# US 2019/0174188 A1 · Fast channel change for a set top box based on channel viewing behaviors > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0174188 A1 - Document ID: US20190174188A1 - Status: Published application - Kind: A1 - Application number: 15/828,493 - Filed: 2017-12-01 - Published: 2019-06-06 - Earliest claimed: 2017-12-01 - Assignees: AT&T Intellectual Property I, L.P. - Applicants: AT&T Intellectual Property I, L.P. - Inventors: Quan Li; Gregory Cooke - Web page: https://patentgrep.com/patent/US20190174188A1 ## CPC - H04N 21/4662: {characterized by learning algorithms} - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 5/048: Fuzzy inferencing - H04N 21/4384: {involving operations to reduce the access time, e.g. fast-tuning for reducing channel switching latency} - H04N 21/44213: {Monitoring of end-user related data (arrangements for monitoring the users' behaviour or opinions in broadcast systems H04H60/33)} - H04N 21/44224: {Monitoring of user activity on external systems, e.g. Internet browsing} - 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 21/4667: {Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections} - H04N 21/4826: {using recommendation lists, e.g. of programmes or channels sorted out according to their score} - G06N 7/02: using fuzzy logic (computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00) - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Aspects of the subject disclosure may include, for example, a method comprising obtaining channel change data for a media processor coupled to a gateway; the channel change data relates to channel changes requested during recurring time periods. A channel viewing profile is determined for the media processor, and a channel viewing map is generated corresponding to a portion of the channel viewing profile for one of the time periods; the channel viewing map comprises a list of predicted channels likely to be requested during a recurrence of the time period. Signals are provided to the media processor corresponding to the predicted channels, each of the predicted channels thereby being available for an instant channel change responsive to a channel change request. Other embodiments are disclosed. ## Claims ### Claim 1 A method, comprising: obtaining, by a processing system including a processor at a gateway, channel change data for a media processor coupled to the gateway, the channel change data relating to channel changes requested via the media processor during each of a plurality of predefined recurring time periods; determining, by the processing system, a channel viewing profile for the media processor, based on the channel change data; generating, by the processing system, a channel viewing map corresponding to a portion of the channel viewing profile for a time period of the plurality of predefined recurring time periods, the channel viewing map comprising a list of predicted channels likely to be requested during a subsequent occurrence of the time period, the predicted channels comprising a portion of channels available at the media processor, the list of predicted channels having a ranking of the predicted channels according to a likelihood of being requested during the subsequent occurrence of the time period; providing, by the processing system, signals to the media processor corresponding to the predicted channels, each of the predicted channels thereby being available for a rapid channel change responsive to a channel change request; determining, by the processing system, a current channel viewing map based on channel change requests during a current time period; comparing, by the processing system, the current channel viewing map with the list of predicted channels; and generating, by the processing system, a new channel viewing map, responsive to a decision to generate the new channel viewing map responsive to a determination that the current channel viewing map does not correspond to the list of predicted channels based on the channel change requests during the current time period. ### Claim 2 (depends on 1) The method of claim 1, wherein the decision to generate the new channel viewing map is made using a fuzzy-logic decision-making procedure. ### Claim 3 (depends on 2) The method of claim 2, wherein the ranking of the predicted channels is based at least in part on a length of time each of the predicted channels was viewed in previous occurrences of the time period. ### Claim 4 (depends on 1) The method of claim 1, wherein the generating the channel viewing map comprises generating a neural network for the media processor and the plurality of predefined recurring time periods, wherein each node of the neural network corresponds to a time period of the plurality of predefined recurring time periods and comprises the list of predicted channels for the time period, and wherein the neural network trains according to channel change data for the plurality of predefined recurring time periods. ### Claim 5 (depends on 1) The method of claim 1, wherein the channel viewing map comprises a number of distribution servers predicted to be required for delivering the signals to the media processor during the time period. ### Claim 6 (depends on 1) The method of claim 1, wherein the time period is a portion of a day of the week, the time period accordingly recurring weekly. ### Claim 7 (depends on 6) The method of claim 6, wherein the day comprises a plurality of unequal time periods. ### Claim 8 (depends on 1) The method of claim 1, wherein each channel of the list of predicted channels is associated with a predicted channel change time, the predicted channel change time being within the time period. ### Claim 9 (depends on 8) The method of claim 8, further comprising causing, by the processing system, a channel change to one of the predicted channels at the predicted channel change time associated with the one of the predicted channels, without