WO2012135791A2 - Personalization of queries, conversations, and searches - Google Patents

Personalization of queries, conversations, and searches Download PDF

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Publication number
WO2012135791A2
WO2012135791A2 PCT/US2012/031736 US2012031736W WO2012135791A2 WO 2012135791 A2 WO2012135791 A2 WO 2012135791A2 US 2012031736 W US2012031736 W US 2012031736W WO 2012135791 A2 WO2012135791 A2 WO 2012135791A2
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WO
WIPO (PCT)
Prior art keywords
user
phrase
action
ontology
agent action
Prior art date
Application number
PCT/US2012/031736
Other languages
French (fr)
Other versions
WO2012135791A3 (en
Inventor
Larry Paul Heck
Madhusudan Chinthakunta
David Mitby
Lisa Stifelman
Original Assignee
Microsoft Corporation
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Priority claimed from US13/077,233 external-priority patent/US20120253789A1/en
Priority claimed from US13/077,455 external-priority patent/US9244984B2/en
Priority claimed from US13/077,431 external-priority patent/US10642934B2/en
Priority claimed from US13/077,396 external-priority patent/US9842168B2/en
Priority claimed from US13/077,368 external-priority patent/US9298287B2/en
Priority claimed from US13/077,303 external-priority patent/US9858343B2/en
Priority claimed from US13/076,862 external-priority patent/US9760566B2/en
Application filed by Microsoft Corporation filed Critical Microsoft Corporation
Priority to EP12765100.8A priority Critical patent/EP2691876A4/en
Publication of WO2012135791A2 publication Critical patent/WO2012135791A2/en
Publication of WO2012135791A3 publication Critical patent/WO2012135791A3/en

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Classifications

    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33Querying
    • G06F16/332Query formulation
    • G06F16/3329Natural language query formulation or dialogue systems
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/903Querying
    • G06F16/9032Query formulation
    • G06F16/90332Natural language query formulation or dialogue systems
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/95Retrieval from the web
    • G06F16/951Indexing; Web crawling techniques
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/95Retrieval from the web
    • G06F16/953Querying, e.g. by the use of web search engines
    • G06F16/9537Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00Handling natural language data
    • G06F40/30Semantic analysis
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/26Speech to text systems

