Papers
6
Total Citations
184
H-Index
4
About
Jianxi Luo is a leading researcher at the intersection of data-driven design, engineering innovation, and patent analytics. His work fundamentally reimagines how designers and engineers leverage large-scale patent data to inspire, structure, and evaluate new product development. Luo’s key contributions center on developing computational methods—including function network analysis and convolutional neural networks—to mine patent databases for design precedents, platform planning, and ideation stimuli. His highly cited 2018 paper on data-driven platform design (57 citations) introduces a systematic approach to balancing product variety with platform sharing, while his 2017 study on mining patent precedents for spherical rolling robots (49 citations) demonstrates a novel method for retrieving relevant prior art. Luo has also explored the cognitive dimensions of design, investigating how analogical distance in patent stimuli affects the novelty and quality of generated concepts (53 citations). His work has been instrumental in moving engineering design from heuristic, manual processes toward rigorous, data-informed methodologies. By enabling designers to search patents not just by text but by visual and functional content, Luo is shaping the future of computational design and innovation.
Research Focus
Key Achievements
Top Papers
- 1Data-Driven Platform Design: Patent Data and Function Network Analysis57 citations · 2018
- 2Does Analogical Distance Affect Performance of Ideation?53 citations · 2018
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- 4Patent stimuli search and its influence on ideation outcomes20 citations · 2017
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