Matching users based on behavior in a social network

• US 9,270,778 B1
• Filed: 12/17/2013
• Issued: 02/23/2016
• Est. Priority Date: 07/05/2007
• Status: Active Grant

• Current Assignee: Invent.ly LLC
• Original Assignee: Invent.ly LLC
• Inventors: Brown, Stephen J.
• Primary Examiner(s): Scott, Randy
• Application Number:
  US14/109,432
• Time in Patent Office: 798 Days
• Field of Search: 705/14.71, 705/14.68, 705/14.46, 705/14.66, 709/204, 709/205, 709/217, 709/219
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/306 User profiles

Abstract

Observation-based user profiling and profile matching are provided. The network behavior of users of a computer-implemented social network are observed and used for user profiling. By observing network behavior instead of necessarily relying on user self-reported data, accurate and objective user profiles can be formed; user profiling is accomplished based on the observed network behaviors with or without the knowledge of the user being profiled. The observed network behaviors can include the customization of a visual graphic, a media preference, a communication preference, or a selection of words from a wordlist. The user profiles can be with respect to a domain and two or more users can be matched based on their profiles with respect to the same domain. User ratings and profile updating based on the ratings are also provided.

Claims

1. A method of matching users based on behavior in a social network, comprising the steps of:

(A) gathering information in a computer by observing one or more of said behaviors of two or more observed users among a plurality of said users of said social network, wherein said behaviors include at least one of (i) one or more reactions to content presented to said observed users, (ii) one or more content created by said observed users and (iii) one or more connections established by said observed users;

(B) generating a plurality of profiles of said observed users based on said information, wherein said behaviors are associated with a plurality of scale factors, respectively;

(C) updating said profiles by refining said scale factors;

(D) matching two or more of said observed users based on said profiles as updated;

(E) communicating between said computer and said observed users that were matched using a communication network; and

(F) establishing a new behavior in response to said information not matching any of said behaviors already known.

2. The method according to claim 1, further comprising the step of:

rating said match based on or more of said behaviors observed from said observed users that were matched.

3. The method according to claim 2, wherein said scale factors are refined based on said rating.

4. The method according to claim 1, further comprising the step of:

rating said match based on one or more inputs received from said observed users that were matched.

5. The method according to claim 1, wherein said reactions are selected by said observed users from a set comprising (i) one or more levels of satisfaction, (ii) a neutral reaction and (iii) one or more levels of dissatisfaction.

6. The method according to claim 1, wherein said content created by said observed users includes one or more among (i) self-descriptions, (ii) self-photographs, (iii) audio, (iv) video and (v) text.

7. The method according to claim 1, wherein said connections include one or more among (i) friends, (ii) social dates, (iii) professional groups, (iv) social groups, (v) events, (vi) products, (vii) jobs, (viii) schools, (ix) education, (x) searches, (xi) links, (xii) media, (xiii) events, (xiv) purchases, (xv) travel, (xvi) communications, (xvii) locations and (xviii) bookmarks.

8. The method according to claim 1, further comprising the step of:

estimating a new scale factor corresponding to said new behavior.

9. An apparatus comprising:

a computer configured to (i) gather information by observing one or more behaviors of two or more observed users among a plurality of users of a social network, wherein said behaviors include at least one of (a) one or more reactions to content presented to said observed users, (b) one or more content created by said observed users and (c) one or more connections established by said observed users, (ii) generate a plurality of profiles of said observed users based on said information, wherein said behaviors are associated with a plurality of scale factors, respectively, (iii) update said profiles by refining said scale factors and (iv) match two or more of said observed users based on said profiles as updated; and

an interface to a communication network configured to provide communications between said computer and said observed users that were matched, wherein said reactions are selected by said observed users from a set comprising (i) one or more levels of satisfaction, (ii) a neutral reaction and (iii) one or more levels of dissatisfaction.

10. The apparatus according to claim 9, wherein said computer is further configured to rate said match based on or more of said behaviors observed from said observed users that were matched.

11. The apparatus according to claim 10, wherein said scale factors are refined based on said rating.

12. The apparatus according to claim 9, wherein said computer is further configured to rate said match based on one or more inputs received from said observed users that were matched.

13. The apparatus according to claim 9, wherein said content created by said observed users includes one or more among (i) self-descriptions, (ii) self-photographs, (iii) audio, (iv) video and (v) text.

14. The apparatus according to claim 9, wherein said connections include one or more among (i) friends, (ii) social dates, (iii) professional groups, (iv) social groups, (v) events, (vi) products, (vii) jobs, (viii) schools, (ix) education, (x) searches, (xi) links, (xii) media, (xiii) events, (xiv) purchases, (xv) travel, (xvi) communications, (xvii) locations and (xviii) bookmarks.

15. The apparatus according to claim 9, wherein said computer is further configured to establish a new behavior in response to said information not matching any of said behaviors already known.

16. The apparatus according to claim 15, wherein said computer is further configured to estimate a new scale factor corresponding to said new behavior.

17. A method of matching users based on behavior in a social network, comprising the steps of:

(A) gathering information in a computer by observing one or more of said behaviors of two or more observed users among a plurality of said users of said social network, wherein said behaviors include at least one of (i) one or more reactions to content presented to said observed users, (ii) one or more content created by said observed users and (iii) one or more connections established by said observed users;

(B) generating a plurality of profiles of said observed users based on said information, wherein said behaviors are associated with a plurality of scale factors, respectively;

(C) updating said profiles by refining said scale factors;

(D) matching two or more of said observed users based on said profiles as updated; and

(E) communicating between said computer and said observed users that were matched using a communication network, wherein said reactions are selected by said observed users from a set comprising (i) one or more levels of satisfaction, (ii) a neutral reaction and (iii) one or more levels of dissatisfaction.

18. The method according to claim 17, further comprising the step of:

rating said match based on or more of said behaviors observed from said observed users that were matched.

19. The method according to claim 18, wherein said scale factors are refined based on said rating.

20. The method according to claim 17, further comprising the step of:

rating said match based on one or more inputs received from said observed users that were matched.