * | 1999-04-06 | 2004-06-08 | Microsoft Corporation | Streaming information appliance with circular buffer for receiving and selectively reading blocks of streaming information |
US6446071B1 (en) * | 1999-04-26 | 2002-09-03 | International Business Machines Corporation | Method and system for user-specific management of applications in a heterogeneous server environment |
US6321190B1 (en) * | 1999-06-28 | 2001-11-20 | Avaya Technologies Corp. | Infrastructure for developing application-independent language modules for language-independent applications |
US6199195B1 (en) * | 1999-07-08 | 2001-03-06 | Science Application International Corporation | Automatically generated objects within extensible object frameworks and links to enterprise resources |
US7152228B2 (en) * | 1999-07-08 | 2006-12-19 | Science Applications International Corporation | Automatically generated objects within extensible object frameworks and links to enterprise resources |
US6275790B1 (en) * | 1999-07-28 | 2001-08-14 | International Business Machines Corporation | Introspective editor system, program, and method for software translation |
US6311151B1 (en) * | 1999-07-28 | 2001-10-30 | International Business Machines Corporation | System, program, and method for performing contextual software translations |
US6442565B1 (en) * | 1999-08-13 | 2002-08-27 | Hiddenmind Technology, Inc. | System and method for transmitting data content in a computer network |
US6490666B1 (en) * | 1999-08-20 | 2002-12-03 | Microsoft Corporation | Buffering data in a hierarchical data storage environment |
US6434568B1 (en) * | 1999-08-31 | 2002-08-13 | Accenture Llp | Information services patterns in a netcentric environment |
US6507833B1 (en) * | 1999-09-13 | 2003-01-14 | Oracle Corporation | Method and apparatus for dynamically rendering components at run time |
US6353925B1 (en) * | 1999-09-22 | 2002-03-05 | Compaq Computer Corporation | System and method for lexing and parsing program annotations |
US6826744B1 (en) * | 1999-10-01 | 2004-11-30 | Vertical Computer Systems, Inc. | System and method for generating web sites in an arbitrary object framework |
US6704737B1 (en) * | 1999-10-18 | 2004-03-09 | Fisher-Rosemount Systems, Inc. | Accessing and updating a configuration database from distributed physical locations within a process control system |
US6728692B1 (en) * | 1999-12-23 | 2004-04-27 | Hewlett-Packard Company | Apparatus for a multi-modal ontology engine |
US6502097B1 (en) * | 1999-12-23 | 2002-12-31 | Microsoft Corporation | Data structure for efficient access to variable-size data objects |
US6721723B1 (en) * | 1999-12-23 | 2004-04-13 | 1St Desk Systems, Inc. | Streaming metatree data structure for indexing information in a data base |
US6654952B1 (en) * | 2000-02-03 | 2003-11-25 | Sun Microsystems, Inc. | Region based optimizations using data dependence graphs |
US6819339B1 (en) * | 2000-02-24 | 2004-11-16 | Eric Morgan Dowling | Web browser with multilevel functions |
EP1272912A2 (en) * | 2000-02-25 | 2003-01-08 | Synquiry Technologies, Ltd | Conceptual factoring and unification of graphs representing semantic models |
US20020062245A1 (en) * | 2000-03-09 | 2002-05-23 | David Niu | System and method for generating real-time promotions on an electronic commerce world wide website to increase the likelihood of purchase |
US6986132B1 (en) * | 2000-04-28 | 2006-01-10 | Sun Microsytems, Inc. | Remote incremental program binary compatibility verification using API definitions |
US6865716B1 (en) * | 2000-05-05 | 2005-03-08 | Aspect Communication Corporation | Method and apparatus for dynamic localization of documents |
US6862610B2 (en) * | 2000-05-08 | 2005-03-01 | Ideaflood, Inc. | Method and apparatus for verifying the identity of individuals |
US6591274B1 (en) * | 2000-05-31 | 2003-07-08 | Sprint Communications Company, L.P. | Computer software framework and method for accessing data from one or more datastores for use by one or more computing applications |
US6658652B1 (en) * | 2000-06-08 | 2003-12-02 | International Business Machines Corporation | Method and system for shadow heap memory leak detection and other heap analysis in an object-oriented environment during real-time trace processing |
JP2002007169A (en) * | 2000-06-23 | 2002-01-11 | Nec Corp | System for measuring grammar comprehension rate |
US6670969B1 (en) * | 2000-06-29 | 2003-12-30 | Curl Corporation | Interface frames for threads |
US7100153B1 (en) * | 2000-07-06 | 2006-08-29 | Microsoft Corporation | Compiler generation of a late binding interface implementation |
US6658416B1 (en) * | 2000-07-10 | 2003-12-02 | International Business Machines Corporation | Apparatus and method for creating an indexed database of symbolic data for use with trace data of a computer program |
US20030070159A1 (en) * | 2000-08-04 | 2003-04-10 | Intrinsic Graphics, Inc. | Object decription language |
US7027975B1 (en) * | 2000-08-08 | 2006-04-11 | Object Services And Consulting, Inc. | Guided natural language interface system and method |
US6981245B1 (en) * | 2000-09-14 | 2005-12-27 | Sun Microsystems, Inc. | Populating binary compatible resource-constrained devices with content verified using API definitions |
US6711672B1 (en) * | 2000-09-22 | 2004-03-23 | Vmware, Inc. | Method and system for implementing subroutine calls and returns in binary translation sub-systems of computers |
US6640231B1 (en) * | 2000-10-06 | 2003-10-28 | Ontology Works, Inc. | Ontology for database design and application development |
US6993568B1 (en) * | 2000-11-01 | 2006-01-31 | Microsoft Corporation | System and method for providing language localization for server-based applications with scripts |
US7111283B2 (en) * | 2000-11-29 | 2006-09-19 | Microsoft Corporation | Program history in a computer programming language |
US6748585B2 (en) * | 2000-11-29 | 2004-06-08 | Microsoft Corporation | Computer programming language pronouns |
US6981031B2 (en) * | 2000-12-15 | 2005-12-27 | International Business Machines Corporation | Language independent message management for multi-node application systems |
US6883087B1 (en) * | 2000-12-15 | 2005-04-19 | Palm, Inc. | Processing of binary data for compression |
