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

SBIR 第一阶段：利用群体智慧和人工智能 (AI) 扩大开放创新，打造更智能、更可持续的时尚

• 批准号: 2223164
• 负责人: HAIYONG ZHANG
• 金额: $ 27.47万
• 依托单位: ILAMBDA, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-15 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223164&HistoricalAwards=false
• 关键词: SBIR; Phase; Scaling; Up; Open

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. 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.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.

这项小型企业创新研究（SBIR）I阶段项目将开发和利用创新的混合智能，即大数据和人工智能（AI）技术与人群智慧的独特组合，以帮助连接和授权独立的设计师和小型到中型零售商/小型零售商/时尚零售商/与供应链型群体一起，使时装型良好的良好型号，使得GROAL GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR GAR INTIOL YERMERS，独特的质量。该项目还旨在帮助时装行业解决过多生产和浪费中最困难，最危险，最紧迫的挑战（导致环境问题）。该项目将通过开发新颖的深度学习时尚推荐模型来推动推荐技术和时尚智能，并有效地结合和整合人类时尚专家的投入和深度学习预测。这些技术将有助于将时尚零售买家和设计（ER）与独特性和排他性相匹配。该项目还将有助于评估时装设计的关键方面，例如独特性和时尚性，并为时尚需求和销售提供更准确的预测。关键的技术创新是两个方面。首先，一种新颖的自学和深度学习驱动的时尚推荐引擎将有效地利用异质的时尚数据（图像，文本，行为和销售），以帮助与时尚购买者和制造商与（新的）设计（ER）在时尚兼容性和其他要求下准确地匹配时尚买家和制造商。其次，混合情报引擎将有效地结合和整合时尚购买者的投入（投票和订单）与深度学习模型，以帮助衡量新设计的时尚独特性，时尚性和销售预测等。该项目可以帮助设计师和零售商跟踪趋势和需求，并保持领先于时尚曲线。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准来诚实地通过评估来诚实地获得支持。