Definitions

  • An augmented conversational understanding architecture may provide a mechanism for personalizing queries, conversations and searches.
  • personal assistant programs and/or search engines often require specialized formatting and syntax. For example, a user's query of "I want to go see 'Inception' around 7" may be ineffective at communicating the user's true intentions when provided to a conventional system.
  • Such systems may generally be incapable of deriving the context that the user is referring to a movie, and that the user desires results informing them of local theatres showing that movie around 7:00.
  • Personalization of user interactions may be provided.
  • a plurality of semantic concepts associated with the user may be loaded. If the phrase is determined to comprise at least one of the plurality of semantic concepts associated with the user, a first action may be performed according to the phrase. If the phrase is determined not to comprise at least one of the plurality of semantic concepts associated with the user, a second action may be performed according to the phrase.
  • FIG. 1 is a block diagram of an operating environment
  • FIG. 2 is a flow chart of a method for providing an augmented reality
  • FIGs. 3A-3B are illustrations of example ontologies.
  • FIG. 4 is a block diagram of a system including a computing device.
  • a cloud (e.g., network storage-based) based service may allow for user personalization of searches, queries or instructions to a personal assistant (e.g., a software program).
  • a personal assistant e.g., a software program.
  • the ability to personalize such queries or instructions may be provided by rule driven techniques in conjunction with various ontologies and using the search terms, instruction statements and the user contexts to provide more accurate search or query results.
  • Natural language speech recognition applications may allow for personalization of searches and actions. Components may focus on the user experience and/or provide a personalization engine, such as via components of a SDS.
  • a user experience component may be available as part of a web search application via a browser running on a general purpose desktop or laptop computer or a specialized computing device such as a smart phone or a information kiosk in a mall.
  • a personalization engine component may store various ontologies, iterate through a query to identify a user's intent, and attempt to match a semantic representation of the query to a particular ontology. For example, ABC Company may populate a shared ontology that may define semantic concepts such as creating an appointment.
  • the semantic concept may be associated with attributes such as calendar servers, scheduling services, and synonyms (e.g., the term "S+" may be defined as a shortcut synonym for setting up a meeting). If the user is an employee of ABC Co., the term S+ ("S plus") may be inherited from the shared ontology and recognized as a shortcut to setting up an appointment using Outlook®.
  • the personalization engine may also use additional user contexts (e.g., location, or previous state information) to merge additional shared ontologies.
  • Other examples of personalization may comprise the user asking for "John Hardy's”; because the user is originally from Minnesota, the SDS may retrieve this information from the user's personal ontology (derived from profile, usage history, and other sources such as contacts and messaging content) and know that the user is looking for the BBQ restaurant located in Rochester, MN. If the user refers to "Rangers” the SDS may be able to infer, based on the personal ontology, that the user intends "NY Rangers” since they are a hockey fan. If the user were known to be a baseball fan, the user's intent may be interpreted as referring instead to the "Texas Rangers.” Such intent deciphering may be in combination with contextual information such as the time of year, what teams are playing that day, etc.
  • a Spoken Language Understanding (SLU) component may receive a spoken or written conversation between users and/or a single-user originating query.
  • the SLU may parse the words of a voice or text conversation and select certain items which may be used to fill out an XML data frame for particular contexts. For example, a restaurant context may have certain slots such as "type of food”, “location/address”, “outdoor dining”, “reservations required”, “hours open”, “day of week”, “time”, “number of persons”, etc.
  • the SLU may attempt to fill different context data frames with both the parsed words from the conversation or query, and with other external information, such as GPS location information.
  • the SLU may keep state during the conversation and fill the slots over the course of the conversation. For example, if user 1 says “How about tonight” and user 2 says “Saturday is better", the SLU may initially fill tonight in the day of the week slot and then fill Saturday in the day of the week slot. If a certain number of the slots in a particular context frame are filled, the SLU may infer that the context is correct and estimate the user intention. The SLU may also prompt the user for more information related to the intent. The SLU may then provide options to the user based on the determined user intent.
  • FIG. 1 is a block diagram of an operating environment 100 comprising a spoken dialog system (SDS) 1 10.
  • SDS 1 10 may comprise assorted computing and/or software modules such as a personal assistant program 112, a dialog manager 114, an ontology database 1 16, and/or a search agent 118.
  • SDS 110 may receive queries and/or action requests from users over network 120. Such queries may be transmitted, for example, from a first user device 130 and/or a second user device 135 such as a computer and/or cellular phone.
  • Network 120 may comprise, for example, a private network, a cellular data network, and/or a public network such as the Internet. Consistent with embodiments of the invention, SDS 110 may be operative to monitor conversations between first user device 130 and second user device 135.
  • the primary component that drives the SDS may comprise dialog manager 114.
  • This component may manage the dialog-based conversation with the user.
  • Dialog manager 114 may determine the intention of the user through a combination of multiple sources of input, such as speech recognition and natural language understanding component outputs, context from the prior dialog turns, user context, and/or results returned from a knowledge base (e.g., search engine). After determining the intention, dialog manager 114 may take an action, such as displaying the final results to the user and/or continuing in a dialog with the user to satisfy their intent.
  • FIG. 2 is a flow chart setting forth the general stages involved in a method
  • Method 200 may be implemented using a computing device 400 as described in more detail below with respect to FIG. 4. Ways to implement the stages of method 200 will be described in greater detail below.
  • Method 200 may begin at starting block 205 and proceed to stage 210 where computing device 400 may identify a plurality of users associated with a conversation.
  • SDS 1 10 may monitor a conversation between a first user of first user device 130 and a second user of second user device 135.
  • the first user and second user may be identified, for example, via an authenticated sign-in with SDS 110 and/or via identifying software and/or hardware IDs associated with their respective devices.
  • Method 200 may then advance to stage 215 where computing device 400 may merge a plurality of ontologies.
  • SDS 110 may load an ontology associated with the first user and the second user from ontology database 1 16.
  • Each of the plurality of ontologies may comprise a plurality of semantic concepts and/or attributes associated with characteristics of at least one of the users, such as a workplace associated a user, a contacts database, a calendar, a previous action, a previous communication made by and/or between the users, a context, and/or a profile.
  • the merger may comprise merging either and/or both users' ontologies with a shared/global ontology.
  • a search engine may provide a shared ontology comprising data gathered and synthesized across many users, while a network application may publish an ontology comprising attributes associated with publicly available applications.
  • a shared ontology may also be associated with an organization and may comprise attributes common to multiple employees. Merging one ontology with another may comprise, for example, creating associations between common terms, adding synonyms to a node, adding additional attribute nodes, sub nodes, and/or branches, and/or adding connections between nodes.
  • Method 200 may then advance to stage 220 where computing device 400 may receive a natural language phrase from a user.
  • SDS 110 may receive a phrase spoken and/or typed by the user into first user device 130.
  • Method 200 may then advance to stage 225 where computing device 400 may load a model associated with a spoken dialog system.
  • SDS 110 may load a language dictionary associated with the user's preferred spoken language.
  • Method 200 may then advance to stage 230 where computing device 400 may translate the natural language phrase into an agent action.
  • the phrase may be scanned for concepts that correlate to a search domain and/or an executable action associated with a network application. Words such as "dinner tonight" may scan to a "restaurant" search domain associated with a search action.
  • Each domain may be associated with a plurality of slots that may comprise attributes for defining the scope of the action. For example, a restaurant domain may comprise slots for party size, type of cuisine, time, whether outdoor seating is available, etc. Dialog manager 114 may attempt to fill these slots based on the natural language phrase.
  • Method 200 may then advance to stage 235 where computing device 400 may determine whether the recognition is acceptable. For example, dialog manager 114 may be unable to fill enough slots to provide a complete action, and/or additional phrases may be received from the initial user and/or another user involved in the conversation that modify the agent action prior to execution.
  • method 200 may advance to stage 240 where computing device 400 may receive an update to the agent action.
  • dialog manager may create a restaurant domain agent action for making a reservation.
  • dialog manager may return to stage 230 to translate the new input and update the action accordingly.
  • method 200 may advance to stage 245 where computing device 400 may perform the action.
  • dialog manager 114 may create a lunch appointment calendar event.
  • Method 200 may then advance to stage 250 where computing device 400 may display at least one result associated with the performed action to at least one of the plurality of users. For example, SDS 1 10 may populate the created lunch appointment to calendars associated with each of the first user and second user and/or display a confirmation that the event was created on their respective user devices. Method 200 may then end at stage 255.
  • FIG. 3A is an illustration of a shared ontology 300.
  • An ontology may generally comprise a plurality of semantic relationships between concept nodes.
  • Each concept node may comprise a generalized grouping, an abstract idea, and/or a mental symbol and that node's associated attributes.
  • one concept may comprise a person associated with attributes such as name, job function, home location, etc.
  • the ontology may comprise, for example, a semantic relationship between the person concept and a job concept connected by the person's job function attribute.
  • Shared ontology 300 may comprise a plurality of concept nodes 310(A)-(F). Each of the concept nodes may be associated with attribute nodes.
  • person concept node 310(C) may be associated with a plurality of attributes 315(A)-(D). Attributes may be further associated with sub-nodes, such as where contact info attribute node 315(B) is associated with a plurality of sub-nodes 320(A)-(C). Similarly, attribute nodes may be associated with synonyms, such as where name attribute node 315(A) is associated with a nicknames synonym 325.
  • Concept nodes 310(A)-(F) may be interconnected via a plurality of semantic relationships 330(A)-(B). For example, person attribute 310(C) may be connected to location attribute 310(F) via work semantic relationship 330(A) and/or home semantic relationship 330(B).
  • FIG. 3B is an illustration of a personal ontology 350 comprising a user concept node 360.
  • User concept node 360 may comprise a plurality of attribute nodes 370(A)-(D) associated with user details such as preferences, activities, relationships, and/or previous choices.
  • User concept node 360 may comprise a semantic connection 375 associated with another concept node, such as a second user node 380 associated with a child of the user.
  • An embodiment consistent with the invention may comprise a system for providing a context-aware environment.
  • the system may comprise a memory storage and a processing unit coupled to the memory storage.
  • the processing unit may be operative to receive a phrase from a user, load an ontology associated with the user, determine whether the phrase comprises at least one semantic concept associated with the ontology, and, if not, perform a first action according to the phrase.
  • the processing unit may be operative to perform a second action according to the phrase.
  • the phrase may comprise a spoken natural language phrase and the processing unit may be operative to convert the spoken phrase to a text-based phrase.
  • the natural language phrase may comprise a typed phrase.
  • the ontology may comprise, for example, terms and/or concepts associated with the user's workplace, previous actions, learned phrasing, slang, contact-derived references (e.g., "Billy-boy” equates to a contact named Bill Smith, Jr.), and/or previous communications.
  • Another embodiment consistent with the invention may comprise a system for providing a personalized user interaction.
  • the system may comprise a memory storage and a processing unit coupled to the memory storage.
  • the processing unit may be operative to receive a phrase from a user, load an ontology associated with the user, translate the received phrase into an agent action, determine whether the phrase comprises at least one of the semantic concepts associated with the ontology, and, if so, modify the agent action, perform the modified agent action, and display at least one result associated with the performed agent action to the user.
  • the agent action may comprise, for example, a search query, and being operative to modify the action may comprise the processing unit being operative to add a term to the query and/or replace a term of the query with a synonym.
  • the agent action may comprise performing a task within an application, wherein an attribute associated with the ontology comprises a shortcut synonym associated with a semantic concept of performing the task within the application (e.g., a spoken command "exit" may be translated into application tasks of saving all open files and quitting the application).
  • the context associated with the user may comprise, for example, a location of the user, a time the phrase was received, and a date the phrase was received.
  • the received phrase may be associated with a conversation between the user and at least one second user.
  • the processing unit may then be operative to receive a second phrase from the second user, load a second ontology associated with the second user, merge the two users' ontologies, translate the second received phrase into a second agent action, determine whether the second phrase comprises a semantic concept associated with the merged ontologies, and, if so, modify the agent action, perform the modified agent action, and display at least one result associated with the performed agent action to the second user.
  • Yet another embodiment consistent with the invention may comprise a system for providing a context-aware environment.
  • the system may comprise a memory storage and a processing unit coupled to the memory storage.
  • the processing unit may be operative to identify a plurality of users associated with a conversation, merge a plurality of ontologies, each associated with one of the users, receive a first natural language phrase from a first user of the plurality of users, translate the natural language phrase into an agent action, and determine whether the agent action is associated with at least one of the semantic concepts associated with the merged ontologies.
  • the processing unit may be operative to modify the agent action.
  • the processing unit may then be operative to receive a second natural language phrase from a second user of the plurality of users, and determine whether the second natural language phrase is associated with the agent action. If so, the processing unit may be operative to update the agent action according to the second natural language phrase. The processing unit may then be operative to perform the agent action and display at least one result associated with the performed agent action to at least one of the plurality of users.
  • FIG. 4 is a block diagram of a system including computing device 400.
  • the aforementioned memory storage and processing unit may be implemented in a computing device, such as computing device 400 of FIG. 4. Any suitable combination of hardware, software, or firmware may be used to implement the memory storage and processing unit.
  • the memory storage and processing unit may be implemented with computing device 400 or any of other computing devices 418, in combination with computing device 400.
  • the aforementioned system, device, and processors are examples and other systems, devices, and processors may comprise the aforementioned memory storage and processing unit, consistent with embodiments of the invention.
  • computing device 400 may comprise operating environment 100 as described above. System 100 may operate in other environments and is not limited to computing device 400.
  • a system consistent with an embodiment of the invention may include a computing device, such as computing device 400.
  • computing device 400 may include at least one processing unit 402 and a system memory 404.
  • system memory 404 may comprise, but is not limited to, volatile (e.g., random access memory (RAM)), non- volatile (e.g., read-only memory (ROM)), flash memory, or any combination.
  • System memory 404 may include operating system 405, one or more programming modules 406, and may include personal assistant program 1 12. Operating system 405, for example, may be suitable for controlling computing device 400's operation.
  • embodiments of the invention may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 4 by those components within a dashed line 408.
  • Computing device 400 may have additional features or functionality.
  • computing device 400 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape.
  • additional storage is illustrated in FIG. 4 by a removable storage 409 and a non-removable storage 410.
  • Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.
  • System memory 404, removable storage 409, and non-removable storage 410 are all computer storage media examples (i.e., memory storage.)
  • Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 400. Any such computer storage media may be part of device 400.
  • Computing device 400 may also have input device(s) 412 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc.
  • Output device(s) 414 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.
  • Computing device 400 may also contain a communication connection 416 that may allow device 400 to communicate with other computing devices 418, such as over a network in a distributed computing environment, for example, an Intranet or the Internet.
  • Communication connection 416 is one example of communication media.
  • Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media.
  • modulated data signal may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal.
  • communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.
  • wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.
  • RF radio frequency
  • computer readable media may include both storage media and communication media.
  • program modules and data files may be stored in system memory 404, including operating system 405.
  • programming modules 406 e.g., personal assistant program 112 may perform processes including, for example, one or more of method 200's stages as described above. The aforementioned process is an example, and processing unit 402 may perform other processes.
  • Other programming modules that may be used in accordance with
  • embodiments of the present invention may include electronic mail and contacts applications, word processing applications, spreadsheet applications, database
  • program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types.
  • embodiments of the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor- based or programmable consumer electronics, minicomputers, mainframe computers, and the like.
  • Embodiments of the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote memory storage devices.
  • embodiments of the invention may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors.
  • Embodiments of the invention may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to, mechanical, optical, fluidic, and quantum technologies.
  • embodiments of the invention may be practiced within a general purpose computer or in any other circuits or systems.
  • Embodiments of the invention may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media.
  • the computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process.
  • the computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process.
  • the present invention may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.).
  • embodiments of the present invention may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system.
  • a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • the computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • CD-ROM portable compact disc read-only memory
  • the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

Abstract

Personalization of user interactions may be provided. Upon receiving a phrase from a user, a plurality of semantic concepts associated with the user may be loaded. If the phrase is determined to comprise at least one of the plurality of semantic concepts associated with the user, a first action may be performed according to the phrase. If the phrase is determined not to comprise at least one of the plurality of semantic concepts associated with the user, a second action may be performed according to the phrase.