US6885985B2 (en) * | 2000-12-18 | 2005-04-26 | Xerox Corporation | Terminology translation for unaligned comparable corpora using category based translation probabilities |
US6678677B2 (en) * | 2000-12-19 | 2004-01-13 | Xerox Corporation | Apparatus and method for information retrieval using self-appending semantic lattice |
US6950793B2 (en) * | 2001-01-12 | 2005-09-27 | International Business Machines Corporation | System and method for deriving natural language representation of formal belief structures |
US7249018B2 (en) * | 2001-01-12 | 2007-07-24 | International Business Machines Corporation | System and method for relating syntax and semantics for a conversational speech application |
US6539460B2 (en) * | 2001-01-19 | 2003-03-25 | International Business Machines Corporation | System and method for storing data sectors with header and trailer information in a disk cache supporting memory compression |
US6964014B1 (en) * | 2001-02-15 | 2005-11-08 | Networks Associates Technology, Inc. | Method and system for localizing Web pages |
US20020133523A1 (en) * | 2001-03-16 | 2002-09-19 | Anthony Ambler | Multilingual graphic user interface system and method |
US6847974B2 (en) * | 2001-03-26 | 2005-01-25 | Us Search.Com Inc | Method and apparatus for intelligent data assimilation |
US6721943B2 (en) * | 2001-03-30 | 2004-04-13 | Intel Corporation | Compile-time memory coalescing for dynamic arrays |
US7024546B2 (en) * | 2001-04-03 | 2006-04-04 | Microsoft Corporation | Automatically enabling editing languages of a software program |
US20030005412A1 (en) * | 2001-04-06 | 2003-01-02 | Eanes James Thomas | System for ontology-based creation of software agents from reusable components |
US7210022B2 (en) * | 2001-05-15 | 2007-04-24 | Cloudshield Technologies, Inc. | Apparatus and method for interconnecting a processor to co-processors using a shared memory as the communication interface |
US7099885B2 (en) * | 2001-05-25 | 2006-08-29 | Unicorn Solutions | Method and system for collaborative ontology modeling |
US7266832B2 (en) * | 2001-06-14 | 2007-09-04 | Digeo, Inc. | Advertisement swapping using an aggregator for an interactive television system |
US20030004703A1 (en) * | 2001-06-28 | 2003-01-02 | Arvind Prabhakar | Method and system for localizing a markup language document |
US20030009323A1 (en) * | 2001-07-06 | 2003-01-09 | Max Adeli | Application platform for developing mono-lingual and multi-lingual systems and generating user presentations |
US7003764B2 (en) * | 2001-10-12 | 2006-02-21 | Sun Microsystems, Inc. | Method and apparatus for dynamic configuration of a lexical analysis parser |
US7432940B2 (en) * | 2001-10-12 | 2008-10-07 | Canon Kabushiki Kaisha | Interactive animation of sprites in a video production |
CA2359831A1 (en) * | 2001-10-24 | 2003-04-24 | Ibm Canada Limited-Ibm Canada Limitee | Method and system for multiple level parsing |
US20030210329A1 (en) * | 2001-11-08 | 2003-11-13 | Aagaard Kenneth Joseph | Video system and methods for operating a video system |
US7308449B2 (en) | 2002-02-01 | 2007-12-11 | John Fairweather | System and method for managing collections of data on a network |
WO2003063949A2 (en) * | 2002-02-01 | 2003-08-07 | The Cleveland Clinic Foundation | Adjustable simulation device and method of using same |
WO2003085493A2 (en) * | 2002-03-29 | 2003-10-16 | Agilent Technologies, Inc. | Method and system for predicting multi-variable outcomes |
US7155438B2 (en) * | 2002-05-01 | 2006-12-26 | Bea Systems, Inc. | High availability for event forwarding |
US7093023B2 (en) * | 2002-05-21 | 2006-08-15 | Washington University | Methods, systems, and devices using reprogrammable hardware for high-speed processing of streaming data to find a redefinable pattern and respond thereto |
US6915291B2 (en) * | 2002-06-07 | 2005-07-05 | International Business Machines Corporation | Object-oriented query execution data structure |
US7127520B2 (en) * | 2002-06-28 | 2006-10-24 | Streamserve | Method and system for transforming input data streams |
US6970969B2 (en) | 2002-08-29 | 2005-11-29 | Micron Technology, Inc. | Multiple segment data object management |
US7464254B2 (en) * | 2003-01-09 | 2008-12-09 | Cisco Technology, Inc. | Programmable processor apparatus integrating dedicated search registers and dedicated state machine registers with associated execution hardware to support rapid application of rulesets to data |
US7340724B2 (en) * | 2003-08-15 | 2008-03-04 | Laszlo Systems, Inc. | Evaluating expressions in a software environment |
US7624385B2 (en) * | 2005-03-30 | 2009-11-24 | Alcatel-Lucent Usa Inc. | Method for handling preprocessing in source code transformation |
US7512634B2 (en) * | 2006-06-05 | 2009-03-31 | Tarari, Inc. | Systems and methods for processing regular expressions |
US7831607B2 (en) * | 2006-12-08 | 2010-11-09 | Pandya Ashish A | Interval symbol architecture for programmable intelligent search memory |
-
2003
- 2003-02-03 US US10/357,304 patent/US7308449B2/en active Active
- 2003-02-03 WO PCT/US2003/003157 patent/WO2004002044A2/en not_active Application Discontinuation
- 2003-02-03 WO PCT/US2003/003251 patent/WO2003065180A2/en not_active Application Discontinuation
- 2003-02-03 AU AU2003210789A patent/AU2003210789A1/en not_active Abandoned
- 2003-02-03 US US10/357,326 patent/US7328430B2/en active Active
- 2003-02-03 WO PCT/US2003/003067 patent/WO2003065252A1/en not_active Application Discontinuation
- 2003-02-03 EP EP03735120A patent/EP1527414A2/en not_active Withdrawn
- 2003-02-03 US US10/357,290 patent/US20030172053A1/en not_active Abandoned
- 2003-02-03 AU AU2003210803A patent/AU2003210803A1/en not_active Abandoned
- 2003-02-03 WO PCT/US2003/003227 patent/WO2003065240A1/en not_active Application Discontinuation
- 2003-02-03 WO PCT/US2003/003032 patent/WO2003065171A2/en not_active Application Discontinuation
- 2003-02-03 AU AU2003269798A patent/AU2003269798A1/en not_active Abandoned
- 2003-02-03 US US10/357,325 patent/US7158984B2/en not_active Expired - Lifetime
- 2003-02-03 AU AU2003217312A patent/AU2003217312A1/en not_active Abandoned
- 2003-02-03 US US10/357,324 patent/US7210130B2/en active Active
- 2003-02-03 US US10/357,284 patent/US7555755B2/en active Active
