Tracing Technology Evolution Pathways by Combining Tech Mining and Patent Citation Analysis

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

Tracing Technology Evolution Pathways by Combining Tech Mining and Patent Citation Analysis

Huang, Y Zhang, Y

Ma, J Porter, A Wang, X

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Publication Type:: Conference Proceeding
Citation:: 2015
Issue Date:: 2015-08-02

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Field	Value	Language
dc.contributor.author	Huang, Y	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301	en_US
dc.contributor.author	Ma, J	en_US
dc.contributor.author	Porter, A	en_US
dc.contributor.author	Wang, X	en_US
dc.date	2015-08-02	en_US
dc.date.issued	2015-08-02	en_US
dc.identifier.citation	2015	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36799
dc.description.abstract	With the flexibility and complexity of modern science-based technologies, traditional statistical analysis may fail to capture the details of technology evolution, especially for newly emerging science and technologies (NESTs). Tracking technology evolution pathways can support industrial, governmental, and academic decision-making involving future development trends. Patents provide one of several important NEST data resources pertinent to developmental paths. In this paper, we draw upon text analyses, augmented with expert knowledge, to identify key NEST sub-domains and component technologies. We then complement those analyses with patent citation analyses to help track developmental progressions. We identify key sub-domain patents associated with particular component technology trajectories, then extract pivotal patents via citation analyses. We compose evolutionary pathways by combining citation and topical intelligence. We present empirical analyses of Dye Sensitized Solar Cells (DSSCs) to illustrate our approach. We believe this offers potential to inform NEST management by spotting prime opportunities for innovation.	en_US
dc.relation.ispartof	2015 Portland International Conference on Management of Engineering & Technology	en_US
dc.title	Tracing Technology Evolution Pathways by Combining Tech Mining and Patent Citation Analysis	en_US
dc.type	Conference Proceeding
utslib.location.activity	Portland, USA	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Portland, USA
pubs.embargo.period	Not known	en_US
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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.consider-herdc	false	en_US
pubs.finish-date	2015-08-06	en_US
pubs.start-date	2015-08-02	en_US

Abstract:

With the flexibility and complexity of modern science-based technologies, traditional statistical analysis may fail to capture the details of technology evolution, especially for newly emerging science and technologies (NESTs). Tracking technology evolution pathways can support industrial, governmental, and academic decision-making involving future development trends. Patents provide one of several important NEST data resources pertinent to developmental paths. In this paper, we draw upon text analyses, augmented with expert knowledge, to identify key NEST sub-domains and component technologies. We then complement those analyses with patent citation analyses to help track developmental progressions. We identify key sub-domain patents associated with particular component technology trajectories, then extract pivotal patents via citation analyses. We compose evolutionary pathways by combining citation and topical intelligence. We present empirical analyses of Dye Sensitized Solar Cells (DSSCs) to illustrate our approach. We believe this offers potential to inform NEST management by spotting prime opportunities for innovation.

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