Profiling and identification of television viewers

• US 7,260,823 B2
• Filed: 10/31/2001
• Issued: 08/21/2007
• Est. Priority Date: 01/11/2001
• Status: Active Grant

• Current Assignee: Prime Research Alliance E, LLC
• Original Assignee: Prime Research Alliance E., Inc.
• Inventors: Eldering, Charles A, Schlack, John A., Sutton, Andrew, Rampulla, Andrew J
• Primary Examiner(s): Miller, John
• Assistant Examiner(s): Chowdhury, Sumaiya A.
• Application Number:
  US09/998,979
• Publication Number:
  US 20020129368A1
• Time in Patent Office: 2,120 Days
• Field of Search: 725/46, 725/9, 725/10, 725/14
• US Class Current: 725/9
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04H 2201/30 characterised by the use of a return channel, e.g. for collecting users' opinions, for returning broadcast space/time information or for requesting data
• CPC Class Codes: H04H 60/33 Arrangements for monitoring the users' behaviour or opinions
• CPC Class Codes: H04H 60/37 for identifying segments of broadcast information, e.g. scenes or extracting programme ID
• CPC Class Codes: H04H 60/45 for identifying users
• CPC Class Codes: H04H 60/46 for recognising users' preferences
• CPC Class Codes: H04H 60/66 for using the result on distributors' side
• CPC Class Codes: H04H 60/94 Telephonic networks for accessing the Internet H04H60/84
• CPC Class Codes: H04H 60/97 using uplink of the CATV systems
• CPC Class Codes: H04L 67/30 Profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04N 21/23113 involving housekeeping operations for stored content, e.g. prioritizing content for deletion because of storage space restrictions storage management, e.g. defragmentation G06F3/0604; snloading stored programs G06F9/445; housekeeping operations in file systems, e.g. deletion policies G06F16/10; buffering arrangements in a network node or in an end terminal in packet networks H04L49/90
• CPC Class Codes: H04N 21/23424 involving splicing one content stream with another content stream, e.g. for inserting or substituting an advertisement
• CPC Class Codes: H04N 21/23608 Remultiplexing multiplex streams, e.g. involving modifying time stamps or remapping the packet identifiers
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25883 being end-user demographical data, e.g. age, family status or address arrangements for identifying locations of users in broadcast systems H04H60/52
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; 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 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/4344 Remultiplexing of multiplex streams, e.g. by modifying time stamps or remapping the packet identifiers
• CPC Class Codes: H04N 21/44016 involving splicing one content stream with another content stream, e.g. for substituting a video clip
• CPC Class Codes: H04N 21/441 : Acquiring end-user identification , e.g. using personal code sent by the remote control or by inserting a card
• 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 21/466 : 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 21/4661 : 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 21/4662 : characterized by learning algorithms
• CPC Class Codes: H04N 21/6582 : Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 21/812 : involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 7/17318 : Direct or substantially direct transmission and handling of requests

Abstract

Profiling a television viewer by monitoring and processing, locally at a television viewing environment, the viewer'"'"'s interactivity (e.g., via a remote control unit) with the television viewing environment. Generating one or more profiles for each viewer based on one or more of the multitude of interactions of each viewer and on, in general, the viewing habits and preferences of the viewer. Automatically and reliably, detecting or inferring at a particular time, which specific individual or individuals, are actually interacting with the TV in household comprising more than one individual. Such viewer identification and profile generation can be used to facilitate the delivery of targeted content, including targeted advertising.

Claims

1. A method for profiling a plurality of identities of a television viewing audience based on the interactivity of the identities with a television, the method comprising:

detecting an initiation of a television viewing session;

detecting a termination of the television viewing session;

monitoring user interactions with the television;

filtering the user interactions into at least one interaction category;

processing the user interactions in each of the at least one interaction categories in order to create at least one category profile for each associated interaction category, wherein each of the at least one category profiles identifies attributes about the user for that category of interaction;

generating an interaction profile by combining all of the category profiles, wherein said monitoring, filtering, processing and generating are continually performed for the television viewing session in order to generate a session profile;

generating a signature profile based on the session profile wherein said generating a signature profile includes correlating the session profile to a plurality of pre-existing signature profiles, determining if the session profile satisfies a correlation threshold with at least one of the pre-existing signature profiles, selecting the pre-existing signature profile having highest correlation value over the correlation threshold, and updating the selected pre-existing signature profile to include the session profile;

comparing the updated signature profile to the plurality of pre-existing signature profiles to determine whether the updated signature profile exceeds a threshold correlation with at least one of the other pre-existing signature profiles; and

combining the updated signature profile with a most correlated pre-existing viewing signature profile if the correlation threshold was exceeded.

