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

• US 20030101449A1
• Filed: 01/09/2002
• Published: 05/29/2003
• Est. Priority Date: 01/09/2001
• Status: Active Grant

• Current Assignee: Thomson Licensing SAS (Vantiva SA)
• Original Assignee: Thomson Licensing SAS (Vantiva SA)
• Inventors: Humpleman, Richard, Zhou, Yiming, Bentolila, Isaac, Ismail, Isaac Labeeb
• Granted Patent: US 8,495,680 B2
• Time in Patent Office: Days
• US Class Current: 725/10
• 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 of determining a television viewer'"'"'s viewing habits, the method which comprises:

recording a viewer'"'"'s monitor behavior with data item variables selected from the group consisting of watch date, watch start time, watch duration, and watch channel;

inputting historical data information regarding demographic information tagged to the viewer;

inputting program guide information; and

associating the 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 models.

2. The method according to claim 1, wherein the step of defining the knowledge base comprises calculating a parameterized transition matrix defining the viewer'"'"'s viewing habits, the transition matrix containing information of program transitions initiated by the viewer.

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

4. The method according to claim 2, which comprises 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 1, which comprises providing feedback information with the viewer'"'"'s monitor behavior by recording a click stream.

6. The method according to claim 1, which comprises parameterizing the viewer'"'"'s monitor behavior with a double random pseudo hidden Markov process, and 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 between the active behavioral clusters.

7. The method according to claim 6, which comprises 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.

8. A machine-readable medium having stored thereon a plurality of processor-executable instructions for implementing a function of:

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

combining the statistical state machine families into global statistical state machines defined in a global probability density function;

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; 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.

9. The machine readable medium according to claim 8, wherein the state transitions represent a television viewer'"'"'s monitor behavior and the statistical state machines are selected from the group consisting of watch date, watch start time, watch duration, and watch channel.

10. The machine readable medium according to claim 8, wherein the global profile represents demographic cluster information of the viewer in terms of the statistical state machine transition models.

11. The machine readable medium according to claim 8, wherein the state machines are defined in a parameterized transition matrix defining the viewer'"'"'s viewing habits, the transition matrix containing information of program transitions initiated by the viewer.

12. The machine readable medium according to claim 11, wherein the transition matrix is one of at least two concurrent transition matrices including a channel matrix and a genre matrix.

13. The machine readable medium according to claim 8, wherein the transition matrix is a two-dimensional matrix with transitions from television channels to television channels in temporal form.

14. The machine readable medium according to claim 8, configured to parameterize the viewer'"'"'s monitor behavior with a double random pseudo hidden Markov process, and 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 between the active behavioral clusters.

15. The machine readable medium according to claim 8, which comprises 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.