Guiding data-driven design ideation by knowledge distance

Data-driven conceptual design methods and tools aim to inspire human ideation for new concepts by providing external inspirational stimuli. In prior studies, the stimuli have been limited in terms of coverage, granularity, retrieval guidance. Here, we present a knowledge-based expert system that provides across semantic, document field levels simultaneously from all fields engineering technology follows creativity theories guide use according knowledge distance. The is centered on network patent classification system, store organize world’s cumulative data technological knowledge, solutions total database statistically-estimated distance between fields. turn, guides network-based exploration inferences near far generate ideas analogy combination. With two case showcase effectiveness using explore retrieve multilevel both problem solving open-ended innovation. These studies also demonstrate computer-aided process, which data-driven, computationally augmented, theoretically grounded, visually inspiring, rapid.