Papers
2
Total Citations
67
H-Index
2
About
Di Feng is a rising scholar in engineering design and innovation, whose work centers on product conceptual design, knowledge-driven decision-making, and computational creativity. Her research uniquely bridges patent text mining, knowledge graphs, and design-by-analogy (DbA) to enhance product innovation. Feng’s most-cited paper (2024, 64 citations) introduces a patent text-based decision-making framework that fuses incomplete evaluation semantics with scheme beliefs, enabling more robust and objective design choices from vast patent repositories. Her latest work (2025) advances DbA by constructing a knowledge graph that structures analogical knowledge retrieval, reducing reliance on designer intuition and systematically promoting cross-domain idea generation. This contribution is particularly notable for formalizing a previously ad hoc process, offering a scalable tool for innovation teams. With her publications already garnering attention in the design research community, Feng is establishing herself as a key voice in integrating artificial intelligence with engineering design. Her work holds significant promise for students and researchers interested in data-driven design methodologies, patent analytics, and computational support for creative problem-solving.
Research Focus
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Top Papers
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