The demand for innovation in product design necessitates a prolific ideation phase. Conversational AI (CAI) systems that use Large Language Models (LLMs) such as GPT (Generative Pre-trained Transformer) have been shown to be fruitful in augmenting human creativity, providing numerous novel and diverse ideas. Despite the success in ideation quantity, the qualitative assessment of these ideas remains challenging and traditionally reliant on expert human evaluation. This method suffers from limitations such as human judgment errors, bias, and oversight. Addressing this gap, our study introduces a comprehensive mathematical framework for automated analysis to objectively evaluate the plethora of ideas generated by CAI systems and/or humans. This framework is particularly advantageous for novice designers who lack experience in selecting promising ideas. By converting the ideas into higher dimensional vectors and quantitatively measuring the diversity between them using tools such as UMAP, DBSCAN and PCA, the proposed method provides a reliable and objective way of selecting the most promising ideas, thereby enhancing the efficiency of the ideation phase.

翻译：产品设计创新的需求要求一个多产的创意生成阶段。使用大型语言模型（如GPT）的对话式人工智能系统已被证明能有效增强人类创造力，提供大量新颖且多样化的创意。尽管在创意数量上取得了成功，对这些创意的质量评估仍然具有挑战性，传统上依赖于专家的人工评估。这种方法存在诸如人为判断错误、偏见和疏漏等局限性。为弥补这一不足，本研究引入了一个全面的数学框架进行自动化分析，以客观评估由对话式人工智能系统和/或人类生成的大量创意。该框架对于缺乏筛选潜力创意经验的新手设计师尤为有益。通过将创意转换为高维向量，并利用UMAP、DBSCAN和PCA等工具定量测量创意间的多样性，所提出的方法提供了一种可靠且客观的方式来筛选最具潜力的创意，从而提高了创意生成阶段的效率。