# US 2023/0410147 A1 · Audience proposal creation and spot scheduling utilizing a framework for audience rating estimation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0410147 A1 - Document ID: US20230410147A1 - Status: Published application - Kind: A1 - Application number: 18/321,950 - Filed: 2023-05-23 - Published: 2023-12-21 - Earliest claimed: 2015-11-02 - Assignees: Turner Broadcasting System, Inc. - Applicants: Turner Broadcasting System, Inc. - Inventors: Wassim Samir Chaar; David James Benoit; José Antonio Carbajal Orozco - Web page: https://patentgrep.com/patent/US20230410147A1 ## CPC - G06Q 30/0254: {based on statistics} - G06Q 30/0273: Determination of fees for advertising - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract A system determines a constraint associated with a pending deal for an advertiser based on target cost per thousand (CPM) reduction goal, demographics CPM cap, or established parameter. Rates are generated for each selling title for a week for a duration and a network of the pending deal. In certain time period for first channel of first network, buckets are determined based on sum of program attributes and time attributes for each second channel and weighing factor. Target audience rating estimates are acquired based on a predictive model, the buckets, the target CPM reduction goal, and the demographics CPM cap for plurality of networks. First proposal information is generated based on first distribution information of an audience spot and modified target CPM of a proposal associated with the pending deal based on target audience rating estimates. Audience spot is scheduled across the network for a selling title and week combination. ## Claims ### Claim 1 (canceled) ### Claim 2 A computer-implemented method, comprising: determining, by one or more processors, a plurality of channel buckets for a target channel; determining, by the one or more processors, a plurality of bucketized channel vectors for the target channel, wherein: (i) the plurality of bucketized channel vectors comprises a respective bucketized channel vector for each of the plurality of channel buckets, and (ii) a bucketized channel vector of the plurality of bucketized channel vectors is based at least in part on a plurality of program attributes and time attributes for each of a plurality of channels within a channel bucket corresponding to the bucketized channel vector; determining, by the one or more processors, a plurality of targeted audience rating estimates for the target channel based at least in part on the plurality of bucketized channel vectors; and initiating, by the one or more processors, a scheduling of one or more audience spots for a pending deal based at least in part on the plurality of targeted audience rating estimates. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein the bucketized channel vector is based at least in part on a sum of the plurality of program attributes and time attributes for each of the plurality of channels within the channel bucket. ### Claim 4 (depends on 2) The computer-implemented method of claim 2, wherein the bucketized channel vector is based at least in part on one or more observed consumer behavior patterns. ### Claim 5 (depends on 2) The computer-implemented method of claim 2, wherein each targeted audience rating estimate of the plurality of targeted audience rating estimates corresponds to a percentage of viewers that are engaged in a particular targeted activity. ### Claim 6 (depends on 2) The computer-implemented method of claim 2, wherein the plurality of targeted audience rating estimates corresponds to a plurality of selling title-weeks across the target channel and one or more affiliated channels. ### Claim 7 (depends on 2) The computer-implemented method of claim 2, further comprising: determining, by the one or more processors, a distribution model for the pending deal based on the plurality of targeted audience rating estimates; and initiating, by the one or more processors, the scheduling of the one or more audience spots based at least in part on the distribution model. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, wherein the distribution model is generated based at least in part on a budget and a target cost per thousand (CPM) for the pending deal. ### Claim 9 (depends on 2) The computer-implemented method of claim 2, wherein the plurality of time attributes is indicative of one or more of a quarter, seasonality, day of week, half hour, or holiday. ### Claim 10 (depends on 2) The computer-implemented method of claim 2, wherein the plurality of program attributes is indicative of one or more of a genre, repeat, premiere, live, or duration. ### Claim 11 A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to: determine a plurality of channel buckets for a target channel; determine a plurality of bucketized channel vectors for the target channel, wherein: (i) the plurality of bucketized channel vectors comprises a respective bucketized channel vector for each of the plurality of channel buckets, and (ii) a bucketized channel vector of the plurality of bucketized channel vectors is based at least in part on a plurality of program attributes and time attributes for each of a plurality of channels within a channel bucket corresponding to the bucketized channel vector; determine a plurality of targeted audience rating estimates for the target channel based at least in part on the plurality of bucketized channel vectors; and initiate a scheduling of one or more audience spots for a pending deal based at least in part on the plurality of targeted audience rating estimates. ### Claim 12 (depends on 11) The computing system of claim 11, wherein the bucketized channel vector is based at least in part on a sum of the plurality of program attributes and time attributes for each of the plurality of channels within the channel bucket. ### Claim 13 (depends on 11) The computing system of claim 11, wherein the bucketized channel vector is based at least in part on one or more observed consumer behavior patterns. ### Claim 14 (depends on 11) The computing system of claim 11, wherein each targeted audience rating estimate of the plurality of targeted audience rating estimates corresponds to a percentage of viewers that are engaged in a particular targeted activity. ### Claim 15 (depends on 11) The computing system of claim 11, wherein the plurality of targeted audience rating estimates corresponds to a plurality of selling title-weeks across the target channel and one or more affiliated channels. ### Claim 16 (depends on 11) The computing system of claim 11, wherein the one or more processors are further configured to: determine a distribution model for the pending deal based on the plurality of targeted audience rating estimates; and initiate the scheduling of the one or more audience spots based at least in part on the distribution model. ### Claim 17 One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: determine a plurality of channel buckets for a target channel; determine a plurality of bucketized channel vectors for the target channel, wherein: (i) the plurality of bucketized channel vectors comprises a respective bucketized channel vector for each of the plurality of channel buckets, and (ii) a bucketized channel vector of the plurality of bucketized channel vectors is based at least in part on a plurality of program attributes and time attributes for each of a plurality of channels within a channel bucket corresponding to the bucketized channel vector; determine a plurality of targeted audience rating estimates for the target channel based at least in part on the plurality of bucketized channel vectors; and initiate a scheduling of one or more audience spots for a pending deal based at least in part on the plurality of targeted audience rating estimates. ### Claim 18 (depends on 17) The one or more non-transitory computer-readable storage media of claim 17, wherein the instructions further cause the one or more processors to: determine a distribution model for the pending deal based on the plurality of targeted audience rating estimates; and initiate the scheduling of the one or more audience spots based at least in part on the distribution model. ### Claim 19 (depends on 18) The one or more non-transitory computer-readable storage media of claim 18, wherein the distribution model is generated based at least in part on a budget and a target cost per thousand (CPM) for the pending deal. ### Claim 20 (depends on 17) The one or more non-transitory computer-readable storage media of claim 17, wherein the plurality of time attributes is indicative of one or more of a quarter, seasonality, day of week, half hour, or holiday. ### Claim 21 (depends on 17) The one or more non-transitory computer-readable storage media of claim 17, wherein the plurality of program attributes is indicative of one or more of a genre, repeat, premiere, live, or duration.