I-Corps: Artificial intelligence-based image processing to automate fashion e-commerce

这个i-Corps项目的更广泛的影响/商业潜力是开发软件工具，使基于互联网的商务(电子商务)的几项基本任务自动化。电子商务是一个快速增长的细分市场，预计到2023年销售额将达到6.5万亿美元。如此快速的增长随之而来的是产品发现方面的挑战。丰富的产品属性/产品标签使产品更容易被发现。然而，填充产品属性/标签是一项耗时且主要是手动的任务。通过利用基于人工智能(AI)的图像处理，这些任务可以自动完成，从而为电子商务商店节省时间、精力和费用。此外，准确的标签可以用来生成几种有用的功能，以改善用户体验，并增加电子商务商店的销售额和收入。基于人工智能的自动化工具可能会为电子商务店主提供获取先进技术的途径，并使中小型企业能够在竞争激烈的时尚电子商务细分市场中保持生存。这个i-Corps项目是基于图像处理技术的开发，该技术使用机器学习和人工智能(AI)来准确识别与图像相关的多个(50+)属性。这些属性用于自动和准确地标记商店中的产品，与手动标记相比，成本和时间只需一小部分。准确的产品标签使人们可以在网上很容易地找到产品。AI算法生成的标签还用于提供几个附加功能，如完成外观和产品捆绑。这些功能显著改善了购物者的用户体验，并为店主带来了更多的销售和收入。拟议的技术还包括资源优化，以使人工智能支持的处理在商业上可行。这样的资源优化确保了先进的基于人工智能的技术以合理的成本可用于时尚电子商务等动态环境。此外，AI算法、基于机器学习的决策树和输出之间的过程流确保结果几乎即时显示在店面。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of software tools to automate several essential tasks for internet-based commerce (e-commerce). E-commerce is a rapidly growing market segment, predicted to reach $6.5 trillion in sales by 2023. Consequent to such rapid growth are the resultant challenges in product discovery. Rich product attributes/product tagging makes products easily discoverable. However, populating product attributes/tags is a time-consuming, mostly manual task. By leveraging artificial intelligence (AI)-based image processing, these tasks may be automated, saving time, effort, and expense for e-commerce stores. In addition, accurate tags may be used to generate several useful functionalities to improve user experience as well as increase sales and revenue for e-commerce stores. AI-based automation tools may provide e-commerce store owners with access to advanced technologies and empower small and mid-sized businesses to stay viable in the highly competitive fashion e-commerce market segment. This I-Corps project is based on the development of image processing technology that uses machine learning and artificial intelligence (AI) to accurately recognize multiple (50+) attributes associated with an image. These attributes are used to automatically and accurately tag products in the store at a fraction of a cost and time compared to manual tagging. Accurate tagging of products results in making products easily discoverable online. The AI-algorithm generated tags also are used to provide several additional functionalities such as complete-the-look and product bundling. Such functionalities significantly improve the user experience for shoppers and lead to increased sales and revenue for store owners. The proposed technology also includes resource optimization to make AI-powered processing commercially viable. Such resource optimization ensures that advanced AI-based technologies are available for dynamic environments such as fashion e-commerce at reasonable costs. In addition, the process flow between the AI-algorithm, machine learning-based decision tree, and the output ensures that the results are displayed at the storefront almost instantaneously.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.