Rentrak Corporation

Comscore, Inc.

Rentak Corporation

Michael J Vinson

Amir Yazdani

Bruce Goerlich

Aaron J Harsh

Maria Loper

Kim Hadfield

Caroline Horner

Charles Palit

Bill Engel

Frank Pecjak

Cameron Meierhoefer

John Clougherty

Joseph R Ruthruff

Josh Chasin

Lin Qin

Soyoung Park

Melissa Martin

Marla Loper

Naoko Shimada

A method may include receiving training data including tuning data, household member data, and initial person level distributions. The method may further include aggregating the tuning data from one or more user devices associated with a household to generate an observed household distribution, and calculating, via a prediction model, an implied household viewership distribution based on the person level distribution associated with one or more members of the household. The method may further include comparing the implied household distribution to the observed household distribution of the household, adjusting the prediction model and/or the person level distributions such that the implied household distribution more closely aligns with the observed household distribution, and generating a report with the person level distributions. A system and a non-transitory computer-readable medium may perform the method.

Systems and methods of personifying viewership data

Predict whether active viewability is taking place and/or the likelihood that active viewability will be taking place in the future is described. Historical viewing data may be obtained. One or more probability distribution functions may be generated based on the historical viewing data. One or more survival curves may be determined based on the one or more probability distribution functions. Current viewing data may be obtained. Whether viewability is active in at least one of a current time or a specified future time may be predicted based on the one or more probability distribution functions and the current viewing data. Alternatively, this prediction may be performed via a machine learning model trained on the historical viewing data. Whether to perform a function may be determined based on the prediction.

Predictive detection of real-time and future viewability

Techniques for projecting household-level viewing events are described herein. Population data may be accessed including classes of a plurality of demographic attributes for households in a market. Representative household units (RHUs) may be generated, and the RHUs may be assigned a class for each of the demographic attributes and a quota based on the demographic attributes of a plurality of panelist households. Each of the panelist households may be assigned to one of the RHUs based on at least one panelist classes matching the classes for respective demographic attributes of the RHU, and the number of matching panelist households assigned to each of the RHU may be based on the quota. Panelist viewing data representing viewing events associated with the panelist household may be accessed. A report may be generated with the classes of the RHUs and the panelist viewing data of the assigned panelist households.

Deterministic household assignment model

Prediction models for managing viewership data are disclosed. An amount of time users are displayed content is initially obtained. The obtained amounts may be for each content distributor that distributes channels, for each of the channels with respect to which sets of content are displayed, for each of the sets that comprises content displayed during past periods, and for each of the displayed content. A set of features associated with each of the displays is obtained and a target period is selected from among the past periods. A model is used to predict an amount of time users were displayed content during the target period, for each of the sets, channels, and distributors, based on the obtained sets of features associated with the displays during the target period and on the obtained amounts for the displays during the past periods that precede the target period. A comparison, respectively for the same displays during the target period, of each of the obtained amounts to each of the predicted amounts is performed, and an anomaly is detected based on the comparisons. Finally, the anomaly is alerted.

Systems and methods for predicting viewership and detecting anomalies

A method includes receiving audio data from a microphone of a device associated with a user. The audio data includes audio received by the microphone from a first source and from a second source. The audio data is correlated with a first audio signature and a second audio signature. Then, based on the correlation of the audio data with the first audio signature, first content broadcast from the first source is identified. Second content broadcast from the second source is also identified based on the correlation of the audio data with the second audio signature. It is then determined that the device is closer to the first source than the second source based on audio qualities and/or temporal aspects of the audio data. The first content is associated with the user based on the determination that the device is closer to the first source.

Audio correlation for viewership determination

Audience engagement is analyzed by receiving at least one analysis parameter comprising a content selection parameter, identifying one or more content based on the content selection parameter, determining viewership for the one or more content, determining one or more keywords for the one or more content based on the content, filtering social media messages based on the determined one or more keywords, and calculating an audience engagement measurement corresponding to the one or more content based on the viewership and the social media messages.

