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research-article

Mining Patent Precedents for Data-driven Design: The Case of Spherical Rolling Robots

[+] Author and Article Information
Binyang Song

Engineering Product Development Pillar & SUTD-MIT International Design Center, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore
binyang_song@mymail.sutd.edu.sg

Jianxi Luo

Engineering Product Development Pillar & SUTD-MIT International Design Center, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore
luo@sutd.edu.sg

1Corresponding author.

ASME doi:10.1115/1.4037613 History: Received February 20, 2017; Revised August 03, 2017

Abstract

Data-driven engineering designers often search for design precedents in the patent database 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 resultant patents do not sufficiently or accurately capture the prior design knowledge base. This paper proposes an iterative and heuristic methodology to exhaustively search for patents as precedents of the design of a specific technology or product for data-driven next design. The patent retrieval methodology integrates the mining of patent texts, citation relationships and inventor information to identify relevant patents; in particular, 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 identifying self-propelled spherical rolling robot patents. Furthermore, we present two approaches to further analyse and make sense of the retrieved patent data for exploring new spherical rolling robot design opportunities. Our research contributes to patent data-driven design.

Copyright (c) 2017 by ASME
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