Systems and methods of profiling, matching and optimizing performance of large networks of individuals

• US 20040210661A1
• Filed: 01/14/2004
• Published: 10/21/2004
• Est. Priority Date: 01/14/2003
• Status: Abandoned Application

• Current Assignee: Weattract.Com, Co.
• Original Assignee: Weattract.Com, Co.
• Inventors: Thompson, Mark Gregory
• Application Number:
  US10/757,091
• Publication Number:
  US 20040210661A1
• Time in Patent Office: Days
• US Class Current: 709/228
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 67/306 User profiles

Abstract

The invention relates to integrating multiple methods of profiling user attributes and preferences, using expert systems to code attributes of objects, predicting goodness of fit between users and candidates or objects, searching for compatible matches, optimizing searching effectiveness, customizing information and commerce to fit user preferences and attributes, and assisting the users to form and maintain new connections with their matches. More specifically, the inventive methods relate to offering integrative solutions to situations where large networks of people seek to find optimal fits between the mutual preferences and attributes. The invention also relates to systems that leverage user feedback and observations of user behavior to create user-dependent logic. Finally, the methods relate to interventions designed to enhance performance via automated coaching, educational course, targeted reinforcement, and peer support and feedback.

Claims

1. A method of profiling, matching and optimizing performance of large networks of individuals, comprising obtaining information of a user'"'"'s preferences relative to a target, synthesizing the information into conclusions, estimating the fit between the user'"'"'s preferences and a potential target'"'"'s attributes, predicting an outcome of an encounter between a user and a target, observing the outcome between the user and the target and obtaining feedback from the user and the target after the occurrence of the encounter.

2. The method of claim 1, further comprising obtaining input from one or more targets prior to the encounter between the user and said one or more targets.

3. The method of claim 1, further comprising classifying the user and target on the basis of the estimated fit quality between the user'"'"'s preferences and the target'"'"'s attributes.

4. The method of claim 1, wherein the information of a user'"'"'s preferences is obtained by one more modules involved in direct assessment, oblique assessment and input, wherein said input is obtained from peers or external judges.

19. The method of claim 1 wherein information of the user'"'"'s preferences relative to a target is obtained by a combination of one or more processes including direct assessment, indirect assessment, feedback from a target group and tagging the user'"'"'s preferences and attributes via a coding process.

5. A method of matching large networks of individuals with one or more targets, comprising matching a user with a first set of potentially compatible targets based on mutually corresponding preferences, the first set being generated with a set of user-independent search criteria, modifying the user-independent search criteria with feedback from the user and criteria established by the user, and generating subsequent sets of potentially compatible targets based on the modified criteria.

6. The method of claim 5, further excluding a target from the sets of potentially compatible targets when the target has at least one characteristic inconsistent with a deal breaker criteria established by the user.

7. The method of claim 5, further comprising obtaining additional information from the user or a target when the information to ascertain a match between the user and the target is incomplete.

8. The method of claim 5, wherein the user established feedback and criteria includes an assessment of the user'"'"'s preference to specific target characteristics.

9. The method of claim 5, further comprising linking the user to a matched target.

10. The method of claim 5, wherein the step of matching the user with potentially compatible targets is further based on mutually corresponding persona preferences, the user'"'"'s persona preferences measured along stated dimensions and along implicit dimensions.

11. A system for profiling, matching and optimizing performance of large networks of individuals, comprising a server having a processor for executing a software application and for exchanging data related to the software application with a user over a network medium, wherein the software application contains logic to assess the preferences of the user along explicit and implicit dimensions and to match the user with potentially compatible targets based on the user'"'"'s preferences.

12. The system of claim 11, wherein the software application contains logic to categorize the user with individuals having similar preferences for one or more targets.

13. The system of claim 11, wherein the software application contains logic to reassess the user'"'"'s preferences based on feedback received from the user.

14. The system of claim 11, wherein the software application contains logic to estimate the fit between the user'"'"'s preferences and a potential target'"'"'s attributes.

15. The system of claim 11, wherein the software application contains logic to predict an outcome of an encounter between a user and a target.

16. The system of claim 11, wherein the software application contains logic to observing the outcome between the user and the target.

17. A method for matching a user to a target, said method comprising the steps of:

accessing a guide that serves as an agent for facilitating the search and match process and customizing related information;

allowing a user to personalize the guide'"'"'s personality, image, representation, voice and other features;

performing tests that directly assess the user'"'"'s preferences and attributes or the object'"'"'s attributes via self-report clues and counter clues;

performing tests that indirectly or obliquely assess the user'"'"'s or the object'"'"'s preferences and attributes via implicit methods;

obtaining feedback from a target group regarding the user'"'"'s preferences and attributes or the object'"'"'s attributes;

tagging the attributes of a user or an object via a semi-automated system involving human expert judgment;

reporting the presented conclusions on the preferences and attributes of the user and the object in order to promote education and gain further feedback;

aggregating multiples clues and synthesizing the clues, while estimating the parameters and confidence levels in light of missing, inexact and contradictory information;

customizing the presentation of information, education and advertising and facilitating commerce based on a user'"'"'s or object'"'"'s preferences and attributes;

estimating the fit quality between the user'"'"'s preferences and attributes and the preferences and attributes of a pool of potential candidates and objects;

providing the user control over the domain to be searched and the level of tolerance for false positives and false negatives;

clustering heterogeneous groups of users and objects into homogeneous subgroups based on similarities in preferences and attributes;

classifying the object on the basis of the user'"'"'s satisfaction with the object;

searching and ranking a pool of objects based on the estimated fit quality with the user'"'"'s preferences and attributes;

predicting an outcome following one or more encounters between the user and the object;

optimizing the quality of the search and match process based on adjustment to the search and match parameters to narrow the gap between predicted and observed behavior;

observing the user'"'"'s behavior to assess the gap between predicted user and observed user actions and reactions

obtaining feedback from the user and the object following at least one encounter between the user and the object;

offering advice to the user that is tailored to the user'"'"'s assessed goals and readiness for change;

preparing the information between the user and the object prior to any encounter between the user and the candidate;

preparing a user for an encounter with a target by sharing information on the target regarding areas of mutual compatibility while simultaneously priming expectations, trust and familiarity through the implicit use of custom images and words;

synthesizing and presenting feedback received from potential targets to the user in a manner that fosters readiness for change;

providing intervention to facilitate desired changes in the preferences and attributes of the user or the object; and

testing the impact and effectiveness of words and images through an automated system that randomly pulls and systematically evaluates the stimuli from a large pool of media.

20. The method of claim 17 wherein estimation of the fit quality between the user and the object is based upon previously derived encounters between comparable users and objects or statistical modeling of scenarios that compare a single search result to the percentiles of all projected fit results.

18. A system for matching a user to a target comprising a server having a processor for executing a software application, wherein the user is guided by a customized and personalized agent in the execution of the software application in the areas of data collection, data presentation, or self-improvement.