Matching Social Network Users

• US 20100125632A1
• Filed: 11/20/2009
• Published: 05/20/2010
• Est. Priority Date: 11/20/2008
• Status: Active Grant

• Current Assignee: Melissa Leonard
• Original Assignee: MeetMyKind LLC
• Inventors: Leonard, Melissa
• Granted Patent: US 8,060,573 B2
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

Systems and methods are disclosed for matching of individuals to one another using a matching model. The matching model matches social network users based on ratings given by users one to another, as well as, as appropriate and available, explicit attributes indicated by users and other data such as location data and system usage data.

Claims

1. A computer system for recommending prospects to social network users, the client computer comprising:

a user input device

a display device

a processor

a memory

wherein the processor and the memory comprise circuits and software for

obtaining personal attribute information from a first plurality of the users;

obtaining desired-prospect attribute information from a second plurality of the users;

identifying a plurality of prospects to recommend to the users based on the personal attribute information and the desired-prospect attribute information;

obtaining ratings of a plurality of prospects from a third plurality of the users;

recommending a subset of the prospects to a fourth plurality of the users, wherein the recommended prospects have acceptable proximities to the users based upon the ratings, the desired-prospect attribute information and the personal attribute information.

2. The computer system of claim 1 wherein recommending comprises

scoring user-prospect matches to indicate quality of match between respective prospects and users, wherein the scores are derived from weighting plural matched attribute information of the prospects to the users'"'"' desired-prospect attributes.

3. The computer system of claim 1, the comprising circuits and software further for providing to a fifth plurality of the users respective preliminary lists of prospects, and obtaining from the fifth plurality of users each user'"'"'s rating of the prospects in the user'"'"'s respective preliminary list,

wherein the ratings reflect the user'"'"'s opinion of a quality of match of the prospects in the preliminary list,

wherein the fifth plurality of the users comprises a subset of the first plurality of users, the second plurality of users and the third plurality of the users.

4. The computer system of claim 1, the circuits and software further for using the ratings to estimate which prospects are most like other prospects based on similarity of ratings of the prospects.

5. The computer system of claim 1 wherein at least some of the users are also prospects, the circuits and software for further for

identifying users that have rated each other high and low, and using these identifications in conjunction with an estimate of how similar one user is to the other to estimate how the users will rate other users that they have not yet rated.

6. The computer system of claim 1 wherein

if a prospect is deemed to be a potential match for a given user based on a combination of proximity data and other data, then evaluating whether the prospect'"'"'s attributes match the user'"'"'s desired-attributes.

7. The computer system of claim 1 wherein recommending further comprises categorizing the prospects.

8. The computer system of claim 7 wherein the categories are selected from the group consisting of dating, friendship, activities, networking, deal making and reuniting.

9. The computer system of claim 1 wherein the circuits and software for obtaining ratings of at least some of other users.

10. The computer system of claim 1 wherein recommending further comprises scoring matches of users and prospects.

11. The computer system of claim 10, the circuits and software further for

adjusting the scores based on the attributes of the prospect being rated.

12. The computer system of claim 10, the circuits and software further for

adjusting the scores based on the ratings.

13. The computer system of claim 10, the circuits and software further for

invalidating ratings by a given user if the given user has a pattern of ratings which fail to correlate to ratings by other users.

14. A process for recommending prospects to social network users, the process comprising:

obtaining personal attribute information from a first plurality of the users;

obtaining desired-prospect attribute information from a second plurality of the users;

identifying a plurality of prospects to recommend to the users based on the personal attribute information and the desired-prospect attribute information;

obtaining ratings of a plurality of prospects from a third plurality of the users;

recommending a subset of the prospects to a fourth plurality of the users, wherein the recommended prospects have acceptable proximities to the users based upon the ratings, the desired-prospect attribute information and the personal attribute information.

15. The process of claim 14 wherein recommending comprises

scoring user-prospect matches to indicate quality of match between respective prospects and users, wherein the scores are derived from weighting plural matched attribute information of the prospects to the users'"'"' desired-prospect attributes.

16. The process of claim 14 further comprising providing to a fifth plurality of the users respective preliminary lists of prospects, and obtaining from the fifth plurality of users each user'"'"'s rating of the prospects in the user'"'"'s respective preliminary list,

wherein the ratings reflect the user'"'"'s opinion of a quality of match of the prospects in the preliminary list,

wherein the fifth plurality of the users comprises a subset of the first plurality of users, the second plurality of users and the third plurality of the users.

17. The process of claim 14 comprising using the ratings to estimate which prospects are most like other prospects based on similarity of ratings of the prospects.

18. The process of claim 14 wherein at least some of the users are also prospects, the process further comprising

identifying users that have rated each other high and low, and using these identifications in conjunction with an estimate of how similar one user is to the other to estimate how the users will rate other users that they have not yet rated.

19. The process of claim 14 wherein

if a prospect is deemed to be a potential match for a given user based on a combination of proximity data and other data, then evaluating whether the prospect'"'"'s attributes match the user'"'"'s desired-attributes.

20. The process of claim 14 wherein recommending further comprises categorizing the prospects.

21. The process of claim 20 wherein the categories are selected from the group consisting of dating, friendship, activities, networking, deal making and reuniting.

22. The process of claim 21 wherein at least some of the users rate other users.

23. The process of claim 21 wherein recommending further comprises scoring matches of users and prospects.

24. The process of claim 23 further comprising

adjusting the scores based on the attributes of the prospect being rated.

25. The process of claim 23 further comprising

adjusting the scores based on the ratings.

26. The process of claim 23 further comprising

invalidating ratings by a given user if the given user has a pattern of ratings which fail to correlate to ratings by other users.

27. Apparatus comprising a storage medium storing a program having instructions which when executed by a processor will cause the processor to recommend prospects to social network users, the instructions of the program for:

obtaining personal attribute information from a first plurality of the users;

obtaining desired-prospect attribute information from a second plurality of the users;

identifying a plurality of prospects to recommend to the users based on the personal attribute information and the desired-prospect attribute information;

obtaining ratings of a plurality of prospects from a third plurality of the users;

recommending a subset of the prospects to a fourth plurality of the users, wherein the recommended prospects have acceptable proximities to the users based upon the ratings, the desired-prospect attribute information and the personal attribute information.