US20030101451A1
Expired / Lapsed
US
⏱️ 0.0 years remaining
System, method, and software application for targeted advertising via behavioral model clustering, and preference programming based on behavioral model clusters
Assignee: Thomson Licensing SAS
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Filing: 2002-01-09
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Granted: —
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Expires: 2022-01-09
Abstract
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).
Inventors
- 👤 Isaac Bentolila
- 👤 Yiming Zhou
- 👤 Isaac Ismail
- 👤 Richard Humpleman
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-26 21:44
CPC Classifications
H; H04; H04N; H04N21/00; H04N21/20; H04N21/25; H04N21/266; H04N21/2668; G; G06; G06Q; G06Q30/00; G06Q30/02; G06Q30/0241; G06Q30/0251; G06Q30/0254; G06Q30/0255; G06Q30/0269; G06Q30/0271; H04N21/251; H04N21/252; H04N21/40; H04N21/43; H04N21/442; H04N21/44213; H04N21/44222; H04N21/45; H04N21/4508; H04N21/4532; H04N21/454; H04N21/466; H04N21/4662; H04N21/4663; H04N21/4667; H04N21/47; H04N21/482; H04N21/4826; H04N7/00; H04N7/16; H04N7/162; H04N7/163; H04N7/173; H04N7/17309
Claims Analysis
Total Claims: 24
Claim 1
Independent Claim
90 words
1 . A television rating system for targeted program delivery, comprising: a clustering engine receiving television viewing data input, processing the viewing data input, and generating user profiles targeting advertising category groups; a client-side system adapted to classify a television user into at least one advertising category group; a contextual behavioral profiling system connected to said client-side system and determining a television user's viewing behavior with content and usage-related preferences; and a behavioral model database connected to said profiling system and storing therein information with the television user's viewing behavior.
Claim 2
Dependent Claim
90 words
1 . A television rating system for targeted program delivery, comprising: a clustering engine receiving television viewing data input, processing the viewing data input, and generating user profiles targeting advertising category groups; a client-side system adapted to classify a television user into at least one advertising category group; a contextual behavioral profiling system connected to said client-side system and determining a television user's viewing behavior with content and usage-related preferences; and a behavioral model database connected to said profiling system and storing therein information with the television user's viewing behavior.
Claim 3
Dependent Claim
44 words
2 . The system according to claim 1 , wherein said clustering engine is a software agent residing in a central computer system at a television distribution head-end and is programmed to create template behavioral profiles corresponding to targeted advertising categories of television viewers.
Claim 4
Dependent Claim
37 words
3 . The system according to claim 2 , wherein said clustering engine is trained substantially exclusively on tagged viewing data from a given target group to learn a most general profile of the given target group.
Claim 5
Dependent Claim
55 words
4 . The system according to claim 2 , wherein said clustering engine is programmed to generalize viewer's profiles in each group into a representative aggregation for a respective advertising category, and to form advertising category profiles by aggregating all dimensions most strongly in common for the given group and most unique across target groups.
Claim 6
Dependent Claim
54 words
5 . The system according to claim 1 , which further comprises an advertisement manager connected to query said behavioral model database, said advertisement manager being programmed to parameterize behavioral profiles of said behavioral model database and to download the parameterized behavioral profiles to an advertising category membership agent residing at said client-side system.
Claim 7
Dependent Claim
58 words
6 . The system according to claim 5 , wherein said advertising category membership agent is configured to reconstruct the downloaded parameterized targeting models, and apply a clustering engine to the television user's history to determine a most likely advertising category the user belongs to and store the results as targeting category probabilities in a user category database.
Claim 8
Dependent Claim
45 words
7 . The system according to claim 5 , which further comprises targeting agents and presentation agents disposed at said client-side system for combining the targeting category probabilities and relevant preference information to selectively capture, store, and display advertisements downloaded in accordance with the optimization.
Claim 9
Dependent Claim
110 words
8 . In an interactive display system with a head-end side distributing program content and a client side receiving the program content and selectively displaying the program content in accordance with a user's selection, a preference engine for determining the user's preferred program content, comprising: a user monitoring device connected at the client side to record contextual transition behaviors profiling one or more users and to continually build a knowledgebase of preferences and contextual transition behaviors profiling the one or more users; and a device for providing to the one or more users the program content in accordance with the user's demographic information and with the contextual transition behavior profile.