Description

PERSONALIZATION OF QUERIES, CONVERSATIONS, AND SEARCHES
BACKGROUND
[001] An augmented conversational understanding architecture may provide a mechanism for personalizing queries, conversations and searches. In some situations, personal assistant programs and/or search engines often require specialized formatting and syntax. For example, a user's query of "I want to go see 'Inception' around 7" may be ineffective at communicating the user's true intentions when provided to a conventional system. Such systems may generally be incapable of deriving the context that the user is referring to a movie, and that the user desires results informing them of local theatres showing that movie around 7:00.
SUMMARY
[002] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter. Nor is this Summary intended to be used to limit the claimed subject matter's scope.
[003] Personalization of user interactions may be provided. Upon receiving a phrase from a user, a plurality of semantic concepts associated with the user may be loaded. If the phrase is determined to comprise at least one of the plurality of semantic concepts associated with the user, a first action may be performed according to the phrase. If the phrase is determined not to comprise at least one of the plurality of semantic concepts associated with the user, a second action may be performed according to the phrase.
[004] Both the foregoing general description and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing general description and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.
BRIEF DESCRIPTION OF THE DRAWINGS
[005] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present invention. In the drawings: [006] FIG. 1 is a block diagram of an operating environment;
[007] FIG. 2 is a flow chart of a method for providing an augmented
conversational understanding architecture;
[008] FIGs. 3A-3B are illustrations of example ontologies; and
[009] FIG. 4 is a block diagram of a system including a computing device.
DETAILED DESCRIPTION
[010] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While embodiments of the invention may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the invention. Instead, the proper scope of the invention is defined by the appended claims.
[011] A cloud (e.g., network storage-based) based service may allow for user personalization of searches, queries or instructions to a personal assistant (e.g., a software program). The ability to personalize such queries or instructions may be provided by rule driven techniques in conjunction with various ontologies and using the search terms, instruction statements and the user contexts to provide more accurate search or query results.
[012] Natural language speech recognition applications may allow for personalization of searches and actions. Components may focus on the user experience and/or provide a personalization engine, such as via components of a SDS. A user experience component may be available as part of a web search application via a browser running on a general purpose desktop or laptop computer or a specialized computing device such as a smart phone or a information kiosk in a mall. A personalization engine component may store various ontologies, iterate through a query to identify a user's intent, and attempt to match a semantic representation of the query to a particular ontology. For example, ABC Company may populate a shared ontology that may define semantic concepts such as creating an appointment. The semantic concept may be associated with attributes such as calendar servers, scheduling services, and synonyms (e.g., the term "S+" may be defined as a shortcut synonym for setting up a meeting). If the user is an employee of ABC Co., the term S+ ("S plus") may be inherited from the shared ontology and recognized as a shortcut to setting up an appointment using Outlook®. The personalization engine may also use additional user contexts (e.g., location, or previous state information) to merge additional shared ontologies.
[013] Other examples of personalization may comprise the user asking for "John Hardy's"; because the user is originally from Minnesota, the SDS may retrieve this information from the user's personal ontology (derived from profile, usage history, and other sources such as contacts and messaging content) and know that the user is looking for the BBQ restaurant located in Rochester, MN. If the user refers to "Rangers" the SDS may be able to infer, based on the personal ontology, that the user intends "NY Rangers" since they are a hockey fan. If the user were known to be a baseball fan, the user's intent may be interpreted as referring instead to the "Texas Rangers." Such intent deciphering may be in combination with contextual information such as the time of year, what teams are playing that day, etc.
[014] A Spoken Language Understanding (SLU) component (e.g., a translator) may receive a spoken or written conversation between users and/or a single-user originating query. The SLU may parse the words of a voice or text conversation and select certain items which may be used to fill out an XML data frame for particular contexts. For example, a restaurant context may have certain slots such as "type of food", "location/address", "outdoor dining", "reservations required", "hours open", "day of week", "time", "number of persons", etc. The SLU may attempt to fill different context data frames with both the parsed words from the conversation or query, and with other external information, such as GPS location information. The SLU may keep state during the conversation and fill the slots over the course of the conversation. For example, if user 1 says "How about tonight" and user 2 says "Saturday is better", the SLU may initially fill tonight in the day of the week slot and then fill Saturday in the day of the week slot. If a certain number of the slots in a particular context frame are filled, the SLU may infer that the context is correct and estimate the user intention. The SLU may also prompt the user for more information related to the intent. The SLU may then provide options to the user based on the determined user intent.
[015] FIG. 1 is a block diagram of an operating environment 100 comprising a spoken dialog system (SDS) 1 10. SDS 1 10 may comprise assorted computing and/or software modules such as a personal assistant program 112, a dialog manager 114, an ontology database 1 16, and/or a search agent 118. SDS 110 may receive queries and/or action requests from users over network 120. Such queries may be transmitted, for example, from a first user device 130 and/or a second user device 135 such as a computer and/or cellular phone. Network 120 may comprise, for example, a private network, a cellular data network, and/or a public network such as the Internet. Consistent with embodiments of the invention, SDS 110 may be operative to monitor conversations between first user device 130 and second user device 135.
[016] Spoken dialog systems enable people to interact with computers with their voice. The primary component that drives the SDS may comprise dialog manager 114. This component may manage the dialog-based conversation with the user. Dialog manager 114 may determine the intention of the user through a combination of multiple sources of input, such as speech recognition and natural language understanding component outputs, context from the prior dialog turns, user context, and/or results returned from a knowledge base (e.g., search engine). After determining the intention, dialog manager 114 may take an action, such as displaying the final results to the user and/or continuing in a dialog with the user to satisfy their intent.
[017] FIG. 2 is a flow chart setting forth the general stages involved in a method
200 consistent with an embodiment of the invention for providing a personalized user experience. Method 200 may be implemented using a computing device 400 as described in more detail below with respect to FIG. 4. Ways to implement the stages of method 200 will be described in greater detail below. Method 200 may begin at starting block 205 and proceed to stage 210 where computing device 400 may identify a plurality of users associated with a conversation. For example, SDS 1 10 may monitor a conversation between a first user of first user device 130 and a second user of second user device 135. The first user and second user may be identified, for example, via an authenticated sign-in with SDS 110 and/or via identifying software and/or hardware IDs associated with their respective devices.
[018] Method 200 may then advance to stage 215 where computing device 400 may merge a plurality of ontologies. For example, SDS 110 may load an ontology associated with the first user and the second user from ontology database 1 16. Each of the plurality of ontologies may comprise a plurality of semantic concepts and/or attributes associated with characteristics of at least one of the users, such as a workplace associated a user, a contacts database, a calendar, a previous action, a previous communication made by and/or between the users, a context, and/or a profile. Consistent with embodiments of the invention, the merger may comprise merging either and/or both users' ontologies with a shared/global ontology. For example, a search engine may provide a shared ontology comprising data gathered and synthesized across many users, while a network application may publish an ontology comprising attributes associated with publicly available applications. A shared ontology may also be associated with an organization and may comprise attributes common to multiple employees. Merging one ontology with another may comprise, for example, creating associations between common terms, adding synonyms to a node, adding additional attribute nodes, sub nodes, and/or branches, and/or adding connections between nodes.
[019] Method 200 may then advance to stage 220 where computing device 400 may receive a natural language phrase from a user. For example, SDS 110 may receive a phrase spoken and/or typed by the user into first user device 130.
[020] Method 200 may then advance to stage 225 where computing device 400 may load a model associated with a spoken dialog system. For example, SDS 110 may load a language dictionary associated with the user's preferred spoken language.
[021] Method 200 may then advance to stage 230 where computing device 400 may translate the natural language phrase into an agent action. For example, the phrase may be scanned for concepts that correlate to a search domain and/or an executable action associated with a network application. Words such as "dinner tonight" may scan to a "restaurant" search domain associated with a search action. Each domain may be associated with a plurality of slots that may comprise attributes for defining the scope of the action. For example, a restaurant domain may comprise slots for party size, type of cuisine, time, whether outdoor seating is available, etc. Dialog manager 114 may attempt to fill these slots based on the natural language phrase.
[022] Method 200 may then advance to stage 235 where computing device 400 may determine whether the recognition is acceptable. For example, dialog manager 114 may be unable to fill enough slots to provide a complete action, and/or additional phrases may be received from the initial user and/or another user involved in the conversation that modify the agent action prior to execution.
[023] In such cases, method 200 may advance to stage 240 where computing device 400 may receive an update to the agent action. For example, dialog manager may create a restaurant domain agent action for making a reservation. Upon receiving a phrase from a user such as "what about tomorrow instead?", dialog manager may return to stage 230 to translate the new input and update the action accordingly. [024] Otherwise, once the action is acceptable, method 200 may advance to stage 245 where computing device 400 may perform the action. For example, dialog manager 114 may create a lunch appointment calendar event.
[025] Method 200 may then advance to stage 250 where computing device 400 may display at least one result associated with the performed action to at least one of the plurality of users. For example, SDS 1 10 may populate the created lunch appointment to calendars associated with each of the first user and second user and/or display a confirmation that the event was created on their respective user devices. Method 200 may then end at stage 255.
