Automatic user profiling

• US 7,657,907 B2
• Filed: 09/30/2002
• Issued: 02/02/2010
• Est. Priority Date: 09/30/2002
• Status: Expired due to Fees

• Current Assignee: Sharp Kabushiki Kaisha (Hon Hai Precision Industry Co., Ltd.)
• Original Assignee: Sharp Laboratories of America Incorporated (Hon Hai Precision Industry Co., Ltd.)
• Inventors: Fennan, A. Mufit, van Beek, Petrus J. L., Errico, James H.
• Primary Examiner(s): Miller; John W
• Assistant Examiner(s): PARRY, CHRISTOPHER L
• Application Number:
  US10/261,550
• Publication Number:
  US 20040073918A1
• Time in Patent Office: 2,682 Days
• Field of Search: 725 25- 29, 725/34, 725/46, 725/47, 725/14, 725/45
• US Class Current: 725/46
• CPC Class Codes: H04H 60/31 Arrangements for monitoring the use made of the broadcast services
• CPC Class Codes: H04H 60/46 for recognising users' preferences
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/454 Content or additional data filtering, e.g. blocking advertisements
• CPC Class Codes: H04N 21/4661 Deriving a combined profile for a plurality of end-users of the same client, e.g. for family members within a home user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/47 End-user applications
• CPC Class Codes: H04N 21/47214 for content reservation or setting reminders; for requesting event notification, e.g. of sport results or stock market stock exchange data over packet-switching network H04L12/1804; push services over packet-switching network H04L12/1859; notification of incoming messages in packet switching networks H04L51/224
• CPC Class Codes: H04N 21/47217 for controlling playback functions for recorded or on-demand content, e.g. using progress bars, mode or play-point indicators or bookmarks specific graphical features in visual interfaces H04N21/4312
• CPC Class Codes: H04N 21/4755 for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306

Abstract

A user profiling system preferably includes a set of fuzzy logic operators. Based upon the fuzzy logic operators the user profile may be updated.

Claims

1. A method of describing video content comprising:

(a) an electronic processing device receiving a profile that contains a plurality of preferences that each have non-binary values that together describe video content that is potentially desirable to a user, said values each indicating a degree of desirability of a respective said preference to said user, and providing a mapping table used by a fuzzy inference system to combine a plurality of said preferences into a single test that is applied to program content descriptions so as to automatically identify programming preferable to said user, where said mapping table includes at least one combinatorial operator, and said fuzzy inference system is capable of using any soft said combinatorial operator present in said mapping table when combining said preferences into said single test, where said soft combinatorial operator alters a logical combinatorial operation to provide a different, non-zero value than the altered said logical combinatorial operation, by using a content affinity function that measures the relationship between a user'"'"'s affinity for respective said preferences and the said non-binary values associated with said preferences;

(b) said processing device automatically modifying said profile based upon video content manually selected by said user; and

(c) wherein said plurality of different preferences have different weights relative to each other, said weights being independent of the respective said values of said plurality of preferences.

2. The method of claim 1 wherein said modifying includes adding additional preferences to said profile.

3. The method of claim 1 wherein said modifying includes removing preferences from said profile.

4. The method of claim 3 wherein said removing is at least partially based on a temporal time period.

5. The method of claim 1 wherein user preferences included in said mapping table are automatically selected based upon input said program content descriptions.

6. The method of claim 2 wherein said additional preferences are added without user intervention.

7. The method of claim 1 wherein said different weights may be selectively modified.

8. The method of claim 1 wherein said user may select desired said combinatorial operators when creating a said profile, at least one selectable said combinatorial operator being a soft said combinatorial operator.

9. A method of describing video content comprising:

(a) an electronic processing device receiving a stored profile that contains a plurality of preferences that together describe video content that is potentially desirable to a user, each preference including a field for storing a value indicative of the desirability of an attribute of program content, and providing a mapping table used by a fuzzy inference system to combine a plurality of said preferences into a single test that is applied to program content descriptions so as to automatically identify programming preferable to said user, where said mapping table includes at least one combinatorial operator, and said fuzzy inference system is capable of using any soft said combinatorial operator present in said mapping table when combining said preferences into said single test, where said soft combinatorial operator alters a logical combinatorial operation to provide a different, non-zero value than the altered said logical combinatorial operation, by using a content affinity function that measures the relationship between a user'"'"'s affinity for respective said preferences and the said non-binary values associated with said preferences;

(b) said processing device removing preferences, including an associated said field of removed said preferences, from said profile and free from intervention from said user.

10. The method of claim 9 wherein said removing of preferences is at least partially based on a temporal time period.

11. The method of claim 10 wherein said removing of preferences is based upon other preferences being added to said profile.

