A patent time series processing component for technology intelligence by trend identification functionality

Chen, H; Zhang, G; Zhu, D; Lu, J

A patent time series processing component for technology intelligence by trend identification functionality

Chen, H

Zhang, G

Zhu, D Lu, J

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Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2014, 26 (2), pp. 345 - 353
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Chen, H https://orcid.org/0000-0002-0893-1817	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Zhu, D	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Neural Computing and Applications, 2014, 26 (2), pp. 345 - 353	en_US
dc.identifier.issn	0941-0643	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32786
dc.identifier.uri	http://hdl.handle.net/10453/35441
dc.description.abstract	© 2014, Springer-Verlag London. Technology intelligence indicates the concept and applications that transform data hidden in patents or scientific literatures into technical insight for technology strategy-making support. The existing frameworks and applications of technology intelligence mainly focus on obtaining text-based knowledge with text mining components. However, what is the corresponding technological trend of the knowledge over time is seldom taken into consideration. In order to capture the hidden trend turning points and improve the framework of existing technology intelligence, this paper proposes a patent time series processing component with trend identification functionality. We use piecewise linear representation method to generate and quantify the trend of patent publication activities, then utilize the outcome to identify trend turning points and provide trend tags to the existing text mining component, thus making it possible to combine the text-based and time-based knowledge together to support technology strategy making more satisfactorily. A case study using Australia patents (year 1983–2012) in Information and Communications Technology industry is presented to demonstrate the feasibility of the component when dealing with real-world tasks. The result shows that the new component identifies the trend reasonably well, at the same time learns valuable trend turning points in historical patent time series.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110103733
dc.relation.ispartof	Neural Computing and Applications	en_US
dc.relation.isbasedon	10.1007/s00521-014-1616-y	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A patent time series processing component for technology intelligence by trend identification functionality	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	26	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2014, Springer-Verlag London. Technology intelligence indicates the concept and applications that transform data hidden in patents or scientific literatures into technical insight for technology strategy-making support. The existing frameworks and applications of technology intelligence mainly focus on obtaining text-based knowledge with text mining components. However, what is the corresponding technological trend of the knowledge over time is seldom taken into consideration. In order to capture the hidden trend turning points and improve the framework of existing technology intelligence, this paper proposes a patent time series processing component with trend identification functionality. We use piecewise linear representation method to generate and quantify the trend of patent publication activities, then utilize the outcome to identify trend turning points and provide trend tags to the existing text mining component, thus making it possible to combine the text-based and time-based knowledge together to support technology strategy making more satisfactorily. A case study using Australia patents (year 1983–2012) in Information and Communications Technology industry is presented to demonstrate the feasibility of the component when dealing with real-world tasks. The result shows that the new component identifies the trend reasonably well, at the same time learns valuable trend turning points in historical patent time series.

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http://hdl.handle.net/10453/32786