16 results about "Demographics" patented technology

A method for creating a personalized immersive safety simulator includes collecting demographic information relating to a first individual, identifying existing information relating to a work environment of the first individual, and combining the demographic information and the existing information into a semantic model for predicting risk to individuals in the work environment. Training scenarios displayable in an augmented reality or virtual reality device may be created using the combined information. The semantic model may be queried based on the individual and a real-world incident and near miss report related to the work environment or information in a safety protocol or safety standard. The semantic model may be enhanced by integrating information about high-risk zones, high-risk activities, and high-risk demographics into the semantic model, the integrated information being unique to the work environment associated with the semantic model. The semantic model may be updated based on observed tendencies or scene changes.

A disclosed example includes accessing computer-generated impression records, the computer-generated impression records based on network communications received at a server of a database proprietor from client devices, the computer-generated impression records indicative of accesses to media at the client devices; accessing self-reported demographic data and behavioral data from a database of the database proprietor, the self-reported demographic data and the behavioral data corresponding to user accounts registered with the database proprietor and associated with the client devices; comparing the self-reported demographic data with a probability distribution of higher-accuracy demographic data; determining different adjustments for corresponding ones of self-reported demographics of the self-reported demographic data based on the comparison; adjusting the corresponding ones of the self-reported demographics based on corresponding ones of the different adjustments to generate adjusted user demographic data; and assigning the adjusted user demographic data to corresponding ones of the computer-generated impression records.

A disclosed example includes accessing computer-generated impression records, the computer-generated impression records based on network communications received at a server of a database proprietor from client devices, the computer-generated impression records indicative of accesses to media at the client devices; accessing self-reported demographic data and behavioral data from a database of the database proprietor, the self-reported demographic data and the behavioral data corresponding to user accounts registered with the database proprietor and associated with the client devices; comparing the self-reported demographic data with a probability distribution of higher-accuracy demographic data; determining different adjustments for corresponding ones of self-reported demographics of the self-reported demographic data based on the comparison; adjusting the corresponding ones of the self-reported demographics based on corresponding ones of the different adjustments to generate adjusted user demographic data; and assigning the adjusted user demographic data to corresponding ones of the computer-generated impression records.

Demographic estimation system, comprising: a feature generator for generating features from return path data (RPD) reported by set-top boxes associated with return path data households; a neural network to process the features generated from the return path data and predict demographic classification probabilities for the return path data households, wherein the neural network is to be trained based on forum data reported by measurement devices that monitor media devices associated with the participant households; a demographic assignment engine for assigning one or more demographic categories to corresponding ones of the return path data households based on the predicted demographic classification probabilities; and a visitor assignment engine for assigning virtual visitors to at least a subset of the corresponding ones of the return path data households based on the one or more demographic categories assigned to the corresponding ones of the return path data households.

Methods and apparatus to apply household-level weights to audience measurement data at a household-member level are disclosed. An example method to determine demographics of populations to measure media audiences of populations includes determining demographics for members of a first household of a sub-population. First demographics of a first member of the first household are different than second demographics of a second member of the first household. The example method includes calculating a first household-level weight for the first household based on a demographics distribution of the sub-population and aggregate demographics of a population. The example method includes applying the first household-level weight to the first demographics of the first member, applying the first household-level weight to the second demographics of the second member, and estimating a demographics distribution of the population to measure a media audience of the population based on the weighted first demographics and the weighted second demographics.

Disclosed herein are system, apparatus, article of manufacture, method, and / or computer program product embodiments, and / or combinations and sub-combinations thereof, for demographic predictions for content items. An example embodiment operates by assigning weights representing demographics to a first plurality of nodes of a predictive model and assigning predictive values representing predicted demographics to a second plurality of nodes of the model. Pairwise distances between the predictive values for the nodes of the second plurality of nodes and the weighted values of the first plurality of nodes may be calculated and the shortest calculated pairwise distances may be used to assign demographics for content items corresponding to nodes of the first plurality of nodes to content items corresponding nodes of the second plurality of nodes. When content is requested, a content item for which the same demographic has been assigned may be recommended to the requestor.

An appearance of a character depicted in a media asset may be altered, for example, by a trained machine learning model, based on demographic attributes of a consumer of the media asset, such as the age or ethnicity of the consumer. A character alteration rule may limit some types of alteration, for example, re-aging a character that would depict a minor in some situation. Altering the appearance of one or more characters depicted in the media asset may also entail altering the appearance of a first character based on demographics of the consumer and automatically altering the appearance of a second character based on the second character's relationship with the first character. The re-aging the second character may be proportional to the re-aging of the first character.

A disclosed example includes accessing computer-generated impression records, the computer-generated impression records based on network communications received at a server of a database proprietor from client devices, the computer-generated impression records indicative of accesses to media at the client devices; accessing self-reported demographic data and behavioral data from a database of the database proprietor, the self-reported demographic data and the behavioral data corresponding to user accounts registered with the database proprietor and associated with the client devices; comparing the self-reported demographic data with a probability distribution of higher-accuracy demographic data; determining different adjustments for corresponding ones of self-reported demographics of the self-reported demographic data based on the comparison; adjusting the corresponding ones of the self-reported demographics based on corresponding ones of the different adjustments to generate adjusted user demographic data; and assigning the adjusted user demographic data to corresponding ones of the computer-generated impression records.

