System and method for generating and managing user preference information for scheduled and stored television programs

• US 8,352,984 B2
• Filed: 05/28/2002
• Issued: 01/08/2013
• Est. Priority Date: 06/12/1998
• Status: Expired due to Term

• Current Assignee: Thomson Licensing SAS (Vantiva SA)
• Original Assignee: Thomson Licensing (Vantiva SA)
• Inventors: Gogoi, Amar, Ismail, Labeeb, Godbole, Santosh, Humpleman, Richard
• Primary Examiner(s): NGUYEN BA, HOANG VU A
• Application Number:
  US10/156,153
• Publication Number:
  US 20020199193A1
• Time in Patent Office: 3,878 Days
• Field of Search: 725/46, 725/14, 348/180, 386/296, 386/297, 705/14.53, 705/14.66, 705/14.67
• US Class Current: 725/46
• CPC Class Codes: H04N 21/26283 for associating distribution time parameters to content, e.g. to generate electronic program guide data
• CPC Class Codes: H04N 21/4147 PVR [Personal Video Recorder] H04N5/76 takes precedence ; arrangements for broadcast with accumulation-type receivers H04H20/40
• CPC Class Codes: H04N 21/4332 by placing content in organized collections, e.g. local EPG data repository interfaces, Database management systems or updating for information retrieval G06F16/23; details of retrieval of video data and associated meta data in video database G06F16/739
• CPC Class Codes: H04N 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• 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 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• 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 21/47 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 21/4823 using a channel name
• CPC Class Codes: H04N 5/775 between a recording apparatus and a television receiver
• CPC Class Codes: H04N 5/781 on disks or drums
• CPC Class Codes: H04N 5/782 on tape
• CPC Class Codes: H04N 7/163 by receiver means only
• CPC Class Codes: H04N 9/8042 involving data reduction

Abstract

Television program availability and recordings are personalized by learning the program preferences of the TV and PDR user. This is effected over a period of time by observing, recording and processing user activity. A viewing record module agent (VRM) and a program information viewing history agent (CDM) are software agents that, according to built-in algorithms, operate on user activity and other events to ultimately produce preference profile information in special purpose relational databases (CDB, viewing history database).

Claims

1. A method of personalizing television program availability, comprising:

observing user activity and program usage behavior of a television program user over a period of time;

cross-referencing individual programs of a list of available programs against a viewing behavior of the television program user where different pluralities of programs from said list of available programs are associated with different category types;

generating, from the user activity and the program usage behavior, preference profile information, and storing the preference profile information in a relational database; and

applying the preference profile information to managing television programs for the user, wherein preference profile information includes assigned program preference ratings, said assigned program preference ratings being determined by a ratio of a total time period of watched programs in a category over an expected available maximum expected time period of programs in the category.

2. The method according to claim 1, wherein the observing, cross referencing and generating steps are performed with distributed software agents.

3. The method according to claim 2, wherein the distributed software agents operate autonomously with built-in algorithms operating on user activity and other events to produce the preference profile information in a special purpose relational database.

4. The method according to claim 3, wherein the distributed software agents are programmed to operate on data items selected from the group consisting of data representing user control events, external EPG information, click-stream data, viewing records, channel lineup lists, and a string table representing internal program lineup availability.

5. The method according to claim 1, further comprising defining a program history relational database for preference determination with index numbers representing external program information text strings.

6. The method according to claim 1, further comprising defining maintenance operations for the relational database including creating, changing, generalizing, enhancing, and expanding program information category data rows in the database.

7. The method according to claim 1, further comprising continually updating the relational database by accumulating time information, program information, and category data row items when a program is watched.

8. The method according to claim 1, further comprising continually updating the relational database with data-dependent input accumulating available time for viewing per each data program information category data row.

9. The method according to claim 1, further comprising maintaining in the relational database a number of forms of pre-processed information representing continually updated user preference information.

10. The method according to claim 9, wherein the pre-processed information includes accumulations of time for watched programs, and split category rows.

11. The method according to claim 1, further comprising capping an available time period of a given program or category for repeated live programs and stored programs.

12. The method according to claim 11, further comprising capping a value for the available time period at one program time period or one program time period per session.

13. The method according to claim 12, further comprising determining separate users based on a probability of being a given user.

14. The method according to claim 1, further comprising accumulating available time in a time period only after the program or category is first watched.

15. The method according to claim 1, further comprising accumulating a watched time period only after the program or category is watched for a given minimum time.

16. The method according to claim 1, further comprising defining a multi-user system and accumulating watched time periods and available time periods separately for each user.

17. The method according to claim 1, further comprising managing the relational database by forcing obsolescence of given data rows by adding available time periodically to force a decay of an assigned preference rating even if there is no actual program available.

18. The method according to claim 1, further comprising deleting a given data row if an assigned preference rating of the row falls below a predetermined deletion threshold.

19. The method according to claim 1, further comprising enabling user input via a user interface (GUI) to control management setting of the relational database.

20. The method of claim 1, wherein the television programs are automatically selected for the user.

21. The method of claim 1, wherein said category types are genres.

27. The method of claim 1, wherein an available and expected to be viewed program has a history of being viewed or is a recorded program available for selection that has been partially watched.

22. A method of determining a television user'"'"'s program preferences, comprising:

observing via a program determination engine user activity and program usage behavior of a television user over a period of time;

said program determination engine determining for each of a plurality of programs a time period for available time and each of said plurality of programs is associated with different category types;

said program determination engine defining a preference profile information of the television user with an assigned program preference rating being determined by a ratio of total time period of watched programs in a category over an expected available maximum expected time period of programs in the category; and

applying the preference profile information to managing television programs for the user.

23. The method according to claim 22, further comprising observing user activity with regard to stored and recorded programs and defining user preference profile based on the stored and recorded programs.

24. The method according to claim 22, further comprising defining a time period of available time with a cap of only one program time period per accounting period or with a cap of one program time period per viewing session.

25. The method according to claim 22, further comprising defining a time period of available time with a cap of only one program time period per accounting period and a cap of one program time period per viewing session, and enabling the television user to select from the two caps for generating the preference rating.

26. The method according to claim 22, further comprising storing the preference profile information in a relational database.