SBIR Phase I: Machine Vision for Content-based Video Marketing Analytics

Period of Performance: 07/01/2016 - 06/30/2017

$225K

Phase 1 SBIR

Recipient Firm

Perceptive Automata, Inc.
1 Broadway 5th Fl Array
Cambridge, MA 02142
Firm POC, Principal Investigator

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to protect consumer privacy while continuing to enable the ad-supported Internet model. Current tracking-based consumer targeting approaches inherently erode consumer privacy, surreptitiously tracking users across many different web sites in an effort to gather demographic and behavioral data. On the flip side of the coin, marketers need to collect such data to successfully reach their audiences, and the revenue that marketers pour into advertising online has become an essential component of the economics of the internet. Today, this delicate balance of competing pros and cons is further threatened by the rise of ad-blocking software, which erodes the value of internet ad placement. The video marketing analytics capability developed in this project will limit marketers' need for invasive consumer data, while improving consumer experience. In the commercial realm, marketers would value the opportunity to target their ads in the most emotionally consonant, least disruptive, and most engaging manner possible. This technology will provide marketers with the capability to watch millions of videos algorithmically, thus enabling a more streamlined and customized viewer experience than has ever before been possible on television or on the Internet. This Small Business Innovation Research Phase I project seeks to develop commercial applications for Perceptual Annotation, a technology developed with NSF funding that allows detailed measurements of human performance to be infused into a machine learning process, allowing the machine learner to both perform better and to perform in a way that is more consistent with humans. By adding this new category of human-derived supervisory signal into a machine learning process, the proposers have demonstrated that it is possible to significantly boost machine vision performance, allowing machines to generalize better to new, previously unseen images. While the company's technology has been rigorously validated on large-scale "in the wild" academic datasets, a major technical drive in the proposed SBIR Phase I activities will be to shift the company's efforts to the analysis of "live," enormous, and ever-expanding data sets such as online videos. A second major drive of the proposed Phase I work will be the construction of "second stage" machine learning models that take perceptual-annotation-based machine ratings as an input and output actionable marketing decisions.