Data-driven engineering designers often search for design precedents in patent databases to learn about relevant prior arts, seek design inspiration, or assess the novelty of their own new inventions. However, patent retrieval relevant to the design of a specific product or technology is often unstructured and unguided, and the resultant patents do not sufficiently or accurately capture the prior design knowledge base. This paper proposes an iterative and heuristic methodology to comprehensively search for patents as precedents of the design of a specific technology or product for data-driven design. The patent retrieval methodology integrates the mining of patent texts, citation relationships, and inventor information to identify relevant patents; particularly, the search keyword set, citation network, and inventor set are expanded through the designer's heuristic learning from the patents identified in prior iterations. The method relaxes the requirement for initial search keywords while improving patent retrieval completeness and accuracy. We apply the method to identify self-propelled spherical rolling robot (SPSRRs) patents. Furthermore, we present two approaches to further integrate, systemize, visualize, and make sense of the design information in the retrieved patent data for exploring new design opportunities. Our research contributes to patent data-driven design.
Mining Patent Precedents for Data-Driven Design: The Case of Spherical Rolling Robots
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 20, 2017; final manuscript received August 3, 2017; published online October 2, 2017. Assoc. Editor: Ying Liu.
Song, B., and Luo, J. (October 2, 2017). "Mining Patent Precedents for Data-Driven Design: The Case of Spherical Rolling Robots." ASME. J. Mech. Des. November 2017; 139(11): 111420. https://doi.org/10.1115/1.4037613
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