System and method for behavioral model clustering in television usage, targeted advertising via model clustering, and preference programming based on behavioral model clusters

• US 8,495,680 B2
• Filed: 01/09/2002
• Issued: 07/23/2013
• Est. Priority Date: 01/09/2001
• Status: Expired due to Fees

• Current Assignee: Thomson Licensing SAS (Vantiva SA)
• Original Assignee: Thomson Licensing (Vantiva SA)
• Inventors: Bentolila, Isaac, Zhou, Yiming, Ismail, Isaac Labeeb, Humpleman, Richard
• Primary Examiner(s): SHELEHEDA, JAMES R
• Application Number:
  US10/043,698
• Publication Number:
  US 20030101449A1
• Time in Patent Office: 4,213 Days
• Field of Search: 725/9, 725/35, 725/46, 725/14
• US Class Current: 725/46
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0271 Personalized advertisement
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• 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 21/44222 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 21/4663 involving probabilistic networks, e.g. Bayesian networks
• 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 21/4826 using recommendation lists, e.g. of programs or channels sorted out according to their score
• CPC Class Codes: H04N 7/163 by receiver means only
• CPC Class Codes: H04N 7/17309 Transmission or handling of upstream communications

Abstract

The method and system for TV user profile data prediction and modeling allows accurate and narrowly focused behavioral clustering. A client-side system classifies television consumers into representative user profiles. The profiles target individual user advertising and program preference category groups. A contextual behavioral profiling system determines the user'"'"'s monitor behavior and content preferences, and the system may be continually updated with user information. A behavioral model database is queried by various system modules. The programming, including targeted advertising for television and interactive television is based on the profile data prediction, modeling and preference determination. The system is enabled to present a complete program sequence to the viewer based on the preference determination and stored programming. The latter is referred to as automatic program sequence (virtual channel) creation and the virtual channel can be presented as a separate channel in an electronic programming guide (EPG).

Claims

1. A method comprising:

selecting a plurality of predetermined demographic groups including externally selected characteristics including historical data from a plurality of actual viewers and historical actual electronic program guide (EPG) data to associate viewers with;

recording a viewer'"'"'s monitor behavior with data item variables including watched channel, watching start time, at least one of watching date and watching duration, a first ratio of time watched to time available for at least one non-hopping program, and a second ratio of time watched to time available for at least one program with hopping, wherein hopping represents an act of leaving and returning to the same program, wherein the first ratio exclusively corresponds to non-hopped programs and said second ratio exclusively corresponds to hopped programs;

associating a particular demographic group of the plurality of demographic groups with the viewer;

from a server-side system, inputting historical data information regarding demographic information tagged to the viewer for the viewer'"'"'s demographic group;

generating preferred program guide information based on the historical data information for the viewer'"'"'s demographic group and based on bias metrics;

inputting the preferred program guide information for the viewer'"'"'s demographic group;

at a client side system, associating the preferred program guide information with the viewer'"'"'s monitor behavior; and

defining therefrom a knowledge base with demographic group cluster information of the viewer in terms of statistical state machine transition models.

2. The method according to claim 1, wherein defining the knowledge base comprises calculating a parameterized transition matrix defining the viewer'"'"'s viewing habits, the parameterized transition matrix containing information of program transitions initiated by the viewer, and wherein the row number and the column number of the element represent the first and second states.

3. The method according to claim 2, further comprising defining at least two concurrent transition matrices including a channel matrix and a genre matrix.

4. The method according to claim 2, further comprising defining the transition matrix as a two-dimensional matrix with transitions from television channels to television channels in temporal form.

5. The method according to claim 2, wherein the parameterized transition matrix is in a temporal form.

6. The method according to claim 2, wherein the parameterized transition matrix includes a first matrix for TV watching activities and second matrix for TV channel surfing.

7. The method according to claim 1, further comprising providing feedback information with the viewer'"'"'s monitor behavior by recording a click stream.

8. The method according to claim 1, further comprising parameterizing the viewer'"'"'s monitor behavior with a pseudo hidden Markov process.

9. The method according to claim 8, wherein the pseudo hidden Markov process is a double random process.

10. The method according to claim 9, further comprising defining the double random process with a plurality of dimensions, and determining parallel statistical state machine transition events in at least two of three state categories including channel, genre, and title.

11. The method according to claim 9, further comprising:

defining a low level statistical state machine modeling a behavioral cluster and a top-level statistical state machine with active behavioral clusters and an interaction among the active behavioral clusters.

12. The method according to claim 1, wherein the data items have a probability function with a confidence level, the method further comprising:

updating the historical data information upon determining that a given data item has a probability function with a higher confidence level than a previous data item.

13. The method of claim 1, wherein the statistical state machine transition models employ a parameterized transition matrix, and wherein the transition matrix comprises an element indicating a transition from a first state to a second state, and wherein each of the first and second states is indicated by one of a row and a column of the transition matrix.

14. The method of claim 1, wherein each of the plurality of demographic groups are defined by viewing monitor information including watch date, watch start time, watch duration and watch channel, and associated demographic information.