- 2003-02-03 US US10/357,259 patent/US7143087B2/en active Active
- 2003-02-03 WO PCT/US2003/003085 patent/WO2003065173A2/en not_active Application Discontinuation
- 2003-02-03 US US10/357,289 patent/US7369984B2/en active Active
- 2003-02-03 AU AU2003225542A patent/AU2003225542A1/en not_active Abandoned
- 2003-02-03 WO PCT/US2003/003068 patent/WO2003065212A1/en not_active Application Discontinuation
- 2003-02-03 US US10/357,286 patent/US20040024720A1/en not_active Abandoned
- 2003-02-03 AU AU2003210795A patent/AU2003210795A1/en not_active Abandoned
- 2003-02-03 WO PCT/US2003/003110 patent/WO2003065175A2/en not_active Application Discontinuation
- 2003-02-03 AU AU2003214975A patent/AU2003214975A1/en not_active Abandoned
- 2003-02-03 WO PCT/US2003/003205 patent/WO2003065179A2/en not_active Application Discontinuation
- 2003-02-03 AU AU2003216161A patent/AU2003216161A1/en not_active Abandoned
- 2003-02-03 WO PCT/US2003/003151 patent/WO2003065177A2/en not_active Application Discontinuation
- 2003-02-03 WO PCT/US2003/003201 patent/WO2003065213A1/en not_active Application Discontinuation
- 2003-02-03 WO PCT/US2003/003066 patent/WO2003065634A2/en not_active Application Discontinuation
- 2003-02-03 US US10/357,283 patent/US7240330B2/en active Active
- 2003-02-03 US US10/357,288 patent/US7103749B2/en active Active
-
2006
- 2006-06-16 US US11/455,304 patent/US7533069B2/en not_active Expired - Lifetime
- 2006-07-10 US US11/484,220 patent/US7685083B2/en active Active - Reinstated
-
2007
- 2007-07-11 US US11/776,299 patent/US8099722B2/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6732090B2 (en) * | 2001-08-13 | 2004-05-04 | Xerox Corporation | Meta-document management system with user definable personalities |
US6778979B2 (en) * | 2001-08-13 | 2004-08-17 | Xerox Corporation | System for automatically generating queries |
US6820075B2 (en) * | 2001-08-13 | 2004-11-16 | Xerox Corporation | Document-centric system with auto-completion |
Cited By (254)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9369770B2 (en) | 1999-11-04 | 2016-06-14 | Xdrive, Llc | Network personal digital video recorder system (NPDVR) |
US10397224B2 (en) | 1999-11-04 | 2019-08-27 | Oath Inc. | Network personal digital video recorder system (NPDVR) |
US9378212B2 (en) | 1999-11-04 | 2016-06-28 | Xdrive, Llc | Methods and systems for providing file data and metadata |
US7979856B2 (en) | 2000-06-21 | 2011-07-12 | Microsoft Corporation | Network-based software extensions |
US8271369B2 (en) * | 2003-03-12 | 2012-09-18 | Norman Gilmore | Financial modeling and forecasting system |
US20090177961A1 (en) * | 2003-03-24 | 2009-07-09 | Microsoft Corporation | Designing Electronic Forms |
US7925621B2 (en) | 2003-03-24 | 2011-04-12 | Microsoft Corporation | Installing a solution |
US8918729B2 (en) | 2003-03-24 | 2014-12-23 | Microsoft Corporation | Designing electronic forms |
US9229917B2 (en) | 2003-03-28 | 2016-01-05 | Microsoft Technology Licensing, Llc | Electronic form user interfaces |
US20050021670A1 (en) * | 2003-06-27 | 2005-01-27 | Oracle International Corporation | Method and apparatus for supporting service enablers via service request composition |
US20050015340A1 (en) * | 2003-06-27 | 2005-01-20 | Oracle International Corporation | Method and apparatus for supporting service enablers via service request handholding |
US7873716B2 (en) | 2003-06-27 | 2011-01-18 | Oracle International Corporation | Method and apparatus for supporting service enablers via service request composition |
US9239821B2 (en) | 2003-08-01 | 2016-01-19 | Microsoft Technology Licensing, Llc | Translation file |
US8892993B2 (en) | 2003-08-01 | 2014-11-18 | Microsoft Corporation | Translation file |
US9268760B2 (en) | 2003-08-06 | 2016-02-23 | Microsoft Technology Licensing, Llc | Correlation, association, or correspondence of electronic forms |
US8429522B2 (en) | 2003-08-06 | 2013-04-23 | Microsoft Corporation | Correlation, association, or correspondence of electronic forms |
US7873541B1 (en) * | 2004-02-11 | 2011-01-18 | SQAD, Inc. | System and method for aggregating advertising pricing data |
US20080222514A1 (en) * | 2004-02-17 | 2008-09-11 | Microsoft Corporation | Systems and Methods for Editing XML Documents |
US9565297B2 (en) | 2004-05-28 | 2017-02-07 | Oracle International Corporation | True convergence with end to end identity management |
US9038082B2 (en) | 2004-05-28 | 2015-05-19 | Oracle International Corporation | Resource abstraction via enabler and metadata |
US20060020501A1 (en) * | 2004-07-22 | 2006-01-26 | Leicht Howard J | Benefit plans |
US20060053382A1 (en) * | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for facilitating user interaction with multi-relational ontologies |
US20060053171A1 (en) * | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for curating one or more multi-relational ontologies |
US7493333B2 (en) | 2004-09-03 | 2009-02-17 | Biowisdom Limited | System and method for parsing and/or exporting data from one or more multi-relational ontologies |
US7496593B2 (en) | 2004-09-03 | 2009-02-24 | Biowisdom Limited | Creating a multi-relational ontology having a predetermined structure |
US20060074833A1 (en) * | 2004-09-03 | 2006-04-06 | Biowisdom Limited | System and method for notifying users of changes in multi-relational ontologies |
US20060074836A1 (en) * | 2004-09-03 | 2006-04-06 | Biowisdom Limited | System and method for graphically displaying ontology data |
US20060053174A1 (en) * | 2004-09-03 | 2006-03-09 | Bio Wisdom Limited | System and method for data extraction and management in multi-relational ontology creation |
US7505989B2 (en) | 2004-09-03 | 2009-03-17 | Biowisdom Limited | System and method for creating customized ontologies |
US20060053175A1 (en) * | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for creating, editing, and utilizing one or more rules for multi-relational ontology creation and maintenance |
US20060053172A1 (en) * | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for creating, editing, and using multi-relational ontologies |
US20060053173A1 (en) * | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for support of chemical data within multi-relational ontologies |
