# US 8,789,080 B2 · Viewing behavior learning apparatus > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,789,080 B2 - Document ID: US8789080B2 - Status: Granted - Kind: B2 - Application number: 12/235,630 - Filed: 2008-09-23 - Published: 2009-04-23 - Granted: 2014-07-22 - Earliest claimed: 2007-10-19 - Assignees: Kabushiki Kaisha Toshiba - Applicants: Kouichirou Mori; Tomoko Murakami; Ryohei Orihara - Inventors: Kouichirou Mori; Tomoko Murakami; Ryohei Orihara - Web page: https://patentgrep.com/patent/US8789080B2 ## CPC - H04N 7/163: {by receiver means only} - 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)} - H04N 21/4663: {involving probabilistic networks, e.g. Bayesian networks} - H04N 21/4667: {Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections} - H04N 21/4668: {for recommending content, e.g. movies} - 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)} - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract A viewing behavior learning apparatus includes: a viewing history acquiring unit that acquires a viewing record that indicates an attribute of a program and start and end times during which a viewer viewed the program; a viewing history dividing unit that divides the viewing record every discretized time points that is discretized by a unit time; a viewing history replicating unit that discretizes a viewing time period denoted by the divided viewing records by interval shorter than the unit time to obtain discretized viewing time periods; a viewing behavior storage unit that builds a model of the viewing behavior by use of a Bayesian network having the viewing time period and the attribute as random variables and stores a conditional probability table of the Bayesian network; and a viewing behavior updating unit that updates the conditional probability table using the discretized viewing time periods. ## Claims ### Claim 1 A viewing behavior learning apparatus for learning a viewing behavior of a viewer who views a program including at least one of video or audio, the viewing behavior learning apparatus comprising: a memory storing one or more instructions; and a processor configured to, upon executing the one or more instructions, perform: acquiring a viewing record that indicates an attribute of the program and a viewing time point that indicates a start time and an end time during which the program was viewed; dividing the viewing record into divided viewing records according to discretized time points discretized by a unit time, the divided viewing records respectively denoting a viewing time period during which the program was viewed; discretizing the viewing time period for the respective divided viewing records by an interval shorter than the unit time to yield discretized viewing time periods, replicate the divided viewing records to yield a number of replicated viewing records equal to a number of the discretized viewing time periods, and determine respective viewing frequency values for the divided viewing records based on the replicated viewing records; building a model of the viewing behavior by use of a Bayesian network having the viewing time point and the attribute of the program as random variables, and to store a conditional probability table of the Bayesian network; and updating a subset of records of the conditional probability table corresponding to the divided viewing records using the respective viewing frequency values. ### Claim 2 (depends on 1) The apparatus according to claim 1, wherein the unit time is one hour and the interval is one minute. ### Claim 3 (depends on 1) The viewing behavior learning apparatus according to claim 1, wherein the memory storing a conditional frequency table corresponding to the conditional probability table, and the updating instruction further comprises: updating the conditional frequency table by use of the viewing frequency values; and updating the conditional probability table using the conditional frequency table. ### Claim 4 (depends on 1) The apparatus according to claim 1, wherein the records of the conditional probability table correspond to respective discretized time points for respective days of the week. ### Claim 5 A method for learning a viewing behavior for a program including at least one of video or audio, comprising: electronically acquiring, from a television, a viewing record indicating an attribute of the program and a viewing time point indicating a start time and an end time during which the program was viewed; dividing the viewing record into divided viewing records according to discretized time points discretized by a unit time, wherein the divided viewing records respectively denote a viewing time period during which the program was viewed; discretizing the viewing time period for the respective divided viewing records by an interval shorter than the unit time to yield discretized viewing time periods; replicating the divided viewing records to yield a number of replicated viewing records equal to a number of the discretized viewing time periods; determining respective viewing frequency values for the divided viewing records based on the replicated viewing records; building a model of the viewing behavior by use of a Bayesian network having the viewing time point and the attribute of the program as random variables; storing a conditional probability table of the Bayesian network; and updating a subset of records of the conditional probability table corresponding to the divided viewing records using the respective viewing frequency values. ### Claim 6 (depends on 5) The method of claim 5, further comprising setting the unit time to one hour, and setting the interval to one minute. ### Claim 7 (depends on 5) The method of claim 5, further comprising: storing a conditional frequency table corresponding to the conditional probability table; updating the conditional frequency table by use of the viewing frequency values; and updating the conditional probability table using the conditional frequency table. ### Claim 8 (depends on 5) The method of claim 5, wherein the storing the conditional probability table comprises storing the conditional probability table wherein the records correspond to respective discretized time points for respective days of a week. ### Claim 9 A non-transitory computer-readable memory having stored thereon computer-executable instructions that, in response to execution, cause a computer system to perform operations, comprising: acquiring a viewing record indicating an attribute of a program and a viewing time point indicating a start time and an end time during which the program was viewed; dividing the viewing record into divided viewing records according to discretized time points discretized by a unit time, wherein the divided viewing records denote respective viewing time periods during which the program was viewed; discretizing the viewing time periods for the respective divided viewing records by an interval shorter than the unit time to yield discretized viewing time periods; replicating the divided viewing records to yield a number of replicated viewing records equal to a number of the discretized viewing time periods; determining respective viewing frequency values for the divided viewing records based on the replicated viewing records; building a model of the viewing behavior by use of a Bayesian network having the viewing time point and the attribute of the program as random variables; storing a conditional probability table of the Bayesian network; and updating a subset of records of the conditional probability table corresponding to the divided viewing records using the respective viewing frequency values. ### Claim 10 (depends on 9) The non-transitory computer-readable memory of claim 9, the operations further comprising setting the unit time to one hour, and setting the interval to one minute. ### Claim 11 (depends on 9) The non-transitory computer-readable memory of claim 9, the operations further including comprising: storing a conditional frequency table corresponding to the conditional probability table; updating the conditional frequency table by use of the viewing frequency values; and updating the conditional probability table using the conditional frequency table. ### Claim 12 (depends on 9) The non-transitory computer-readable memory of claim 9, wherein the storing the conditional probability table comprises storing the conditional probability table comprising the records, wherein the records correspond to respective discretized time points for respective days of a week.