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Research Papers: D3 Applications and Case Studies

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

[+] Author and Article Information
Binyang Song

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

Jianxi Luo

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

1Corresponding author.

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.

J. Mech. Des 139(11), 111420 (Oct 02, 2017) (15 pages) Paper No: MD-17-1157; doi: 10.1115/1.4037613 History: Received February 20, 2017; Revised August 03, 2017

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.

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Copyright © 2017 by ASME
Topics: Design , Patents
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Figures

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Fig. 1

Two SSPRR examples. (a) Propulsion provided by a pendulum mechanism; (b) propulsion provided by a self-contained electric cart. Picture courtesy of Peixian Li, Yu Hui Wee, Zhi Yuan Sherwin Lau, Jabez Leong Kit, Yang Hu, Joseph Pua, Kenneth Lim, Padmanathan Suhasini, Sherry Tay, and Threvin Anand in the “30.007 Engineering Design and Project Engineering” classes at SUTD in 2015 and 2016, respectively.

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Fig. 2

An example of the search process through the citation network

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Fig. 3

An example of a search via patent–inventor linkages

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Fig. 4

Flowchart of the proposed patent search method

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Fig. 5

Citation network of SPSRR patents (the numbers are patent IDs)

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Fig. 6

Inventor-sharing network of SPSRR patents (the numbers are patent IDs)

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Fig. 7

Historical expansion of the SPSRR patent set

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Fig. 8

Design knowledge space expansion of SPSRRs over time: as of 1917 (a), 1945 (b), 2000 (c), and 2016 (d)

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Fig. 9

The neighboring technology classes to existing SPSRR designs

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Fig. 10

The co-occurrence network of functions identified in SPSRR patents

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