US20100211677A1 (en) * | 2004-09-15 | 2010-08-19 | Qurio Holdings, Inc. | Peer proxy binding |
US8305892B2 (en) | 2004-09-15 | 2012-11-06 | Qurio Holdings, Inc. | Peer proxy binding |
US7719971B1 (en) | 2004-09-15 | 2010-05-18 | Qurio Holdings, Inc. | Peer proxy binding |
US20060116912A1 (en) * | 2004-12-01 | 2006-06-01 | Oracle International Corporation | Managing account-holder information using policies |
US8032920B2 (en) * | 2004-12-27 | 2011-10-04 | Oracle International Corporation | Policies as workflows |
US20060143686A1 (en) * | 2004-12-27 | 2006-06-29 | Oracle International Corporation | Policies as workflows |
US20060212574A1 (en) * | 2005-03-01 | 2006-09-21 | Oracle International Corporation | Policy interface description framework |
US8321498B2 (en) | 2005-03-01 | 2012-11-27 | Oracle International Corporation | Policy interface description framework |
US20060288011A1 (en) * | 2005-06-21 | 2006-12-21 | Microsoft Corporation | Finding and consuming web subscriptions in a web browser |
US8832571B2 (en) | 2005-06-21 | 2014-09-09 | Microsoft Corporation | Finding and consuming web subscriptions in a web browser |
US9104773B2 (en) | 2005-06-21 | 2015-08-11 | Microsoft Technology Licensing, Llc | Finding and consuming web subscriptions in a web browser |
US8751936B2 (en) | 2005-06-21 | 2014-06-10 | Microsoft Corporation | Finding and consuming web subscriptions in a web browser |
US20090013266A1 (en) * | 2005-06-21 | 2009-01-08 | Microsoft Corporation | Finding and Consuming Web Subscriptions in a Web Browser |
US20060288329A1 (en) * | 2005-06-21 | 2006-12-21 | Microsoft Corporation | Content syndication platform |
US9894174B2 (en) | 2005-06-21 | 2018-02-13 | Microsoft Technology Licensing, Llc | Finding and consuming web subscriptions in a web browser |
US8661459B2 (en) | 2005-06-21 | 2014-02-25 | Microsoft Corporation | Content syndication platform |
US9762668B2 (en) | 2005-06-21 | 2017-09-12 | Microsoft Technology Licensing, Llc | Content syndication platform |
US20070011145A1 (en) * | 2005-07-08 | 2007-01-11 | Matthew Snyder | System and method for operation control functionality |
US8620667B2 (en) * | 2005-10-17 | 2013-12-31 | Microsoft Corporation | Flexible speech-activated command and control |
US20070088556A1 (en) * | 2005-10-17 | 2007-04-19 | Microsoft Corporation | Flexible speech-activated command and control |
US9210234B2 (en) | 2005-12-05 | 2015-12-08 | Microsoft Technology Licensing, Llc | Enabling electronic documents for limited-capability computing devices |
US7571151B1 (en) * | 2005-12-15 | 2009-08-04 | Gneiss Software, Inc. | Data analysis tool for analyzing data stored in multiple text files |
US9177047B2 (en) * | 2005-12-20 | 2015-11-03 | Araicom Research Llc | System, method and computer program product for information sorting and retrieval using a language-modeling kernal function |
US20110270829A1 (en) * | 2005-12-20 | 2011-11-03 | Araicom Research Llc | System, method and computer program product for information sorting and retrieval using a language-modeling kernal function |
US20070150821A1 (en) * | 2005-12-22 | 2007-06-28 | Thunemann Paul Z | GUI-maker (data-centric automated GUI-generation) |
WO2007085304A1 (en) * | 2006-01-27 | 2007-08-02 | Swiss Reinsurance Company | System for automated generation of database structures and/or databases and a corresponding method |
US20070179826A1 (en) * | 2006-02-01 | 2007-08-02 | International Business Machines Corporation | Creating a modified ontological model of a business machine |
US20070204017A1 (en) * | 2006-02-16 | 2007-08-30 | Oracle International Corporation | Factorization of concerns to build a SDP (Service delivery platform) |
US9245236B2 (en) | 2006-02-16 | 2016-01-26 | Oracle International Corporation | Factorization of concerns to build a SDP (service delivery platform) |
US7764701B1 (en) | 2006-02-22 | 2010-07-27 | Qurio Holdings, Inc. | Methods, systems, and products for classifying peer systems |
US7779004B1 (en) | 2006-02-22 | 2010-08-17 | Qurio Holdings, Inc. | Methods, systems, and products for characterizing target systems |
US8280843B2 (en) | 2006-03-03 | 2012-10-02 | Microsoft Corporation | RSS data-processing object |
US20070208759A1 (en) * | 2006-03-03 | 2007-09-06 | Microsoft Corporation | RSS Data-Processing Object |
US8768881B2 (en) | 2006-03-03 | 2014-07-01 | Microsoft Corporation | RSS data-processing object |
US7979803B2 (en) | 2006-03-06 | 2011-07-12 | Microsoft Corporation | RSS hostable control |
US7593927B2 (en) | 2006-03-10 | 2009-09-22 | Microsoft Corporation | Unstructured data in a mining model language |
US20070214164A1 (en) * | 2006-03-10 | 2007-09-13 | Microsoft Corporation | Unstructured data in a mining model language |
US20140052735A1 (en) * | 2006-03-31 | 2014-02-20 | Daniel Egnor | Propagating Information Among Web Pages |
US8990210B2 (en) * | 2006-03-31 | 2015-03-24 | Google Inc. | Propagating information among web pages |
US7596549B1 (en) | 2006-04-03 | 2009-09-29 | Qurio Holdings, Inc. | Methods, systems, and products for analyzing annotations for related content |
US8005841B1 (en) | 2006-04-28 | 2011-08-23 | Qurio Holdings, Inc. | Methods, systems, and products for classifying content segments |
US8396848B2 (en) | 2006-06-26 | 2013-03-12 | Microsoft Corporation | Customizable parameter user interface |
US20070299823A1 (en) * | 2006-06-26 | 2007-12-27 | Microsoft Corporation | Customizable parameter user interface |
US8615573B1 (en) | 2006-06-30 | 2013-12-24 | Quiro Holdings, Inc. | System and method for networked PVR storage and content capture |
US9118949B2 (en) | 2006-06-30 | 2015-08-25 | Qurio Holdings, Inc. | System and method for networked PVR storage and content capture |
US7873988B1 (en) | 2006-09-06 | 2011-01-18 | Qurio Holdings, Inc. | System and method for rights propagation and license management in conjunction with distribution of digital content in a social network |
US8244694B2 (en) * | 2006-09-12 | 2012-08-14 | International Business Machines Corporation | Dynamic schema assembly to accommodate application-specific metadata |
US20080065678A1 (en) * | 2006-09-12 | 2008-03-13 | Petri John E | Dynamic schema assembly to accommodate application-specific metadata |
