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Research Papers: D3 and Lifecycle

InnoGPS for Data-Driven Exploration of Design Opportunities and Directions: The Case of Google Driverless Car Project

[+] Author and Article Information
Jianxi Luo

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

Bowen Yan

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

Kristin Wood

Engineering Product Development Pillar and
SUTD-MIT International Design Centre,
Singapore University of Technology and Design,
8 Somapah Road,
Singapore 487372, Singapore

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 4, 2017; final manuscript received August 5, 2017; published online October 2, 2017. Assoc. Editor: Yan Wang.

J. Mech. Des 139(11), 111416 (Oct 02, 2017) (13 pages) Paper No: MD-17-1203; doi: 10.1115/1.4037680 History: Received March 04, 2017; Revised August 05, 2017

Engineers and technology firms must continually explore new design opportunities and directions to sustain or thrive in technology competition. However, the related decisions are normally based on personal gut feeling or experiences. Although the analysis of user preferences and market trends may shed light on some design opportunities from a demand perspective, design opportunities are always conditioned or enabled by the technological capabilities of designers. Herein, we present a data-driven methodology for designers to analyze and identify what technologies they can design for the next, based on the principle—what a designer can currently design condition or enable what it can design next. The methodology is centered on an empirically built network map of all known technologies, whose distances are quantified using more than 5 million patent records, and various network analytics to position a designer according to the technologies that they can design, navigate technologies in the neighborhood, and identify feasible paths to far fields for novel opportunities. Furthermore, we have integrated the technology space map, and various map-based functions for designer positioning, neighborhood search, path finding, and knowledge discovery and learning, into a data-driven visual analytic system named InnoGPS. InnoGPS is a global position system (GPS) for finding innovation positions and directions in the technology space, and conceived by analogy from the GPS that we use for positioning, neighborhood search, and direction finding in the physical space.

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Copyright © 2017 by ASME
Topics: Design , Patents
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Figures

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Fig. 1

Flow diagram summarizing the overall methodology

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Fig. 2

Accumulative probability to enter a new domain given its proximity to prior technology positions of Google

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Fig. 3

Maximum spanning tree of the patent technology network and the front-end interface of InnoGPS

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Fig. 4

The original technology space position of Google, from 1997 to 1999

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Fig. 5

Technology domains proximate to G06-Computing in the technology space

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Fig. 6

Design capability-building paths from G06-Computing to B60-Vehicles in General on MST

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Fig. 7

The technology space positions of Google over time: (a) 1997–1999, (b) 1997–2000, (c) 1997–2004, and (d) 1997–2006. Technology classes are highlighted with darkness corresponding to the numbers of Google's patents in respective classes.

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Fig. 8

The technology space positions of Google from 1997 to 2016

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