Rovi Guides, Inc.

Rovi Product Corporation

Steven Bennett

Matthew Emans

Samuel Meyer

Randall Kelley

Walter Richard Klappert

Xiaoxi Xu

A consumption probability metric may be generated for a media asset. An aggregated forecast predicting user consumption of a media asset is received. A plurality of probabilities, each corresponding to a user of a plurality of users, is received, each indicating how likely a respective user is to consume the media asset. A weight for the plurality of users is calculated representing a ratio of the total number of users to a number of users in the plurality of users. A disaggregated forecast predicting user consumption of a media asset is determined based on the weight for the plurality of users and the plurality of probabilities. A modification factor is computed based on the aggregated forecast and the disaggregated forecast. A metric is generated that includes a plurality of user identifiers associated with the plurality of users and a plurality of modified probabilities each modified by the modification factor.

Systems and methods for generating consumption probability metrics

Systems and methods for a framework for generating predictive models for media planning. In some aspects, control circuitry receives, from a database, reference data associated with a program. The control circuitry receives a future date for insertion of an advertisement during transmission of the program. The control circuitry determines a prediction period between a current date and the future date. The control circuitry determines whether the prediction period exceeds a threshold period. If the prediction period does not exceed the threshold period, the control circuitry selects a first type for a predictive model. If the prediction period exceeds the threshold period, the control circuitry selects a second type for the predictive model. The control circuitry trains the predictive model according to the selected type and based on the reference data. The control circuitry predicts, based on the predictive model, an average audience for insertion of the advertisement.

Systems and methods for a framework for generating predictive models for media planning

Methods and systems are disclosed for an optimization system that recommends amounts of resources for use in reaching viewers during a media asset. The optimization system, using control circuitry, receives a user input of a first value of a resource and determines a first rate at which a number of unique household viewers reached during a first media asset changes for the first value. The optimization system compares the first rate to a threshold rate and determines whether the first rate equals or exceeds the threshold rate. The optimization system, in response to determining that the first rate does not equal or exceed the threshold rate, recommends a second value to the user.

Systems and methods for non-linear optimization of audience reach

Methods and systems for performing non-linear optimization of reach are described herein. The methods and systems may be used to compute the reach associated with different advertisement campaigns involving different combinations of spots. The method includes computing probabilities that a user will access a certain spot. The method includes retrieving a weight associated with the targeted user demographic. The method includes computing a first reach value using a first non-linear function, and a second reach value using a second non-linear function. The method includes comparing the two reach values to each other and to a predetermined reach threshold. The higher reach value larger than the predetermined reach threshold is selected and information associated with the selected reach value is provided to the user.

Growing community of inventors

Somerville, MA, United States of America

Steven Bennett