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.

Copyright © 2017 by ASME
Topics: Design , Patents
Your Session has timed out. Please sign back in to continue.


Reid, T. N. , Frischknecht, B. D. , and Papalambros, P. Y. , 2012, “ Perceptual Attributes in Product Design: Fuel Economy and Silhouette-Based Perceived Environmental Friendliness Tradeoffs in Automotive Vehicle Design,” ASME J. Mech. Des., 134(4), p. 041006. [CrossRef]
Chen, W. , Hoyle, C. , and Wassenaar, H. J. , 2012, Decision-Based Design: Integrating Consumer Preferences Into Engineering Design, Springer Science & Business Media, London.
Wang, M. , Chen, W. , Huang, Y. , Contractor, N. S. , and Fu, Y. , 2016, “ Modeling Customer Preferences Using Multidimensional Network Analysis in Engineering Design,” Des. Sci., 2, p. e11.
Burnap, A. , Pan, Y. , Liu, Y. , Ren, Y. , Lee, H. , Gonzalez, R. , and Papalambros, P. Y. , 2016, “ Improving Design Preference Prediction Accuracy Using Feature Learning,” ASME J. Mech. Des., 138(7), p. 071404. [CrossRef]
Kang, N. , Ren, Y. , Feinberg, F. M. , and Papalambros, P. Y. , 2016, “ Public Investment and Electric Vehicle Design: A Model-Based Market Analysis Framework With Application to a USA–China Comparison Study,” Des. Sci., 2, p. e6.
Ma, J. , and Kim, H. M. , 2014, “ Continuous Preference Trend Mining for Optimal Product Design With Multiple Profit Cycles,” ASME J. Mech. Des., 136(6), p. 061002. [CrossRef]
Jin, J. , Ji, P. , and Liu, Y. , 2014, “ Prioritising Engineering Characteristics Based on Customer Online Reviews for Quality Function Deployment,” J. Eng. Des., 25(7–9), pp. 303–324. [CrossRef]
Weisberg, R. W. , 2006, Creativity: Understanding Innovation in Problem Solving, Science, Invention, and the Arts, Wiley, Hoboken, NJ.
Arthur, W. B. , 2009, The Nature of Technology: What It Is and How It Evolves, Simon and Schuster, New York. [PubMed] [PubMed]
Hatchuel, A. , and Weil, B. , 2009, “ CK Design Theory: An Advanced Formulation,” Res. Eng. Des., 19(4), pp. 181–192. [CrossRef]
Tang, V. , and Luo, J. , 2013, “ Idea Matrix and Creativity Operators,” DS 75-7: 19th International Conference on Engineering Design (ICED), Design for Harmonies, Seoul, South Korea, Aug. 19–22, pp. 301–316.
Alstott, J. , Triulzi, G. , Yan, B. , and Luo, J. , 2017, “ Mapping Technology Space by Normalizing Patent Networks,” Scientometrics, 110(1), pp. 443–479. [CrossRef]
Fleming, L. , and Sorenson, O. , 2004, “ Science as a Map in Technological Search,” Strategic Manage. J., 25(8–9), pp. 909–928. [CrossRef]
Hatchuel, A. , and Weil, B. , 2003, “ A New Approach of Innovative Design: An Introduction to CK Theory,” DS 31: The 14th International Conference on Engineering Design, ICED, Stockholm, Sweden, Aug. 19–21, Paper No. DS31_1794FPC.
Hatchuel, A. , Le Masson, P. , and Weil, B. , 2004, “ C-K Theory in Practice: Lessons From Industrial Applications,” DS 32: The Eighth International Design Conference, DESIGN, Dubrovnik, Croatia, May 18–21, pp. 245–258.
Luo, J. , 2015, “ The United Innovation Process: Integrating Science, Design, and Entrepreneurship as Sub-Processes,” Des. Sci., 1, p. e2.
De Groot, A. D. , 1965, Thoughtand Choice in Chess, Mouton, The Hague, The Netherlands.
Chase, W. G. , and Simon, H. A. , 1973, “ Perception in Chess,” Cognit. Psychol., 4(1), pp. 55–81. [CrossRef]
Chi, M. T. , Feltovich, P. J. , and Glaser, R. , 1981, “ Categorization and Representation of Physics Problems by Experts and Novices,” Cognit. Sci., 5(2), pp. 121–152. [CrossRef]
Ericsson, K. A. , 1999, “ Creative Expertise as Superior Reproducible Performance: Innovative and Flexible Aspects of Expert Performance,” Psychol. Inquiry, 10(4), pp. 329–333.
