Subscriber characterization system with filters

• US 7,240,355 B1
• Filed: 03/01/2000
• Issued: 07/03/2007
• Est. Priority Date: 12/03/1998
• Status: Expired due to Fees

• Current Assignee: Prime Research Alliance E, LLC
• Original Assignee: Prime Research Alliance E., Inc.
• Inventors: Eldering, Charles A., Sylla, M. Lamine, Blasko, John P.
• Primary Examiner(s): Beliveau, Scott E.
• Assistant Examiner(s): SHELEHEDA, JAMES R
• Application Number:
  US09/516,983
• Time in Patent Office: 2,680 Days
• Field of Search: 725/45, 725/46, 725/47, 725/48
• US Class Current: 725/46
• CPC Class Codes: H04N 21/2353 specifically adapted to content descriptors, e.g. coding, compressing or processing of metadata
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/44008 involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• CPC Class Codes: H04N 21/4415 using biometric characteristics of the user, e.g. by voice recognition or fingerprint scanning cryptography using biological data H04L9/3231; authentication in networks using biometric H04L63/0861
• CPC Class Codes: H04N 21/44204 Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched monitoring of user activities for profile generation for accessing a video database G06F16/739; protecting generic digital content where the protection is independent of the precise nature of the content G06F21/10; arrangements for monitoring the use made of the broadcast services in broadcast systems H04H60/31
• CPC Class Codes: H04N 21/44209 Monitoring of downstream path of the transmission network originating from a server, e.g. bandwidth variations of a wireless network arrangements for maintenance or administration in data switching networks involving bandwidth and capacity management H04L41/0896
• CPC Class Codes: H04N 21/44218 Detecting physical presence or behaviour of the user, e.g. using sensors to detect if the user is leaving the room or changes his face expression during a TV program methods or arrangements for recognising human body or animal bodies or body parts G06V40/10; methods or arrangements for acquiring or recognising human faces, facial parts, facial sketches, facial expressions G06V40/16; methods or arrangements for recognising movements or behaviour G06V40/20; arrangements for identifying users in broadcast systems H04H60/45
• 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 21/4663 involving probabilistic networks, e.g. Bayesian networks
• CPC Class Codes: H04N 21/4665 involving classification methods, e.g. Decision trees
• 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 21/482 End-user interface for program selection broadcast systems using EPGs H04H60/72
• CPC Class Codes: H04N 21/84 Generation or processing of descriptive data, e.g. content descriptors systems specially adapted for using meta-information in broadcast systems H04H60/73

Abstract

A subscriber characterization system with filters in which the subscriber'"'"'s selections are monitored, including monitoring of the time duration programming is watched, the volume at which the programming is listened to, and any available information regarding the type of programming, including category and sub-category of the programming. The raw subscriber selection data is then processed to eliminate data associated with irrelevant activities such as channel surfing, channel jumping, or extended periods of inactivity. The actual subscriber selection data is used to form program characteristics vectors. The programming characteristics vectors can be used in combination with the actual subscriber selection data to form a subscriber profile. Heuristic rules indicating the relationships between programming choices and demographics can be applied to generate additional probabilistic subscriber profiles regarding demographics and programming and product interests.

Claims

1. In a video network, a computer-implemented method of determining the number of people in a household, the method comprising:

(a) monitoring viewer interactions with a multimedia device;

(b) processing the viewer interactions to obtain viewer interaction data corresponding to the viewer interactions;

(c) retrieving one or more previously developed heuristic rules, wherein the previously developed heuristic rules relate at least one aspect of the viewer interaction data to the number of people in the household and wherein the previously developed heuristic rules have been previously developed through the application of at least one heuristic process;

(d) applying one or more of the previously developed heuristic rules to at least a subset of the viewer interaction data; and

(e) inferring the number of people in the household from the viewer interaction data based on the application of the previously developed heuristic rules.

2. The method of claim 1, wherein the heuristic rules are probabilistic in nature.

3. The method of claim 1, wherein the inferred number of people in the household is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.

4. The method of claim 1, wherein said monitoring includes monitoring at least some subset of channel changes, volume changes, record commands, and time of viewer interaction.

5. The method of claim 1, wherein step (b) includes evaluating channel change commands and associated viewing times to determine the viewer interaction data.

6. The method of claim 1, wherein the viewer interaction data includes at least some subset of

viewing time per channel, category, and network;

channel changes per time period;

average volume per time period, channel, category, and network; and

dwell time per channel, category, and network.

7. The method of claim 1, wherein the heuristic rules are predefined.

8. The method of claim 1, wherein the heuristic rules remain unchanged at least during steps (c), (d) and (e).

9. The method of claim 1, wherein the heuristic rules create an inferential link between the viewer interaction data and the number of people in the household.

10. The method of claim 1, wherein the application of the heuristic rules provides a predictive value that the household has the number of people inferred in step (e).