receiving an input of a channel change request for the one of the predicted channels. ### Claim 10 (depends on 9) The method of claim 9, wherein the one of the predicted channels has a highest ranking of the predicted channels. ### Claim 11 A device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations comprising: obtaining channel change data for a media processor coupled to a gateway, the channel change data relating to channel changes requested via the media processor during each of a plurality of predefined recurring time periods; determining a channel viewing profile for the media processor, based on the channel change data; generating a neural network for the media processor and the time periods, wherein each node of the neural network comprises a channel viewing map corresponding to a portion of the channel viewing profile for a time period of the plurality of predefined recurring time periods, the node accordingly comprising a list of predicted channels likely to be requested during a subsequent occurrence of the time period, the predicted channels comprising a portion of channels available at the media processor, the list of predicted channels having a ranking of the predicted channels according to a likelihood of being requested during the subsequent occurrence of the time period, and wherein the neural network trains according to channel change data for the plurality of predefined recurring time periods; providing signals to the media processor corresponding to the predicted channels, each of the predicted channels thereby being available for a rapid channel change responsive to a channel change request; determining a current channel viewing map based on channel change requests during a current time period; comparing the current channel viewing map with the list of predicted channels; and generating a new channel viewing map, responsive to a determination that the current channel viewing map does not correspond to the list of predicted channels based on the channel change requests during the current time period. ### Claim 12 (depends on 11) The device of claim 11, wherein the generating the new channel viewing map is made using a fuzzy-logic decision-making procedure. ### Claim 13 (depends on 11) The device of claim 11, wherein each node of the network corresponds to a time period of the plurality of predefined recurring time periods and comprises the list of predicted channels for the time period. ### Claim 14 (depends on 11) The device of claim 11, wherein the time period is a portion of a day of the week, the time period accordingly recurring weekly. ### Claim 15 (depends on 11) The device of claim 11, wherein each channel of the list of predicted channels is associated with a predicted channel change time, the predicted channel change time being within the time period. ### Claim 16 (depends on 15) The device of claim 15, wherein the operations further comprise causing a channel change to one of the predicted channels at the predicted channel change time associated with the one of the predicted channels, without receiving an input of a channel change request for the one of the predicted channels. ### Claim 17 A non-transitory machine-readable medium comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations comprising: obtaining channel change data for a media processor coupled to a gateway, the channel change data relating to channel changes requested via the media processor during each of a plurality of time periods; determining a channel viewing profile for the media processor, based on the channel change data; generating a neural network for the media processor and the time periods, wherein each node of the neural network comprises a channel viewing map corresponding to a portion of the channel viewing profile for a time period of the plurality of time periods, the node accordingly comprising a list of predicted channels likely to be requested during a subsequent occurrence of the time period, the predicted channels comprising a portion of channels available at the media processor, the list of predicted channels having a ranking of the predicted channels according to a likelihood of being requested during the subsequent occurrence of the time period, and wherein the neural network trains according to channel change data for the plurality of time periods; providing signals to the media processor corresponding to the predicted channels, each of the predicted channels thereby being available for a rapid channel change responsive to a channel change request; determining a current channel viewing map based on channel change requests during a current time period; comparing the current channel viewing map with the list of predicted channels; and generating a new channel viewing map, responsive to a decision to generate the new channel viewing map responsive to a determination that the current channel viewing map does not correspond to the list of predicted channels based on the channel change requests during the current time period. ### Claim 18 (depends on 17) The non-transitory machine-readable medium of claim 17, wherein the decision to generate the new channel viewing map is made using a fuzzy-logic decision-making procedure. ### Claim 19 (depends on 17) The non-transitory machine-readable medium of claim 17, wherein each channel of the list of predicted channels is associated with a predicted channel change time, the predicted channel change time being within the time period, and wherein the plurality of time periods comprise a plurality of predefined recurring time periods. ### Claim 20 (depends on 19) The non-transitory machine-readable medium of claim 19, wherein the operations further comprise causing a channel change to one of the predicted channels at the predicted channel change time associated with the one of the predicted channels, without receiving an input of a channel change request for the one of the predicted channels.