Linsey, J. S. , 2007, “ Design-by-Analogy and Representation in Innovative Engineering Concept Generation,” Ph.D. thesis, The University of Texas at Austin, Austin, TX.
Linsey, J. , Markman, A. , and Wood, K. , 2012, “ Design by Analogy: A Study of the WordTree Method for Problem Re-Representation,” ASME J. Mech. Des., 134(4), p. 041009. [CrossRef]
Fleming, L. , and Sorenson, O. , 2001, “ Technology as a Complex Adaptive System: Evidence From Patent Data,” Res. Policy, 30(7), pp. 1019–1039. [CrossRef]
Uzzi, B. , Mukherjee, S. , Stringer, M. , and Jones, B. , 2013, “ Atypical Combinations and Scientific Impact,” Science, 342(6157), pp. 468–472. [CrossRef] [PubMed]
He, Y. , and Luo, J. , 2017, “ Novelty, Conventionality, and Value of Invention,” Design Computing and Cognition (DCC), Evanston, IL, June 27–29, pp. 23–38.
Yan, B. , and Luo, J. , 2017, “ Measuring Technological Distance for Patent Mapping,” J. Assoc. Inf. Sci. Technol., 68(2), pp. 423–437. [CrossRef]
Shai, O. , and Reich, Y. , 2004, “ Infused Design. I. Theory,” Res. Eng. Des., 15(2), pp. 93–107.
Reich, Y. , and Shai, O. , 2012, “ The Interdisciplinary Engineering Knowledge Genome,” Res. Eng. Des., 23(3), pp. 251–264. [CrossRef]
Altshuller, H. , 1994, The Art of Inventing (and Suddenly the Inventor Appeared), Technical Innovation Center, Worcester, MA.
Szykman, S. , Sriram, R. D. , Bochenek, C. , Racz, J. W. , and Senfaute, J. , 2000, “ Design Repositories: Engineering Design's New Knowledge Base,” IEEE Intell. Syst. Their Appl., 15(3), pp. 48–55. [CrossRef]
Bohm, M. R. , Vucovich, J. P. , and Stone, R. B. , 2008, “ Using a Design Repository to Drive Concept Generation,” ASME J. Comput. Inf. Sci. Eng., 8(1), p. 014502. [CrossRef]
Chakrabarti, A. K. , Dror, I. , and Eakabuse, N. , 1991, “ Interorganizational Transfer of Knowledge: An Analysis of Patent Citations of a Defense Firm,” Technology Management: The New International Language, Portland, OR, Oct. 27–31, pp. 510–515.
Indukuri, K. V. , Ambekar, A. A. , and Sureka, A. , 2007, “ Similarity Analysis of Patent Claims Using Natural Language Processing Techniques,” International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), Sivakasi, Tamil Nadu, Dec. 13–15, pp. 169–175.
Kasravi, K. , and Risov, M. , 2007, “ Patent Mining-Discovery of Business Value From Patent Repositories,” 40th Annual Hawaii International Conference on System Sciences (HICSS), Big Island, Hawaii, Jan. 3–6, pp. 1–10.
Moehrle, M. , and Geritz, A. , 2004, “ Developing Acquisition Strategies Based on Patent Maps,” 13th International Conference on Management of Technology (IAMOT), Washington, DC, May 13–15 and Apr. 3–7, pp. 19–29.
Zhang, L. , Li, L. , and Li, T. , 2015, “ Patent Mining: A Survey,” ACM SIGKDD Explor. Newsl., 16(2), pp. 1–19. [CrossRef]
Bonino, D. , Ciaramella, A. , and Corno, F. , 2010, “ Review of the State-of-the-Art in Patent Information and Forthcoming Evolutions in Intelligent Patent Informatics,” World Pat. Inf., 32(1), pp. 30–38. [CrossRef]
Cascini, G. , and Russo, D. , 2007, “ Computer-Aided Analysis of Patents and Search for TRIZ Contradictions,” Int. J. Prod. Dev., 4(1–2), pp. 52–67. [CrossRef]
Zhang, R. , Cha, J. , and Lu, Y. , 2007, “ A Conceptual Design Model Using Axiomatic Design, Functional Basis and TRIZ,” IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, Dec. 2–4, pp. 1807–1810.
Li, Z. , Tate, D. , Lane, C. , and Adams, C. , 2012, “ A Framework for Automatic TRIZ Level of Invention Estimation of Patents Using Natural Language Processing, Knowledge-Transfer and Patent Citation Metrics,” Comput.-Aided Des., 44(10), pp. 987–1010. [CrossRef]
Fantoni, G. , Apreda, R. , Dell'Orletta, F. , and Monge, M. , 2013, “ Automatic Extraction of Function–Behaviour–State Information From Patents,” Adv. Eng. Inf., 27(3), pp. 317–334. [CrossRef]
