71 results about "Demographics" patented technology

A consumer profiling and advertisement selection system is presented in which consumers or subscribers can be characterized based on their purchase or viewing habits. The result of this process is a consumer characterization vector describing the probabilistic demographics and product preferences of the subscriber or viewer. Advertisement characterization vectors describing an actual or hypothetical market for a product or desired viewing audience can be determined. The ad characteristics including an ad demographic vector, an ad product category and an ad product preference vector is transmitted along with a consumer ID. The consumer ID is used to retrieve a consumer characterization vector which is correlated with the ad characterization vector to determine the suitability of the advertisement to the consumer. A price for displaying the advertisement can be determined based on the results of the correlation of the ad characteristics with the consumer characterization vector.

A video demographics analysis system selects a training set of videos to use to correlate viewer demographics and video content data. The video demographics analysis system extracts demographic data from viewer profiles related to videos in the training set and creates a set of demographic distributions, and also extracts video data from videos in the training set. The video demographics analysis system correlates the viewer demographics with the video data of videos viewed by that viewer. Using the prediction model produced by the machine learning process, a new video about which there is no a priori knowledge can be associated with a predicted demographic distribution specifying probabilities of the video appealing to different types of people within a given demographic category, such as people of different ages within an age demographic category.

The present invention is a system and framework for automatically measuring and correlating visual characteristics of people and accumulating the data for the purpose of demographic and behavior analysis. The demographic and behavior characteristics of people are extracted from a sequence of images using techniques from computer vision. The demographic and behavior characteristics are combined with a timestamp and a location marker to provide a feature vector of a person at a particular time at a particular location. These feature vectors are then accumulated and aggregated automatically in order to generate a data set that can be statistically analyzed, data mined and / or queried.

A method for obtaining demographic and / or preferences information for the display of targeted advertisements into applications on mobile platforms. The described method has advantages of directly connecting the collected demographic information and preferences to the application in question and allowing any type of demographic or preference information to be requested and collected.

A demographics analysis trains classifier models for predicting demographic attribute values of videos and users not already having known demographics. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of videos using video features such as demographics of video uploaders, textual metadata, and / or audiovisual content of videos. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of users (e.g., anonymous users) using user features based on prior video viewing periods of users. For example, viewing-period based user features can include individual viewing period statistics such as total videos viewed. Further, the viewing-period based features can include distributions of values over the viewing period, such as distributions in demographic attribute values of video uploaders, and / or distributions of viewings over hours of the day, days of the week, and the like.

A data processing system generates recommendations for on-line shopping by scoring recommendations matching the customer's cart contents using by assessing and ranking each candidate recommendation by the expected incremental margin associated with the recommendation being issued (as compared to the expected margin associated with the recommendation not being issued) by taking into consideration historical associations, knowledge of the layout of the site, the complexity of the product being sold, the user's session behavior, the quality of the selling point messages, product life cycle, substitutability, demographics and / or other considerations relating to the customer purchase environment. In an illustrative implementation, scoring inputs for each candidate recommendation (such as relevance, exposure, clarity and / or pitch strength) are included in a probabilistic framework (such as a Bayesian network) to score the effectiveness of the candidate recommendation and / or associated selling point messages by comparing a recommendation outcome (e.g., purchase likelihood or expected margin resulting from a given recommendation) against a non-recommendation outcome (e.g., the purchase likelihood or expected margin if no recommendation is issued). In addition, a probabilistic framework may also be used to select a selling point message for inclusion with a selected candidate recommendation by assessing the relative strength of the selling point messages by factoring in a user profile match factor (e.g., the relative likelihood that the customer matches the various user case profiles).

A video demographics analysis system selects a training set of videos to use to correlate viewer demographics and video content data. The video demographics analysis system extracts demographic data from viewer profiles related to videos in the training set and creates a set of demographic distributions, and also extracts video data from videos in the training set. The video demographics analysis system correlates the viewer demographics with the video data of videos viewed by that viewer. Using the prediction model produced by the machine learning process, a new video about which there is no a priori knowledge can be associated with a predicted demographic distribution specifying probabilities of the video appealing to different types of people within a given demographic category, such as people of different ages within an age demographic category.

A targeted marketing system and method are provided that provide a customer with customer questions, receive responses to the customer questions from the customer, and store data associated with the responses. The customer is provided with a feedback page that graphically illustrates data associated with the customer's standing in a selected peer group. The customer is provided with options operable to adjust the customer's actual demographic to a hypothetical demographic, and data associated with hypothetical demographic changes from the customer is received and processed. Hypothetical feedback information is then displayed that graphically illustrates the hypothetical standing of the customer within the selected peer group such that the customer can see the effect of the hypothetical demographic changes.

The present disclosure relates to the measurement of the audience characteristics or demographics which is or can be exposed to displayed interactive content depicted on a screen. The audience can include persons who are engaged in interacting with the interactive content as well as persons who are observers of the interactive content and / or the active participants. The content can include one or more targeted advertisements contained in or associated with interactive content, and can be varied based on the audience characteristics or demographics. The audience characteristics or demographics can be used to accurately charge advertisers for the impressions created by the content displayed and can also or alternatively be used to determine and / or trigger what content is actually displayed.