[026] FIG. 3A is an illustration of a shared ontology 300. An ontology may generally comprise a plurality of semantic relationships between concept nodes. Each concept node may comprise a generalized grouping, an abstract idea, and/or a mental symbol and that node's associated attributes. For example, one concept may comprise a person associated with attributes such as name, job function, home location, etc. The ontology may comprise, for example, a semantic relationship between the person concept and a job concept connected by the person's job function attribute. Shared ontology 300 may comprise a plurality of concept nodes 310(A)-(F). Each of the concept nodes may be associated with attribute nodes. For example, person concept node 310(C) may be associated with a plurality of attributes 315(A)-(D). Attributes may be further associated with sub-nodes, such as where contact info attribute node 315(B) is associated with a plurality of sub-nodes 320(A)-(C). Similarly, attribute nodes may be associated with synonyms, such as where name attribute node 315(A) is associated with a nicknames synonym 325. Concept nodes 310(A)-(F) may be interconnected via a plurality of semantic relationships 330(A)-(B). For example, person attribute 310(C) may be connected to location attribute 310(F) via work semantic relationship 330(A) and/or home semantic relationship 330(B).
[027] FIG. 3B is an illustration of a personal ontology 350 comprising a user concept node 360. User concept node 360 may comprise a plurality of attribute nodes 370(A)-(D) associated with user details such as preferences, activities, relationships, and/or previous choices. User concept node 360 may comprise a semantic connection 375 associated with another concept node, such as a second user node 380 associated with a child of the user.
[028] An embodiment consistent with the invention may comprise a system for providing a context-aware environment. The system may comprise a memory storage and a processing unit coupled to the memory storage. The processing unit may be operative to receive a phrase from a user, load an ontology associated with the user, determine whether the phrase comprises at least one semantic concept associated with the ontology, and, if not, perform a first action according to the phrase. In response to determining that the phrase does comprises a semantic concept associated with the ontology, the processing unit may be operative to perform a second action according to the phrase. The phrase may comprise a spoken natural language phrase and the processing unit may be operative to convert the spoken phrase to a text-based phrase. Consistent with embodiments of the invention, the natural language phrase may comprise a typed phrase.
[029] The ontology may comprise, for example, terms and/or concepts associated with the user's workplace, previous actions, learned phrasing, slang, contact-derived references (e.g., "Billy-boy" equates to a contact named Bill Smith, Jr.), and/or previous communications.
[030] Another embodiment consistent with the invention may comprise a system for providing a personalized user interaction. The system may comprise a memory storage and a processing unit coupled to the memory storage. The processing unit may be operative to receive a phrase from a user, load an ontology associated with the user, translate the received phrase into an agent action, determine whether the phrase comprises at least one of the semantic concepts associated with the ontology, and, if so, modify the agent action, perform the modified agent action, and display at least one result associated with the performed agent action to the user.
[031] The agent action may comprise, for example, a search query, and being operative to modify the action may comprise the processing unit being operative to add a term to the query and/or replace a term of the query with a synonym. The agent action may comprise performing a task within an application, wherein an attribute associated with the ontology comprises a shortcut synonym associated with a semantic concept of performing the task within the application (e.g., a spoken command "exit" may be translated into application tasks of saving all open files and quitting the application). The context associated with the user may comprise, for example, a location of the user, a time the phrase was received, and a date the phrase was received.
[032] The received phrase may be associated with a conversation between the user and at least one second user. The processing unit may then be operative to receive a second phrase from the second user, load a second ontology associated with the second user, merge the two users' ontologies, translate the second received phrase into a second agent action, determine whether the second phrase comprises a semantic concept associated with the merged ontologies, and, if so, modify the agent action, perform the modified agent action, and display at least one result associated with the performed agent action to the second user.
[033] Yet another embodiment consistent with the invention may comprise a system for providing a context-aware environment. The system may comprise a memory storage and a processing unit coupled to the memory storage. The processing unit may be operative to identify a plurality of users associated with a conversation, merge a plurality of ontologies, each associated with one of the users, receive a first natural language phrase from a first user of the plurality of users, translate the natural language phrase into an agent action, and determine whether the agent action is associated with at least one of the semantic concepts associated with the merged ontologies. In response to determining that the phrase comprises the semantic concept associated with the merged ontologies, the processing unit may be operative to modify the agent action. The processing unit may then be operative to receive a second natural language phrase from a second user of the plurality of users, and determine whether the second natural language phrase is associated with the agent action. If so, the processing unit may be operative to update the agent action according to the second natural language phrase. The processing unit may then be operative to perform the agent action and display at least one result associated with the performed agent action to at least one of the plurality of users.
[034] FIG. 4 is a block diagram of a system including computing device 400. Consistent with an embodiment of the invention, the aforementioned memory storage and processing unit may be implemented in a computing device, such as computing device 400 of FIG. 4. Any suitable combination of hardware, software, or firmware may be used to implement the memory storage and processing unit. For example, the memory storage and processing unit may be implemented with computing device 400 or any of other computing devices 418, in combination with computing device 400. The aforementioned system, device, and processors are examples and other systems, devices, and processors may comprise the aforementioned memory storage and processing unit, consistent with embodiments of the invention. Furthermore, computing device 400 may comprise operating environment 100 as described above. System 100 may operate in other environments and is not limited to computing device 400.
[035] With reference to FIG. 4, a system consistent with an embodiment of the invention may include a computing device, such as computing device 400. In a basic configuration, computing device 400 may include at least one processing unit 402 and a system memory 404. Depending on the configuration and type of computing device, system memory 404 may comprise, but is not limited to, volatile (e.g., random access memory (RAM)), non- volatile (e.g., read-only memory (ROM)), flash memory, or any combination. System memory 404 may include operating system 405, one or more programming modules 406, and may include personal assistant program 1 12. Operating system 405, for example, may be suitable for controlling computing device 400's operation. Furthermore, embodiments of the invention may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 4 by those components within a dashed line 408.
[036] Computing device 400 may have additional features or functionality. For example, computing device 400 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 4 by a removable storage 409 and a non-removable storage 410. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory 404, removable storage 409, and non-removable storage 410 are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 400. Any such computer storage media may be part of device 400. Computing device 400 may also have input device(s) 412 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc. Output device(s) 414 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.
[037] Computing device 400 may also contain a communication connection 416 that may allow device 400 to communicate with other computing devices 418, such as over a network in a distributed computing environment, for example, an Intranet or the Internet. Communication connection 416 is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term "modulated data signal" may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. The term computer readable media as used herein may include both storage media and communication media.
[038] As stated above, a number of program modules and data files may be stored in system memory 404, including operating system 405. While executing on processing unit 402, programming modules 406 (e.g., personal assistant program 112) may perform processes including, for example, one or more of method 200's stages as described above. The aforementioned process is an example, and processing unit 402 may perform other processes. Other programming modules that may be used in accordance with
embodiments of the present invention may include electronic mail and contacts applications, word processing applications, spreadsheet applications, database
applications, slide presentation applications, drawing or computer-aided application programs, etc.
[039] Generally, consistent with embodiments of the invention, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor- based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Embodiments of the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
[040] Furthermore, embodiments of the invention may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the invention may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to, mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the invention may be practiced within a general purpose computer or in any other circuits or systems.
[041] Embodiments of the invention, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present invention may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present invention may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[042] The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory
(ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
[043] Embodiments of the present invention, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the invention. The
functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
[044] While certain embodiments of the invention have been described, other embodiments may exist. Furthermore, although embodiments of the present invention have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, floppy disks, or a CD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods' stages may be modified in any manner, including by reordering stages and/or inserting or deleting stages, without departing from the invention.
[045] All rights including copyrights in the code included herein are vested in and the property of the Applicant. The Applicant retains and reserves all rights in the code included herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.
[046] While the specification includes examples, the invention's scope is indicated by the following claims. Furthermore, while the specification has been described in language specific to structural features and/or methodological acts, the claims are not limited to the features or acts described above. Rather, the specific features and acts described above are disclosed as example for embodiments of the invention.