15. The method of claim 14, wherein the associating the particular demographic group of the plurality of demographic groups with the viewer is based on the viewer'"'"'s monitor behavior.

16. The method of claim 15, wherein the viewer'"'"'s monitor behavior is past monitor behavior.

17. The method of claim 1, wherein each of the plurality of demographic groups is selected based on predetermined criteria selected external to the server-side system and the client-side system.

18. A non-transitory computer-readable storage medium encoded with a plurality of processor executable instructions for implementing a function of:

selecting a plurality of predefined demographic groups including externally selected characteristics including historical data from a plurality of actual viewers and historical actual electronic program guide (EPG) data, the demographic groups defined by viewing monitor information including watch date, watch start time, watch duration and watch channel, EPG data and associated demographic information, a first ratio of time watched to time available for at least one non-hopping program, and a second ratio of time watched to time available for at least one program with hopping, wherein hopping represents an act of leaving and returning to the same program, wherein the first ratio exclusively corresponds to non-hopped programs and said second ratio exclusively corresponds to hopped programs;

associating a particular demographic group of the plurality of demographic groups with each viewer based on monitor behavior;

capturing state transitions by defining monitor behavior in a plurality of statistical state machine families each representing viewing behavior of the particular demographic group;

at a client-side system, combining the statistical state machine families into global statistical state machines defined in a global probability density function based on the particular demographic group;

updating and reinforcing the global probability density function upon determining that a given probability function has a higher confidence level than a previous probability density function based in part on bias metrics; and

outputting a global profile based on the global probability density function, wherein the global profile is suitable for determining programming content of a television server for classes of viewers.

19. The non-transitory computer-readable storage medium according to claim 18, wherein the state transitions represent a television viewer'"'"'s monitor behavior and the statistical state machines are selected from the group consisting of watched channel, watching start time, and at least one of watching date and watching duration.

20. The non-transitory computer-readable storage medium according to claim 18, wherein the global profile represents demographic cluster information of a viewer in terms of a statistical state machine transition matrix.

21. The non-transitory computer-readable storage medium according to claim 20, further comprising instructions for:

at the client-side system, associating program guide information with the viewer'"'"'s monitor behavior; and

defining therefrom a knowledge base with demographic cluster information of the viewer in terms of statistical state machine transition matrices.

22. The non-transitory computer-readable storage medium according to claim 18, wherein the state machines are defined in a parameterized transition matrix defining the viewer'"'"'s viewing habits, the transition matrix comprising an element indicating information of a program transition initiated by the viewer.

23. The non-transitory computer-readable storage medium according to claim 22, wherein the parameterized transition matrix is one of at least two concurrent transition matrices including a channel matrix and a genre matrix.

24. The non-transitory computer-readable storage medium according to claim 22, wherein the parameterized transition matrix is a two-dimensional matrix with transitions from television channels to television channels in temporal form.

25. The non-transitory computer-readable storage medium according to claim 18, further comprising instructions for parameterizing the viewer'"'"'s monitor behavior with a pseudo hidden Markov process.

26. The non-transitory computer-readable storage medium according to claim 25, wherein the pseudo hidden Markov process is a double random process.

27. The non-transitory computer-readable storage medium according to claim 26, further comprising:

defining a low-level statistical state machine modeling a behavioral cluster and a top-level statistical state machine with active behavioral clusters and an interaction among the active behavioral clusters.

28. The non-transitory computer-readable storage medium according to claim 18, further comprising instructions for defining a double random process with a plurality of dimensions, and determining parallel statistical state machine transition events in at least two of three state categories including channel, genre, and title.

29. The non-transitory computer readable storage medium of claim 18, wherein the instructions further comprise describing the state transitions in a parameterized transition matrix.

30. The non-transitory computer readable storage medium of claim 29, wherein the transition matrix comprises an element indicating a transition from a first state to a second state, and wherein each of the first and second states is indicated by one of a row and a column of the transition matrix.

31. A method comprising:

selecting externally generated groups defined by externally selected demographics including historical data from a plurality of actual viewers and historical actual electronic program guide (EPG) data to associate viewers with;

recording a viewer'"'"'s monitor behavior with data item variables including watched channel, watching start time, at least one of watching date and watching duration, a first ratio of time watched to time available for at least one non-hopping program, and a second ratio of time watched to time available for at least one program with hopping, wherein hopping represents an act of leaving and returning to the same program, wherein the first ratio exclusively corresponds to non-hopped programs and said second ratio exclusively corresponds to hopped programs;

associating a particular group of the externally generated groups with the viewer based on the viewer'"'"'s monitor behavior;

from a server-side system, inputting historical data information regarding demographic information and monitor behavior tagged to the viewer for the viewer'"'"'s particular associated group;

generating preferred program guide information based on the historical data information for the viewer'"'"'s particular associated group and based on bias metrics;

inputting the preferred program guide information for the viewer'"'"'s particular associated group;

at a client-side system, associating the preferred program guide information with the viewer'"'"'s monitor behavior; and

defining there from a knowledge base with associated group cluster information of the viewer in terms of statistical state machine transition models, wherein the generated groups are predefined externally to the client-side system and the server-side system.