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

Huang, Y; Zhang, Y; Ma, J; Porter, A; Wang, X; Guo, Y

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

Huang, Y Zhang, Y Ma, J Porter, A Wang, X Guo, Y

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Anticipating Future Innovation Pathways Through Large Data Analysis, 2016, pp. 153-172
Issue Date:: 2016-07-01

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Field	Value	Language
dc.contributor.author	Huang, Y
dc.contributor.author	Zhang, Y
dc.contributor.author	Ma, J
dc.contributor.author	Porter, A
dc.contributor.author	Wang, X
dc.contributor.author	Guo, Y
dc.date.accessioned	2022-11-07T06:59:27Z
dc.date.available	2022-11-07T06:59:27Z
dc.date.issued	2016-07-01
dc.identifier.citation	Anticipating Future Innovation Pathways Through Large Data Analysis, 2016, pp. 153-172
dc.identifier.isbn	978-3-319-39054-3
dc.identifier.uri	http://hdl.handle.net/10453/163285
dc.description.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.
dc.format.extent	18
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Anticipating Future Innovation Pathways Through Large Data Analysis
dc.relation.isbasedon	10.1007/978-3-319-39056-7_9
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Generating Competitive Technical Intelligence Using Topical Analysis, Patent Citation Analysis, and Term Clumping Analysis
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-11-07T06:59:26Z
pubs.place-of-publication	Germany
pubs.publication-status	Published
dc.location	Germany

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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http://hdl.handle.net/10453/163285