The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Oct. 13, 2015
Filed:
Oct. 29, 2013
Rajeev Sharma, State College, PA (US);
Namsoon Jung, State College, PA (US);
Joonhwa Shin, State College, PA (US);
Rajeev Sharma, State College, PA (US);
Namsoon Jung, State College, PA (US);
Joonhwa Shin, State College, PA (US);
VideoMining Corporation, State College, PA (US);
Abstract
The present invention provides a comprehensive method to design an automatic media audience measurement system that can estimate the site-wide audience of a media of interest (e.g., the site-wide viewership of a target display) based on the measurements of a subset of the actual audience sampled from a limited space in the site. This invention enables (1) the placement of sensors in optimal positions for the viewership data measurement and (2) the estimation of the site-wide viewership of the target display by performing the viewership extrapolation based on the sampled viewership data. The viewership extrapolation problem is formulated in a way that the time-varying crowd dynamics around the target display is an important decisive factor as well as the sampled viewership data at a given time in yielding the estimated site-wide viewership. To solve this problem, the system elements that affect the viewership—site, display, crowd, and audience—and their relationships are first identified in terms of the visibility, the viewership relevancy, and the crowd occupancy. The optimal positions of the sensors are determined to cover the maximum area of the viewership with high probabilities. The viewership extrapolation function is then modeled and learned from the sampled viewership data, the site-wide viewership data, and the crowd dynamics measurements while removing the noise in the sampled viewership data using the viewership relevancy of the measurements to the target display.