2. The method of claim 1, wherein the at least one interaction category includes at least some subset of channels and volume levels.

3. The method of claim 1, wherein

said monitoring includes monitoring time associated with each of the user interactions;

and

said processing includes processing at least some subset of data from at least some subset of the at least one interaction category with respect to time in order to create at least one category profile.

4. The method of claim 3, wherein the at least one category profile includes at least some subset of channel viewing duration, channel dwell time, channel surf order, channel holding factor, channel volume level, channel selects, and channel change frequency.

5. The method of claim 1, further comprising storing the signature profile.

6. The method of claim 1, wherein said updating includes updating the selected pre-existing signature profile by adding the session profile on a time weighted basis.

7. The method of claim 1, wherein said updating is done at a predetermined time interval.

8. The method of claim 7, wherein the predetermined time interval is an end of day part.

9. The method of claim 1, wherein said updating is done once the viewing session has been terminated.

10. The method of claim 1, wherein said comparing includes comparing at least a portion of the category profiles that make up the updated signature profile with corresponding portions of category profiles that make up the plurality of pre-existing signature profiles.

11. The method of claim 10, wherein said comparing at least a portion of the category profiles includes applying weighting factors to the category profiles.

12. The method of claim 1, wherein said combining combines the updated viewing signature profile and the most correlated pre-existing signature profile on a time weighted basis.

13. The method of claim 1, further comprising

determining whether the session profile satisfies a minimum session threshold;

and

discarding the session profile when the viewing session profile does not satisfy the minimum session threshold.

14. The method of claim 7, wherein the minimum session threshold is a minimum session duration.

15. The method of claim 5, further comprising

ranking the pre-existing signature profiles based on viewing attributes, when total number of viewing signature profiles stored exceeds a pre-determined maximum number, and

discarding lowest ranked pre-existing signature profile.

16. The method of claim 15, wherein the viewing attributes include at least a subset of total viewing duration and recency of signature profile modification.

17. The method of claim 1, wherein the at least one category profile is a viewing duration profile identifying various viewing duration attributes for the viewing session.

18. The method of claim 17, wherein the various viewing duration attributes include at least some subset of average viewing duration, viewing duration by day, viewing duration by day-part, channel viewing duration, source network viewing duration, program genre viewing duration, program category viewing duration, and program viewing duration.

19. The method of claim 1, wherein the at least one category profile is a channel change frequency profile identifying various channel change frequency attributes for the viewing session.

20. The method of claim 1, wherein the at least one category profile is a holding factor profile identifying various holding factor attributes for the viewing session.

21. The method of claim 1, wherein the at least one category profile is a dwell time profile identifying various dwell time factor attributes for the viewing session.

22. The method of claim 1, wherein the at least one category profile is a surf profile identifying various surfing attributes for the viewing session.

23. The method of claim 22, wherein the surf profile identifies attributes including at least some subset of network surfing, channel surfing, and EPG surfing.

24. The method of claim 1, wherein the at least one category profile is delineated by day or day-part.

25. The method of claim 1, wherein the at least one category profile is a probable demographic trait profile predicting various demographic attributes associated with users during the viewing session.

26. The method of claim 25, wherein the probable demographic trait profile identifies attributes including at least some subset of probable gender, probable age, probable education-level, probable income-level, and probable ethnicity.

27. The method of claim 1, wherein the user interactions include at least a subset of channel changes, volume changes, record commands, EPG activation.

28. The method of claim 1, wherein said detecting an initiation includes detecting a television power-on event as the initiation of the television viewing session.

29. The method of claim 1, wherein said detecting an initiation includes detecting interactivity of the television viewing audience with the television as the initiation of the television viewing session.

30. The method of claim 1, wherein said detecting an initiation includes detecting a predetermined event as the initiation of the television viewing session.

31. The method of claim 30, wherein the predetermined event is a day or day-part transition.

32. The method of claim 1, wherein said detecting a termination includes detecting a television power-off event as the termination of the television viewing session.

