A Novel Mathematical Framework for Objective Characterization of Ideas
This addresses the challenge for novice designers in selecting promising ideas, but it is incremental as it applies existing mathematical tools to a specific domain.
The study tackled the problem of qualitative assessment of ideas generated by conversational AI or humans, which traditionally relies on error-prone expert evaluation, by introducing a mathematical framework that converts ideas into vectors and measures diversity to objectively select promising ideas, enhancing ideation efficiency.
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.