Souili, A. , Cavallucci, D. , Rousselot, F. , and Zanni, C. , 2015, “ Starting From Patents to Find Inputs to the Problem Graph Model of IDM-TRIZ,” Procedia Eng., 131, pp. 150–161. [CrossRef]
Mukherjea, S. , Bamba, B. , and Kankar, P. , 2005, “ Information Retrieval and Knowledge Discovery Utilizing a Biomedical Patent Semantic Web,” IEEE Trans. Knowl. Data Eng., 17(8), pp. 1099–1110. [CrossRef]
Fu, K. , Murphy, J. , Yang, M. , Otto, K. , Jensen, D. , and Wood, K. , 2015, “ Design-by-Analogy: Experimental Evaluation of a Functional Analogy Search Methodology for Concept Generation Improvement,” Res. Eng. Des., 26(1), pp. 77–95. [CrossRef]
Fu, K. , Cagan, J. , Kotovsky, K. , and Wood, K. , 2013, “ Discovering Structure in Design Databases Through Functional and Surface Based Mapping,” ASME J. Mech. Des., 135(3), p. 031006. [CrossRef]
Chan, J. , Fu, K. , Schunn, C. , Cagan, J. , Wood, K. , and Kotovsky, K. , 2011, “ On the Benefits and Pitfalls of Analogies for Innovative Design: Ideation Performance Based on Analogical Distance, Commonness, and Modality of Examples,” ASME J. Mech. Des., 133(8), p. 081004. [CrossRef]
Chan, J. , Dow, S. P. , and Schunn, C. D. , 2015, “ Do the Best Design Ideas (Really) Come From Conceptually Distant Sources of Inspiration?,” Des. Stud., 36, pp. 31–58. [CrossRef]
Fu, K. , Chan, J. , Cagan, J. , Kotovsky, K. , Schunn, C. , and Wood, K. , 2013, “ The Meaning of ‘Near’ and ‘Far’: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output,” ASME J. Mech. Des., 135(2), p. 021007. [CrossRef]
Srinivansan, V. , Song, B. , Luo, J. , Subburaj, K. , Rajesh Elara, M. , Blessing, L. , and Wood, K. , 2017, “ Investigating Effects of Analogical Distance on Performance of Ideation,” ASME International Design Engineering Technical Conference (IDETC), Cleveland, OH, Aug. 6–9, Paper No. DETC2017-67752.
Gentner, D. , and Markman, A. B. , 1997, “ Structure Mapping in Analogy and Similarity,” Am. Psychol., 52(1), pp. 45–56. [CrossRef]
Ward, T. B. , 1998, “ Analogical Distance and Purpose in Creative Thought: Mental Leaps Versus Mental Hops,” Advances in Analogy Research: Integration of Theory and Data From the Cognitive, Computational, and Neural Sciences, New Bulgarian University, Sofia, Bulgaria.
Tseng, I. , Moss, J. , Cagan, J. , and Kotovsky, K. , 2008, “ The Role of Timing and Analogical Similarity in the Stimulation of Idea Generation in Design,” Des. Stud., 29(3), pp. 203–221. [CrossRef]
Wilson, J. O. , Rosen, D. , Nelson, B. A. , and Yen, J. , 2010, “ The Effects of Biological Examples in Idea Generation,” Des. Stud., 31(2), pp. 169–186. [CrossRef]
Gick, M. L. , and Holyoak, K. J. , 1980, “ Analogical Problem Solving,” Cognit. Psychol., 12(3), pp. 306–355. [CrossRef]
Leydesdorff, L. , Kushnir, D. , and Rafols, I. , 2014, “ Interactive Overlay Maps for U.S. Patent (USPTO) Data Based on International Patent Classification (IPC),” Scientometrics, 98(3), pp. 1583–1599. [CrossRef]
Kay, L. , Newman, N. , Youtie, J. , Porter, A. L. , and Rafols, I. , 2014, “ Patent Overlay Mapping: Visualizing Technological Distance,” J. Assoc. Inf. Sci. Technol., 65(12), pp. 2432–2443. [CrossRef]
Engelsman, E. C. , and van Raan, A. F. , 1994, “ A Patent-Based Cartography of Technology,” Res. Policy, 23(1), pp. 1–26. [CrossRef]
Jaccard, P. , 1901, “ Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines,” Bull. Soc. Vaudoise Sci. Nat., 37, pp. 241–272.
Von Wartburg, I. , Teichert, T. , and Rost, K. , 2005, “ Inventive Progress Measured by Multi-Stage Patent Citation Analysis,” Res. Policy, 34(10), pp. 1591–1607. [CrossRef]
