METHOD AND SYSTEM FOR COMPUTERIZED SEARCHING AND MATCHING USING EMOTIONAL PREFERENCE

Abstract

A method and system for capturing emotional preference of human subjects, generating machine-readable emotional code and using the code to optimize computerized searching and matching operations between entities is disclosed. The entity can be a human user, a product, or a service. The emotional code can thus be a universal language expressing human emotion that communicates among entities. After understanding the sending parties' emotional profile, the receiving party can adapt its operation to achieve more optimum results.

Description

BRIEF DESCRIPTION OF FIGURES

FIG. 1 is the block diagram of the emotional profiling system in one embodiment.

FIG. 2 is the block diagram of the computerized emotional profiling server according to one example of the present invention.

FIG. 3 a shows the major software modules and databases of the emotional profile cataloguing system.

FIG. 3 b shows the internal composition of the survey form design toolbox.

FIG. 4 illustrates a specific example of the user interface when the user is doing the survey.

FIG. 5 shows another specific example of the user interface when the user is doing the survey.

FIG. 6 is the illustration of a specific data representation example of the emotional code or Visual DNA.

FIG. 7 a is a specific example of auto image tagger, where tagging has not been done yet.

FIG. 7 b is the specific example of auto image tagger, where tagging is partially done.

FIG. 8 a shows a specific example of user interface of the search engine with a diverse range of results

FIG. 8 b shows a specific example of user interface of the search engine with a more homogeneous range of results

FIG. 9 shows a specific example of the internal architecture of the hardware and software system of the matching system.

Claims

1. A method of codifying human emotion into a machine-readable language by a first computer application comprising the steps of: (a) creating multimedia object of entity; said multimedia object being selectable by a user through an end-user system;(b) obtaining human emotional preferences through capturing user responses from a plurality of users while they make selections from a plurality of said multimedia objects through said end-user system; said selection implicitly revealing said user's emotional preference;(c) categorizing said user responses into categories; and(d) assigning category label to each category; said category label being an emotional code in said machine-readable language,whereby said user's emotional preference may be digitally characterized and conveyed to a second computer application; and wherein said second computer application is able to adapt its operation based on the interpretation of said emotional code.

2. The method of claim 1, wherein said multimedia objects are digital images.

3. The method of claim 1, wherein said multimedia objects are attached with a plurality of keywords; said keywords describing the human feeling towards a said multimedia object.

4. The method of claim 3, wherein said user response may be at least one of the following responses: (a) positive response indicating said multimedia object said user prefers;(b) negative response indicating said multimedia object said user dislikes;(c) don't know response indicating said multimedia object said user does not know;(d) said user's comments on said positive response;(e) said user's comments on said negative response; and(f) said user's comments on said don't know response.

5. The method of claim 4, wherein said categorizing step further comprising the steps of: (a) retrieving said user responses from said user;(b) tallying up the number of occurrences of each keyword on a list of keywords, said list of keywords aggregated from said multimedia objects said user selected;(c) invoking an expert system that categorizes said user to at least one category based on said user responses and result of said tallying step.

6. The method of claim 5, wherein said expert system is a rule-based expert system that captures psychology expertise in categorizing said user based on said user responses.

7. The method of claim 5, wherein said expert system is a neural network classification system that classifies said user responses into said categories.

8. The method of claim 1, wherein said categorizing step further comprising the steps of: (a) retrieving said user responses from said plurality of users;(b) invoking a cluster analysis module to categorize said plurality of user responses into at least one cluster; and(c) assigning said category label to each said cluster.

9. The method of claim 8, wherein said invoking step invokes a statistical clustering module to generate a cluster mean parameter and a cluster variance parameter for each said cluster; said cluster mean parameter being a multi-dimensional vector defining the cluster center, and said cluster variance parameter being a multi-dimensional matrix.

10. The method of claim 1 further comprising associating said emotional codes to an entity; said associating step comprising(a) obtaining an entity multimedia object of said entity;(b) choosing a plurality of representative multimedia objects; each representative multimedia object pre-selected to represent a broad category of human emotion(c) pre-assigning at least one said emotional code to each of a plurality of representative multimedia objects;(d) displaying said plurality of representative multimedia objects and said entity multimedia object on a display device for a second user to operate;(e) requesting said second user to select among said plurality of representative multimedia objects a first representative multimedia object that is most similar to said entity object; and(f) assigning said emotional code pre-assigned to said first representative multimedia object to said entity.

11. A method according to claim 10 further comprising a search step for an enterprise to present a filtered plurality of entities to a third user when said third user reveals his emotional code to said enterprise, said search step further comprising the steps of: (a) defining a peripheral region using said third user's emotional code, said peripheral region centered around said third user's emotional code;(b) selecting a plurality of said entities having emotional code falling within said peripheral region;(c) presenting said plurality of said entities from step (b) to said third user as said filtered plurality of entitiessuch that irrelevant entities whose emotional code does not remotely match said third user's emotional code will not be presented to said third user.