US7801971B1 (en) | 2006-09-26 | 2010-09-21 | Qurio Holdings, Inc. | Systems and methods for discovering, creating, using, and managing social network circuits |
US7925592B1 (en) | 2006-09-27 | 2011-04-12 | Qurio Holdings, Inc. | System and method of using a proxy server to manage lazy content distribution in a social network |
US8554827B2 (en) | 2006-09-29 | 2013-10-08 | Qurio Holdings, Inc. | Virtual peer for a content sharing system |
US7782866B1 (en) | 2006-09-29 | 2010-08-24 | Qurio Holdings, Inc. | Virtual peer in a peer-to-peer network |
US20090254459A1 (en) * | 2006-10-23 | 2009-10-08 | Chipin Inc. | Method and system for providing a widget usable in affiliate marketing |
WO2008070320A3 (en) * | 2006-10-23 | 2008-08-07 | Chipin Inc | Method and system for providing a widget for displaying multimedia content |
US20080097906A1 (en) * | 2006-10-23 | 2008-04-24 | Carnet Williams | Method and system for providing a widget usable in financial transactions |
WO2008070320A2 (en) * | 2006-10-23 | 2008-06-12 | Chipin Inc. | Method and system for providing a widget for displaying multimedia content |
US20080098325A1 (en) * | 2006-10-23 | 2008-04-24 | Carnet Williams | Method and system for facilitating social payment or commercial transactions |
US8560840B2 (en) | 2006-10-23 | 2013-10-15 | InMobi Pte Ltd. | Method and system for authenticating a widget |
US9183002B2 (en) | 2006-10-23 | 2015-11-10 | InMobi Pte Ltd. | Method and system for providing a widget for displaying multimedia content |
US20080104496A1 (en) * | 2006-10-23 | 2008-05-01 | Carnet Williams | Method and system for facilitating social payment or commercial transactions |
US20080097871A1 (en) * | 2006-10-23 | 2008-04-24 | Carnet Williams | Method and system for providing a widget usable in affiliate marketing |
US9311647B2 (en) | 2006-10-23 | 2016-04-12 | InMobi Pte Ltd. | Method and system for providing a widget usable in financial transactions |
US7565332B2 (en) | 2006-10-23 | 2009-07-21 | Chipin Inc. | Method and system for providing a widget usable in affiliate marketing |
US20080098289A1 (en) * | 2006-10-23 | 2008-04-24 | Carnet Williams | Method and system for providing a widget for displaying multimedia content |
US20080098290A1 (en) * | 2006-10-23 | 2008-04-24 | Carnet Williams | Method and system for providing a widget for displaying multimedia content |
US20080215879A1 (en) * | 2006-10-23 | 2008-09-04 | Carnet Williams | Method and system for authenticating a widget |
US8117022B2 (en) * | 2006-12-07 | 2012-02-14 | Linker Sheldon O | Method and system for machine understanding, knowledge, and conversation |
US20080140387A1 (en) * | 2006-12-07 | 2008-06-12 | Linker Sheldon O | Method and system for machine understanding, knowledge, and conversation |
US8739296B2 (en) | 2006-12-11 | 2014-05-27 | Qurio Holdings, Inc. | System and method for social network trust assessment |
US8276207B2 (en) | 2006-12-11 | 2012-09-25 | Qurio Holdings, Inc. | System and method for social network trust assessment |
US7730216B1 (en) | 2006-12-14 | 2010-06-01 | Qurio Holdings, Inc. | System and method of sharing content among multiple social network nodes using an aggregation node |
US7650371B2 (en) * | 2006-12-14 | 2010-01-19 | Microsoft Corporation | Finalizable object usage in software transactions |
US20080147757A1 (en) * | 2006-12-14 | 2008-06-19 | Microsoft Corporation | Finalizable object usage in software transactions |
US8135800B1 (en) | 2006-12-27 | 2012-03-13 | Qurio Holdings, Inc. | System and method for user classification based on social network aware content analysis |
US7840903B1 (en) | 2007-02-26 | 2010-11-23 | Qurio Holdings, Inc. | Group content representations |
US10922332B2 (en) | 2007-03-02 | 2021-02-16 | Verizon Media Inc. | Digital asset management system (DAMS) |
US20080263103A1 (en) * | 2007-03-02 | 2008-10-23 | Mcgregor Lucas | Digital asset management system (DAMS) |
US11899683B2 (en) | 2007-03-02 | 2024-02-13 | Verizon Patent And Licensing Inc. | Digital asset management system |
US9811576B2 (en) | 2007-03-02 | 2017-11-07 | Oath Inc. | Digital asset management system (DAMS) |
US20080228748A1 (en) * | 2007-03-16 | 2008-09-18 | John Fairweather | Language independent stemming |
US8015175B2 (en) * | 2007-03-16 | 2011-09-06 | John Fairweather | Language independent stemming |
US8744055B2 (en) | 2007-03-23 | 2014-06-03 | Oracle International Corporation | Abstract application dispatcher |
US8214503B2 (en) | 2007-03-23 | 2012-07-03 | Oracle International Corporation | Factoring out dialog control and call control |
US8321594B2 (en) | 2007-03-23 | 2012-11-27 | Oracle International Corporation | Achieving low latencies on network events in a non-real time platform |
US8675852B2 (en) | 2007-03-23 | 2014-03-18 | Oracle International Corporation | Using location as a presence attribute |
US20080288966A1 (en) * | 2007-03-23 | 2008-11-20 | Oracle International Corporation | Call control enabler abstracted from underlying network technologies |
US8230449B2 (en) | 2007-03-23 | 2012-07-24 | Oracle International Corporation | Call control enabler abstracted from underlying network technologies |
US20080232567A1 (en) * | 2007-03-23 | 2008-09-25 | Oracle International Corporation | Abstract application dispatcher |
US20080235380A1 (en) * | 2007-03-23 | 2008-09-25 | Oracle International Corporation | Factoring out dialog control and call control |
US20080235327A1 (en) * | 2007-03-23 | 2008-09-25 | Oracle International Corporation | Achieving low latencies on network events in a non-real time platform |
US20080235230A1 (en) * | 2007-03-23 | 2008-09-25 | Oracle International Corporation | Using location as a presence attribute |
US20130159116A1 (en) * | 2007-08-14 | 2013-06-20 | John Nicholas Gross | Method for predicting news content |
US20140316911A1 (en) * | 2007-08-14 | 2014-10-23 | John Nicholas Gross | Method of automatically verifying document content |
US8775406B2 (en) * | 2007-08-14 | 2014-07-08 | John Nicholas Gross | Method for predicting news content |
US9177014B2 (en) * | 2007-08-14 | 2015-11-03 | John Nicholas and Kristin Gross Trust | Method of automatically verifying document content |