System and methods for analyzing content engagement in conjunction with social media

Methods and systems are provided herein for calibrating subject data based on reference data, so that the calibrated subject data more closely represents a target population. The methods and systems include partitioning a reference data set into a plurality of reference data partitions using a data partitioning scheme, each reference data partition associated with a characteristic; and partitioning a subject data set into a plurality of subject data partitions using the data partitioning scheme, each subject data partition associated with a characteristic that corresponds to the characteristic associated with a reference data partition of the plurality of reference data partitions; identifying a variable present in the reference data set that is not present in the subject data set; and calculating a value of the variable for each reference data partition based on a rate of occurrence of the variable in each reference data partition.

Systems and methods for calibrating user and consumer data

A system and method to analyze the effectiveness of advertisements of presented content. The advertisements include advertisements for which the behavior desired by the advertiser is to drive a viewer to perform a certain action, such as to watch particular television program content. The system determines if an advertisement is effective by correlating exposure to presented advertisements with the subsequent presentation of content that was promoted in the advertisements. To perform the analysis, the system collects tune data from set top boxes, digital video recorders (DVRs), or other component capable of storing tune data related to the content presented on an associated display component.

System and method for analyzing the effectiveness of content advertisements

A computer system may include at least one processor and at least one memory storing instructions that, when executed, cause the at least one processor to perform a process. The process may include receiving audio data from a user device, and accessing content data including at least one audio signature associated with video content. The process may also include correlating the audio data with the at least one audio signature and identifying recognized video content based on the correlation of the audio data with the at least one audio. The process may also include receiving tuning data including content being presented on a display component. The process may further include correlating the recognized video content with the tuning data, determining viewed video content based on the correlation of the recognized video content with the tuning data, and storing the viewed video content in a user array.

Various embodiments disclose a system and methods for media content analysis based at least in part upon social media data. In some embodiments, a computer system may determine a viewership associated with a selected piece of televised content such as a television show or advertisement. The computer system may also identify social media data such as social media messages associated with the selected piece of televised content. The computer system may calculate an audience engagement measurement corresponding to the selected piece of televised content based upon the social media data and the viewership.

Systems and methods for analyzing viewing behavior of users viewing video content and advertisements are provided. A system may periodically or continuously receive tune data reflecting the viewing behavior of users, analyze the viewing behavior to determine a location and time period of advertisement blocks in a viewed segment, and determine parameters reflecting a comparison between a number of viewers that viewed the advertisement blocks and a number of viewers that viewed non-advertised content. The system may also deliver a report of the analysis to advertisers, agencies, media sellers, or other parties that are interested in measuring the effectiveness of advertisements on users. The analysis by the system can be done over multiple different types of content-distribution platforms.

Systems and method for analyzing advertisement pods

Various embodiments disclose a system and methods for media content analysis based at least in part upon social media data. In some embodiments, a computer system may identify social media messages corresponding to televised content. For example, following the uploading of a television show (or advertisement) to an Internet website, or transmission across a television network, viewers and non-viewers of the show may begin commenting upon the show. The system may correlate the number of viewers of the show with the social media messages to generate one or more metrics describing relations between the show's viewership and the show's social media effects.

A system and method for analyzing the viewing behavior of end users that are viewing video content to determine the likely presence of one or more advertisements being viewed and to assess the viewers who stop viewing during the course of such advertisements. The system periodically or continuously receives tune data reflecting the viewing behavior of end users, analyzes the cumulative viewing behavior reflected by the received tune data, and delivers a report of the analysis to advertisers, agencies, media sellers, or other parties that are interested in measuring the effectiveness of advertisements on end users. The analysis by the system can be done over multiple different types of content-distribution platforms.

Growing community of inventors

Piedmont, CA, United States of America

Michael J Vinson