Claims

CLAIMS WHAT IS CLAIMED IS:
1. A method for providing a personalized user interaction, the method comprising:
receiving a phrase from a user;
loading an ontology associated with the user
merging the ontology associated with the user with at least one shared ontology; translating the received phrase into an agent action; and
performing the agent action.
2. The method of claim 1, further comprising:
determining whether the user approves the agent action; and
in response to determining that the user does not approve the agent action, receiving an update to the agent action from the user.
3. The method of claim 1, wherein the ontology associated with the user comprises at least one semantic concept associated with the user.
4. The method of claim 3, wherein the at least one semantic concept is associated with at least one of the following: a previous action of the user, a workplace of the user, a location of the user, a contact database of the user, a previous communication of the user, a preference of the user, a social relationship of the user, and an interest of the user.
5. The method of claim 1, wherein expanding the phrase comprises abstracting at least one word of the phrase into a plurality of synonyms.
6. A computer-readable medium which stores a set of instructions which when executed performs a method for providing a personalized user interaction, the method executed by the set of instructions comprising:
receiving a phrase from a user;
translating the received phrase into an agent action;
loading an ontology associated with the user, wherein the ontology comprises a plurality of semantic concepts associated with at least one of the following: a workplace associated with the user, a contacts database associated with the user, a calendar associated with the user, a previous action associated with the user, a previous communication associated with the user, a context associated with the user, and a profile associated with the user; determining whether the phrase comprises at least one of the plurality of semantic concepts associated with the ontology; and
in response to determining that the phrase comprises the at least one of the plurality of semantic concepts associated with the ontology:
modifying the agent action according to the ontology,
performing the modified agent action, and
displaying at least one result associated with the performed agent action to the user.
7. The computer-readable medium of claim 6, wherein the agent action comprises a search query and wherein modifying the action comprises replacing at least one term of the search query with a synonym of the at least one of the plurality of semantic concepts associated with the ontology.
8. The computer-readable medium of claim 6, wherein the context associated with the user comprises at least one of the following: a location of the user, a time the phrase was received, and a date the phrase was received.
9. The computer-readable medium of claim 6, further comprising:
receiving a second phrase from at least one second user;
loading a second ontology associated with the at least one second user;
merging the second ontology with the ontology associated with the user;
determining whether the second phrase comprises a response to the received phrase;
in response to determining that the second phrase comprises the response to the received phrase, determining whether the second phrase comprises at least one second semantic concept associated with the merged ontologies; and
in response to determining that the second phrase comprises the at least one second semantic concept associated with the merged ontologies:
updating the agent action,
performing the updated agent action, and
displaying at least one result associated with the performed updated agent action to the first user and the second user.
10. A system for providing a personalized user interaction, the system comprising:
a memory storage; and a processing unit coupled to the memory storage, wherein the processing unit is operative to:
identify a plurality of users associated with a conversation,
merge a plurality of ontologies, wherein each of the plurality of ontologies is associated with at least one of the plurality of users and wherein each of the plurality of ontologies comprises a plurality of semantic concepts associated with at least one of the following: a workplace associated with the at least one user, a contacts database associated with the at least one user, a calendar associated with the at least one user, a previous action associated with the at least one user, a previous communication associated with the at least one user, a context associated with the at least one user, and a profile associated with the at least one user,
receive a first natural language phrase from a first user of the plurality of users, translate the natural language phrase into an agent action according to the merged ontology,
determine whether the agent action comprises an acceptable action,
in response to determining that the agent action does not comprise an acceptable action:
receive a second natural language phrase from at least one of the plurality of users; and
update the agent action according to the received second natural language phrase, perform the action, and
display at least one result associated with the performed action to at least one of the plurality of users.
PCT/US2012/031736 2011-03-31 2012-03-30 Personalization of queries, conversations, and searches WO2012135791A2 (en)

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US13/077,233 US20120253789A1 (en) 2011-03-31 2011-03-31 Conversational Dialog Learning and Correction
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US13/077,455 2011-03-31
US13/077,233 2011-03-31
US13/077,455 US9244984B2 (en) 2011-03-31 2011-03-31 Location based conversational understanding
US13/077,396 2011-03-31
US13/077,431 US10642934B2 (en) 2011-03-31 2011-03-31 Augmented conversational understanding architecture
US13/077,303 2011-03-31
US13/077,396 US9842168B2 (en) 2011-03-31 2011-03-31 Task driven user intents
US13/077,368 US9298287B2 (en) 2011-03-31 2011-03-31 Combined activation for natural user interface systems
US13/077,368 2011-03-31
US13/077,431 2011-03-31
US13/077,303 US9858343B2 (en) 2011-03-31 2011-03-31 Personalization of queries, conversations, and searches
US13/076,862 US9760566B2 (en) 2011-03-31 2011-03-31 Augmented conversational understanding agent to identify conversation context between two humans and taking an agent action thereof

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PCT/US2012/030636 WO2012135157A2 (en) 2011-03-31 2012-03-27 Task driven user intents
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3144931A1 (en) * 2015-09-17 2017-03-22 Samsung Electronics Co., Ltd. Dialog management apparatus and method