The present disclosure relates to a demographic estimation system, method, and non-transitory computer-readable storage medium. Example methods, devices, systems, and articles of manufacture (e.g., physical storage media) are disclosed that implement neural network processing of set-top box return path data to estimate household demographics. The example demographic estimation system disclosed herein includes a feature generator that generates features based on return path data reported from a set-top box associated with a return path data household. The disclosed example demographic estimation system also includes a neural network that processes the features generated based on the return path data to predict demographic classification probabilities for the return path data household, the neural network being trained based on panel data reported from a meter that monitors media devices associated with panelist households. The disclosed example demographic estimation system also includes a demographic assignment engine that assigns one or more demographic categories to the respective return path data household in the return path data household based on the predicted demographic classification probabilities.

Methods and apparatus to apply household-level weights to audience measurement data at a household-member level are disclosed. An example method to determine demographics of populations to measure media audiences of populations includes determining demographics for members of a first household of a sub-population. First demographics of a first member of the first household are different than second demographics of a second member of the first household. The example method includes calculating a first household-level weight for the first household based on a demographics distribution of the sub-population and aggregate demographics of a population. The example method includes applying the first household-level weight to the first demographics of the first member, applying the first household-level weight to the second demographics of the second member, and estimating a demographics distribution of the population to measure a media audience of the population based on the weighted first demographics and the weighted second demographics.

Disclosed herein are system, apparatus, article of manufacture, method, and / or computer program product embodiments, and / or combinations and sub-combinations thereof, for demographic predictions for content items. An example embodiment operates by assigning weights representing demographics to a first plurality of nodes of a predictive model and assigning predictive values representing predicted demographics to a second plurality of nodes of the model. Pairwise distances between the predictive values for the nodes of the second plurality of nodes and the weighted values of the first plurality of nodes may be calculated and the shortest calculated pairwise distances may be used to assign demographics for content items corresponding to nodes of the first plurality of nodes to content items corresponding nodes of the second plurality of nodes. When content is requested, a content item for which the same demographic has been assigned may be recommended to the requestor.

An appearance of a character depicted in a media asset may be altered, for example, by a trained machine learning model, based on demographic attributes of a consumer of the media asset, such as the age or ethnicity of the consumer. A character alteration rule may limit some types of alteration, for example, re-aging a character that would depict a minor in some situation. Altering the appearance of one or more characters depicted in the media asset may also entail altering the appearance of a first character based on demographics of the consumer and automatically altering the appearance of a second character based on the second character's relationship with the first character. The re-aging the second character may be proportional to the re-aging of the first character.

A disclosed example includes accessing computer-generated impression records, the computer-generated impression records based on network communications received at a server of a database proprietor from client devices, the computer-generated impression records indicative of accesses to media at the client devices; accessing self-reported demographic data and behavioral data from a database of the database proprietor, the self-reported demographic data and the behavioral data corresponding to user accounts registered with the database proprietor and associated with the client devices; comparing the self-reported demographic data with a probability distribution of higher-accuracy demographic data; determining different adjustments for corresponding ones of self-reported demographics of the self-reported demographic data based on the comparison; adjusting the corresponding ones of the self-reported demographics based on corresponding ones of the different adjustments to generate adjusted user demographic data; and assigning the adjusted user demographic data to corresponding ones of the computer-generated impression records.

A data processing system for recommending insurance plans implements obtaining an electronic copy of demographic information associated with a user; analyzing the demographic information with a first machine learning model to recommend a bundle of insurance policies based on the demographic information, wherein the first machine learning model is configured to group insured people having similar demographics into clusters and to generate the bundle of insurance policies based on predicted medical insurance consumption associated with a respective group into which the model predicts that the first user falls; customizing the recommended bundle of insurance policies based on the demographic information associated with the user to generate a customized bundle of insurance policies; generating an insurance recommendation report that presents the customized bundle of insurance policies to the user; and causing a user interface of a display of a computing device associated with the user to present the insurance recommendation report.

A data processing system for recommending insurance plans implements obtaining an electronic copy of demographic information associated with a user; analyzing the demographic information with a first machine learning model to recommend a bundle of insurance policies based on the demographic information, wherein the first machine learning model is configured to group insured people having similar demographics into clusters and to generate the bundle of insurance policies based on predicted medical insurance consumption associated with a respective group into which the model predicts that the first user falls; customizing the recommended bundle of insurance policies based on the demographic information associated with the user to generate a customized bundle of insurance policies; generating an insurance recommendation report that presents the customized bundle of insurance policies to the user; and causing a user interface of a display of a computing device associated with the user to present the insurance recommendation report.

Methods, apparatus, systems, and articles of manufacture are disclosed to determine unique audience size via clustered data. An example apparatus includes memory, machine readable instructions, and processor circuitry to at least one of execute or instantiate the machine readable instructions. The example processor circuitry is to form a first matrix identifying matching and non-matching instances of first values of respective demographics of an audience of media and second values of the respective demographics. Also, the processor circuitry is to normalize the first matrix to generate an account sharing adjustment matrix. Additionally, the processor circuitry is to apply the account sharing adjustment matrix to a vector of one or more counts of impressions of the media to correct for one or more members of the audience that share a user account registered with a database proprietor, the one or more counts of the impressions corresponding to the respective demographics.