SBIR/STTR Research Award

SBIR Phase I:Scaling Up Open Innovation with Crowd Wisdom and Artificial Intelligence (AI) for Smarter and More Sustainable Fashion

Award identifier 16223f0c3f3c0fcf41e8f1768bdee23b4e29ce844c2d07665ddedba6fbe0e2e2Awarding agency National Science Foundation

Award amount

$274,667

Program and phase

SBIR · Phase I

Award year

2023

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
ILAMBDA INC
Proposal award date
2023-02-15
Contract end date
2023-07-31
UEI
N9F1HMNSKYB6
Solicitation number
NSF 22-551
Topic code
AA

Research abstract

This Small Business Innovation Research (SBIR) Phase I project will develop and leverage an innovative hybrid intelligence, i.e., a unique combination of Big Data and artificial intelligence (AI) technologies with the wisdom of crowds, to help connect and empower both independent designers and small-to-medium-sized retailers/fashion buyers (together with supply chain partners), to help bring the original, unique, trendy designs with great garment quality to fashion consumers. The project also aims to help the fashion industry to tackle some of its hardest, most critical, and most urgent challenges in overproduction and waste (resulting in environmental issues). The project will advance recommendation technology and fashion intelligence by developing novel deep learning-powered fashion recommendation models, and effectively combine and integrate human fashion experts’ input and deep learning predictions. These techniques will help match fashion retail buyers and design(er)s, with the consideration of uniqueness and exclusivity.The project will also help evaluate key aspects of the fashion designs, such as uniqueness and trendiness, and provide more accurate predictions on fashion demands and sales. _x000D_ _x000D_ The key technology innovations are two-fold. First, a novel self-supervised and deep learning-powered fashion recommendation engine will effectively utilize the heterogeneous fashion data (images, text, behaviors, and sales) to help accurately match fashion buyers and manufacturers with the (new) design(er)s under style compatibility and other requirements. Second, a hybrid intelligence engine will effectively combine and integrate fashion buyers' input (votes and orders) with deep learning models to help measure fashion uniqueness, trendiness, and sales forecasts, etc., of the new designs. The project can help both designers and retailers track the trends and the demands and stay ahead of the fashion curve._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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

2 SBIR/STTR records share this UEI (2023–2026). Reported award amounts total $1,524,601. Reported amounts are not federal obligations.

Phases: Phase II, Phase I

Agencies: National Science Foundation

Agency intelligence

National Science Foundation

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