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Generating Competitive Technical Intelligence Using Topical Analysis, Patent Citation Analysis, and Term Clumping Analysis

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Anticipating Future Innovation Pathways Through Large Data Analysis

Part of the book series: Innovation, Technology, and Knowledge Management ((ITKM))

Abstract

Because of the flexibility and complexity of Newly Emerging Science and Technologies (NESTs), traditional statistical analysis fails to capture technology evolution in detail. Tracking technology evolution pathways supports industrial, governmental, and academic decisions to guide future development trends. Patents are one of the most important NESTs data sources and are pertinent to developmental paths. This paper draws upon text analyses, augmented by expert knowledge, to identify key NESTs sub-domains and component technologies. We then complement those analyses with patent citation analysis to help track developmental progressions. We identify key sub-domain patents, associated with particular component technology trajectories, then extract pivotal patents via citation analysis. We compose evolutionary pathways by combining citation and topical intelligence obtained through term clumping. We demonstrate our approach with empirical analysis of dye-sensitized solar cells (DSSCs), as an example of a promising NESTs, contributing to the remarkable growth in the renewable energy industry. The systematic approach we proposed not only offers a macro-perspective covering technology development levels and future trends, but also makes R&D information accessible for micro-level probes as needed. We work to uncover developmental trends and to compile mentions of possible applications, and this study informs NESTs management by spotting prime opportunities for innovation.

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Acknowledgments

We acknowledge support from the US National Science Foundation (NSF) (Award No. 1064146), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Award No. 13YJC630042) and the National High Technology Research and Development Program of China (Grant No. 2014AA015105). Besides, we are grateful for the scholarship provided by the China Scholarship Council (CSC Student ID 201406030005). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the supporters.

Author information

Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Ying Huang, Yi Zhang, Jing Ma, Xuefeng Wang & Ying Guo

  2. Centre for Quantum Computation & Intelligent Systems, University of Technology Sydney, Sydney, 2007, NSW, Australia

    Yi Zhang

  3. School of Public Policy, Georgia Institute of Technology, Atlanta, 30332, GA, USA

    Alan L. Porter

  4. Search Technology Inc., Atlanta, 30092, GA, USA

    Alan L. Porter

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  1. Ying Huang
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  3. Jing Ma
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  4. Alan L. Porter
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  6. Ying Guo
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Editors and Affiliations

  1. Dept. of Engineering and Technology Mana, Portland State University, Portland, Oregon, USA

    Tugrul U. Daim

  2. Search Technology, Inc, Norcross, Georgia, USA

    Denise Chiavetta

  3. Georgia Institute of Technology, Roswell, Georgia, USA

    Alan L. Porter

  4. Higher School of Economics, National Research University, Moscow, Russia

    Ozcan Saritas

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Huang, Y., Zhang, Y., Ma, J., Porter, A.L., Wang, X., Guo, Y. (2016). Generating Competitive Technical Intelligence Using Topical Analysis, Patent Citation Analysis, and Term Clumping Analysis. In: Daim, T., Chiavetta, D., Porter, A., Saritas, O. (eds) Anticipating Future Innovation Pathways Through Large Data Analysis. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-39056-7_9

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