WO2009025681A3 (en) * | 2007-08-20 | 2009-08-27 | James Heidenreich | System to customize the facilitation of development and documentation of user thinking about an arbitrary problem |
WO2009025681A2 (en) * | 2007-08-20 | 2009-02-26 | James Heidenreich | System to customize the facilitation of development and documentation of user thinking about an arbitrary problem |
US9111285B2 (en) | 2007-08-27 | 2015-08-18 | Qurio Holdings, Inc. | System and method for representing content, user presence and interaction within virtual world advertising environments |
US8073810B2 (en) | 2007-10-29 | 2011-12-06 | Oracle International Corporation | Shared view of customers across business support systems (BSS) and a service delivery platform (SDP) |
US20090112875A1 (en) * | 2007-10-29 | 2009-04-30 | Oracle International Corporation | Shared view of customers across business support systems (bss) and a service delivery platform (sdp) |
US20090125595A1 (en) * | 2007-11-14 | 2009-05-14 | Oracle International Corporation | Intelligent message processing |
US8539097B2 (en) | 2007-11-14 | 2013-09-17 | Oracle International Corporation | Intelligent message processing |
US8161171B2 (en) | 2007-11-20 | 2012-04-17 | Oracle International Corporation | Session initiation protocol-based internet protocol television |
US8370506B2 (en) | 2007-11-20 | 2013-02-05 | Oracle International Corporation | Session initiation protocol-based internet protocol television |
US20090132717A1 (en) * | 2007-11-20 | 2009-05-21 | Oracle International Corporation | Session initiation protocol-based internet protocol television |
US9563721B2 (en) | 2008-01-16 | 2017-02-07 | Ab Initio Technology Llc | Managing an archive for approximate string matching |
US8775441B2 (en) | 2008-01-16 | 2014-07-08 | Ab Initio Technology Llc | Managing an archive for approximate string matching |
US20090182728A1 (en) * | 2008-01-16 | 2009-07-16 | Arlen Anderson | Managing an Archive for Approximate String Matching |
US9654515B2 (en) | 2008-01-23 | 2017-05-16 | Oracle International Corporation | Service oriented architecture-based SCIM platform |
US20090193433A1 (en) * | 2008-01-24 | 2009-07-30 | Oracle International Corporation | Integrating operational and business support systems with a service delivery platform |
US8966498B2 (en) | 2008-01-24 | 2015-02-24 | Oracle International Corporation | Integrating operational and business support systems with a service delivery platform |
US8589338B2 (en) | 2008-01-24 | 2013-11-19 | Oracle International Corporation | Service-oriented architecture (SOA) management of data repository |
US20090193057A1 (en) * | 2008-01-24 | 2009-07-30 | Oracle International Corporation | Service-oriented architecture (soa) management of data repository |
US20090201917A1 (en) * | 2008-02-08 | 2009-08-13 | Oracle International Corporation | Pragmatic approaches to ims |
US8401022B2 (en) | 2008-02-08 | 2013-03-19 | Oracle International Corporation | Pragmatic approaches to IMS |
US20090228584A1 (en) * | 2008-03-10 | 2009-09-10 | Oracle International Corporation | Presence-based event driven architecture |
US8914493B2 (en) | 2008-03-10 | 2014-12-16 | Oracle International Corporation | Presence-based event driven architecture |
US20090307671A1 (en) * | 2008-06-06 | 2009-12-10 | Cornell University | System and method for scaling simulations and games |
US8443350B2 (en) | 2008-06-06 | 2013-05-14 | Cornell University | System and method for scaling simulations and games |
US8458703B2 (en) | 2008-06-26 | 2013-06-04 | Oracle International Corporation | Application requesting management function based on metadata for managing enabler or dependency |
US20100031147A1 (en) * | 2008-07-31 | 2010-02-04 | Chipln Inc. | Method and system for mixing of multimedia content |
US10819530B2 (en) | 2008-08-21 | 2020-10-27 | Oracle International Corporation | Charging enabler |
US20100049640A1 (en) * | 2008-08-21 | 2010-02-25 | Oracle International Corporation | Charging enabler |
US20100058436A1 (en) * | 2008-08-21 | 2010-03-04 | Oracle International Corporation | Service level network quality of service policy enforcement |
US8090848B2 (en) | 2008-08-21 | 2012-01-03 | Oracle International Corporation | In-vehicle multimedia real-time communications |
US8505067B2 (en) | 2008-08-21 | 2013-08-06 | Oracle International Corporation | Service level network quality of service policy enforcement |
US20100049826A1 (en) * | 2008-08-21 | 2010-02-25 | Oracle International Corporation | In-vehicle multimedia real-time communications |
US9607103B2 (en) | 2008-10-23 | 2017-03-28 | Ab Initio Technology Llc | Fuzzy data operations |
US11615093B2 (en) | 2008-10-23 | 2023-03-28 | Ab Initio Technology Llc | Fuzzy data operations |
US9191434B2 (en) | 2008-10-31 | 2015-11-17 | Disney Enterprises, Inc. | System and method for managing digital media content |
US9413813B2 (en) | 2008-10-31 | 2016-08-09 | Disney Enterprises, Inc. | System and method for providing media content |
US9235572B2 (en) * | 2008-10-31 | 2016-01-12 | Disney Enterprises, Inc. | System and method for updating digital media content |
US8903841B2 (en) | 2009-03-18 | 2014-12-02 | Teradata Us, Inc. | System and method of massively parallel data processing |
US7966340B2 (en) | 2009-03-18 | 2011-06-21 | Aster Data Systems, Inc. | System and method of massively parallel data processing |
US20110134804A1 (en) * | 2009-06-02 | 2011-06-09 | Oracle International Corporation | Telephony application services |
US8879547B2 (en) | 2009-06-02 | 2014-11-04 | Oracle International Corporation | Telephony application services |
US8583830B2 (en) | 2009-11-19 | 2013-11-12 | Oracle International Corporation | Inter-working with a walled garden floor-controlled system |
US20110119404A1 (en) * | 2009-11-19 | 2011-05-19 | Oracle International Corporation | Inter-working with a walled garden floor-controlled system |
US20110125913A1 (en) * | 2009-11-20 | 2011-05-26 | Oracle International Corporation | Interface for Communication Session Continuation |
US9269060B2 (en) | 2009-11-20 | 2016-02-23 | Oracle International Corporation | Methods and systems for generating metadata describing dependencies for composable elements |