Claim 10
Dependent Claim
110 words
8 . In an interactive display system with a head-end side distributing program content and a client side receiving the program content and selectively displaying the program content in accordance with a user's selection, a preference engine for determining the user's preferred program content, comprising: a user monitoring device connected at the client side to record contextual transition behaviors profiling one or more users and to continually build a knowledgebase of preferences and contextual transition behaviors profiling the one or more users; and a device for providing to the one or more users the program content in accordance with the user's demographic information and with the contextual transition behavior profile.
Claim 11
Dependent Claim
29 words
9 . The preference engine according to claim 8 , wherein said user monitoring device models the user's behavioral interaction with advertising program content and with entertainment program content.
Claim 12
Dependent Claim
60 words
10 . The preference engine according to claim 8 , connected to receive from the head-end metadata describing advertising content and metadata describing entertainment program content, and programmed to establish content preferences by combining metadata information with the contextual transition behavior profile, and to build a relational knowledge base with associations between the user's behavior, demographics, and program content preferences.
Claim 13
Dependent Claim
65 words
11 . The preference engine according to claim 8 programmed to model patterns of usage behaviors with a behavioral model and to extract key usage information from the behavioral model into a behavioral database, wherein each entry in the behavioral database has a confidence value associated therewith reflects an estimate of a structural and sampling quality of the data used to calculate the database entry.
Claim 14
Dependent Claim
129 words
12 . In a program content delivery system having a head-end side and a client side, a system for targeted program delivery, comprising: a central data system at the head-end side receiving viewing data selected from the group consisting of watch data, watch start time, watch duration, and watch channel, demographic information describing a program user, and an electronic program guide with metadata describing a program content; a demographic cluster knowledge base acquirer receiving behavioral data of the user and outputting a knowledge base in form of a transition matrix with weight sets, the transition matrix predicting a demographic group of the user; and a program content generating module providing to the client side streams of program content including advertisements based on the predicted demographic group of the user.
Claim 15
Dependent Claim
129 words
12 . In a program content delivery system having a head-end side and a client side, a system for targeted program delivery, comprising: a central data system at the head-end side receiving viewing data selected from the group consisting of watch data, watch start time, watch duration, and watch channel, demographic information describing a program user, and an electronic program guide with metadata describing a program content; a demographic cluster knowledge base acquirer receiving behavioral data of the user and outputting a knowledge base in form of a transition matrix with weight sets, the transition matrix predicting a demographic group of the user; and a program content generating module providing to the client side streams of program content including advertisements based on the predicted demographic group of the user.
Claim 16
Dependent Claim
34 words
13 . The system according to claim 12 , which further comprises a realtime feedback link for delivering to said central data system realtime information concerning a user's viewing behavior with click stream data.
Claim 17
Dependent Claim
23 words
14 . The system according to claim 12 , wherein said demographic cluster knowledge base acquirer is based on a hidden Markov model.
Claim 18
Dependent Claim
43 words
15 . The system according to claim 12 , wherein said demographic cluster knowledge base acquirer and said program content generating module are software modules each adapted to be stored on a machine-readable medium in the form of a plurality of processor-executable instructions.
Claim 19
Dependent Claim
31 words
16 . The system according to claim 12 , wherein said demographic cluster knowledge base acquirer generates demographic cluster information of the user in terms of statistical state machine transition models.
Claim 20
Dependent Claim
32 words
17 . The system according to claim 16 , wherein the state machines are defined in the transition matrix, and the transition matrix contains information of program transitions initiated by the viewer.
Claim 21
Dependent Claim
30 words
18 . The system according to claim 12 , wherein the transition matrix is one of at least two concurrent transition matrices including a channel matrix and a genre matrix.
Claim 22
Dependent Claim
28 words
19 . The system according to claim 12 , wherein the transition matrix is a two-dimensional matrix with transitions from television channels to television channels in temporal form.
Claim 23
Dependent Claim
61 words
20 . The system according to claim 12 , wherein said demographic cluster knowledge base acquirer is configured to parameterize the user's behavior with a double random pseudo hidden Markov process, and to define 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.
Claim 24
Dependent Claim
55 words
21 . The system according to claim 12 , wherein said demographic cluster knowledge base acquirer is configured to define a double random process with a plurality of dimensions, and to determine parallel statistical state machine transition events in at least two of three state categories including channel, genre, and title of the program content.
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