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About

Binyang Song is a leading researcher in data-driven engineering design, with a focus on product platform planning, analogical design, and patent mining. His work bridges computational methods and design theory to enhance innovation efficiency. Song’s most-cited paper, "Data-Driven Platform Design: Patent Data and Function Network Analysis" (57 citations), introduces a novel approach to balancing economy of scale and scope in product families by leveraging patent data for platform planning. In "Does Analogical Distance Affect Performance of Ideation?" (53 citations), he investigates how the distance of stimuli influences the novelty and quality of design solutions, offering critical insights for ideation strategies. His research on patent precedents, such as in "Mining Patent Precedents for Data-Driven Design: The Case of Spherical Rolling Robots" (49 citations), demonstrates how to systematically retrieve and apply prior art for design inspiration. Song’s work has significant impact, with his papers collectively cited over 180 times, and his studies on patent stimuli search and function network analysis provide practical tools for designers. His contributions advance the field of data-driven design, making him a key figure in engineering innovation.