System and method for behavioral model clustering in television usage, targeted advertising via model clustering, and preference programming based on behavioral model clusters
First Claim
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.
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Accused Products
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).
120 Citations
31 Claims
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1. A method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A non-transitory computer-readable storage medium encoded with a plurality of processor executable instructions for implementing a function of:
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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. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
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31. A method comprising:
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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.
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Specification