Families Citing this family (202)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8677377B2 (en) 2005-09-08 2014-03-18 Apple Inc. Method and apparatus for building an intelligent automated assistant
US9318108B2 (en) 2010-01-18 2016-04-19 Apple Inc. Intelligent automated assistant
US8977255B2 (en) 2007-04-03 2015-03-10 Apple Inc. Method and system for operating a multi-function portable electronic device using voice-activation
US10002189B2 (en) 2007-12-20 2018-06-19 Apple Inc. Method and apparatus for searching using an active ontology
US9330720B2 (en) 2008-01-03 2016-05-03 Apple Inc. Methods and apparatus for altering audio output signals
US8996376B2 (en) 2008-04-05 2015-03-31 Apple Inc. Intelligent text-to-speech conversion
US20100030549A1 (en) 2008-07-31 2010-02-04 Lee Michael M Mobile device having human language translation capability with positional feedback
US8676904B2 (en) 2008-10-02 2014-03-18 Apple Inc. Electronic devices with voice command and contextual data processing capabilities
US10241752B2 (en) 2011-09-30 2019-03-26 Apple Inc. Interface for a virtual digital assistant
US20120311585A1 (en) 2011-06-03 2012-12-06 Apple Inc. Organizing task items that represent tasks to perform
US10241644B2 (en) 2011-06-03 2019-03-26 Apple Inc. Actionable reminder entries
US10276170B2 (en) 2010-01-18 2019-04-30 Apple Inc. Intelligent automated assistant
US8682667B2 (en) 2010-02-25 2014-03-25 Apple Inc. User profiling for selecting user specific voice input processing information
US10032127B2 (en) 2011-02-18 2018-07-24 Nuance Communications, Inc. Methods and apparatus for determining a clinician's intent to order an item
US9262612B2 (en) 2011-03-21 2016-02-16 Apple Inc. Device access using voice authentication
US10642934B2 (en) 2011-03-31 2020-05-05 Microsoft Technology Licensing, Llc Augmented conversational understanding architecture
US9842168B2 (en) 2011-03-31 2017-12-12 Microsoft Technology Licensing, Llc Task driven user intents
US9760566B2 (en) 2011-03-31 2017-09-12 Microsoft Technology Licensing, Llc Augmented conversational understanding agent to identify conversation context between two humans and taking an agent action thereof
US9064006B2 (en) 2012-08-23 2015-06-23 Microsoft Technology Licensing, Llc Translating natural language utterances to keyword search queries
US10057736B2 (en) 2011-06-03 2018-08-21 Apple Inc. Active transport based notifications
US10134385B2 (en) 2012-03-02 2018-11-20 Apple Inc. Systems and methods for name pronunciation
US10417037B2 (en) 2012-05-15 2019-09-17 Apple Inc. Systems and methods for integrating third party services with a digital assistant
US9721563B2 (en) 2012-06-08 2017-08-01 Apple Inc. Name recognition system
CN104704797B (en) 2012-08-10 2018-08-10 纽昂斯通讯公司 Virtual protocol communication for electronic equipment
US9547647B2 (en) 2012-09-19 2017-01-17 Apple Inc. Voice-based media searching
KR20230137475A (en) 2013-02-07 2023-10-04 애플 인크. Voice trigger for a digital assistant
CN105190628B (en) * 2013-03-01 2019-10-11 纽昂斯通讯公司 The method and apparatus for determining the intention of the subscription items of clinician
US10652394B2 (en) 2013-03-14 2020-05-12 Apple Inc. System and method for processing voicemail
US9436287B2 (en) * 2013-03-15 2016-09-06 Qualcomm Incorporated Systems and methods for switching processing modes using gestures
US10748529B1 (en) 2013-03-15 2020-08-18 Apple Inc. Voice activated device for use with a voice-based digital assistant
WO2014197334A2 (en) 2013-06-07 2014-12-11 Apple Inc. System and method for user-specified pronunciation of words for speech synthesis and recognition
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
US9728184B2 (en) 2013-06-18 2017-08-08 Microsoft Technology Licensing, Llc Restructuring deep neural network acoustic models
US9311298B2 (en) 2013-06-21 2016-04-12 Microsoft Technology Licensing, Llc Building conversational understanding systems using a toolset
US9589565B2 (en) * 2013-06-21 2017-03-07 Microsoft Technology Licensing, Llc Environmentally aware dialog policies and response generation
US10296160B2 (en) 2013-12-06 2019-05-21 Apple Inc. Method for extracting salient dialog usage from live data
CN104714954A (en) * 2013-12-13 2015-06-17 中国电信股份有限公司 Information searching method and system based on context understanding
US20150170053A1 (en) * 2013-12-13 2015-06-18 Microsoft Corporation Personalized machine learning models
US20170017501A1 (en) 2013-12-16 2017-01-19 Nuance Communications, Inc. Systems and methods for providing a virtual assistant
US10015770B2 (en) 2014-03-24 2018-07-03 International Business Machines Corporation Social proximity networks for mobile phones
US9529794B2 (en) 2014-03-27 2016-12-27 Microsoft Technology Licensing, Llc Flexible schema for language model customization
US20150278370A1 (en) * 2014-04-01 2015-10-01 Microsoft Corporation Task completion for natural language input
US10111099B2 (en) 2014-05-12 2018-10-23 Microsoft Technology Licensing, Llc Distributing content in managed wireless distribution networks
US9874914B2 (en) 2014-05-19 2018-01-23 Microsoft Technology Licensing, Llc Power management contracts for accessory devices
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
US9715875B2 (en) 2014-05-30 2017-07-25 Apple Inc. Reducing the need for manual start/end-pointing and trigger phrases
US9430463B2 (en) 2014-05-30 2016-08-30 Apple Inc. Exemplar-based natural language processing
US10170123B2 (en) 2014-05-30 2019-01-01 Apple Inc. Intelligent assistant for home automation
US9717006B2 (en) 2014-06-23 2017-07-25 Microsoft Technology Licensing, Llc Device quarantine in a wireless network
JP6275569B2 (en) 2014-06-27 2018-02-07 株式会社東芝 Dialog apparatus, method and program
US9338493B2 (en) 2014-06-30 2016-05-10 Apple Inc. Intelligent automated assistant for TV user interactions
US9916328B1 (en) 2014-07-11 2018-03-13 Google Llc Providing user assistance from interaction understanding
US10146409B2 (en) * 2014-08-29 2018-12-04 Microsoft Technology Licensing, Llc Computerized dynamic splitting of interaction across multiple content
US9818400B2 (en) 2014-09-11 2017-11-14 Apple Inc. Method and apparatus for discovering trending terms in speech requests
US10127911B2 (en) 2014-09-30 2018-11-13 Apple Inc. Speaker identification and unsupervised speaker adaptation techniques
US10074360B2 (en) 2014-09-30 2018-09-11 Apple Inc. Providing an indication of the suitability of speech recognition
US9668121B2 (en) 2014-09-30 2017-05-30 Apple Inc. Social reminders
KR102188268B1 (en) * 2014-10-08 2020-12-08 엘지전자 주식회사 Mobile terminal and method for controlling the same
CN107003723A (en) * 2014-10-21 2017-08-01 罗伯特·博世有限公司 For the response selection in conversational system and the method and system of the automation of composition
KR102329333B1 (en) 2014-11-12 2021-11-23 삼성전자주식회사 Query processing apparatus and method
US9836452B2 (en) * 2014-12-30 2017-12-05 Microsoft Technology Licensing, Llc Discriminating ambiguous expressions to enhance user experience
CN107112016B (en) 2015-01-05 2020-12-29 谷歌有限责任公司 Multi-modal state cycling
US10572810B2 (en) 2015-01-07 2020-02-25 Microsoft Technology Licensing, Llc Managing user interaction for input understanding determinations
WO2016129767A1 (en) * 2015-02-13 2016-08-18 주식회사 팔락성 Online site linking method
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
US10460227B2 (en) 2015-05-15 2019-10-29 Apple Inc. Virtual assistant in a communication session
US10200824B2 (en) 2015-05-27 2019-02-05 Apple Inc. Systems and methods for proactively identifying and surfacing relevant content on a touch-sensitive device
US10083688B2 (en) 2015-05-27 2018-09-25 Apple Inc. Device voice control for selecting a displayed affordance
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
US9792281B2 (en) * 2015-06-15 2017-10-17 Microsoft Technology Licensing, Llc Contextual language generation by leveraging language understanding
US20160378747A1 (en) 2015-06-29 2016-12-29 Apple Inc. Virtual assistant for media playback
US10249297B2 (en) 2015-07-13 2019-04-02 Microsoft Technology Licensing, Llc Propagating conversational alternatives using delayed hypothesis binding
US10671428B2 (en) 2015-09-08 2020-06-02 Apple Inc. Distributed personal assistant
US10740384B2 (en) 2015-09-08 2020-08-11 Apple Inc. Intelligent automated assistant for media search and playback
US10747498B2 (en) 2015-09-08 2020-08-18 Apple Inc. Zero latency digital assistant
US10331312B2 (en) 2015-09-08 2019-06-25 Apple Inc. Intelligent automated assistant in a media environment
US10262654B2 (en) * 2015-09-24 2019-04-16 Microsoft Technology Licensing, Llc Detecting actionable items in a conversation among participants
US10970646B2 (en) * 2015-10-01 2021-04-06 Google Llc Action suggestions for user-selected content
US10691473B2 (en) 2015-11-06 2020-06-23 Apple Inc. Intelligent automated assistant in a messaging environment
US10956666B2 (en) 2015-11-09 2021-03-23 Apple Inc. Unconventional virtual assistant interactions
KR102393928B1 (en) * 2015-11-10 2022-05-04 삼성전자주식회사 User terminal apparatus for recommanding a reply message and method thereof
WO2017090954A1 (en) * 2015-11-24 2017-06-01 Samsung Electronics Co., Ltd. Electronic device and operating method thereof
KR102502569B1 (en) 2015-12-02 2023-02-23 삼성전자주식회사 Method and apparuts for system resource managemnet
US10049668B2 (en) 2015-12-02 2018-08-14 Apple Inc. Applying neural network language models to weighted finite state transducers for automatic speech recognition
US10223066B2 (en) 2015-12-23 2019-03-05 Apple Inc. Proactive assistance based on dialog communication between devices
US9905248B2 (en) 2016-02-29 2018-02-27 International Business Machines Corporation Inferring user intentions based on user conversation data and spatio-temporal data
US9978396B2 (en) 2016-03-16 2018-05-22 International Business Machines Corporation Graphical display of phone conversations
US10587708B2 (en) * 2016-03-28 2020-03-10 Microsoft Technology Licensing, Llc Multi-modal conversational intercom
US11487512B2 (en) 2016-03-29 2022-11-01 Microsoft Technology Licensing, Llc Generating a services application
US10158593B2 (en) * 2016-04-08 2018-12-18 Microsoft Technology Licensing, Llc Proactive intelligent personal assistant