11. The method of claim 1, further comprising:

(f) accessing the inferred number of people in the household, wherein the accessing includes predictive values of the inferred number of people in the household and at least one other number of people in the household, and wherein the predictive values are assigned by the heuristic rules based on the viewer interaction data.

12. In a video network, a computer-implemented method of determining the number of people in a household, the method comprising:

(a) monitoring viewer interactions with a multimedia device, the viewer interactions occurring during one or more interaction sessions;

(b) processing the viewer interactions to obtain viewer interaction data;

(c) retrieving one or more previously developed heuristic rules, wherein the previously developed heuristic rules relate at least one aspect of the viewer interaction data to the number of people in the household and wherein the previously developed heuristic rules have been previously developed through the application of at least one heuristic process;

(d) applying one or more of the previously developed heuristic rules to at least a subset of the viewer interaction data, for each interaction session; and

(e) inferring the number of people in the household from the viewer interaction data for each interaction session based on the application of the previously developed heuristic rules.

13. The method of claim 12, wherein said step (b) includes processing the viewer interactions for an interaction session to generate session interaction data for each interaction session.

14. The method of claim 12, wherein step (b) includes processing the viewer interactions for multiple interaction sessions to generate average interaction data for the multiple interaction sessions.

15. The method of claim 14, wherein the heuristic rules are applied to the average interaction data.

16. The method of claim 12, wherein the heuristic rules are probabilistic in nature.

17. The method of claim 12, wherein the inferred number of people in the household is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.

18. The method of claim 12, wherein said monitoring includes monitoring at least some subset of channel changes, volume changes, record commands, and time of viewer interaction.

19. The method of claim 12, wherein step (b) includes evaluating channel change commands and associated viewing times to group the viewer interaction data.

20. The method of claim 12, wherein the viewer interaction data includes at least some subset of

viewing time per channel, category, and network;

channel changes per time period;

average volume per time period, channel, category, and network; and

dwell time per channel, category, and network.

21. The method of claim 12, wherein the heuristic rules are predefined.

22. The method of claim 12, wherein the heuristic rules remain unchanged at least during steps (c), (d) and (e).

23. The method of claim 12, wherein the heuristic rules create an inferential link between the viewer interaction data and the number of people in the household.

24. The method of claim 12, wherein the application of the heuristic rules provides a predictive value that the household has the number of people inferred in step (e).

25. The method of claim 12, further comprising:

(f) accessing the inferred number of people in the household, wherein the accessing includes predictive values of the inferred number of people in the household and at least one other number of people in the household, and wherein the predictive values are assigned by the heuristic rules based on the viewer interaction data.

26. In a video network, a computer-implemented method of determining the number of people in a household, the method comprising:

(a) monitoring viewer interactions with a multimedia device, the viewer interactions occurring during one or more viewing periods;

(b) processing the viewer interactions to obtain viewer interaction data;

(c) retrieving one or more previously developed heuristic rules, wherein the previously developed heuristic rules relate at least one aspect of the viewer interaction data to the number of people in the household and wherein the previously developed heuristic rules have been previously developed through the application of at least one heuristic process;

(d) applying one or more of the previously developed heuristic rules to the viewer interaction data for each viewing period; and

(e) inferring the number of people in the household from the viewer interaction data based on the application of the previously developed heuristic rules.

27. The method of claim 26, wherein said step (b) includes processing the viewer interactions for a viewing period to generate period interaction data for each viewing period.

28. The method of claim 26, wherein step (b) includes processing the viewer interactions for multiple viewing periods to generate average interaction data for the multiple viewing periods.

29. The method of claim 28, wherein the heuristic rules are applied to the average interaction data.

30. The method of claim 26, wherein the heuristic rules are probabilistic in nature.

31. The method of claim 26, wherein the inferred number of people in the household is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.

32. The method of claim 26, wherein said monitoring includes monitoring at least some subset of channel changes, volume changes, record commands, and time of viewer interaction.

33. The method of claim 26, wherein step (b) includes evaluating channel change commands and associated viewing times to group the viewer interaction data.

34. The method of claim 26, wherein the viewer interaction data includes at least some subset of

viewing time per channel, category, and network;

channel changes per time period;

average volume per time period, channel, category, and network; and

dwell time per channel, category, and network.

35. The method of claim 26, wherein the heuristic rules are predefined.

36. The method of claim 26, wherein the heuristic rules remain unchanged at least during steps (c), (d) and (e).

37. The method of claim 26, wherein the heuristic rules create an inferential link between the viewer interaction data and the number of people in the household.

38. The method of claim 26, wherein the application of the heuristic rules provides a predictive value that the household has the number of people inferred in step (e).

39. The method of claim 26, further comprising:

(f) accessing the inferred number of people in the household, wherein the accessing includes predictive values of the inferred number of people in the household and at least one other number of people in the household, and wherein the predictive values are assigned by the heuristic rules based on the viewer interaction data.