US20110145278A1 (en) * | 2009-11-20 | 2011-06-16 | Oracle International Corporation | Methods and systems for generating metadata describing dependencies for composable elements |
US20110125909A1 (en) * | 2009-11-20 | 2011-05-26 | Oracle International Corporation | In-Session Continuation of a Streaming Media Session |
US8533773B2 (en) | 2009-11-20 | 2013-09-10 | Oracle International Corporation | Methods and systems for implementing service level consolidated user information management |
US20110126261A1 (en) * | 2009-11-20 | 2011-05-26 | Oracle International Corporation | Methods and systems for implementing service level consolidated user information management |
US20110145347A1 (en) * | 2009-12-16 | 2011-06-16 | Oracle International Corporation | Global presence |
US9509790B2 (en) | 2009-12-16 | 2016-11-29 | Oracle International Corporation | Global presence |
US9503407B2 (en) | 2009-12-16 | 2016-11-22 | Oracle International Corporation | Message forwarding |
US8935240B2 (en) * | 2009-12-24 | 2015-01-13 | At&T Intellectual Property I, L.P. | Method and apparatus for automated end to end content tracking in peer to peer environments |
US20130262429A1 (en) * | 2009-12-24 | 2013-10-03 | At&T Intellectual Property I, L.P. | Method and apparatus for automated end to end content tracking in peer to peer environments |
US20130060803A1 (en) * | 2010-05-17 | 2013-03-07 | Green Sql Ltd | Database translation system and method |
US9135297B2 (en) * | 2010-05-17 | 2015-09-15 | Green Sql Ltd. | Database translation system and method |
US9684712B1 (en) * | 2010-09-28 | 2017-06-20 | EMC IP Holding Company LLC | Analyzing tenant-specific data |
US11884487B2 (en) | 2010-12-15 | 2024-01-30 | Symbotic Llc | Autonomous transport vehicle with position determining system and method therefor |
US11279557B2 (en) | 2010-12-15 | 2022-03-22 | Symbotic Llc | Bot position sensing |
US10053286B2 (en) | 2010-12-15 | 2018-08-21 | Symbotic, LLC | Bot position sensing |
US10221014B2 (en) | 2010-12-15 | 2019-03-05 | Symbotic, LLC | Bot position sensing |
US9309050B2 (en) | 2010-12-15 | 2016-04-12 | Symbotic, LLC | Bot position sensing |
US9008884B2 (en) | 2010-12-15 | 2015-04-14 | Symbotic Llc | Bot position sensing |
US8898172B2 (en) * | 2011-05-11 | 2014-11-25 | Google Inc. | Parallel generation of topics from documents |
US9776794B2 (en) | 2011-09-09 | 2017-10-03 | Symbotic, LLC | Storage and retrieval system case unit detection |
US9517885B2 (en) | 2011-09-09 | 2016-12-13 | Symbotic Llc | Storage and retrieval system case unit detection |
US9242800B2 (en) | 2011-09-09 | 2016-01-26 | Symbotic, LLC | Storage and retrieval system case unit detection |
US8954188B2 (en) | 2011-09-09 | 2015-02-10 | Symbotic, LLC | Storage and retrieval system case unit detection |
US8832113B2 (en) * | 2011-09-12 | 2014-09-09 | Fujitsu Limited | Data management apparatus and system |
US20130066883A1 (en) * | 2011-09-12 | 2013-03-14 | Fujitsu Limited | Data management apparatus and system |
US9037589B2 (en) * | 2011-11-15 | 2015-05-19 | Ab Initio Technology Llc | Data clustering based on variant token networks |
US10572511B2 (en) | 2011-11-15 | 2020-02-25 | Ab Initio Technology Llc | Data clustering based on candidate queries |
US10503755B2 (en) | 2011-11-15 | 2019-12-10 | Ab Initio Technology Llc | Data clustering, segmentation, and parallelization |
US9361355B2 (en) | 2011-11-15 | 2016-06-07 | Ab Initio Technology Llc | Data clustering based on candidate queries |
US20130124524A1 (en) * | 2011-11-15 | 2013-05-16 | Arlen Anderson | Data clustering based on variant token networks |
US9529829B1 (en) * | 2011-11-18 | 2016-12-27 | Veritas Technologies Llc | System and method to facilitate the use of processed data from a storage system to perform tasks |
US20130132402A1 (en) * | 2011-11-21 | 2013-05-23 | Nec Laboratories America, Inc. | Query specific fusion for image retrieval |
US8762390B2 (en) * | 2011-11-21 | 2014-06-24 | Nec Laboratories America, Inc. | Query specific fusion for image retrieval |
US10140357B2 (en) | 2011-12-12 | 2018-11-27 | International Business Machines Corporation | Anomaly, association and clustering detection |
US9292690B2 (en) * | 2011-12-12 | 2016-03-22 | International Business Machines Corporation | Anomaly, association and clustering detection |
US20140074838A1 (en) * | 2011-12-12 | 2014-03-13 | International Business Machines Corporation | Anomaly, association and clustering detection |
US9171158B2 (en) | 2011-12-12 | 2015-10-27 | International Business Machines Corporation | Dynamic anomaly, association and clustering detection |
US9589046B2 (en) | 2011-12-12 | 2017-03-07 | International Business Machines Corporation | Anomaly, association and clustering detection |
US9235396B2 (en) * | 2011-12-13 | 2016-01-12 | Microsoft Technology Licensing, Llc | Optimizing data partitioning for data-parallel computing |
US20130152057A1 (en) * | 2011-12-13 | 2013-06-13 | Microsoft Corporation | Optimizing data partitioning for data-parallel computing |
US9336217B2 (en) | 2012-03-29 | 2016-05-10 | Empire Technology Development Llc | Determining user key-value storage needs from example queries |
US20130262382A1 (en) * | 2012-03-29 | 2013-10-03 | Empire Technology Development, Llc | Determining user key-value storage needs from example queries |
US8849757B2 (en) * | 2012-03-29 | 2014-09-30 | Empire Technology Development Llc | Determining user key-value storage needs from example queries |
US9286571B2 (en) | 2012-04-01 | 2016-03-15 | Empire Technology Development Llc | Machine learning for database migration source |
US20130290326A1 (en) * | 2012-04-25 | 2013-10-31 | Yevgeniy Lebedev | System for dynamically linking tags with a virtual repository of a registered user |
US8914387B2 (en) * | 2012-04-26 | 2014-12-16 | Sap Ag | Calculation models using annotations for filter optimization |
US20130290354A1 (en) * | 2012-04-26 | 2013-10-31 | Sap Ag | Calculation Models Using Annotations For Filter Optimization |
US20130290369A1 (en) * | 2012-04-30 | 2013-10-31 | Craig Peter Sayers | Contextual application recommendations |
US8856168B2 (en) * | 2012-04-30 | 2014-10-07 | Hewlett-Packard Development Company, L.P. | Contextual application recommendations |
