Subscriber characterization system

• US 7,150,030 B1
• Filed: 12/03/1998
• Issued: 12/12/2006
• 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
• Primary Examiner(s): BUI, KIEU OANH T
• Application Number:
  US09/204,888
• Time in Patent Office: 2,931 Days
• Field of Search: 725/12, 725/14, 725/34, 725/35, 725/46, 725/9, 725/15, 725/16, 725/17, 725/18, 725/19, 725/20
• 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: H04H 60/74 using programme related information, e.g. title, composer or interpreter
• 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 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• 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 21/4662 characterized by learning algorithms
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02
• 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 is presented 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 characterization system can extract textual information related to the programming from closed captioning data, electronic program guides, or other text sources associated with the programming. The extracted information is used to form program characteristics vectors. The programming characteristics vectors can be used in combination with the 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 information regarding demographics and programming and product interests.

Claims

1. In a video network, a computer-implemented method of inferring the gender of a viewer, the method comprising:

(a) monitoring viewer interactions with a multimedia device to create a viewing record;

(b) applying one or more heuristic rules to the viewing record, wherein the heuristic rules assign a probabilistic measure of gender based on one or more aspects of the viewing record; and

(c) inferring the gender of the viewer based on the probabilistic measure.

2. The method of claim 1 wherein the viewing record includes the number of channel changes made by the viewer.

3. The method of claim 2 wherein the heuristic rules assign the probabilistic measure based on the number of channel changes.

4. The method of claim 1 wherein step (a) includes evaluating channel change commands and associated viewing times to create the viewing record.

5. The method of claim 1, wherein the heuristic rules assign probabilities of a viewing record being associated with a gender group based on the viewing record.

6. 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.

7. The method of claim 1 wherein the viewing record includes the number of channel changes made per time period.

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

9. In a video network, a computer-implemented method of inferring the gender of a viewer, the method comprising:

(a) monitoring channels viewed in a multimedia device by the viewer to create a viewing record;

(b) applying one or more heuristic rules to the viewing record, wherein the heuristic rules assign a probabilistic measure of gender based on the viewing record; and

(c) inferring the gender of the viewer based on the probabilistic measure.

10. The method of claim 9 wherein the heuristic rules assign the probabilistic measure based on monitored channel information in the viewing record.

11. The method of claim 9 wherein the viewing record includes the number of channel changes made by the viewer.

12. The method of claim 11 wherein the heuristic rules assign the probabilistic measure based on the number of channel changes.

13. The method of claim 9, wherein the heuristic rules assign probabilities of a viewing record being associated with a gender group based on the viewing record.

14. The method of claim 9 wherein the viewing record includes the number of channel changes made per time period.

15. The method of claim 9, wherein the heuristic rules are predefined.

16. In a video network, a computer-implemented method of inferring the gender of a viewer, the method comprising:

(a) monitoring viewer interactions with a multimedia device to create a viewing record, wherein the viewing record includes the number of channel changes made by the viewer within a time period;

(b) applying one or more heuristic rules to the viewing record, wherein the heuristic rules assign a probabilistic measure of gender based on the number of channel changes in the time period; and

(c) inferring the gender of the viewer based on the probabilistic measure.

17. The method of claim 16 wherein the time period is pre-defined.

18. The method of claim 16 wherein step (a) includes evaluating channel change commands and associated viewing times to create the viewing record.

19. The method of claim 16, wherein the heuristic rules assign probabilities of a viewing record being associated with a gender group based on the viewing record.

20. The method of claim 16, wherein the heuristic rules are predefined.

21. In a video network, a computer-implemented method of inferring the income of a subscriber, the method comprising:

(a) monitoring viewer interactions with a multimedia device to create a viewing record;

(b) applying one or more heuristic rules to the viewing record, wherein the heuristic rules assign a probabilistic measure of income based on one or more aspects of the viewing record; and

(c) inferring the income of the subscriber based on the probabilistic measure.

22. The method of claim 21 wherein the viewing record includes the number of channel changes made by the subscriber.

23. The method of claim 22 wherein the heuristic rules assign the probabilistic measure based on the number of channel changes.

24. The method of claim 21 wherein step (a) includes evaluating channel change commands and associated viewing times to create the viewing record.

25. The method of claim 21, wherein the heuristic rules assign probabilities of a viewing record being associated with an income group based on the viewing record.

26. The method of claim 21, wherein said monitoring includes monitoring at least some subset of channel changes, volume changes, record commands, and time of viewer interaction.

27. The method of claim 21 wherein the viewing record includes the number of channel changes made per time period.

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

29. In a video network, a computer-implemented method of inferring the income of a subscriber, the method comprising:

(a) monitoring channels viewed in a multimedia device by the subscriber to create a viewing record;

(b) applying one or more heuristic rules to the viewing record, wherein the heuristic rules assign a probabilistic measure of income based on the viewing record; and

(c) inferring the income of the subscriber based on the probabilistic measure.

30. The method of claim 29 wherein the heuristic rules assign the probabilistic measure based on monitored channel information in the viewing record.

31. The method of claim 29 wherein the viewing record includes the number of channel changes made by the subscriber.

32. The method of claim 31 wherein the heuristic rules assign the probabilistic measure based on the number of channel changes.

33. The method of claim 29, wherein the heuristic rules assign probabilities of a viewing record being associated with an income group based on the viewing record.

34. The method of claim 29 wherein the viewing record includes the number of channel changes made per time period.

35. The method of claim 29, wherein the heuristic rules are predefined.

36. In a video network, a computer-implemented method of inferring the income of a subscriber, the method comprising:

(a) monitoring viewer interactions with a multimedia device to create a viewing record, wherein the viewing record includes the number of channel changes made by the subscriber within a time period;

(b) applying one or more heuristic rules to the viewing record, wherein the heuristic rules assign a probabilistic measure of income based on the number of channel changes in the time period; and

(c) inferring the income of the subscriber based on the probabilistic measure.

37. The method of claim 36 wherein the time period is pre-defined.

38. The method of claim 36 wherein step (a) includes evaluating channel change commands and associated viewing times to create the viewing record.

39. The method of claim 36, wherein the heuristic rules assign probabilities of a viewing record being associated with an income group based on the viewing record.

40. The method of claim 36, wherein the heuristic rules are predefined.