BioSpark: An End-to-End Generative System for Biological-Analogical Inspirations and Ideation

• DOI: 10.1145/3613905.3651035
• Publication Date: 2024-05-11T08:15:21Z
• Authors: Hyeonsu B Kang; David Chuan-En Lin; Nikolas Martelaro; Aniket Kittur; Yan-Ying Chen; Matthew K. Hong
• Keywords: FOS: Computer and information sciences; Computer Science - Human-Computer Interaction; Human-Computer Interaction (cs.HC)

Nature is often used to inspire solutions for complex engineering problems, but achieving its full potential is challenging due to difficulties in discovering relevant analogies and synthesizing from them. Here, we present an end-to-end system, BioSpark, that generates biological-analogical mechanisms and provides an interactive interface to comprehend and synthesize from them. BioSpark pipeline starts with a small seed set of mechanisms and expands it using an iteratively constructed taxonomic hierarchies, overcoming data sparsity in manual expert curation and limited conceptual diversity in automated analogy generation via LLMs. The interface helps designers with recognizing and understanding relevant analogs to design problems using four main interaction features. We evaluate the biological-analogical mechanism generation pipeline and showcase the value of BioSpark through case studies. We end with discussion and implications for future work in this area.<br/>NeurIPS 2023 Workshop on Machine Learning for Creativity and Design<br/>