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

• 批准号: 2050052
• 负责人: Mohsen Moghaddam
• 金额: $ 41.66万
• 依托单位: Northeastern University
• 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=2050052&HistoricalAwards=false
• 关键词: Collaborative; Research; User; Reviews; Centered

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.

该项目研究设计过程，其中用户未满足的需求从社交媒体，在线论坛和电子商务平台中引出，并使用人工智能（AI）转化为设计师的新概念建议。这种动机源于越来越多的用户生成的反馈，以及缺乏先进的计算方法来从这些数据中提取有用的设计知识和见解。该研究将建立一个严格的计算基础，（1）使用先进的自然语言处理（NLP）算法从在线评论中大规模激发用户需求，（2）使用新的生成对抗网络（GAN）算法将激发的需求转化为新概念的视觉和功能方面。理论创新将促进对人工智能如何在早期产品开发过程中增强设计师的性能和创造力的基本理解。该项目将通过为设计创新、包容和有竞争力的产品创造隐性机会，提高国家在创新方面的竞争力。聚合研究团队将为STEM学生、教师和代表性不足的少数群体制定外展计划，并与行业和研究利益相关者合作，以确保技术与市场的契合和成功传播。该项目的总体目标是建立一个变革性的、数据驱动的移情设计范式，增强设计师发现和解决大规模用户关键但潜在需求的能力。该项目将创建可扩展和计算效率高的NLP算法，从评论中捕获普通用户的需求，识别底层使用上下文，并推断极端用例以促进潜在需求的激发。焦点小组和采访涉及90名设计专家和众包评估人员将进行测试的第一个研究假设：NLP算法引起的需求是不明显的，难以识别，并提供显着的价值和原创性。该项目将建立新的GAN架构和算法，用于基于潜在用户需求的形式和功能的生成设计。将开发新的多模态深度回归模型，以根据用户对现有产品的反馈评估生成样本的质量。将进行涉及50名受试者和50名评估人员的实验室研究，以测试第二个研究假设：GAN生成的设计建议显着提高了人类设计师生成的设计概念的质量和多样性。该项目将通过促进设计师与人工智能的共同创造和创新，以用户的同理心为中心，弥合用户需求发现和设计成果之间的差距，从而产生广泛的社会成果。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Special Issue: Emerging Technologies and Methods for Early-Stage Product Design and Development

特刊：早期产品设计和开发的新兴技术和方法

• DOI: 10.1115/1.4056744
• 发表时间: 2023
• 期刊: Journal of Mechanical Design
• 影响因子: 3.3
• 作者: Moghaddam, Mohsen;Marion, Tucker;Holtta-Otto, Katja;Fu, Kate;Olechowski, Alison;McComb, Christopher
• 通讯作者: McComb, Christopher

DDE-GAN: Integrating a Data-driven Design Evaluator into Generative Adversarial Networks for Desirable and Diverse Concept Generation

DDE-GAN：将数据驱动的设计评估器集成到生成对抗网络中，以生成理想且多样化的概念

• DOI: 10.1115/1.4056500
• 发表时间: 2022
• 期刊: Journal of Mechanical Design
• 影响因子: 3.3
• 作者: Yuan, Chenxi;Marion, Tucker;Moghaddam, Mohsen
• 通讯作者: Moghaddam, Mohsen

Attribute-Sentiment-Guided Summarization of User Opinions From Online Reviews

属性情感引导的在线评论用户意见总结

• DOI: 10.1115/1.4055736
• 发表时间: 2023
• 期刊: Journal of Mechanical Design
• 影响因子: 3.3
• 作者: Han, Yi;Nanda, Gaurav;Moghaddam, Mohsen
• 通讯作者: Moghaddam, Mohsen

Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model

• DOI: 10.1115/1.4052366
• 发表时间: 2021-09
• 期刊: Journal of Mechanical Design
• 影响因子: 3.3
• 作者: Chenxi Yuan;T. Marion;Mohsen Moghaddam

Aspect-Sentiment-Guided Opinion Summarization for User Need Elicitation From Online Reviews

方面情感引导的意见总结，用于从在线评论中获取用户需求

• DOI: 10.1115/detc2022-90108
• 发表时间: 2022
• 期刊: Aspect-Sentiment-Guided Opinion Summarization for User Need Elicitation From Online Reviews
• 作者: Han, Yi;Moghaddam, Mohsen;Suthar, Meet Tusharbhai;Nanda, Gaurav
• 通讯作者: Nanda, Gaurav