US10945129B2 (en) * 2016-04-29 2021-03-09 Microsoft Technology Licensing, Llc Facilitating interaction among digital personal assistants
US10409876B2 (en) * 2016-05-26 2019-09-10 Microsoft Technology Licensing, Llc. Intelligent capture, storage, and retrieval of information for task completion
US10242667B2 (en) * 2016-06-03 2019-03-26 Maluuba Inc. Natural language generation in a spoken dialogue system
US10249300B2 (en) 2016-06-06 2019-04-02 Apple Inc. Intelligent list reading
US11227589B2 (en) 2016-06-06 2022-01-18 Apple Inc. Intelligent list reading
US10282218B2 (en) * 2016-06-07 2019-05-07 Google Llc Nondeterministic task initiation by a personal assistant module
US10049663B2 (en) 2016-06-08 2018-08-14 Apple, Inc. Intelligent automated assistant for media exploration
DK179309B1 (en) 2016-06-09 2018-04-23 Apple Inc Intelligent automated assistant in a home environment
US10067938B2 (en) 2016-06-10 2018-09-04 Apple Inc. Multilingual word prediction
US10586535B2 (en) 2016-06-10 2020-03-10 Apple Inc. Intelligent digital assistant in a multi-tasking environment
DK201670540A1 (en) * 2016-06-11 2018-01-08 Apple Inc Application integration with a digital assistant
DK179415B1 (en) 2016-06-11 2018-06-14 Apple Inc Intelligent device arbitration and control
DK179343B1 (en) 2016-06-11 2018-05-14 Apple Inc Intelligent task discovery
US10216269B2 (en) * 2016-06-21 2019-02-26 GM Global Technology Operations LLC Apparatus and method for determining intent of user based on gaze information
EP3504639A1 (en) * 2016-08-23 2019-07-03 Illumina, Inc. Semantic distance systems and methods for determining related ontological data
US10474753B2 (en) 2016-09-07 2019-11-12 Apple Inc. Language identification using recurrent neural networks
US10446137B2 (en) * 2016-09-07 2019-10-15 Microsoft Technology Licensing, Llc Ambiguity resolving conversational understanding system
US10503767B2 (en) * 2016-09-13 2019-12-10 Microsoft Technology Licensing, Llc Computerized natural language query intent dispatching
US10043516B2 (en) 2016-09-23 2018-08-07 Apple Inc. Intelligent automated assistant
US9940390B1 (en) * 2016-09-27 2018-04-10 Microsoft Technology Licensing, Llc Control system using scoped search and conversational interface
CN115858730A (en) 2016-09-29 2023-03-28 微软技术许可有限责任公司 Conversational data analysis
US10535005B1 (en) 2016-10-26 2020-01-14 Google Llc Providing contextual actions for mobile onscreen content
JP6697373B2 (en) 2016-12-06 2020-05-20 カシオ計算機株式会社 Sentence generating device, sentence generating method and program
US10593346B2 (en) 2016-12-22 2020-03-17 Apple Inc. Rank-reduced token representation for automatic speech recognition
US11204787B2 (en) 2017-01-09 2021-12-21 Apple Inc. Application integration with a digital assistant
EP3552114A4 (en) * 2017-02-08 2020-05-20 Semantic Machines, Inc. Natural language content generator
US10643601B2 (en) * 2017-02-09 2020-05-05 Semantic Machines, Inc. Detection mechanism for automated dialog systems
CN116991971A (en) * 2017-02-23 2023-11-03 微软技术许可有限责任公司 Extensible dialog system
WO2018156978A1 (en) 2017-02-23 2018-08-30 Semantic Machines, Inc. Expandable dialogue system
US10798027B2 (en) * 2017-03-05 2020-10-06 Microsoft Technology Licensing, Llc Personalized communications using semantic memory
US10237209B2 (en) * 2017-05-08 2019-03-19 Google Llc Initializing a conversation with an automated agent via selectable graphical element
US10417266B2 (en) 2017-05-09 2019-09-17 Apple Inc. Context-aware ranking of intelligent response suggestions
DK201770383A1 (en) 2017-05-09 2018-12-14 Apple Inc. User interface for correcting recognition errors
DK201770439A1 (en) 2017-05-11 2018-12-13 Apple Inc. Offline personal assistant
DK180048B1 (en) 2017-05-11 2020-02-04 Apple Inc. MAINTAINING THE DATA PROTECTION OF PERSONAL INFORMATION
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
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
DK179496B1 (en) 2017-05-12 2019-01-15 Apple Inc. USER-SPECIFIC Acoustic Models
DK179745B1 (en) 2017-05-12 2019-05-01 Apple Inc. SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL 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
US10403278B2 (en) 2017-05-16 2019-09-03 Apple Inc. Methods and systems for phonetic matching in digital assistant services
DK179560B1 (en) 2017-05-16 2019-02-18 Apple Inc. Far-field extension for digital assistant services
US10311144B2 (en) 2017-05-16 2019-06-04 Apple Inc. Emoji word sense disambiguation
US20180336275A1 (en) 2017-05-16 2018-11-22 Apple Inc. Intelligent automated assistant for media exploration
US20180336892A1 (en) 2017-05-16 2018-11-22 Apple Inc. Detecting a trigger of a digital assistant
US10664533B2 (en) * 2017-05-24 2020-05-26 Lenovo (Singapore) Pte. Ltd. Systems and methods to determine response cue for digital assistant based on context
US10679192B2 (en) * 2017-05-25 2020-06-09 Microsoft Technology Licensing, Llc Assigning tasks and monitoring task performance based on context extracted from a shared contextual graph
US10657328B2 (en) 2017-06-02 2020-05-19 Apple Inc. Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling
US10742435B2 (en) * 2017-06-29 2020-08-11 Google Llc Proactive provision of new content to group chat participants
US11132499B2 (en) 2017-08-28 2021-09-28 Microsoft Technology Licensing, Llc Robust expandable dialogue system
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
US10546023B2 (en) 2017-10-03 2020-01-28 Google Llc Providing command bundle suggestions for an automated assistant
US10636424B2 (en) 2017-11-30 2020-04-28 Apple Inc. Multi-turn canned dialog
US11341422B2 (en) 2017-12-15 2022-05-24 SHANGHAI XIAOl ROBOT TECHNOLOGY CO., LTD. Multi-round questioning and answering methods, methods for generating a multi-round questioning and answering system, and methods for modifying the system
CN110019718B (en) * 2017-12-15 2021-04-09 上海智臻智能网络科技股份有限公司 Method for modifying multi-turn question-answering system, terminal equipment and storage medium
US10733982B2 (en) 2018-01-08 2020-08-04 Apple Inc. Multi-directional dialog
US10839160B2 (en) * 2018-01-19 2020-11-17 International Business Machines Corporation Ontology-based automatic bootstrapping of state-based dialog systems
US10733375B2 (en) 2018-01-31 2020-08-04 Apple Inc. Knowledge-based framework for improving natural language understanding
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
KR102635811B1 (en) * 2018-03-19 2024-02-13 삼성전자 주식회사 System and control method of system for processing sound data
US10818288B2 (en) 2018-03-26 2020-10-27 Apple Inc. Natural assistant interaction
US10909331B2 (en) 2018-03-30 2021-02-02 Apple Inc. Implicit identification of translation payload with neural machine translation
US10685075B2 (en) 2018-04-11 2020-06-16 Motorola Solutions, Inc. System and method for tailoring an electronic digital assistant query as a function of captured multi-party voice dialog and an electronically stored multi-party voice-interaction template
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
US10984780B2 (en) 2018-05-21 2021-04-20 Apple Inc. Global semantic word embeddings using bi-directional recurrent neural networks
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
DK201870355A1 (en) 2018-06-01 2019-12-16 Apple Inc. Virtual assistant operation in multi-device environments
DK180639B1 (en) 2018-06-01 2021-11-04 Apple Inc DISABILITY OF ATTENTION-ATTENTIVE VIRTUAL ASSISTANT
US10892996B2 (en) 2018-06-01 2021-01-12 Apple Inc. Variable latency device coordination
US11076039B2 (en) 2018-06-03 2021-07-27 Apple Inc. Accelerated task performance
WO2020044990A1 (en) 2018-08-29 2020-03-05 パナソニックIpマネジメント株式会社 Power conversion system and power storage system
US11010561B2 (en) 2018-09-27 2021-05-18 Apple Inc. Sentiment prediction from textual data
US10839159B2 (en) 2018-09-28 2020-11-17 Apple Inc. Named entity normalization in a spoken dialog system
US11170166B2 (en) 2018-09-28 2021-11-09 Apple Inc. Neural typographical error modeling via generative adversarial networks
US11462215B2 (en) 2018-09-28 2022-10-04 Apple Inc. Multi-modal inputs for voice commands
US11475898B2 (en) 2018-10-26 2022-10-18 Apple Inc. Low-latency multi-speaker speech recognition
US11638059B2 (en) 2019-01-04 2023-04-25 Apple Inc. Content playback on multiple devices
CN111428721A (en) * 2019-01-10 2020-07-17 北京字节跳动网络技术有限公司 Method, device and equipment for determining word paraphrases and storage medium
US11348573B2 (en) 2019-03-18 2022-05-31 Apple Inc. Multimodality in digital assistant systems
US11217251B2 (en) 2019-05-06 2022-01-04 Apple Inc. Spoken notifications
US11423908B2 (en) 2019-05-06 2022-08-23 Apple Inc. Interpreting spoken requests
US11307752B2 (en) 2019-05-06 2022-04-19 Apple Inc. User configurable task triggers
US11475884B2 (en) 2019-05-06 2022-10-18 Apple Inc. Reducing digital assistant latency when a language is incorrectly determined
US11140099B2 (en) 2019-05-21 2021-10-05 Apple Inc. Providing message response suggestions
DK180129B1 (en) 2019-05-31 2020-06-02 Apple Inc. User activity shortcut suggestions
US11496600B2 (en) 2019-05-31 2022-11-08 Apple Inc. Remote execution of machine-learned models
US11289073B2 (en) 2019-05-31 2022-03-29 Apple Inc. Device text to speech
DK201970510A1 (en) 2019-05-31 2021-02-11 Apple Inc Voice identification in digital assistant systems
US11360641B2 (en) 2019-06-01 2022-06-14 Apple Inc. Increasing the relevance of new available information
US11227599B2 (en) 2019-06-01 2022-01-18 Apple Inc. Methods and user interfaces for voice-based control of electronic devices
WO2021056255A1 (en) 2019-09-25 2021-04-01 Apple Inc. Text detection using global geometry estimators
US11038934B1 (en) 2020-05-11 2021-06-15 Apple Inc. Digital assistant hardware abstraction
US11061543B1 (en) 2020-05-11 2021-07-13 Apple Inc. Providing relevant data items based on context
US11755276B2 (en) 2020-05-12 2023-09-12 Apple Inc. Reducing description length based on confidence
US11490204B2 (en) 2020-07-20 2022-11-01 Apple Inc. Multi-device audio adjustment coordination
US11438683B2 (en) 2020-07-21 2022-09-06 Apple Inc. User identification using headphones
US11783827B2 (en) 2020-11-06 2023-10-10 Apple Inc. Determining suggested subsequent user actions during digital assistant interaction
EP4174848A1 (en) * 2021-10-29 2023-05-03 Televic Rail NV Improved speech to text method and system