33. The method of claim 1, wherein said detecting a termination includes detecting inactivity of the television viewing audience with the television as the termination of the television viewing session.

34. The method of claim 1, wherein said detecting a termination includes detecting a predetermined event as the termination of the television viewing session.

35. The method of claim 1, wherein said detecting a termination includes detecting that a different identity is interacting with the television as the termination of the television viewing session.

36. The method of claim 35, wherein said detecting that a different identity is interacting with the television includes detecting when current interactions with the television deviate from the session profile generated to that point in the session.

37. The method of claim 36, wherein said detecting when current interactions with the television deviate from the session profile generated to that point includes comparing at least a portion of the category profiles that make up the current interactions with corresponding portions of category profiles that make up the session profile generated to that point.

38. The method of claim 37, wherein said comparing at least a portion of the category profiles includes applying weighting factors to the category profiles.

39. The method of claim 1, wherein said detecting a termination includes

iteratively correlating the session profile to a plurality of pre-existing signature profiles;

matching the session profile to one of the plurality of pre-existing viewing signature profiles having the highest conelation if the correlation is above a predefined matching threshold;

iteratively correlating the session proffle to the matched signature profile; and

terminating the viewing session when the session profile deviates beyond a pre-determined termination threshold from the matched signature profile.

40. The method of claim 39, wherein said iteratively correlating the session profile to a plurality of pro-existing signature profiles includes correlating at least a portion of the category profiles that make up the session profile with corresponding portions of category profiles that make up the plurality of pre-existing signature profiles.

41. The method of claim 40, wherein said correlating at least a portion of the category profiles includes applying weighting factors to the category profiles.

42. The method of claim 39, wherein said iteratively correlating the session profile to the matched signature profile includes correlating at least a portion of the category profiles tat make up the session profile with corresponding portions of category profiles that make up the matched signature profile.

43. The method of claim 42, wherein said correlating at least a portion of the category profiles includes applying weighting factors to the category profiles.

44. The method of claim 1, further comprising receiving specific content for display on the television based on the session profile.

45. The method of claim 1, further comprising

correlating the session profile with advertisement profiles;

and

selecting an advertisement associated with highest correlation advertisement profile for display on the television.

46. The method of claim 45, wherein said correlating includes correlating at least a portion of the category profiles that make up the session profile with corresponding portions of advertisement profiles.

47. The method of claim 46, wherein said correlating at least a portion of the category profiles includes applying weighting factors to the category profiles.

48. The method of claim 1, further comprising retrieving data associated with at least a subset of the user interactions, wherein said filtering includes filtering the user interactions into the at least one interaction category based at least in part on the associated data.

49. The method of claim 48, wherein the retrieved data includes at least some subset of programs, networks, program genre, and program categories associated with each channel selected during the session.

50. A system for profiling a plurality of identities of a television viewing audience based on the interactivity of the identities with a television, the system comprising:

means for detecting an initiation of a television viewing session;

means for detecting a termination of the television viewing session;

means for monitoring user interactions with the television;

means for filtering the user interactions into at least one interaction category;

means for processing the user interactions in each of the at least one interaction categories in order to create at least one category profile for each associated interaction category, wherein each of the at least one category profiles identifies attributes about the user for that category of interaction;

means for generating an interaction profile by combining all of the category profiles, wherein said monitoring, filtering, processing and generating are continually performed for the television viewing session in order to generate a session profile;

means for generating a signature profile based on the session profile wherein said generating a signature profile includes correlating the session profile to a plurality of pre-existing signature profiles, determining if the session profile satisfies a correlation threshold with at least one of the pre-existing signature profiles, selecting the pre-existing signature profile having highest correlation value over the correlation threshold, and updating the selected pre-existing signature profile to include the session profile;

means for comparing the updated signature profile to the plurality of pre-existing signature profiles to determine whether the updated signature profile exceeds a threshold correlation with at least one of the other pre-existing signature profiles; and

means for combining the updated signature profile with a most correlated pre-existing viewing signature profile if the correlation threshold was exceeded.

51. The system of claim 50, wherein

said means for monitoring monitors time associated with each of the user interactions;

and

said means for processing processes at least some subset of data from at least some subset of the at least one interaction category with respect to time in order to create at least one category profile.