12. A method of modifying a profile of a user comprising:

(a) an electronic processing device receiving said profile that contains a plurality of preferences that together describe video content that is potentially desirable to said user and providing a mapping table used by a fuzzy inference system to combine a plurality of said preferences into a single test that is applied to program content descriptions so as to automatically identify programming preferable to said user, where said mapping table includes at least one combinatorial operator, and said fuzzy inference system is capable of using any soft said combinatorial operator present in said mapping table when combining said preferences into said single test, where said soft combinatorial operator alters a logical combinatorial operation to provide a different, non-zero value than the altered said logical combinatorial operation, by using a content affinity function that measures the relationship between a user'"'"'s affinity for respective said preferences and the said non-binary values associated with said preferences;

(b) said processing device modifying said profile based upon video content selected by said user and free from intervention by said user; and

(c) wherein said plurality of preferences are weighted in a hierarchical order.

13. The method of claim 12 wherein said modifying includes adding additional preferences to said profile.

14. The method of claim 12 wherein said modifying includes removing preferences from said profile.

15. The method of claim 12 wherein said preferences include an associated preference weight.

16. A method of describing to video content comprising:

(a) an electronic processing device receiving a stored user profile that contains a plurality of preferences that together describe video content that is potentially desirable to a user and providing a mapping table used by a fuzzy inference system to combine a plurality of said preferences into a single test that is applied to program content descriptions so as to automatically identify programming preferable to said user, where said mapping table includes at least one combinatorial operator, and said fuzzy inference system is capable of using any soft said combinatorial operator present in said mapping table when combining said preferences into said single test, where said soft combinatorial operator alters a logical combinatorial operation to provide a different, non-zero value than the altered said logical combinatorial operation, by using a content affinity function that measures the relationship between a user'"'"'s affinity for respective said preferences and the said non-binary values associated with said preferences;

(b) said processing device modifying at least a first portion of said stored user profile based upon video content selected by said user free from intervention by said user; and

(c) said processing device modifying at least a second portion of said profile based upon video content selected by said user with intervention by said user.

17. The method of claim 16 wherein said first portion is different than said second portion.

18. The method of claim 17 wherein said first portion and said second portion are non-overlapping.

19. A method of modifying a profile of a user comprising:

(a) an electronic processing device receiving said profile that contains a plurality of preferences that together describe video content that is potentially desirable to said user and providing a mapping table used by a fuzzy inference system to combine a plurality of said preferences into a single test that is applied to program content descriptions so as to automatically identify programming preferable to said user, where said mapping table includes at least one combinatorial operator, and said fuzzy inference system is capable of using any soft said combinatorial operator present in said mapping table when combining said preferences into said single test, where said soft combinatorial operator alters a logical combinatorial operation to provide a different, non-zero value than the altered said logical combinatorial operation, by using a content affinity function that measures the relationship between a user'"'"'s affinity for respective said preferences and the said non-binary values associated with said preferences;

(b) said processing device automatically modifying said profile based upon video content selected by said user; and

(c) wherein said modification is based upon different degrees of membership of said preferences in different sets of said preferences.

20. The method of claim 19 wherein said modification includes mapping using a rule base, a fuzzy inference engine, a fuzzification module, and a defuzzification module.

21. The method of claim 19 wherein said modification is based, at least in part, upon the duration that a particular video is viewed.

22. The method of claim 19 wherein said modification is different, based, at least in part, upon said user viewing a portion of a video and said user viewing the entire said video.

23. The method of claim 19 wherein said modification is based upon how said user interacts with said video.

24. The method of claim 23 wherein said interaction is fast forward.

25. The method of claim 23 wherein said interaction is skip.

26. The method of claim 23 wherein said interaction is repeat.

27. The method of claim 23 wherein said interaction is record.

28. A method of modifying a profile of a user comprising:

(a) an electronic processing device receiving said profile that contains a plurality of preferences that together describe audio content that is potentially desirable to said user and providing a mapping table used by a fuzzy inference system to combine a plurality of said preferences into a single test that is applied to program content descriptions so as to automatically identify programming preferable to said user, where said mapping table includes at least one combinatorial operator, and said fuzzy inference system is capable of using any soft said combinatorial operator present in said mapping table when combining said preferences into said single test, where said soft combinatorial operator alters a logical combinatorial operation to provide a different, non-zero value than the altered said logical combinatorial operation, by using a content affinity function that measures the relationship between a user'"'"'s affinity for respective said preferences and the said non-binary values associated with said preferences;

(b) said processing device automatically modifying said profile based upon audio content selected by said user; and

(c) wherein said modification is based upon different degrees of membership of said preferences in different sets of said preferences.

29. The method of claim 28 wherein said modified includes mapping using a rule base, a fuzzy inference engine, a fuzzification module, and a defuzzification module.

30. The method of claim 28 wherein said modification is based, at least in part, upon the duration that a particular audio is listened to.

31. The method of claim 28 wherein said modification is different, based, at least in part, upon said user listening to a portion of a audio and said listener listening to the entire said audio.

32. The method of claim 28 wherein said modification is based upon how said user interacts with said audio.

33. The method of claim 32 wherein said interaction is fast forward.

34. The method of claim 32 wherein said interaction is skip.

35. The method of claim 32 wherein said interaction is repeat.

36. The method of claim 32 wherein said interaction is record.