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070038436A1 (en) 2005-08-10 2007-02-15 Voicebox Technologies, Inc. System and method of supporting adaptive misrecognition in conversational speech

Family Cites Families (71)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5265014A (en) * 1990-04-10 1993-11-23 Hewlett-Packard Company Multi-modal user interface
US5748974A (en) * 1994-12-13 1998-05-05 International Business Machines Corporation Multimodal natural language interface for cross-application tasks
US5970446A (en) * 1997-11-25 1999-10-19 At&T Corp Selective noise/channel/coding models and recognizers for automatic speech recognition
JP2002523828A (en) * 1998-08-24 2002-07-30 ビーシーエル コンピューターズ, インコーポレイテッド Adaptive natural language interface
US6499013B1 (en) * 1998-09-09 2002-12-24 One Voice Technologies, Inc. Interactive user interface using speech recognition and natural language processing
US6332120B1 (en) * 1999-04-20 2001-12-18 Solana Technology Development Corporation Broadcast speech recognition system for keyword monitoring
JP3530109B2 (en) * 1999-05-31 2004-05-24 日本電信電話株式会社 Voice interactive information retrieval method, apparatus, and recording medium for large-scale information database
WO2000073900A1 (en) * 1999-06-01 2000-12-07 Jacquez Geoffrey M Help system for a computer related application
US6598039B1 (en) * 1999-06-08 2003-07-22 Albert-Inc. S.A. Natural language interface for searching database
JP3765202B2 (en) * 1999-07-09 2006-04-12 日産自動車株式会社 Interactive information search apparatus, interactive information search method using computer, and computer-readable medium recording program for interactive information search processing
JP2001125896A (en) * 1999-10-26 2001-05-11 Victor Co Of Japan Ltd Natural language interactive system
US7050977B1 (en) * 1999-11-12 2006-05-23 Phoenix Solutions, Inc. Speech-enabled server for internet website and method
JP2002024285A (en) * 2000-06-30 2002-01-25 Sanyo Electric Co Ltd Method and device for user support
JP2002082748A (en) * 2000-09-06 2002-03-22 Sanyo Electric Co Ltd User support device
US7197120B2 (en) * 2000-12-22 2007-03-27 Openwave Systems Inc. Method and system for facilitating mediated communication
GB2372864B (en) * 2001-02-28 2005-09-07 Vox Generation Ltd Spoken language interface
JP2003115951A (en) * 2001-10-09 2003-04-18 Casio Comput Co Ltd Topic information providing system and topic information providing method
US7224981B2 (en) * 2002-06-20 2007-05-29 Intel Corporation Speech recognition of mobile devices
US7693720B2 (en) * 2002-07-15 2010-04-06 Voicebox Technologies, Inc. Mobile systems and methods for responding to natural language speech utterance
EP1411443A1 (en) * 2002-10-18 2004-04-21 Hewlett Packard Company, a Delaware Corporation Context filter
JP2004212641A (en) * 2002-12-27 2004-07-29 Toshiba Corp Voice input system and terminal device equipped with voice input system
JP2004328181A (en) * 2003-04-23 2004-11-18 Sharp Corp Telephone and telephone network system
JP4441782B2 (en) * 2003-05-14 2010-03-31 日本電信電話株式会社 Information presentation method and information presentation apparatus
JP2005043461A (en) * 2003-07-23 2005-02-17 Canon Inc Voice recognition method and voice recognition device
KR20050032649A (en) * 2003-10-02 2005-04-08 (주)이즈메이커 Method and system for teaching artificial life
US7747601B2 (en) * 2006-08-14 2010-06-29 Inquira, Inc. Method and apparatus for identifying and classifying query intent
US7720674B2 (en) * 2004-06-29 2010-05-18 Sap Ag Systems and methods for processing natural language queries
JP4434972B2 (en) * 2005-01-21 2010-03-17 日本電気株式会社 Information providing system, information providing method and program thereof
ATE510259T1 (en) * 2005-01-31 2011-06-15 Ontoprise Gmbh MAPPING WEB SERVICES TO ONTOLOGIES
GB0502259D0 (en) * 2005-02-03 2005-03-09 British Telecomm Document searching tool and method
CN101120341A (en) * 2005-02-06 2008-02-06 凌圭特股份有限公司 Method and equipment for performing mobile information access using natural language
US7409344B2 (en) * 2005-03-08 2008-08-05 Sap Aktiengesellschaft XML based architecture for controlling user interfaces with contextual voice commands
US20060206333A1 (en) * 2005-03-08 2006-09-14 Microsoft Corporation Speaker-dependent dialog adaptation
WO2006108061A2 (en) * 2005-04-05 2006-10-12 The Board Of Trustees Of Leland Stanford Junior University Methods, software, and systems for knowledge base coordination
US7991607B2 (en) * 2005-06-27 2011-08-02 Microsoft Corporation Translation and capture architecture for output of conversational utterances
US7640160B2 (en) * 2005-08-05 2009-12-29 Voicebox Technologies, Inc. Systems and methods for responding to natural language speech utterance
US7822699B2 (en) * 2005-11-30 2010-10-26 Microsoft Corporation Adaptive semantic reasoning engine
US7627466B2 (en) * 2005-11-09 2009-12-01 Microsoft Corporation Natural language interface for driving adaptive scenarios
US20070136222A1 (en) * 2005-12-09 2007-06-14 Microsoft Corporation Question and answer architecture for reasoning and clarifying intentions, goals, and needs from contextual clues and content
US20070143410A1 (en) * 2005-12-16 2007-06-21 International Business Machines Corporation System and method for defining and translating chat abbreviations
CN100373313C (en) * 2006-01-12 2008-03-05 广东威创视讯科技股份有限公司 Intelligent recognition coding method for interactive input apparatus
US8209407B2 (en) * 2006-02-10 2012-06-26 The United States Of America, As Represented By The Secretary Of The Navy System and method for web service discovery and access
JP4810609B2 (en) * 2006-06-13 2011-11-09 マイクロソフト コーポレーション Search engine dashboard
US20080005068A1 (en) * 2006-06-28 2008-01-03 Microsoft Corporation Context-based search, retrieval, and awareness
US8204739B2 (en) * 2008-04-15 2012-06-19 Mobile Technologies, Llc System and methods for maintaining speech-to-speech translation in the field
CN1963752A (en) * 2006-11-28 2007-05-16 李博航 Man-machine interactive interface technique of electronic apparatus based on natural language
WO2008067676A1 (en) * 2006-12-08 2008-06-12 Medhat Moussa Architecture, system and method for artificial neural network implementation
US20080172359A1 (en) * 2007-01-11 2008-07-17 Motorola, Inc. Method and apparatus for providing contextual support to a monitored communication
US20080172659A1 (en) 2007-01-17 2008-07-17 Microsoft Corporation Harmonizing a test file and test configuration in a revision control system
US20080201434A1 (en) * 2007-02-16 2008-08-21 Microsoft Corporation Context-Sensitive Searches and Functionality for Instant Messaging Applications
US20090076917A1 (en) * 2007-08-22 2009-03-19 Victor Roditis Jablokov Facilitating presentation of ads relating to words of a message
US7720856B2 (en) * 2007-04-09 2010-05-18 Sap Ag Cross-language searching
US8762143B2 (en) * 2007-05-29 2014-06-24 At&T Intellectual Property Ii, L.P. Method and apparatus for identifying acoustic background environments based on time and speed to enhance automatic speech recognition
US7788276B2 (en) * 2007-08-22 2010-08-31 Yahoo! Inc. Predictive stemming for web search with statistical machine translation models
WO2009029905A2 (en) * 2007-08-31 2009-03-05 Powerset, Inc. Identification of semantic relationships within reported speech
US8165886B1 (en) * 2007-10-04 2012-04-24 Great Northern Research LLC Speech interface system and method for control and interaction with applications on a computing system
US8504621B2 (en) * 2007-10-26 2013-08-06 Microsoft Corporation Facilitating a decision-making process
JP2009116733A (en) * 2007-11-08 2009-05-28 Nec Corp Application retrieval system, application retrieval method, monitor terminal, retrieval server, and program
JP5158635B2 (en) * 2008-02-28 2013-03-06 インターナショナル・ビジネス・マシーンズ・コーポレーション Method, system, and apparatus for personal service support
US20090234655A1 (en) * 2008-03-13 2009-09-17 Jason Kwon Mobile electronic device with active speech recognition
CN101499277B (en) * 2008-07-25 2011-05-04 中国科学院计算技术研究所 Service intelligent navigation method and system
US8874443B2 (en) * 2008-08-27 2014-10-28 Robert Bosch Gmbh System and method for generating natural language phrases from user utterances in dialog systems
JP2010128665A (en) * 2008-11-26 2010-06-10 Kyocera Corp Information terminal and conversation assisting program
JP2010145262A (en) * 2008-12-19 2010-07-01 Pioneer Electronic Corp Navigation apparatus
US8326637B2 (en) * 2009-02-20 2012-12-04 Voicebox Technologies, Inc. System and method for processing multi-modal device interactions in a natural language voice services environment
JP2010230918A (en) * 2009-03-26 2010-10-14 Fujitsu Ten Ltd Retrieving device
US8700665B2 (en) * 2009-04-27 2014-04-15 Avaya Inc. Intelligent conference call information agents
US20100281435A1 (en) * 2009-04-30 2010-11-04 At&T Intellectual Property I, L.P. System and method for multimodal interaction using robust gesture processing
KR101622111B1 (en) * 2009-12-11 2016-05-18 삼성전자 주식회사 Dialog system and conversational method thereof
KR101007336B1 (en) * 2010-06-25 2011-01-13 한국과학기술정보연구원 Personalizing service system and method based on ontology
US20120253789A1 (en) 2011-03-31 2012-10-04 Microsoft Corporation Conversational Dialog Learning and Correction

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070038436A1 (en) 2005-08-10 2007-02-15 Voicebox Technologies, Inc. System and method of supporting adaptive misrecognition in conversational speech

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP2691876A4

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3144931A1 (en) * 2015-09-17 2017-03-22 Samsung Electronics Co., Ltd. Dialog management apparatus and method

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