Leydesdorff, L. , and Vaughan, L. , 2006, “ Co‐Occurrence Matrices and Their Applications in Information Science: Extending ACA to the Web Environment,” J. Am. Soc. Inf. Sci. Technol., 57(12), pp. 1616–1628. [CrossRef]
Breschi, S. , Lissoni, F. , and Malerba, F. , 2003, “ Knowledge-Relatedness in Firm Technological Diversification,” Res. Policy, 32(1), pp. 69–87. [CrossRef]
Dibiaggio, L. , and Nesta, L. , 2005, “ Patents Statistics, Knowledge Specialisation and the Organisation of Competencies,” Rev. d’économie Ind., 110(1), pp. 103–126. [CrossRef]
Hinze, S. , Reiss, T. , and Schmoch, U. , 1997, Statistical Analysis on the Distance Between Fields of Technology, Fraunhofer-Inst. Systems and Innovation Research, Karlsruhe, Germany.
Boschma, R. , Balland, P.-A. , and Kogler, D. F. , 2014, “ Relatedness and Technological Change in Cities: The Rise and Fall of Technological Knowledge in U.S. Metropolitan Areas From 1981 to 2010,” Ind. Corporate Change, 24(1), pp. 223–250. [CrossRef]
Rigby, D. L. , 2015, “ Technological Relatedness and Knowledge Space: Entry and Exit of U.S. Cities From Patent Classes,” Reg. Stud., 49(11), pp. 1922–1937. [CrossRef]
Alstott, J. , Triulzi, G. , Yan, B. , and Luo, J. , 2017, “ Inventors' Explorations Across Technology Domains,” Des. Sci., epub.
Song, B. , Triulzi, G. , Alstott, J. , Yan, B. , and Luo, J. , 2016, “ Overlay Patent Network to Analyze the Design Space of a Technology Domain: The Case of Hybrid Electrical Vehicles,” DS 84: The 14th International Design Conference, DESIGN, Cavtat, Croatia, May 16–19, pp. 1145–1154.
Yan, B. , and Luo, J. , 2017, “ Filtering Patent Maps for Visualization of Diversification Paths of Inventors and Organizations,” J. Assoc. Inf. Sci. Technol., 68(6), pp. 1551–1563. [CrossRef]
Kobourov, S. G. , 2013, “ Force-Directed Drawing Algorithms,” Handbook of Graph Drawing and Visualization, R. Tamassia , ed., CRC Press, Boca Raton, FL, pp. 383–408.
Leten, B. , Belderbos, R. , and Van Looy, B. , 2007, “ Technological Diversification, Coherence, and Performance of Firms,” J. Prod. Innovation Manage., 24(6), pp. 567–579. [CrossRef]
Frenken, K. , and Nuvolari, A. , 2004, “ Entropy Statistics as a Framework to Analyse Technological Evolution,” Applied Evolutionary Economics and Complex Systems, Edward Elgar, Cheltenham, UK, pp. 95–133. [CrossRef]
Teece, D. J. , Rumelt, R. , Dosi, G. , and Winter, S. , 1994, “ Understanding Corporate Coherence: Theory and Evidence,” J. Econ. Behav. Organ., 23(1), pp. 1–30. [CrossRef]
Nelson, R. R. , and Winter, S. G. , 1982, An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge, MA.
Winter, S. G. , 2000, “ The Satisficing Principle in Capability Learning,” Strategic Manage. J., 21(10–11), pp. 981–996. [CrossRef]
Winston, P. H. , 1992, Artificial Intelligence, 3rd ed., Addison-Wesley Longman Publishing, Reading, MA.
Guertler, M. R. , von Saucken, C. , Schneider, M. , and Lindemann, U. , 2015, “ How to Search for Open Innovation Partners?,” DS 80-8: The 20th International Conference on Engineering Design (ICED), Milan, Italy, July 27–30, pp. 21–30.


Grahic Jump Location
Fig. 1

Flow diagram summarizing the overall methodology

Grahic Jump Location
Fig. 2

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

Grahic Jump Location
Fig. 3

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

Grahic Jump Location
Fig. 4

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

Grahic Jump Location
Fig. 5

Technology domains proximate to G06-Computing in the technology space

Grahic Jump Location
Fig. 6

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

Grahic Jump Location
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.

Grahic Jump Location
Fig. 8

The technology space positions of Google from 1997 to 2016




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In