Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: A server device obtains sets of input data including input CUME values associated with radio stations from different ratings-data-vendor devices. The input CUME values from a first ratings-data-vendor device are associated with a first limited set of input dayparts, and the input CUME values from a second ratings-data-vendor device are associated with a second limited set of input dayparts. The server obtains a list of media stations and schedule information associated with the media stations, the scheduling information including arbitrary client-specified dayparts. The input CUME values are consolidated, and translated consolidated data is generated by translating the input CUME values for the individual radio stations from the first and second limited set of input dayparts to determine an output CUME value associated with the arbitrary client-specified dayparts different from the input dayparts.

Abstract: A method is provided for enterprise management and bundling of inventory to maximize revenue on perishable products. An electronic data-mart collects factors for the probability and price sensitivity of a buyer. The data-mart also collects rules for inventory scaling, available inventory to sell, budget (goal) information, advertiser payment history, and station performance data to feed to a scenario planner. Once three or more variables exist, algorithms create scenario plans to present the most profitable bundle of offerings. The scenarios are typically pre-approved although presented to local business units for an abnormality failsafe. Once processed by local business units, the scenarios are presented to the buyer. Negotiations typically cause the process to restart.

Abstract: A radio station market analysis program extends Cume values for individual stations to multiple stations (for a particular geographic market, a particular demographic, and a particular daypart) according to the formula C=[1??i=1,n(1?Ci/P)]*P, where n is the number of media stations, P is the population, and Ci is each Cume value. Cume values may be provided for a limited set of input dayparts from Arbitron and Nielson, and are translated to an arbitrary daypart. The arbitrary daypart can represent a sum of component dayparts in a proposed advertising schedule. The reach of the proposed advertising schedule can be further computed based on a hyperbolic function of spot count.

Abstract: A computerized method, system for, and computer-readable medium operable to: determine a set of coordinates corresponding to a user's gaze; determine a user interface (UI) element corresponding to the set of coordinates; return that UI element as being detected and again repeating the determination of the set of coordinates corresponding to the user's gaze; determine if the UI element being returned is the same for a predetermined threshold of time according to a started timer; if the UI element is not the same, reset the started timer and again repeating the determination of the set of coordinates corresponding to the user's gaze; and if the UI element is the same, making the UI element active without requiring any additional action from the user and currently selecting the UI element to receive input.

Abstract: A radio station market analysis program extends Cume values for individual stations to multiple stations (for a particular geographic market, a particular demographic, and a particular daypart) according to the formula C=[1??i=1,n(1?(Ci/P))]*P, where n is the number of media stations, P is the population, and Ci is each Cume value. Cume values may be provided for a limited set of input dayparts from Arbitron and Nielson, and are translated to an arbitrary daypart. The arbitrary daypart can represent a sum of component dayparts in a proposed advertising schedule. The reach of the proposed advertising schedule can be further computed based on a hyperbolic function of spot count.

Abstract: A method is provided for enterprise management and bundling of inventory to maximize revenue on perishable products. An electronic data-mart collects factors for the probability and price sensitivity of a buyer. The data-mart also collects rules for inventory scaling, available inventory to sell, budget (goal) information, advertiser payment history, and station performance data to feed to a scenario planner. Once three or more variables exist, algorithms create scenario plans to present the most profitable bundle of offerings. The scenarios are typically pre-approved although presented to local business units for an abnormality failsafe. Once processed by local business units, the scenarios are presented to the buyer. Negotiations typically cause the process to restart.

Abstract: A method is provided for enterprise management and bundling of inventory to maximize revenue on perishable products. An electronic data-mart collects factors for the probability and price sensitivity of a buyer. The data-mart also collects rules for inventory scaling, available inventory to sell, budget (goal) information, advertiser payment history, and station performance data to feed to a scenario planner. Once three or more variables exist, algorithms create scenario plans to present the most profitable bundle of offerings. The scenarios are typically pre-approved although presented to local business units for an abnormality failsafe. Once processed by local business units, the scenarios are presented to the buyer. Negotiations typically cause the process to restart.

Abstract: A radio station market analysis program extends Cume values for individual stations to multiple stations (for a particular geographic market, a particular demographic, and a particular daypart) according to the formula C=[1??i=1,n(1?(Ci/P))]*P, where n is the number of media stations, P is the population, and Ci is each Cume value. Cume values may be provided for a limited set of input dayparts from Arbitron and Nielson, and are translated to an arbitrary daypart. The arbitrary daypart can represent a sum of component dayparts in a proposed advertising schedule. The reach of the proposed advertising schedule can be further computed based on a hyperbolic function of spot count.

Abstract: A method is provided for enterprise management and bundling of radio, outdoor and entertainment inventory to achieve maximum revenue on perishable products. An electronic data-mart 110 or central information storage and data processing system is established to collect influencing factors for the probability and price sensitivity of a particular advertising buyer 120. The data-mart 110 also collects business rules for inventory scaling, available inventory to sell, budget (goal) information, advertiser payment history, and station performance data to feed to a scenario planner 130. Once three or more variables exist, inventory and pricing fuzzy logic algorithms create scenario plans to present the most profitable bundle of offerings. The scenarios are typically pre-approved although presented to the local business units 150 for an abnormality failsafe. Once processed by the local business units 150, the scenarios are presented to the advertising buyer 120.

Abstract: A method is provided for enterprise management and bundling of radio, outdoor and entertainment inventory to achieve maximum revenue on perishable products. An electronic data-mart 110 or central information storage and data processing system is established to collect influencing factors for the probability and price sensitivity of a particular advertising buyer 120. The data-mart 110 also collects business rules for inventory scaling, available inventory to sell, budget (goal) information, advertiser payment history, and station performance data to feed to a scenario planner 130. Once three or more variables exist, inventory and pricing fuzzy logic algorithms create scenario plans to present the most profitable bundle of offerings. The scenarios are typically pre-approved although presented to the local business units 150 for an abnormality failsafe. Once processed by the local business units 150, the scenarios are presented to the advertising buyer 120.

Abstract: A method is provided for enterprise management and bundling of radio, outdoor and entertainment inventory to achieve maximum revenue on perishable products. An electronic data-mart 110 or central information storage and data processing system is established to collect influencing factors for the probability and price sensitivity of a particular advertising buyer 120. The data-mart 110 also collects business rules for inventory scaling, available inventory to sell, budget (goal) information, advertiser payment history, and station performance data to feed to a scenario planner 130. Once three or more variables exist, inventory and pricing fuzzy logic algorithms create scenario plans to present the most profitable bundle of offerings. The scenarios are typically pre-approved although presented to the local business units 150 for an abnormality failsafe. Once processed by the local business units 150, the scenarios are presented to the advertising buyer 120.

Abstract: A method is provided for enterprise management and bundling of radio, outdoor and entertainment inventory to achieve maximum revenue on perishable products. An electronic data-mart 110 or central information storage and data processing system is established to collect influencing factors for the probability and price sensitivity of a particular advertising buyer 120. The data-mart 110 also collects business rules for inventory scaling, available inventory to sell, budget (goal) information, advertiser payment history, and station performance data to feed to a scenario planner 130. Once three or more variables exist, inventory and pricing fuzzy logic algorithms create scenario plans to present the most profitable bundle of offerings. The scenarios are typically pre-approved although presented to the local business units 150 for an abnormality failsafe. Once processed by the local business units 150, the scenarios are presented to the advertising buyer 120.