SBIR/STTR Research Award

SBIR Phase I:Automated Display Optimization Based on Attention Predictions

Award identifier e9291525496c46360014fb0407f31246db3a091dd0529c8ed8771db3ff797dbaAwarding agency National Science Foundation

Award amount

$0

Program and phase

SBIR · Phase I

Award year

2010

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
EYE-PREDICT LLC
Proposal award date
Not reported
Contract end date
Not reported
UEI
EMT6N5RHL8C5
Solicitation number
NSF 09-609
Topic code
IC

Research abstract

This Small Business Innovation Research (SBIR) Phase I project will establish the feasibility of optimizing the effectiveness of commercial displays using a predictive model of human attention deployment. Traditional solutions for optimizing displays require human data. For example, during A/B testing, the target audience is exposed to alternate versions of a display (comparables) and responses such as clicks are used to determine effectiveness. A key shortcoming of traditional optimization solutions is that the number of comparables that can be tested is severely limited by the available audience. These shortcomings will be eliminated if display optimization could be performed based on computer analyses rather than human data. The Phase I research will establish the extent to which computer-generated attention scores can predict catalog selections as measured by mouse clicks. It is anticipated that catalog items with high attention scores will be selected more frequently than catalog items with low and average attention scores. Billions of dollars are spent annually on optimizing commercial displays using techniques that require human data, such as A/B and multivariate testing, contextual and behavioral targeting, consumer research, etc. If successful, this SBIR project will result in a software-as-a-service solution for optimizing commercial displays based on attention predictions. This proposed innovation will enable large scale display optimizations that are practically impossible to perform based on human data. Another key advantage of automated optimization is that it could be performed before exposing audiences to ineffective displays. As a result, the proposed innovation will increase revenue gains from commercial displays. This innovative solution could be applied to a variety of online and offline displays, including catalogs, shelf plans, and graphic ads. Beyond display optimization, attention models could lead to important scientific and technological advances, commercial applications, and health benefits.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

5 SBIR/STTR records share this UEI (2010–2025). Reported award amounts total $1,572,199. Reported amounts are not federal obligations.

Phases: Phase I

Agencies: National Science Foundation, Department of Health and Human Services

Agency intelligence

National Science Foundation

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