12. The method of claim 11, wherein said categorizing step (in claim 1) invokes a statistical clustering module to categorize said plurality of user responses into at least one cluster whereby each cluster is characterized by a cluster mean parameter and a cluster variance parameter; said peripheral region being centered at said cluster mean parameter; and the extent of said peripheral region being a proportional constant multiplying said cluster variance parameter.

13. A method of filtering irrelevant items from a search operation using machine-readable emotional code; said emotional code generated by categorizing user responses into categories; said user responses captured when said user selects items from a plurality of choices; said selection revealing said user's emotional preferences; and labeling said emotional code to each of said category; and said emotional code assigned to a first entity, said first entity being said user who generates said user responses; and said user responses categorized to said category corresponding to said emotional code assigned to said first entity; a second entity, said second entity being a product by moving a multimedia representation of said product to a second multimedia object that has been assigned a second emotional code, said multimedia representation of said product inheriting said second emotional code, and a third entity, said third entity being a service by moving a multimedia representation of said service to a second multimedia object that has been assigned a third emotional code, said multimedia representation of said service inheriting said third emotional code; said method comprising the steps of: (a) obtaining a search entity; said search entity selected from a group of said first entity, said second entity and said third entity;(b) using said search entity emotional code to define a peripheral region centered around said search entity emotional code;(c) connecting to a database that comprises a plurality of database entities; each of said database entity selected from a group of said first entity, said second entity and said third entity;(d) retrieving a plurality of database entities; each of said database entities having its emotional code falling within said peripheral region; and(e) presenting said plurality of database entities from step (d) to said search entity.

14. A method of filtering irrelevant items from a match operation using machine-readable emotional code; said emotional code generated by categorizing user responses into categories; said user responses captured when said user selects items from a plurality of choices; said selection revealing said user's emotional preferences; and labeling said emotional code to each of said category; and said emotional code assigned to a first entity, said first entity being said user who generates said user responses; and said user responses categorized to said category corresponding to said emotional code assigned to said first entity; a second entity, said second entity being a product by moving a multimedia representation of said product to a second multimedia object that has been assigned a second emotional code, said multimedia representation of said product inheriting said second emotional code, and a third entity, said third entity being a service by moving a multimedia representation of said service to a second multimedia object that has been assigned a third emotional code, said multimedia representation of said service inheriting said third emotional code; said method comprising the steps of: (a) obtaining a match entity; said match entity selected from a group of said first entity, said second entity and said third entity;(b) connecting to a database that comprises a plurality of database entities; each of said database entity selected from a group of said first entity, said second entity and said third entity;(c) retrieving a plurality of database entities; each of said database entities having its emotional code identical to said emotional code of said match entity; and(d) presenting said plurality of database entities from step (c) to said match entity.

15. A computer system for codifying human emotion into a machine readable language, comprising: (a) a profiling server configured to: I. capture the emotional preferences from a plurality of users;II. generate emotional codes from said emotional preferences; andIII. generate a plurality of representative multimedia objects; each associated with at least one said emotional codes;(b) an enterprise system capable of assigning entity emotional codes to a plurality of entity multimedia objects; said entity multimedia object representing an entity offered by an enterprise, and said entity multimedia object inherits at least one said emotional code from said representative multimedia object;(c) an end-user system capable of receiving user emotional code of said user from said server; said user emotional code being said emotional code describing said emotional preferences of said user; and(d) a search and match engine configured to receive said user emotional code from said end-user system and said entity emotional code from said enterprise system; said search and match engine further configured to retrieve a plurality of said entity multimedia object of which entity emotional codes proximate said user emotional code.

16. The computer system in claim 15, wherein said profiling server further comprising: (a) a cataloguing system configured to conduct emotional surveys to capture said emotional preferences; to categorize the results of said emotional surveys into categories, and to assign said emotional codes to said categories;(b) a toolbox for customizing said emotional surveys; and(c) a search engine optimization system for providing said search and match engine the capability of matching using said emotional codes.

17. The computer system in claim 16, wherein said cataloguing system further comprising: (a) a survey module configured to send said emotional surveys to said users and collect said results of said emotional surveys from said users;(b) a plurality of databases for storing and providing relevant information of said emotional surveys; and(c) an analysis module to receive said results of said emotional surveys from said survey module and to categorize said results into categories; said analysis module further assigning category label to each category, said category label being an emotional code in said machine-readable language.

18. The computer system of claim 15, wherein said enterprise system and said search and match engine run on same computer system.

19. The computer system of claim 15, wherein said profile server and said search and match engine run on same computer system.

20. The computer system of claim 15, wherein said end user system is capable of running an Internet browser application and said emotion code is transmitted to said end user system in the form of a computer cookie file.