Collaborative Research: From User Reviews to User-Centered Generative Design: Automated Methods for Augmented Designer Performance

协作研究：从用户评论到以用户为中心的生成设计：增强设计师性能的自动化方法

基本信息

批准号：
2050130
负责人：
Lu Wang
金额：
$ 20.39万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2050130&HistoricalAwards=false
关键词：
Collaborative Research User Reviews Centered

项目摘要

This project investigates design processes where the unmet needs of users are elicited from social media, online forums, and e-commerce platforms, and translated into new concept recommendations for designers using artificial intelligence (AI). The motivation stems from the growing abundance of user-generated feedback and a lack of advanced computational methods for drawing useful design knowledge and insights from that data. The research will establish a rigorous computational foundation that (1) enables large-scale elicitation of user needs from online reviews using advanced natural language processing (NLP) algorithms, and (2) translates the elicited needs into the visual and functional aspects of new concepts using novel generative adversarial networks (GAN) algorithms. The theoretical innovations will advance the fundamental understanding of how AI can augment the performance and creativity of designers in early-stage product development processes. This project will boost national competitiveness in innovation by creating tacit opportunities for designing innovative, inclusive, and competitive products. The convergent research team will create outreach initiatives for STEM students, teachers, and underrepresented minorities, and engage with industry and research stakeholders to ensure technology-market fit and successful dissemination.The overarching goal of this project is to establish a transformative, data-driven paradigm for empathetic design that augments the ability of designers to uncover and address the critical yet latent needs of users at scale. The project will create scalable and computationally efficient NLP algorithms that capture the needs of ordinary users from reviews, identify the underlying usage contexts, and infer extreme use-cases to facilitate latent need elicitation. Focus groups and interviews involving ninety design experts and crowdsourced evaluators will be conducted to test the first research hypothesis: The NLP algorithms elicit needs that are nonobvious, difficult to identify, and provide significant value and originality. The project will build novel GAN architectures and algorithms for generative design of form and function conditioned on the elicited latent user needs. New multimodal deep regression models will be developed to evaluate the quality of the generated samples based on user feedback on existing products. Laboratory studies involving fifty subjects and fifty evaluators will be performed to test the second research hypothesis: The GAN-generated design recommendations significantly improve the quality and variety of the design concepts generated by human designers. The project will lead to broad societal outcomes by fostering designer-AI co-creation and innovation centered on empathy with users to bridge the gap between user need discovery and design outcomes.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.

该项目调查了设计过程，从社交媒体，在线论坛和电子商务平台中提出了用户未满足的需求，并将使用人工智能（AI）的设计师转化为新的概念建议。动机源于越来越多的用户生成的反馈以及缺乏从该数据中汲取有用的设计知识和见解的先进计算方法。该研究将建立一个严格的计算基础，（1）使用高级自然语言处理（NLP）算法从在线评论中大规模诱导用户需求，（2）将引起的需求转化为使用新型概念的视觉和功能方面，使用新颖的生成性对抗网络（GAN）算法。理论创新将提高人们对AI如何增强设计师在早期产品开发过程中的性能和创造力的基本理解。该项目将通过为设计创新，包容和竞争性产品创造默契的机会来提高全国性创新竞争力。融合研究团队将为STEM学生，教师和代表性不足的少数群体创建外展计划，并与行业和研究利益相关者互动，以确保技术市场的拟合和成功传播。该项目的总体目标是建立一个变革性的，数据驱动的范式，以增强与设计者的能力，以扩大设计师的能力，以探讨少数和订婚的人，并在范围内群众满足了一位精选的范围。该项目将创建可扩展和计算高效的NLP算法，从评论中捕获普通用户的需求，确定潜在的用法上下文以及推断极端用例以促进潜在的需求启发。将进行涉及90位设计专家和众包评估人员的焦点小组和访谈，以检验第一个研究假设：NLP算法引起的需求不太明显，难以识别并提供了重要的价值和独创性。该项目将构建新颖的GAN体系结构和算法，以根据引起的潜在用户需求进行形式和功能的生成设计。将开发新的多模式深层回归模型，以根据现有产品的用户反馈来评估生成样品的质量。将进行涉及五十名受试者和五十名评估人员的实验室研究，以检验第二个研究假设：GAN生成的设计建议显着提高了人类设计师产生的设计概念的质量和种类。该项目将通过促进设计师 - ai共同创建和以同理心为中心的用户弥合用户需要发现和设计成果之间的差距。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子的知识和广泛的影响，这将导致广泛的社会成果。