US20140156352A1 - Analytical Tool - Google Patents

Analytical Tool Download PDF

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US20140156352A1
US20140156352A1 US14/144,389 US201314144389A US2014156352A1 US 20140156352 A1 US20140156352 A1 US 20140156352A1 US 201314144389 A US201314144389 A US 201314144389A US 2014156352 A1 US2014156352 A1 US 2014156352A1
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singular
tradeoff
alternative
impact
criterion
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US14/144,389
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Fadi Victor Micaelian
Emil Scoffone
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Auguri Corp
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Auguri Corp
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce
    • G06Q30/02Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201Market modelling; Market analysis; Collecting market data
    • G06Q30/0206Price or cost determination based on market factors
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce
    • G06Q30/06Buying, selling or leasing transactions
    • G06Q30/0601Electronic shopping [e-shopping]
    • G06Q30/0631Item recommendations

Definitions

  • these techniques are most likely not capable of predicting how much a customer is willing to pay for a feature or product, to tradeoff certain features, forecast the impact of a change in a product and predicting which feature would most enhance a product.
  • a method for determining a singular impact of a base criterion includes selecting the base criterion and a trade criterion from a plurality of criteria and selecting a starting alternative and a target alternative.
  • a series of virtual alternatives are then created, initially based on the starting alternative, by sequentially eliminating an impact of each non-selected criteria from the plurality of criteria.
  • a final virtual alternative is compared to the target alternative and the singular impact of the base criterion is determined based on a difference between the final virtual alternative and the target alternative.
  • a method for determining a singular impact of a base criterion includes selecting the base criterion and a trade criterion from a plurality of criteria.
  • a starting alternative and a target alternative are also selected and a series of virtual alternatives are created, initially based on the starting alternative, by sequentially eliminating an impact of each non-selected criteria from the plurality of criteria.
  • a virtual alternative of the series of virtual alternatives is compared to the target alternative wherein the virtual alternative only differs from the target alternative by a value of the base criterion.
  • the singular impact of the base criterion is then determined based on a difference between the final virtual alternative and the target alternative.
  • a method for determining a singular impact of a base criterion includes selecting the base criterion and a trade criterion from “N” criteria.
  • a starting alternative and a target alternative are also selected and “N ⁇ 2” sequential virtual alternatives are created, initially based on the starting alternative, by sequentially eliminating an impact of each non-selected criteria from the “N” criteria.
  • a virtual alternative of the series of virtual alternatives is compared to the target alternative wherein the virtual alternative only differs from the target alternative by a value of the base criterion.
  • the singular impact of the base criterion is then determined based on a difference between the final virtual alternative and the target alternative.
  • a method for analyzing an impact of a desired singular tradeoff for a population of users includes selecting the desired singular tradeoff from the population of users and collecting a plurality of singular tradeoffs in a sequential fashion from the population of users. The plurality of tradeoffs are then processed and analyzed to determine the impact of the desired singular tradeoff.
  • a system for determining a singular impact of a base criterion includes a singular tradeoff engine that accepts a weighted ordered list and operative to determine a singular impact of a base criterion by creating virtual alternatives based on the weighted ordered list. Also included is a function subroutine engine that accepts parametric values from the singular tradeoff engine and operative to develop a new value to the singular tradeoff engine.
  • a method for determining a value a consumer places on a desired product component includes providing a first product without the desired product component and a second product with the desired product component.
  • a series of simulated products are then created, initially based on the first product, by sequentially eliminating an impact of each non-desired product component.
  • a final simulated product is compared to the second product; and the value is determined based on a difference between the final simulated product and the second product.
  • a method for determining a value a consumer places on a desired product component includes providing a first product that does not contain the desired product component and a second product that does contain the desired product component.
  • a series of simulated products are then created, initially based on the first product, by sequentially eliminating an impact of one or more product components that are not the desired product component.
  • a final simulated product is compared to the second product; and the value is determined based on a difference between the final simulated product and the second product.
  • FIG. 1 is a screen shot of a web page allowing for the adjustment of weights
  • FIG. 2 is a chart illustrating a processed utilized by an analytical tool, in accordance with an exemplary embodiment
  • FIG. 3 is a flowchart illustrating a process of an analytical tool for determining a singular impact of a tradeoff, in accordance with an embodiment of the present invention
  • FIG. 4A is a flowchart illustrating a process for analyzing an individual singular tradeoff for a population of users, in accordance with an exemplary embodiment
  • FIG. 4B is a flowchart illustrating a process for storing an individual tradeoff for each attribute, in accordance with an exemplary embodiment.
  • FIG. 5 is a block diagram of an analytical tool system, in accordance with an exemplary embodiment
  • FIG. 6 is a block diagram of an exemplary embodiment of a network
  • FIG. 7 is a block diagram of an exemplary embodiment of a computer.
  • An aspect of the present invention contemplates a variety of methods, systems and data structures for determining a singular impact of a tradeoff or criterion.
  • An analysis systematically eliminates the effect of individual non-changed criteria in order to see what happens if a particular criteria is modified. What results is the individual or singular impact of adjusting that particular criterion.
  • Other aspects are also within the scope of the present invention.
  • “singular” can refer to either one item or to a group of items that are linked in some manner. Additionally, “singular” can also refer to a subcomponent of any economic unit that is capable of being sold.
  • FIG. 1 is a screen shot of a web page allowing for the adjustment of weights.
  • a weight adjustment interface 92 lists a number of properties including a space property 94 , a performance property 96 , a safety property 98 , a gas mileage property 100 , a maintenance cost property 102 , a comfort property 104 , and a price property 106 .
  • a “slider bar” 112 including a diamond shaped indicator 110 (in this example) which can be adjusted in position along the length of the slider bar, as will be appreciated by those skilled in the art.
  • a pointer 108 controlled, for example, by a pointing device of a computer system (pointing device and computer not shown), is used to engage an indicator 110 and to drag to a desired position between the “not important” and the “very important” ends of the slider bar 112 .
  • the making and use of slider bars is well known to those skilled in the art.
  • the position of the indicator 110 along the slider bar 112 is translated into a numeric output, typically a normalized value between zero and one, which is the weight for the criterion.
  • the user input is processed as indicated by the arrow 114 to provide an ordered or ranked list 116 which reflects the preferences of the user.
  • the Ford Focus ZX3 coupe had the best overall score and was ranked #1 based upon the weighted preferences that were input in the weight adjustment section of screen shot 90 .
  • the Ford Escape TWD Sport Utility 4D was ranked #2, the Ford Taurus SE V6 Wagon 4D, ranked at #3, etc.
  • FIG. 2 is a chart illustrating a processed utilized by an analytical tool, in accordance with an exemplary embodiment.
  • Chart 1020 shows two alternatives from a list ranked according to the weights shown in row 1018 . Included in chart 1020 and row 1018 are a plurality of criteria/tradeoffs that include price, horsepower (HP), mileage (MPG) and safety.
  • HP horsepower
  • MPG mileage
  • the analytic tool calculates singular tradeoffs between two criteria by progressively eliminating the contributions of all other criteria. This elimination process is carried out through generation of intermediate “equivalent” alternatives.
  • FIG. 2 shows two such, 1022 and 1024 , used to eliminate the contribution of the safety and mileage criteria, respectively.
  • a Ford motor company would like to determine how much a consumer is willing to pay per horsepower to go from 210 h.p. (the Honda's) to 260 h.p. (the Ford's). Again, it should be understood that the value the consumer is willing to pay per h.p. increase is being determined and not the actual cost to the manufacturer to for the increase.
  • Ford compares their model to the Hyundai model that already has the desired feature—the increased H.P.
  • the algorithm first marks the Hyundai as the target alternative 1026 and the Ford as the starting alternative 1028 .
  • the starting alternative 1028 is converted to the modified virtual alternative 1024 , through virtual alternative 1022 , so that virtual alternative 1024 differs from the target alternative 1026 only in price and horsepower.
  • the algorithm modifies the values of criteria in the starting alternative so that they match the values in the target alternative.
  • the value for the base criterion is left unmodified, and that for the trade criterion is changed depending on the new values of other criteria, as described later.
  • the end result is a virtual alternative that differs from the target in just the values of the base and trade criteria.
  • the target alternative is usually the preferred choice while the starting alternative is the less preferred choice.
  • the trade in the preceding example, is the price which takes the form of a unit of currency.
  • the criteria marked as the trade and the base can be any criteria related to a product that can be adjusted or added on. With that in mind, it is quite clear that, while the preceding example uses automobile related criteria, any economic unit capable of being sold that has subcomponents can be substituted.
  • Some further embodiments can take the form of, but are certainly not limited to, a consumer electronics manufacturer that is planning a new digital camera model, whose target price has been fixed by market considerations within a restricted range.
  • the interesting information is not how much real currency end users would be willing to pay for this or that feature, but rather how much of a feature they are willing to forego in order to get more of another.
  • the algorithms effectively convert one of the two features into a virtual unit of currency, which is then exchanged for the other feature.
  • the designers may want to determine the tradeoff between the lens quality and the digitization speed of the sensor. Depending on how much quality the end user is willing to give up for the ability to take consecutive pictures as quickly as possible, the answer will suggest whether to include more expensive optics, or put more money into a larger acquisition buffer.
  • Yet another embodiment could be a cable broadcast company that wants to introduce a new package that contains strong parental control features.
  • the economic unit is not a hard good, but rather a soft service.
  • the interesting information is how the added parental control features stack up against, say, the width and breadth of the channel offering. In other words, how much “channel selection dollars” are end users willing to “pay” in order to have those new parental control features?
  • the various algorithms employed in this disclosure can answer such questions.
  • FIG. 3 is a flowchart illustrating a process 118 of an analytical tool for determining a singular impact of a tradeoff, in accordance with an exemplary embodiment.
  • the process begins at 120 and in an operation 122 , the base and trade criteria are selected out of the N available criteria.
  • the base criterion is the horsepower
  • the trade criterion the price
  • N is equal to 4 (for price, horsepower, mileage, and safety).
  • the starting virtual alternative and the target alternative are selected; in the preceding example, the starting virtual alternative is the Ford, and the target alternative the Nissan.
  • An iterative loop 126 is then commenced with a counter beginning at 0, incrementing by 1, and looping as long as it is smaller than N; this counter is also an index into the array of criteria.
  • an operation 128 it is decided if the counter corresponds to the index of the base or tradeoff criteria; if it is, then the rest of the loop body is skipped and control is passed back to operation 126 . If the counter corresponds to neither the base nor the trade criterion, then operations 130 and 132 are executed to generate a new virtual alternative.
  • Operation 130 analyzes the current values for the target and virtual alternatives, and, in this example, applies a customizable, and possibly criterion-specific, algorithm to change the value of the base criterion to account for the fact that operation 132 sets the value of the current criterion in the virtual alternative to be the same as in the target alternative. Therefore, the two operations 130 and 132 generate a virtual alternative where the value of the current criterion is identical to the corresponding one in the target alternative, and the value of the base criterion has been adjusted to account for the change in the current criterion. Operations 130 and 132 eliminate the impact of the current criterion from the alternative.
  • the algorithm implemented by operation 130 has access to the current execution context of loop 138 ; this context includes, but is not limited to, the target alternative, the current virtual alternative, the base and trade criteria, and the current criterion as identified by the loop counter.
  • process 118 has been provided with a list of operation 130 algorithms associated with the various criteria. Examples of such algorithms follow, using the setting of FIG. 3 ; note that this is by necessity not a complete list, since the algorithms applied in operation 130 may be highly dependent on the specific setting (for example, they may be highly dependent on the semantics of the trade and base criteria).
  • operation 130 calculates the change in price that would correspond to change the mileage from 15 (the Ford's value) to 18 (the Honda's), or the safety from 4 stars (the Ford's) to 5 stars (the Honda's).
  • operation 130 may implement varied algorithms, of various complexities, tailored to each criterion's semantics.
  • a simple algorithm may use singular impact of tradeoff values that were obtained via other means, such as focus groups or user surveys. For example, an organization may have already established that the typical end user is willing to pay up to $500 for a sunroof, and can use that information to eliminate the contribution of a sunroof when generating automobile-related virtual alternatives.
  • Another algorithm may estimate the tradeoff value as a percentage of the cost of providing the given feature: for example, a digital camera manufacturer knows the additional price of producing a model with a 4 megapixel instead a 3 megapixel sensor, and estimates a user's tradeoff value to be 125% of that cost.
  • complex algorithms may use information like the end-user's tradeoff preferences (the weights used for the ranking) to estimate the percentage of a total price difference to allocate to the adjustment for a specific criterion.
  • FIG. 4A and 4B illustrate various, exemplary, but not limiting implementations of process 138 .
  • FIG. 4A is a flowchart illustrating a process 138 A for analyzing a singular tradeoff from a population of users, in accordance with an exemplary embodiment.
  • the desired singular tradeoff and target user population are selected in operations 1042 and 1044 , respectively; the population will contain Nusers.
  • the loop operation 1046 sets a counter to 0, increases it by 1 with each iteration, and exits when the counter is greater than, or equal to, N.
  • the individual singular tradeoff for user i is collected, in accordance with an embodiment of the present invention, and stored for later processing in operation 1048 . This operation embeds the analytic engine described in FIG.
  • FIG. 4B is a flowchart illustrating a process 138 B for storing an individual singular tradeoff for a set of attributes, in accordance an exemplary embodiment.
  • operation 1062 selects M from N possible attributes for calculation of singular tradeoffs.
  • the loop operation 1064 sets a counter to 0, increases it by 1 with each iteration, and exits when the counter is greater than, or equal to, N.
  • Operation 1066 checks if the criterion at counter i is one of the M selected criteria. If so, operation 1068 calculates the singular tradeoff, in accordance with an embodiment of the present invention, and stores for use by operation 1070 ; otherwise, control is returned to operation 1064 . When all criteria have been processed, operation 1070 performs the desired analyses on the collection of singular tradeoffs. Operation 1072 then ends process 138 A.
  • FIG. 5 is a block diagram of an analytical tool system 160 , in accordance with an exemplary embodiment. Included in system 160 is a singular tradeoff engine 162 and a function subroutine engine 164 . Singular tradeoff engine 162 accepts as input a weighted ordered list and produces a singular tradeoff in conjunction with function subroutine engine 164 . This occurs by exchanging parametric values from the singular tradeoff engine 162 to the function subroutine 164 . In response, the function subroutine engine 162 sends a new value to the singular tradeoff engine 162 .
  • the system 160 for example, can be implemented on a computer system using software to perform processes such as those described above. Alternatively, the system 160 can be implemented in hardware, software or any combination thereof.
  • the engines 160 and 164 can operate on any principle including digital, analog and other computing modalities.
  • FIGS. 6-7 The following description of FIGS. 6-7 is intended to provide an overview of computer hardware and other operating components suitable for performing the methods of the invention described above, but is not intended to limit the applicable environments. Similarly, the computer hardware and other operating components may be suitable as part of the apparatuses of the invention described above.
  • the invention can be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like.
  • the invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • FIG. 6 is a block diagram of an exemplary embodiment of a network 705 , such as the Internet.
  • the term “Internet” as used herein refers to a network of networks which uses certain protocols, such as the TCP/IP protocol, and possibly other protocols such as the hypertext transfer protocol (HTTP) for hypertext markup language (HTML) documents that make up the World Wide Web (web).
  • HTTP hypertext transfer protocol
  • HTML hypertext markup language
  • Access to the Internet 705 is typically provided by Internet service providers (ISP), such as the ISPs 710 and 715 .
  • ISP Internet service providers
  • Users on client systems, such as client computer systems 730 , 740 , 750 , and 760 obtain access to the Internet through the Internet service providers, such as ISPs 710 and 715 .
  • Access to the Internet allows users of the client computer systems to exchange information, receive and send e-mails, and view documents, such as documents which have been prepared in the HTML format.
  • These documents are often provided by web servers, such as web server 720 which is considered to be “on” the Internet.
  • web servers are provided by the ISPs, such as ISP 710 , although a computer system can be set up and connected to the Internet without that system also being an ISP.
  • the web server 720 is typically at least one computer system which operates as a server computer system and is configured to operate with the protocols of the World Wide Web and is coupled to the Internet.
  • the web server 720 can be part of an ISP which provides access to the Internet for client systems.
  • the web server 720 is shown coupled to the server computer system 725 which itself is coupled to web content 795 , which can be considered a form of a media database. While two computer systems 720 and 725 are shown in FIG. 6 , the web server system 720 and the server computer system 725 can be one computer system having different software components providing the web server functionality and the server functionality provided by the server computer system 725 which will be described further below.
  • Client computer systems 730 , 740 , 750 , and 760 can each, with the appropriate web browsing software, view HTML pages provided by the web server 720 .
  • the ISP 710 provides Internet connectivity to the client computer system 730 through the modem interface 735 which can be considered part of the client computer system 730 .
  • the client computer system can be a personal computer system, a network computer, a Web TV system, or other such computer system.
  • the ISP 715 provides Internet connectivity for client systems 740 , 750 , and 760 , although as shown in FIG. 6 , the connections are not the same for these three computer systems.
  • Client computer system 740 is coupled through a modem interface 745 while client computer systems 750 and 760 are part of a LAN.
  • FIG. 6 shows the interfaces 735 and 745 as generically as a “modem,” each of these interfaces can be an analog modem, ISDN modem, cable modem, satellite transmission interface (e.g. “Direct PC”), or other interfaces for coupling a computer system to other computer systems.
  • Client computer systems 750 and 760 may be coupled to a LAN 770 through network interfaces 755 and 765 , which can be Ethernet network or other network interfaces.
  • the LAN 770 is also coupled to a gateway computer system 775 which can provide firewall and other Internet related services for the local area network.
  • This gateway computer system 775 is coupled to the ISP 715 to provide Internet connectivity to the client computer systems 750 and 760 .
  • the gateway computer system 775 can be a conventional server computer system.
  • the web server system 720 can be a conventional server computer system.
  • a server computer system 780 can be directly coupled to the LAN 770 through a network interface 785 to provide files 790 and other services to the clients 750 , 760 , without the need to connect to the Internet through the gateway system 775 .
  • FIG. 7 is a block diagram of an exemplary embodiment of a computer that can be used as a client computer system or a server computer system or as a web server system. Such a computer system can be used to perform many of the functions of an Internet service provider, such as ISP 710 .
  • the computer system 800 interfaces to external systems through the modem or network interface 820 . It will be appreciated that the modem or network interface 820 can be considered to be part of the computer system 800 .
  • This interface 820 can be an analog modem, ISDN modem, cable modem, token ring interface, satellite transmission interface (e.g. “Direct PC”), or other interfaces for coupling a computer system to other computersystems.
  • This interface 820 can be an analog modem, ISDN modem, cable modem, token ring interface, satellite transmission interface (e.g. “Direct PC”), or other interfaces for coupling a computer system to other computersystems.
  • Direct PC satellite transmission interface
  • the computer system 800 includes a processor 810 , which can be a conventional microprocessor such as an Intel Pentium microprocessor or Motorola Power PC microprocessor.
  • Memory 840 is coupled to the processor 810 by a bus 870 .
  • Memory 840 can be dynamic random access memory (DRAM) and can also include static RAM (SRAM).
  • the bus 870 couples the processor 810 to the memory 840 , also to non-volatile storage 850 , to display controller 830 , and to the input/output (I/O) controller 860 .
  • the display controller 830 controls in the conventional manner a display on a display device 835 which can be a cathode ray tube (CRT) or liquid crystal display (LCD).
  • the input/output devices 855 can include a keyboard, disk drives, printers, a scanner, and other input and output devices, including a mouse or other pointing device.
  • the display controller 830 and the I/O controller 860 can be implemented with conventional well known technology.
  • a digital image input device 865 can be a digital camera which is coupled to an I/O controller 860 in order to allow images from the digital camera to be input into the computer system 800 .
  • the non-volatile storage 850 is often a magnetic hard disk, an optical disk, or another form of storage for large amounts of data. Some of this data is often written, by a direct memory access process, into memory 840 during execution of software in the computer system 800 .
  • machine-readable medium or “computer-readable medium” includes any type of storage device that is accessible by the processor 810 and also encompasses a carrier wave that encodes a data signal.
  • the computer system 800 is one example of many possible computer systems which have different architectures.
  • personal computers based on an Intel microprocessor often have multiple buses, one of which can be an input/output (I/O) bus for the peripherals and one that directly connects the processor 810 and the memory 840 (often referred to as a memory bus).
  • the buses are connected together through bridge components that perform any necessary translation due to differing bus protocols.
  • Network computers are another type of computer system that can be used with the present invention.
  • Network computers do not usually include a hard disk or other mass storage, and the executable programs are loaded from a network connection into the memory 840 for execution by the processor 810 .
  • a Web TV system which is known in the art, is also considered to be a computer system according to this embodiment, but it may lack some of the features shown in FIG. 6 , such as certain input or output devices.
  • a typical computer system will usually include at least a processor, memory, and a bus coupling the memory to the processor.
  • the computer system 800 is controlled by operating system software which includes a file management system, such as a disk operating system, which is part of the operating system software.
  • a file management system such as a disk operating system
  • One example of an operating system software with its associated file management system software is the family of operating systems known as Windows® from Microsoft Corporation of Redmond, Wash., and their associated file management systems.
  • Another example of an operating system software with its associated file management system software is the LINUX operating system and its associated file management system.
  • the file management system is typically stored in the non-volatile storage 850 and causes the processor 810 to execute the various acts required by the operating system to input and output data and to store data in memory, including storing files on the non-volatile storage 850 .
  • Some embodiments also relate to apparatus for performing the operations herein.
  • This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer.
  • a computer program may be stored (embodied) in a computer (machine) readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.

Abstract

An exemplary method for determining a singular impact of a base criterion includes selecting the base criterion and a trade criterion from a plurality of criteria and selecting a starting alternative and a target alternative. A series of virtual alternatives are then created, initially based on the starting alternative, by sequentially eliminating an impact of each non-selected criteria from the plurality of criteria. A final virtual alternative is compared to the target alternative and the singular impact of the base criterion is determined based on a difference between the final virtual alternative and the target alternative.

Description

    CROSS REFERENCE TO RELATED APPLICATIONS
  • This application is a continuation application of U.S. patent application Ser. No. 10/981,988, filed Nov. 4, 2004. This application is related to U.S. Pat. No. 6,714,929 and U.S. patent application Ser. No. 09/962,708 both of which are incorporated herein by reference.
  • DESCRIPTION OF THE RELATED ART
  • There are numerous data analysis techniques that are employed by organizations to determine various items such as customer needs, preferences and tradeoffs. These techniques include business intelligence, data mining, marketing analytics and knowledge management/reporting tools. Typically these techniques are based on historical data and therefore are typically inadequate in predicting behavior on new products or markets where historical data is not available.
  • Specifically, these techniques are most likely not capable of predicting how much a customer is willing to pay for a feature or product, to tradeoff certain features, forecast the impact of a change in a product and predicting which feature would most enhance a product.
  • It should be understood that there is a distinction between the cost of an option and the perceived value to a consumer of having that option. For example, it may cost a certain amount of money to a manufacturer to include an option on a product. The figure that a consumer is willing to pay for that option is different and is difficult to determine. Similarly, a consumer may place a premium on a certain grouping of options. Determining that optimal combination can be difficult as well.
  • In view of the foregoing, it may be useful to provide methods and systems that analyze a singular impact of a tradeoff or a singular impact of a group of tradeoffs.
  • SUMMARY OF EMBODIMENTS OF THE INVENTION
  • The present invention is described and illustrated in conjunction with systems, apparatuses and methods of varying scope which are meant to be exemplary and illustrative, not limiting in scope.
  • A method for determining a singular impact of a base criterion, in accordance with an exemplary embodiment, includes selecting the base criterion and a trade criterion from a plurality of criteria and selecting a starting alternative and a target alternative. A series of virtual alternatives are then created, initially based on the starting alternative, by sequentially eliminating an impact of each non-selected criteria from the plurality of criteria. A final virtual alternative is compared to the target alternative and the singular impact of the base criterion is determined based on a difference between the final virtual alternative and the target alternative.
  • A method for determining a singular impact of a base criterion, in accordance with another exemplary embodiment, includes selecting the base criterion and a trade criterion from a plurality of criteria. A starting alternative and a target alternative are also selected and a series of virtual alternatives are created, initially based on the starting alternative, by sequentially eliminating an impact of each non-selected criteria from the plurality of criteria. A virtual alternative of the series of virtual alternatives is compared to the target alternative wherein the virtual alternative only differs from the target alternative by a value of the base criterion. The singular impact of the base criterion is then determined based on a difference between the final virtual alternative and the target alternative.
  • A method for determining a singular impact of a base criterion, in accordance with yet another exemplary embodiment, includes selecting the base criterion and a trade criterion from “N” criteria. A starting alternative and a target alternative are also selected and “N−2” sequential virtual alternatives are created, initially based on the starting alternative, by sequentially eliminating an impact of each non-selected criteria from the “N” criteria. A virtual alternative of the series of virtual alternatives is compared to the target alternative wherein the virtual alternative only differs from the target alternative by a value of the base criterion. The singular impact of the base criterion is then determined based on a difference between the final virtual alternative and the target alternative.
  • A method for analyzing an impact of a desired singular tradeoff for a population of users, in accordance with yet another exemplary embodiment, includes selecting the desired singular tradeoff from the population of users and collecting a plurality of singular tradeoffs in a sequential fashion from the population of users. The plurality of tradeoffs are then processed and analyzed to determine the impact of the desired singular tradeoff.
  • A system for determining a singular impact of a base criterion, in accordance with another exemplary embodiment, includes a singular tradeoff engine that accepts a weighted ordered list and operative to determine a singular impact of a base criterion by creating virtual alternatives based on the weighted ordered list. Also included is a function subroutine engine that accepts parametric values from the singular tradeoff engine and operative to develop a new value to the singular tradeoff engine.
  • A method for determining a value a consumer places on a desired product component, in accordance with an exemplary embodiment, includes providing a first product without the desired product component and a second product with the desired product component. A series of simulated products are then created, initially based on the first product, by sequentially eliminating an impact of each non-desired product component. A final simulated product is compared to the second product; and the value is determined based on a difference between the final simulated product and the second product.
  • A method for determining a value a consumer places on a desired product component, in accordance with an exemplary embodiment, includes providing a first product that does not contain the desired product component and a second product that does contain the desired product component. A series of simulated products are then created, initially based on the first product, by sequentially eliminating an impact of one or more product components that are not the desired product component. A final simulated product is compared to the second product; and the value is determined based on a difference between the final simulated product and the second product.
  • In addition to the aspects and embodiments of the present invention described in this summary, further aspects and embodiments of the invention will become apparent by reference to the drawings and by reading the detailed description that follows.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 is a screen shot of a web page allowing for the adjustment of weights;
  • FIG. 2 is a chart illustrating a processed utilized by an analytical tool, in accordance with an exemplary embodiment;
  • FIG. 3 is a flowchart illustrating a process of an analytical tool for determining a singular impact of a tradeoff, in accordance with an embodiment of the present invention;
  • FIG. 4A is a flowchart illustrating a process for analyzing an individual singular tradeoff for a population of users, in accordance with an exemplary embodiment;
  • FIG. 4B is a flowchart illustrating a process for storing an individual tradeoff for each attribute, in accordance with an exemplary embodiment; and
  • FIG. 5 is a block diagram of an analytical tool system, in accordance with an exemplary embodiment;
  • FIG. 6 is a block diagram of an exemplary embodiment of a network; and
  • FIG. 7 is a block diagram of an exemplary embodiment of a computer.
  • DETAILED DESCRIPTION
  • An aspect of the present invention contemplates a variety of methods, systems and data structures for determining a singular impact of a tradeoff or criterion. An analysis systematically eliminates the effect of individual non-changed criteria in order to see what happens if a particular criteria is modified. What results is the individual or singular impact of adjusting that particular criterion. Other aspects are also within the scope of the present invention. In terms of this disclosure, “singular” can refer to either one item or to a group of items that are linked in some manner. Additionally, “singular” can also refer to a subcomponent of any economic unit that is capable of being sold.
  • FIG. 1 is a screen shot of a web page allowing for the adjustment of weights. In FIG. 1, a weight adjustment interface 92 lists a number of properties including a space property 94, a performance property 96, a safety property 98, a gas mileage property 100, a maintenance cost property 102, a comfort property 104, and a price property 106. Associated with each of these properties is a “slider bar” 112 including a diamond shaped indicator 110 (in this example) which can be adjusted in position along the length of the slider bar, as will be appreciated by those skilled in the art. In a typical interface, a pointer 108 controlled, for example, by a pointing device of a computer system (pointing device and computer not shown), is used to engage an indicator 110 and to drag to a desired position between the “not important” and the “very important” ends of the slider bar 112. The making and use of slider bars is well known to those skilled in the art. The position of the indicator 110 along the slider bar 112 is translated into a numeric output, typically a normalized value between zero and one, which is the weight for the criterion.
  • In practice, the user input is processed as indicated by the arrow 114 to provide an ordered or ranked list 116 which reflects the preferences of the user. As can be seen in the illustration of FIG. 1, the Ford Focus ZX3 coupe had the best overall score and was ranked #1 based upon the weighted preferences that were input in the weight adjustment section of screen shot 90. This was followed by the Ford Escape TWD Sport Utility 4D, which was ranked #2, the Ford Taurus SE V6 Wagon 4D, ranked at #3, etc.
  • FIG. 2 is a chart illustrating a processed utilized by an analytical tool, in accordance with an exemplary embodiment. Chart 1020 shows two alternatives from a list ranked according to the weights shown in row 1018. Included in chart 1020 and row 1018 are a plurality of criteria/tradeoffs that include price, horsepower (HP), mileage (MPG) and safety. The analytic tool calculates singular tradeoffs between two criteria by progressively eliminating the contributions of all other criteria. This elimination process is carried out through generation of intermediate “equivalent” alternatives. FIG. 2 shows two such, 1022 and 1024, used to eliminate the contribution of the safety and mileage criteria, respectively.
  • In a hypothetical situation, a Ford motor company would like to determine how much a consumer is willing to pay per horsepower to go from 210 h.p. (the Honda's) to 260 h.p. (the Ford's). Again, it should be understood that the value the consumer is willing to pay per h.p. increase is being determined and not the actual cost to the manufacturer to for the increase. To determine this perceived value, Ford compares their model to the Honda model that already has the desired feature—the increased H.P. The algorithm first marks the Honda as the target alternative 1026 and the Ford as the starting alternative 1028. Then, the starting alternative 1028 is converted to the modified virtual alternative 1024, through virtual alternative 1022, so that virtual alternative 1024 differs from the target alternative 1026 only in price and horsepower. One can then obtain the price that the consumer is willing to pay per additional horsepower from a ratio between the price difference and the horsepower difference for alternatives 1026 and 1024. This price is referred to in this document as the singular impact of a tradeoff. The elimination process will be explained in more detail, subsequently.
  • It is important to note that, although the algorithm is typically used for price/feature singular impact tradeoff calculations, it is completely generic, and can apply just as well to situations where a tradeoff between two features is desired (for example, horsepower versus mileage). The inputs to the algorithm are as follows:
      • The criterion to be marked as the base criterion; in the example of FIG. 2, this is the horsepower.
      • The criterion to be marked as the trade criterion; in the example of FIG. 2, this is the price.
      • The starting alternative; in the example of FIG. 2, this is the Ford (1028).
      • The target alternative; in the example of FIG. 2, this is the Honda (1026).
  • Given this set of input directives, the algorithm modifies the values of criteria in the starting alternative so that they match the values in the target alternative. The value for the base criterion is left unmodified, and that for the trade criterion is changed depending on the new values of other criteria, as described later. The end result is a virtual alternative that differs from the target in just the values of the base and trade criteria.
  • It should be further noted that the target alternative is usually the preferred choice while the starting alternative is the less preferred choice. It should also be further noted that the trade, in the preceding example, is the price which takes the form of a unit of currency. However, the criteria marked as the trade and the base can be any criteria related to a product that can be adjusted or added on. With that in mind, it is quite clear that, while the preceding example uses automobile related criteria, any economic unit capable of being sold that has subcomponents can be substituted.
  • Some further embodiments can take the form of, but are certainly not limited to, a consumer electronics manufacturer that is planning a new digital camera model, whose target price has been fixed by market considerations within a restricted range. In this situation, the interesting information is not how much real currency end users would be willing to pay for this or that feature, but rather how much of a feature they are willing to forego in order to get more of another. The algorithms effectively convert one of the two features into a virtual unit of currency, which is then exchanged for the other feature. In the case of the digital camera, the designers may want to determine the tradeoff between the lens quality and the digitization speed of the sensor. Depending on how much quality the end user is willing to give up for the ability to take consecutive pictures as quickly as possible, the answer will suggest whether to include more expensive optics, or put more money into a larger acquisition buffer.
  • Yet another embodiment could be a cable broadcast company that wants to introduce a new package that contains strong parental control features. In this case, the economic unit is not a hard good, but rather a soft service. Assuming that, just as for the digital camera, the price has been fixed by market considerations, then the interesting information is how the added parental control features stack up against, say, the width and breadth of the channel offering. In other words, how much “channel selection dollars” are end users willing to “pay” in order to have those new parental control features? The various algorithms employed in this disclosure can answer such questions.
  • The process of eliminating the impact of each criterion will now be explained. FIG. 3 is a flowchart illustrating a process 118 of an analytical tool for determining a singular impact of a tradeoff, in accordance with an exemplary embodiment. The process begins at 120 and in an operation 122, the base and trade criteria are selected out of the N available criteria. In the preceding example, the base criterion is the horsepower, the trade criterion the price, and N is equal to 4 (for price, horsepower, mileage, and safety). In an operation 124, the starting virtual alternative and the target alternative are selected; in the preceding example, the starting virtual alternative is the Ford, and the target alternative the Honda. An iterative loop 126 is then commenced with a counter beginning at 0, incrementing by 1, and looping as long as it is smaller than N; this counter is also an index into the array of criteria. Next, in an operation 128 it is decided if the counter corresponds to the index of the base or tradeoff criteria; if it is, then the rest of the loop body is skipped and control is passed back to operation 126. If the counter corresponds to neither the base nor the trade criterion, then operations 130 and 132 are executed to generate a new virtual alternative.
  • Operation 130 analyzes the current values for the target and virtual alternatives, and, in this example, applies a customizable, and possibly criterion-specific, algorithm to change the value of the base criterion to account for the fact that operation 132 sets the value of the current criterion in the virtual alternative to be the same as in the target alternative. Therefore, the two operations 130 and 132 generate a virtual alternative where the value of the current criterion is identical to the corresponding one in the target alternative, and the value of the base criterion has been adjusted to account for the change in the current criterion. Operations 130 and 132 eliminate the impact of the current criterion from the alternative.
  • Finally, it can be appreciated that, by looping through, the impact of each criterion is eliminated until just the impact of the trade criterion is left and is finally calculated at operation 134 by dividing the difference between the values of the base criteria for the target and final virtual alternatives by the difference between the values of the trade criteria for the same alternatives. Process 118 then ends at operation 136.
  • The algorithm implemented by operation 130 has access to the current execution context of loop 138; this context includes, but is not limited to, the target alternative, the current virtual alternative, the base and trade criteria, and the current criterion as identified by the loop counter. In addition, process 118 has been provided with a list of operation 130 algorithms associated with the various criteria. Examples of such algorithms follow, using the setting of FIG. 3; note that this is by necessity not a complete list, since the algorithms applied in operation 130 may be highly dependent on the specific setting (for example, they may be highly dependent on the semantics of the trade and base criteria).
  • So, since the base and trade criteria are price and HP, respectively, operation 130 calculates the change in price that would correspond to change the mileage from 15 (the Ford's value) to 18 (the Honda's), or the safety from 4 stars (the Ford's) to 5 stars (the Honda's). As was discussed earlier, operation 130 may implement varied algorithms, of various complexities, tailored to each criterion's semantics. A simple algorithm may use singular impact of tradeoff values that were obtained via other means, such as focus groups or user surveys. For example, an organization may have already established that the typical end user is willing to pay up to $500 for a sunroof, and can use that information to eliminate the contribution of a sunroof when generating automobile-related virtual alternatives. Another algorithm may estimate the tradeoff value as a percentage of the cost of providing the given feature: for example, a digital camera manufacturer knows the additional price of producing a model with a 4 megapixel instead a 3 megapixel sensor, and estimates a user's tradeoff value to be 125% of that cost. Finally, complex algorithms may use information like the end-user's tradeoff preferences (the weights used for the ranking) to estimate the percentage of a total price difference to allocate to the adjustment for a specific criterion.
  • FIG. 4A and 4B illustrate various, exemplary, but not limiting implementations of process 138. FIG. 4A is a flowchart illustrating a process 138A for analyzing a singular tradeoff from a population of users, in accordance with an exemplary embodiment. After a start operation 1040, the desired singular tradeoff and target user population are selected in operations 1042 and 1044, respectively; the population will contain Nusers. The loop operation 1046 sets a counter to 0, increases it by 1 with each iteration, and exits when the counter is greater than, or equal to, N. The individual singular tradeoff for user i is collected, in accordance with an embodiment of the present invention, and stored for later processing in operation 1048. This operation embeds the analytic engine described in FIG. 3. Once all singular tradeoffs have been collected, they are processed as a group, for example by calculating the average, or with other statistical techniques, in operation 1052. Finally, the processed singular tradeoffs are used for analysis in operation 1054. The process 138A is then completed at operation 1056.
  • FIG. 4B is a flowchart illustrating a process 138B for storing an individual singular tradeoff for a set of attributes, in accordance an exemplary embodiment. After a start operation 1060, operation 1062 selects M from N possible attributes for calculation of singular tradeoffs. The loop operation 1064 sets a counter to 0, increases it by 1 with each iteration, and exits when the counter is greater than, or equal to, N. Operation 1066 checks if the criterion at counter i is one of the M selected criteria. If so, operation 1068 calculates the singular tradeoff, in accordance with an embodiment of the present invention, and stores for use by operation 1070; otherwise, control is returned to operation 1064. When all criteria have been processed, operation 1070 performs the desired analyses on the collection of singular tradeoffs. Operation 1072 then ends process 138A.
  • FIG. 5 is a block diagram of an analytical tool system 160, in accordance with an exemplary embodiment. Included in system 160 is a singular tradeoff engine 162 and a function subroutine engine 164. Singular tradeoff engine 162 accepts as input a weighted ordered list and produces a singular tradeoff in conjunction with function subroutine engine 164. This occurs by exchanging parametric values from the singular tradeoff engine 162 to the function subroutine 164. In response, the function subroutine engine 162 sends a new value to the singular tradeoff engine 162. The system 160, for example, can be implemented on a computer system using software to perform processes such as those described above. Alternatively, the system 160 can be implemented in hardware, software or any combination thereof. The engines 160 and 164 can operate on any principle including digital, analog and other computing modalities.
  • The following description of FIGS. 6-7 is intended to provide an overview of computer hardware and other operating components suitable for performing the methods of the invention described above, but is not intended to limit the applicable environments. Similarly, the computer hardware and other operating components may be suitable as part of the apparatuses of the invention described above. The invention can be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. The invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • FIG. 6 is a block diagram of an exemplary embodiment of a network 705, such as the Internet. The term “Internet” as used herein refers to a network of networks which uses certain protocols, such as the TCP/IP protocol, and possibly other protocols such as the hypertext transfer protocol (HTTP) for hypertext markup language (HTML) documents that make up the World Wide Web (web). The physical connections of the Internet and the protocols and communication procedures of the Internet are well known to those of skill in the art.
  • ) Access to the Internet 705 is typically provided by Internet service providers (ISP), such as the ISPs 710 and 715. Users on client systems, such as client computer systems 730, 740, 750, and 760 obtain access to the Internet through the Internet service providers, such as ISPs 710 and 715. Access to the Internet allows users of the client computer systems to exchange information, receive and send e-mails, and view documents, such as documents which have been prepared in the HTML format. These documents are often provided by web servers, such as web server 720 which is considered to be “on” the Internet. Often these web servers are provided by the ISPs, such as ISP 710, although a computer system can be set up and connected to the Internet without that system also being an ISP.
  • The web server 720 is typically at least one computer system which operates as a server computer system and is configured to operate with the protocols of the World Wide Web and is coupled to the Internet. Optionally, the web server 720 can be part of an ISP which provides access to the Internet for client systems. The web server 720 is shown coupled to the server computer system 725 which itself is coupled to web content 795, which can be considered a form of a media database. While two computer systems 720 and 725 are shown in FIG. 6, the web server system 720 and the server computer system 725 can be one computer system having different software components providing the web server functionality and the server functionality provided by the server computer system 725 which will be described further below.
  • Client computer systems 730, 740, 750, and 760 can each, with the appropriate web browsing software, view HTML pages provided by the web server 720. The ISP 710 provides Internet connectivity to the client computer system 730 through the modem interface 735 which can be considered part of the client computer system 730. The client computer system can be a personal computer system, a network computer, a Web TV system, or other such computer system.
  • Similarly, the ISP 715 provides Internet connectivity for client systems 740, 750, and 760, although as shown in FIG. 6, the connections are not the same for these three computer systems. Client computer system 740 is coupled through a modem interface 745 while client computer systems 750 and 760 are part of a LAN. While FIG. 6 shows the interfaces 735 and 745 as generically as a “modem,” each of these interfaces can be an analog modem, ISDN modem, cable modem, satellite transmission interface (e.g. “Direct PC”), or other interfaces for coupling a computer system to other computer systems.
  • Client computer systems 750 and 760 may be coupled to a LAN 770 through network interfaces 755 and 765, which can be Ethernet network or other network interfaces. The LAN 770 is also coupled to a gateway computer system 775 which can provide firewall and other Internet related services for the local area network. This gateway computer system 775 is coupled to the ISP 715 to provide Internet connectivity to the client computer systems 750 and 760. The gateway computer system 775 can be a conventional server computer system. Also, the web server system 720 can be a conventional server computer system.
  • Alternatively, a server computer system 780 can be directly coupled to the LAN 770 through a network interface 785 to provide files 790 and other services to the clients 750, 760, without the need to connect to the Internet through the gateway system 775.
  • FIG. 7 is a block diagram of an exemplary embodiment of a computer that can be used as a client computer system or a server computer system or as a web server system. Such a computer system can be used to perform many of the functions of an Internet service provider, such as ISP 710. The computer system 800 interfaces to external systems through the modem or network interface 820. It will be appreciated that the modem or network interface 820 can be considered to be part of the computer system 800. This interface 820 can be an analog modem, ISDN modem, cable modem, token ring interface, satellite transmission interface (e.g. “Direct PC”), or other interfaces for coupling a computer system to other computersystems.
  • The computer system 800 includes a processor 810, which can be a conventional microprocessor such as an Intel Pentium microprocessor or Motorola Power PC microprocessor. Memory 840 is coupled to the processor 810 by a bus 870. Memory 840 can be dynamic random access memory (DRAM) and can also include static RAM (SRAM). The bus 870 couples the processor 810 to the memory 840, also to non-volatile storage 850, to display controller 830, and to the input/output (I/O) controller 860.
  • The display controller 830 controls in the conventional manner a display on a display device 835 which can be a cathode ray tube (CRT) or liquid crystal display (LCD). The input/output devices 855 can include a keyboard, disk drives, printers, a scanner, and other input and output devices, including a mouse or other pointing device. The display controller 830 and the I/O controller 860 can be implemented with conventional well known technology. A digital image input device 865 can be a digital camera which is coupled to an I/O controller 860 in order to allow images from the digital camera to be input into the computer system 800.
  • The non-volatile storage 850 is often a magnetic hard disk, an optical disk, or another form of storage for large amounts of data. Some of this data is often written, by a direct memory access process, into memory 840 during execution of software in the computer system 800. One of skill in the art will immediately recognize that the terms “machine-readable medium” or “computer-readable medium” includes any type of storage device that is accessible by the processor 810 and also encompasses a carrier wave that encodes a data signal.
  • The computer system 800 is one example of many possible computer systems which have different architectures. For example, personal computers based on an Intel microprocessor often have multiple buses, one of which can be an input/output (I/O) bus for the peripherals and one that directly connects the processor 810 and the memory 840 (often referred to as a memory bus). The buses are connected together through bridge components that perform any necessary translation due to differing bus protocols.
  • Network computers are another type of computer system that can be used with the present invention. Network computers do not usually include a hard disk or other mass storage, and the executable programs are loaded from a network connection into the memory 840 for execution by the processor 810. A Web TV system, which is known in the art, is also considered to be a computer system according to this embodiment, but it may lack some of the features shown in FIG. 6, such as certain input or output devices. A typical computer system will usually include at least a processor, memory, and a bus coupling the memory to the processor.
  • In addition, the computer system 800 is controlled by operating system software which includes a file management system, such as a disk operating system, which is part of the operating system software. One example of an operating system software with its associated file management system software is the family of operating systems known as Windows® from Microsoft Corporation of Redmond, Wash., and their associated file management systems. Another example of an operating system software with its associated file management system software is the LINUX operating system and its associated file management system. The file management system is typically stored in the non-volatile storage 850 and causes the processor 810 to execute the various acts required by the operating system to input and output data and to store data in memory, including storing files on the non-volatile storage 850.
  • Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.
  • It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar typically electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
  • Some embodiments also relate to apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored (embodied) in a computer (machine) readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.
  • The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language, and various embodiments may thus be implemented using a variety of programming languages.
  • While this invention has been described in terms of certain embodiments, it will be appreciated by those skilled in the art that certain modifications, permutations and equivalents thereof are within the inventive scope of the present invention. It is therefore intended that the following appended claims include all such modifications, permutations and equivalents as fall within the true spirit and scope of the present invention.

Claims (8)

What is claimed is:
1. A method for analyzing an impact of a desired singular tradeoff for a population of users comprising:
selecting the desired singular tradeoff from the population of users;
collecting a plurality of singular tradeoffs in a sequential fashion from the population of users;
processing the plurality of singular tradeoffs; analyzing the plurality of singular tradeoffs; and
determining the impact of the desired singular tradeoff based on the analyzed plurality of tradeoffs.
2. The method as recited in claim 1 wherein the desired singular tradeoff comprises two or more subcomponents.
3. The method as recited in claim 1 wherein the plurality of singular tradeoffs are subcomponents of an economic unit capable of being sold.
4. The method as recited in claim 3 wherein the economic unit is a plurality of economic units comprising the population of users.
5. The method as recited in claim 1 wherein the impact of the desired singular tradeoff is a unit of currency per the desired singular tradeoff.
6. A computer implemented method for generating a price of an item or a feature based on user preference comprising:
receiving at a server a request for pricing;
receiving an identification of an item or a feature for which pricing is desired;
receiving at least a first preference for the identified item or feature; and
generating in response a price for the identified item or feature.
7. A method as set forth in claim 6 wherein the price is generated in real time.
8. A computer implemented method for generating a price of an item or a feature based on user preference comprising:
receiving at a server a request for pricing;
receiving an identification of an item or a feature for which pricing is desired;
retrieving situational environment information relevant to pricing; and
generating the price based on the identification and the situational environment information.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11195223B2 (en) * 2013-03-15 2021-12-07 Nielsen Consumer Llc Methods and apparatus for interactive evolutionary algorithms with respondent directed breeding

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8126881B1 (en) 2007-12-12 2012-02-28 Vast.com, Inc. Predictive conversion systems and methods
US8868564B1 (en) * 2010-08-15 2014-10-21 John W. Ogilvie Analytic comparison of libraries and playlists
US20120259676A1 (en) 2011-04-07 2012-10-11 Wagner John G Methods and apparatus to model consumer choice sourcing
US10007946B1 (en) 2013-03-07 2018-06-26 Vast.com, Inc. Systems, methods, and devices for measuring similarity of and generating recommendations for unique items
US9465873B1 (en) * 2013-03-07 2016-10-11 Vast.com, Inc. Systems, methods, and devices for identifying and presenting identifications of significant attributes of unique items
US9104718B1 (en) 2013-03-07 2015-08-11 Vast.com, Inc. Systems, methods, and devices for measuring similarity of and generating recommendations for unique items
US9830635B1 (en) 2013-03-13 2017-11-28 Vast.com, Inc. Systems, methods, and devices for determining and displaying market relative position of unique items
WO2014143729A1 (en) 2013-03-15 2014-09-18 Affinnova, Inc. Method and apparatus for interactive evolutionary optimization of concepts
US10127596B1 (en) 2013-12-10 2018-11-13 Vast.com, Inc. Systems, methods, and devices for generating recommendations of unique items
US10147108B2 (en) 2015-04-02 2018-12-04 The Nielsen Company (Us), Llc Methods and apparatus to identify affinity between segment attributes and product characteristics
US10268704B1 (en) 2017-10-12 2019-04-23 Vast.com, Inc. Partitioned distributed database systems, devices, and methods
US11537923B2 (en) * 2020-02-04 2022-12-27 Ford Global Technologies, Llc Predictive methodology to identify potential unknown sweet spots

Citations (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5041972A (en) * 1988-04-15 1991-08-20 Frost W Alan Method of measuring and evaluating consumer response for the development of consumer products
US5321833A (en) * 1990-08-29 1994-06-14 Gte Laboratories Incorporated Adaptive ranking system for information retrieval
US5717865A (en) * 1995-09-25 1998-02-10 Stratmann; William C. Method for assisting individuals in decision making processes
US5734890A (en) * 1994-09-12 1998-03-31 Gartner Group System and method for analyzing procurement decisions and customer satisfaction
US6012051A (en) * 1997-02-06 2000-01-04 America Online, Inc. Consumer profiling system with analytic decision processor
US6049777A (en) * 1995-06-30 2000-04-11 Microsoft Corporation Computer-implemented collaborative filtering based method for recommending an item to a user
US6266649B1 (en) * 1998-09-18 2001-07-24 Amazon.Com, Inc. Collaborative recommendations using item-to-item similarity mappings
US20010010041A1 (en) * 1999-10-06 2001-07-26 Harshaw Bob F. Method for new product development and market introduction
US20020004739A1 (en) * 2000-07-05 2002-01-10 Elmer John B. Internet adaptive discrete choice modeling
US6397212B1 (en) * 1999-03-04 2002-05-28 Peter Biffar Self-learning and self-personalizing knowledge search engine that delivers holistic results
US20020087388A1 (en) * 2001-01-04 2002-07-04 Sev Keil System to quantify consumer preferences
US20020107852A1 (en) * 2001-02-07 2002-08-08 International Business Machines Corporation Customer self service subsystem for context cluster discovery and validation
US20020111780A1 (en) * 2000-09-19 2002-08-15 Sy Bon K. Probability model selection using information-theoretic optimization criterion
US20030018517A1 (en) * 2001-07-20 2003-01-23 Dull Stephen F. Providing marketing decision support
US20030037041A1 (en) * 1994-11-29 2003-02-20 Pinpoint Incorporated System for automatic determination of customized prices and promotions
US20030040952A1 (en) * 2001-04-27 2003-02-27 Keil Sev K. H. System to provide consumer preference information
US6714929B1 (en) * 2001-04-13 2004-03-30 Auguri Corporation Weighted preference data search system and method
US20040225651A1 (en) * 2003-05-07 2004-11-11 Musgrove Timothy A. System and method for automatically generating a narrative product summary
US6826541B1 (en) * 2000-11-01 2004-11-30 Decision Innovations, Inc. Methods, systems, and computer program products for facilitating user choices among complex alternatives using conjoint analysis
US6895388B1 (en) * 1999-11-05 2005-05-17 Ford Motor Company Communication schema of online system and method of locating consumer product in the enterprise production pipeline
US6973418B1 (en) * 2000-04-07 2005-12-06 Hewlett-Packard Development Company, L.P. Modeling decision-maker preferences using evolution based on sampled preferences
US20060026081A1 (en) * 2002-08-06 2006-02-02 Keil Sev K H System to quantify consumer preferences
US7016882B2 (en) * 2000-11-10 2006-03-21 Affinnova, Inc. Method and apparatus for evolutionary design
US7103561B1 (en) * 1999-09-14 2006-09-05 Ford Global Technologies, Llc Method of profiling new vehicles and improvements
US7117163B1 (en) * 2000-06-15 2006-10-03 I2 Technologies Us, Inc. Product substitution search method
US7191143B2 (en) * 2001-11-05 2007-03-13 Keli Sev K H Preference information-based metrics
US7562063B1 (en) * 2005-04-11 2009-07-14 Anil Chaturvedi Decision support systems and methods

Family Cites Families (96)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4839822A (en) 1987-08-13 1989-06-13 501 Synthes (U.S.A.) Computer system and method for suggesting treatments for physical trauma
US4996642A (en) * 1987-10-01 1991-02-26 Neonics, Inc. System and method for recommending items
US5237496A (en) * 1988-12-07 1993-08-17 Hitachi, Ltd. Inventory control method and system
JPH07107668B2 (en) 1989-01-25 1995-11-15 株式会社日立製作所 Inference method of knowledge processing tool
US5305199A (en) * 1992-10-28 1994-04-19 Xerox Corporation Consumable supplies monitoring/ordering system for reprographic equipment
US5712989A (en) * 1993-04-02 1998-01-27 Fisher Scientific Company Just-in-time requisition and inventory management system
US5552995A (en) 1993-11-24 1996-09-03 The Trustees Of The Stevens Institute Of Technology Concurrent engineering design tool and method
US5715444A (en) * 1994-10-14 1998-02-03 Danish; Mohamed Sherif Method and system for executing a guided parametric search
US5758257A (en) * 1994-11-29 1998-05-26 Herz; Frederick System and method for scheduling broadcast of and access to video programs and other data using customer profiles
JP3414873B2 (en) 1995-01-20 2003-06-09 三菱電機株式会社 Car navigation system
US5765143A (en) * 1995-02-28 1998-06-09 Triad Systems Corporation Method and system for inventory management
US6188989B1 (en) * 1995-06-16 2001-02-13 I2 Technologies, Inc. System and method for managing available to promised product (ATP)
US6178406B1 (en) 1995-08-25 2001-01-23 General Electric Company Method for estimating the value of real property
US5819245A (en) * 1995-09-05 1998-10-06 Motorola, Inc. Method of organizing data into a graphically oriented format
US5983220A (en) * 1995-11-15 1999-11-09 Bizrate.Com Supporting intuitive decision in complex multi-attributive domains using fuzzy, hierarchical expert models
US5826260A (en) 1995-12-11 1998-10-20 International Business Machines Corporation Information retrieval system and method for displaying and ordering information based on query element contribution
US6035284A (en) * 1995-12-13 2000-03-07 Ralston Purina Company System and method for product rationalization
WO1997026729A2 (en) * 1995-12-27 1997-07-24 Robinson Gary B Automated collaborative filtering in world wide web advertising
US5970482A (en) 1996-02-12 1999-10-19 Datamind Corporation System for data mining using neuroagents
JPH09231264A (en) * 1996-02-23 1997-09-05 Hitachi Ltd On-line shopping support method and system
US5983237A (en) 1996-03-29 1999-11-09 Virage, Inc. Visual dictionary
NZ286313A (en) * 1996-04-02 1997-06-24 Alan Alexander Maxwell Computer aided decision making in product evaluation
US5867799A (en) * 1996-04-04 1999-02-02 Lang; Andrew K. Information system and method for filtering a massive flow of information entities to meet user information classification needs
US5790426A (en) * 1996-04-30 1998-08-04 Athenium L.L.C. Automated collaborative filtering system
US5903892A (en) * 1996-05-24 1999-05-11 Magnifi, Inc. Indexing of media content on a network
US5890138A (en) * 1996-08-26 1999-03-30 Bid.Com International Inc. Computer auction system
US5918223A (en) * 1996-07-22 1999-06-29 Muscle Fish Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information
US6353822B1 (en) 1996-08-22 2002-03-05 Massachusetts Institute Of Technology Program-listing appendix
US6272467B1 (en) 1996-09-09 2001-08-07 Spark Network Services, Inc. System for data collection and matching compatible profiles
US5963948A (en) * 1996-11-15 1999-10-05 Shilcrat; Esther Dina Method for generating a path in an arbitrary physical structure
US5966126A (en) 1996-12-23 1999-10-12 Szabo; Andrew J. Graphic user interface for database system
US6529877B1 (en) * 1997-03-27 2003-03-04 British Telecommunications Public Limited Company Equipment allocation system
US5899991A (en) 1997-05-12 1999-05-04 Teleran Technologies, L.P. Modeling technique for system access control and management
US5933818A (en) 1997-06-02 1999-08-03 Electronic Data Systems Corporation Autonomous knowledge discovery system and method
US6052122A (en) 1997-06-13 2000-04-18 Tele-Publishing, Inc. Method and apparatus for matching registered profiles
US5963920A (en) * 1997-06-19 1999-10-05 Golconda Screw Incorporated Inventory control system and method
US5963951A (en) 1997-06-30 1999-10-05 Movo Media, Inc. Computerized on-line dating service for searching and matching people
JP3603927B2 (en) 1997-08-08 2004-12-22 アイシン・エィ・ダブリュ株式会社 Vehicle navigation device and navigation method
US6370513B1 (en) * 1997-08-08 2002-04-09 Parasoft Corporation Method and apparatus for automated selection, organization, and recommendation of items
CA2303513A1 (en) * 1997-09-15 1999-03-25 Maintenet Corporation Electronic information network for inventory control and transfer
US5963939A (en) 1997-09-30 1999-10-05 Compaq Computer Corp. Method and apparatus for an incremental editor technology
US6055519A (en) * 1997-10-11 2000-04-25 I2 Technologies, Inc. Framework for negotiation and tracking of sale of goods
US5960414A (en) * 1997-11-25 1999-09-28 Hewlett-Packard Company Method for monitoring excess inventory
US5960422A (en) 1997-11-26 1999-09-28 International Business Machines Corporation System and method for optimized source selection in an information retrieval system
US6018738A (en) * 1998-01-22 2000-01-25 Microsft Corporation Methods and apparatus for matching entities and for predicting an attribute of an entity based on an attribute frequency value
US6249774B1 (en) * 1998-02-23 2001-06-19 Bergen Brunswig Corporation Method for owning, managing, automatically replenishing, and invoicing inventory items
US6009407A (en) * 1998-02-27 1999-12-28 International Business Machines Corporation Integrated marketing and operations decisions-making under multi-brand competition
US6286005B1 (en) * 1998-03-11 2001-09-04 Cannon Holdings, L.L.C. Method and apparatus for analyzing data and advertising optimization
US6064980A (en) * 1998-03-17 2000-05-16 Amazon.Com, Inc. System and methods for collaborative recommendations
US6457052B1 (en) 1998-06-23 2002-09-24 At&T Corp Method and apparatus for providing multimedia buffering capabilities based on assignment weights
US6327574B1 (en) 1998-07-07 2001-12-04 Encirq Corporation Hierarchical models of consumer attributes for targeting content in a privacy-preserving manner
WO2000008539A1 (en) * 1998-08-03 2000-02-17 Fish Robert D Self-evolving database and method of using same
US6266668B1 (en) 1998-08-04 2001-07-24 Dryken Technologies, Inc. System and method for dynamic data-mining and on-line communication of customized information
US6549897B1 (en) 1998-10-09 2003-04-15 Microsoft Corporation Method and system for calculating phrase-document importance
US6321133B1 (en) * 1998-12-04 2001-11-20 Impresse Corporation Method and apparatus for order promising
US6360227B1 (en) * 1999-01-29 2002-03-19 International Business Machines Corporation System and method for generating taxonomies with applications to content-based recommendations
WO2000046701A1 (en) * 1999-02-08 2000-08-10 Huntsman Ici Chemicals Llc Method for retrieving semantically distant analogies
WO2000079460A1 (en) 1999-06-23 2000-12-28 Webango, Inc. Method for buy-side bid management
US6442537B1 (en) 1999-06-24 2002-08-27 Teleran Technologies, Inc. System of generating and implementing rules
AU5934900A (en) 1999-07-16 2001-02-05 Agentarts, Inc. Methods and system for generating automated alternative content recommendations
US6556985B1 (en) 1999-07-23 2003-04-29 Teleran Technologies, Inc. Rule construction and application
US6748484B1 (en) 1999-08-11 2004-06-08 Intel Corporation Match resolution circuit for an associative memory
US6609108B1 (en) 1999-11-05 2003-08-19 Ford Motor Company Communication schema of online system and method of ordering consumer product having specific configurations
US6473751B1 (en) 1999-12-10 2002-10-29 Koninklijke Philips Electronics N.V. Method and apparatus for defining search queries and user profiles and viewing search results
US6732088B1 (en) 1999-12-14 2004-05-04 Xerox Corporation Collaborative searching by query induction
US7882520B2 (en) 2000-12-20 2011-02-01 Tivo Inc. Broadcast program recording overrun and underrun scheduling system
US6889197B2 (en) * 2000-01-12 2005-05-03 Isuppli Inc. Supply chain architecture
US6546388B1 (en) 2000-01-14 2003-04-08 International Business Machines Corporation Metadata search results ranking system
US20020103792A1 (en) 2000-02-03 2002-08-01 Arthur Blank Acumatch cross-matching system
US6584471B1 (en) 2000-02-14 2003-06-24 Leon Maclin System and method for the adaptive, hierarchical receipt, ranking, organization and display of information based upon democratic criteria and resultant dynamic profiling
US6510417B1 (en) 2000-03-21 2003-01-21 America Online, Inc. System and method for voice access to internet-based information
US20010054054A1 (en) 2000-03-27 2001-12-20 Olson Steven Robert Apparatus and method for controllably retrieving and/or filtering content from the world wide web with a profile based search engine
US6499029B1 (en) 2000-03-29 2002-12-24 Koninklijke Philips Electronics N.V. User interface providing automatic organization and filtering of search criteria
US6463428B1 (en) 2000-03-29 2002-10-08 Koninklijke Philips Electronics N.V. User interface providing automatic generation and ergonomic presentation of keyword search criteria
US20020032638A1 (en) 2000-03-31 2002-03-14 Arti Arora Efficient interface for configuring an electronic market
JP3567849B2 (en) 2000-04-06 2004-09-22 日本電気株式会社 Information provision system
US6578022B1 (en) 2000-04-18 2003-06-10 Icplanet Corporation Interactive intelligent searching with executable suggestions
WO2001091002A2 (en) * 2000-05-22 2001-11-29 Manhattan Associates System, method and apparatus for integrated supply chain management
US20030061202A1 (en) 2000-06-02 2003-03-27 Coleman Kevin B. Interactive product selector with fuzzy logic engine
US7058516B2 (en) 2000-06-30 2006-06-06 Bioexpertise, Inc. Computer implemented searching using search criteria comprised of ratings prepared by leading practitioners in biomedical specialties
US7552070B2 (en) 2000-07-07 2009-06-23 Forethought Financial Services, Inc. System and method of planning a funeral
US7191176B2 (en) 2000-07-31 2007-03-13 Mccall Danny A Reciprocal data file publishing and matching system
AU2001281017A1 (en) 2000-08-03 2002-02-18 Unicru, Inc. Electronic employee selection systems and methods
US20030217052A1 (en) 2000-08-24 2003-11-20 Celebros Ltd. Search engine method and apparatus
US6647374B2 (en) 2000-08-24 2003-11-11 Namita Kansal System and method of assessing and rating vendor risk and pricing of technology delivery insurance
US6895406B2 (en) 2000-08-25 2005-05-17 Seaseer R&D, Llc Dynamic personalization method of creating personalized user profiles for searching a database of information
US7082418B2 (en) 2000-10-30 2006-07-25 Monitor Company Group Limited Partnership System and method for network-based personalized education environment
US7356530B2 (en) 2001-01-10 2008-04-08 Looksmart, Ltd. Systems and methods of retrieving relevant information
US6701311B2 (en) 2001-02-07 2004-03-02 International Business Machines Corporation Customer self service system for resource search and selection
US6728706B2 (en) 2001-03-23 2004-04-27 International Business Machines Corporation Searching products catalogs
US20020173978A1 (en) 2001-05-17 2002-11-21 International Business Machines Corporation Method and apparatus for scoring travel itineraries in a data processing system
US7299270B2 (en) 2001-07-10 2007-11-20 Lycos, Inc. Inferring relations between internet objects that are not connected directly
US20020138399A1 (en) 2001-08-07 2002-09-26 Hayes Philip J. Method and system for creating and using a peer-to-peer trading network
US7010527B2 (en) 2001-08-13 2006-03-07 Oracle International Corp. Linguistically aware link analysis method and system
US6928425B2 (en) 2001-08-13 2005-08-09 Xerox Corporation System for propagating enrichment between documents
US7478103B2 (en) 2001-08-24 2009-01-13 Rightnow Technologies, Inc. Method for clustering automation and classification techniques