US9141623B2 (en) | 2012-08-03 | 2015-09-22 | International Business Machines Corporation | System for on-line archiving of content in an object store |
US20140040211A1 (en) * | 2012-08-03 | 2014-02-06 | International Business Machines Corporation | System for on-line archiving of content in an object store |
US9195667B2 (en) * | 2012-08-03 | 2015-11-24 | International Business Machines Corporation | System for on-line archiving of content in an object store |
US8996551B2 (en) * | 2012-10-01 | 2015-03-31 | Longsand Limited | Managing geographic region information |
US8862585B2 (en) * | 2012-10-10 | 2014-10-14 | Polytechnic Institute Of New York University | Encoding non-derministic finite automation states efficiently in a manner that permits simple and fast union operations |
US8943110B2 (en) * | 2012-10-25 | 2015-01-27 | Blackberry Limited | Method and system for managing data storage and access on a client device |
US9165006B2 (en) | 2012-10-25 | 2015-10-20 | Blackberry Limited | Method and system for managing data storage and access on a client device |
US8930328B2 (en) * | 2012-11-13 | 2015-01-06 | Hitachi, Ltd. | Storage system, storage system control method, and storage control device |
US8874617B2 (en) * | 2012-11-14 | 2014-10-28 | International Business Machines Corporation | Determining potential enterprise partnerships |
US20140358975A1 (en) * | 2013-05-30 | 2014-12-04 | ClearStory Data Inc. | Apparatus and Method for Ingesting and Augmenting Data |
US9372913B2 (en) | 2013-05-30 | 2016-06-21 | ClearStory Data Inc. | Apparatus and method for harmonizing data along inferred hierarchical dimensions |
US9613124B2 (en) | 2013-05-30 | 2017-04-04 | ClearStory Data Inc. | Apparatus and method for state management across visual transitions |
US9495436B2 (en) * | 2013-05-30 | 2016-11-15 | ClearStory Data Inc. | Apparatus and method for ingesting and augmenting data |
WO2015080567A1 (en) | 2013-11-27 | 2015-06-04 | Mimos Berhad | A method for converting a knowledge base to binary form |
US9906367B2 (en) * | 2014-08-05 | 2018-02-27 | Sap Se | End-to-end tamper protection in presence of cloud integration |
US9836599B2 (en) * | 2015-03-13 | 2017-12-05 | Microsoft Technology Licensing, Llc | Implicit process detection and automation from unstructured activity |
US20160267268A1 (en) * | 2015-03-13 | 2016-09-15 | Microsoft Technology Licensing, Llc | Implicit process detection and automation from unstructured activity |
US11062142B2 (en) | 2017-06-29 | 2021-07-13 | Accenture Gobal Solutions Limited | Natural language unification based robotic agent control |
US11108798B2 (en) | 2018-06-06 | 2021-08-31 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11611577B2 (en) | 2018-06-06 | 2023-03-21 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11323462B2 (en) | 2018-06-06 | 2022-05-03 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11363043B2 (en) | 2018-06-06 | 2022-06-14 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11374951B2 (en) | 2018-06-06 | 2022-06-28 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11528287B2 (en) | 2018-06-06 | 2022-12-13 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11588838B2 (en) | 2018-06-06 | 2023-02-21 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11297080B2 (en) * | 2018-06-06 | 2022-04-05 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11265338B2 (en) | 2018-06-06 | 2022-03-01 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11637847B2 (en) | 2018-06-06 | 2023-04-25 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11687659B2 (en) | 2018-06-06 | 2023-06-27 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11709946B2 (en) | 2018-06-06 | 2023-07-25 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11921864B2 (en) | 2018-06-06 | 2024-03-05 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11095673B2 (en) | 2018-06-06 | 2021-08-17 | Reliaquest Holdings, Llc | Threat mitigation system and method |
US11222166B2 (en) * | 2019-11-19 | 2022-01-11 | International Business Machines Corporation | Iteratively expanding concepts |
Also Published As
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20040024720A1 (en) | System and method for managing knowledge | |
KR101114023B1 (en) | Content propagation for enhanced document retrieval | |
Kazman et al. | Requirements for integrating software architecture and reengineering models: CORUM II | |
US7289985B2 (en) | Enhanced document retrieval | |
US7734607B2 (en) | Universal visualization platform | |
US20100205238A1 (en) | Methods and apparatus for intelligent exploratory visualization and analysis | |
KR20060085561A (en) | Task oriented user interface model for document centric software application | |
US20210256396A1 (en) | System and method of providing and updating rules for classifying actions and transactions in a computer system | |
WO2023280569A1 (en) | Dynamic web page classification in web data collection | |
Brown et al. | Modelspace: Visualizing the trails of data models in visual analytics systems | |
WO2002054171A2 (en) | System, method, software architecture and business model for an intelligent object based information technology platform | |
Connolly et al. | Harnessing the value of big data analytics | |
Beran et al. | Exploratory analysis of file system metadata for rapid investigation of security incidents | |
Congiusta et al. | Designing grid services for distributed knowledge discovery | |
Leavitt | Data mining for the corporate masses? | |
Wegman et al. | Statistical software for today and tomorrow | |
US7925139B2 (en) | Distributed semantic descriptions of audiovisual content | |
Mosbah | Information Retrieval Approach based on Recursive Query Shifting | |
Utz et al. | Vip-a framework-based approach to robot vision | |
Rivera et al. | Training our first models | |
Haidar et al. | A Graph based Approach to Automatically Chain Distributed Multimedia Indexing Services. | |
CN117370631A (en) | Data processing method, device, electronic equipment, storage medium and program product | |
Kaushik | Survey on Big Data Concepts, T | |
Ceri et al. | Extended memory (xMem) of web interactions | |
Electronic-Business | Search Engine Optimization for E-Business Website |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |