Subscriber Characterization System with Filters

• US 20070240181A1
• Filed: 06/13/2007
• Published: 10/11/2007
• Est. Priority Date: 12/03/1998
• Status: Abandoned Application

• Current Assignee: Prime Research Alliance E, LLC
• Original Assignee: Prime Research Alliance E., Inc.
• Inventors: Eldering, Charles, Lamine, Sylla, Blasko, John
• Application Number:
  US11/762,538
• Publication Number:
  US 20070240181A1
• Time in Patent Office: Days
• US Class Current: 725/14
• 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. A computer-implemented method of determining at least one characteristic of a video or audio presentation being presented to a viewer, 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 at least one characteristic of the video or audio presentation, 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 at least one characteristic of the video or audio presentation being presented to the viewer 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 at least one inferred characteristic is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.

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

(a) viewing time per channel, category, or network;

(b) channel changes per time;

(c) average volume per time period, channel, category, or network; and

(d) dwell time per channel, category, or network.

5. The method of claim 1, wherein the at least one heuristic process incorporates at least two types of analysis selected from the group consisting of exploratory problem-solving, self-learning, discovery, experiments, trial and error, inferences, educated guesses, market studies, human knowledge and experience.

6. The method of claim 1, further comprising:

(f) reporting the at least one inferred characteristic, wherein the report includes predictive values of the at least one inferred characteristic and at least one other characteristic, and wherein the predictive values are assigned by the heuristic rules based on the viewer interaction data.

7. A computer-implemented method of determining at least one demographic attribute of a video or audio presentation being presented to a viewer, 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 at least one demographic attribute of the video or audio presentation, 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 at least one demographic attribute of the video or audio presentation being watched by a viewer based on the application of the previously developed heuristic rules.

8. The method of claim 7, wherein the heuristic rules are probabilistic in nature.

9. The method of claim 7, wherein the at least one demographic attribute is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.

10. The method of claim 7, wherein the viewer interaction data includes at least some subset of:

(a) viewing time per channel, category, or network;

(b) channel changes per time;

(c) average volume per time period, channel, category, or network; and

(d) dwell time per channel, category, or network.

11. The method of claim 7, wherein the at least one heuristic process incorporates at least two types of analysis selected from the group consisting of exploratory problem-solving, self-learning, discovery, experiments, trial and error, inferences, educated guesses, market studies, human knowledge and experience.

12. The method of claim 7, further comprising:

(f) reporting the at least one demographic attribute, wherein the report includes predictive values of the at least one demographic attribute and at least one other demographic attribute, and wherein the predictive values are assigned by the heuristic rules based on the viewer interaction data.

13. A computer-implemented method of determining which viewer or viewers of an audience of a video or audio presentation are viewing the video or audio presentation, 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 of at least one viewer;

(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 at least one characteristic of a viewer, 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 which viewer or viewers of a plurality of viewers of the audience are viewing the video or audio presentation based on the application of the previously developed heuristic rules.

14. The method of claim 13, wherein the heuristic rules of step (d) are applied at least to a subset of previously stored viewer interaction data.

15. The method of claim 14, wherein the inferring of step (e) utilizes the application of the heuristic rules to the previously stored viewer interaction data.

16. The method of claim 13, wherein the heuristic rules are probabilistic in nature.

17. The method of claim 13, wherein the at least on inferred characteristic is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.

18. The method of claim 13, wherein the viewer interaction data includes at least some subset of:

(a) viewing time per channel, category, or network;

(b) channel changes per time;

(c) average volume per time period, channel, category, or network; and

(d) dwell time per channel, category, or network.

19. The method of claim 13, wherein the at least one heuristic process incorporates at least two types of analysis selected from the group consisting of exploratory problem-solving, self-learning, discovery, experiments, trial and error, inferences, educated guesses, market studies, human knowledge and experience.

20. A computer-implemented method of determining which viewer or viewers in a household are viewing a video or audio presentation, 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 of the household;

(c) retrieving on or more previously developed heuristic rules, wherein the previously developed heuristic rules relate viewer interaction data to at least one characteristic of a viewer, 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 which viewer or viewers in the household are viewing the video or audio presentation based on the application of heuristic rules.

21. The method of claim 20, wherein the heuristic rules of step (d) are applied at least to a subset of previously stored viewer interaction data.

22. The method of claim 21, wherein the inferring of step (e) utilizes the application of the heuristic rules to the previously stored viewer interaction data.

23. The method of claim 20, wherein the heuristic rules are probabilistic in nature.

24. The method of claim 20, wherein the at least on inferred characteristic is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.

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

(a) viewing time per channel, category, or network;

(b)channel changes per time;

(c) average volume per time period, channel, category, or network; and

(d) dwell time per channel, category, or network.

26. The method of claim 20, wherein the at least one heuristic process incorporates at least two types of analysis selected from the group consisting of exploratory problem-solving, self-learning, discovery, experiments, trial and error, inferences, educated guesses, market studies, human knowledge and experience.