Patent Citations (29)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5041972A (en) * 1988-04-15 1991-08-20 Frost W Alan Method of measuring and evaluating consumer response for the development of consumer products
US5321833A (en) * 1990-08-29 1994-06-14 Gte Laboratories Incorporated Adaptive ranking system for information retrieval
US5734890A (en) * 1994-09-12 1998-03-31 Gartner Group System and method for analyzing procurement decisions and customer satisfaction
US20030037041A1 (en) * 1994-11-29 2003-02-20 Pinpoint Incorporated System for automatic determination of customized prices and promotions
US6049777A (en) * 1995-06-30 2000-04-11 Microsoft Corporation Computer-implemented collaborative filtering based method for recommending an item to a user
US5717865A (en) * 1995-09-25 1998-02-10 Stratmann; William C. Method for assisting individuals in decision making processes
US6012051A (en) * 1997-02-06 2000-01-04 America Online, Inc. Consumer profiling system with analytic decision processor
US6266649B1 (en) * 1998-09-18 2001-07-24 Amazon.Com, Inc. Collaborative recommendations using item-to-item similarity mappings
US6397212B1 (en) * 1999-03-04 2002-05-28 Peter Biffar Self-learning and self-personalizing knowledge search engine that delivers holistic results
US7103561B1 (en) * 1999-09-14 2006-09-05 Ford Global Technologies, Llc Method of profiling new vehicles and improvements
US20010010041A1 (en) * 1999-10-06 2001-07-26 Harshaw Bob F. Method for new product development and market introduction
US6895388B1 (en) * 1999-11-05 2005-05-17 Ford Motor Company Communication schema of online system and method of locating consumer product in the enterprise production pipeline
US6973418B1 (en) * 2000-04-07 2005-12-06 Hewlett-Packard Development Company, L.P. Modeling decision-maker preferences using evolution based on sampled preferences
US7117163B1 (en) * 2000-06-15 2006-10-03 I2 Technologies Us, Inc. Product substitution search method
US20020004739A1 (en) * 2000-07-05 2002-01-10 Elmer John B. Internet adaptive discrete choice modeling
US20020111780A1 (en) * 2000-09-19 2002-08-15 Sy Bon K. Probability model selection using information-theoretic optimization criterion
US6826541B1 (en) * 2000-11-01 2004-11-30 Decision Innovations, Inc. Methods, systems, and computer program products for facilitating user choices among complex alternatives using conjoint analysis
US7016882B2 (en) * 2000-11-10 2006-03-21 Affinnova, Inc. Method and apparatus for evolutionary design
US20020087388A1 (en) * 2001-01-04 2002-07-04 Sev Keil System to quantify consumer preferences
US20020107852A1 (en) * 2001-02-07 2002-08-08 International Business Machines Corporation Customer self service subsystem for context cluster discovery and validation
US6714929B1 (en) * 2001-04-13 2004-03-30 Auguri Corporation Weighted preference data search system and method
US20030040952A1 (en) * 2001-04-27 2003-02-27 Keil Sev K. H. System to provide consumer preference information
US20030018517A1 (en) * 2001-07-20 2003-01-23 Dull Stephen F. Providing marketing decision support
US7191143B2 (en) * 2001-11-05 2007-03-13 Keli Sev K H Preference information-based metrics
US20060026081A1 (en) * 2002-08-06 2006-02-02 Keil Sev K H System to quantify consumer preferences
US7596505B2 (en) * 2002-08-06 2009-09-29 True Choice Solutions, Inc. System to quantify consumer preferences
US20050004880A1 (en) * 2003-05-07 2005-01-06 Cnet Networks Inc. System and method for generating an alternative product recommendation
US20040225651A1 (en) * 2003-05-07 2004-11-11 Musgrove Timothy A. System and method for automatically generating a narrative product summary
US7562063B1 (en) * 2005-04-11 2009-07-14 Anil Chaturvedi Decision support systems and methods

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11195223B2 (en) * 2013-03-15 2021-12-07 Nielsen Consumer Llc Methods and apparatus for interactive evolutionary algorithms with respondent directed breeding
US11574354B2 (en) 2013-03-15 2023-02-07 Nielsen Consumer Llc Methods and apparatus for interactive evolutionary algorithms with respondent directed breeding

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