Modeling technological topic changes in patent claims

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

Modeling technological topic changes in patent claims

Chen, H

Zhang, Y

Zhang, G

Zhu, D Lu, J

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Publication Type:: Conference Proceeding
Citation:: Portland International Conference on Management of Engineering and Technology, 2015, 2015-September pp. 2049 - 2059
Issue Date:: 2015-09-21

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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, Y https://orcid.org/0000-0002-7731-0301	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	2015-09-21	en_US
dc.identifier.citation	Portland International Conference on Management of Engineering and Technology, 2015, 2015-September pp. 2049 - 2059	en_US
dc.identifier.isbn	9781890843328	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37291
dc.identifier.uri	http://hdl.handle.net/10453/37534
dc.description.abstract	© 2014 Portland International Conference on Management of Engineering and Technology. Patent claims usually embody the most essential terms and the core technological scope to define the protection of an invention, which makes them the ideal resource for patent content and topic change analysis. However, manually conducting content analysis on massive technical terms is very time consuming and laborious. Even with the help of traditional text mining techniques, it is still difficult to model topic changes over time, because single keywords alone are usually too general or ambiguous to represent a concept. Moreover, term frequency which used to define a topic cannot separate polysemous words that are actually describing a different theme. To address this issue, this research proposes a topic change identification approach based on Latent Dirichlet Allocation to model and analyze topic changes with minimal human intervention. After textual data cleaning, underlying semantic topics hidden in large archives of patent claims are revealed automatically. Concepts are defined by probability distributions over words instead of term frequency, so that polysemy is allowed. A case study using patents published in the United States Patent and Trademark Office (USPTO) from 2009 to 2013 with Australia as their assignee country is presented to demonstrate the validity of the proposed topic change identification approach. The experimental result shows that the proposed approach can be used as an automatic tool to provide machine-identified topic changes for more efficient and effective R&D management assistance.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140101366
dc.relation.ispartof	Portland International Conference on Management of Engineering and Technology	en_US
dc.relation.isbasedon	10.1109/PICMET.2015.7273098	en_US
dc.title	Modeling technological topic changes in patent claims	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015-September	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	USA, Portland, OR
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.publication-status	Published	en_US
pubs.volume	2015-September	en_US

Abstract:

© 2014 Portland International Conference on Management of Engineering and Technology. Patent claims usually embody the most essential terms and the core technological scope to define the protection of an invention, which makes them the ideal resource for patent content and topic change analysis. However, manually conducting content analysis on massive technical terms is very time consuming and laborious. Even with the help of traditional text mining techniques, it is still difficult to model topic changes over time, because single keywords alone are usually too general or ambiguous to represent a concept. Moreover, term frequency which used to define a topic cannot separate polysemous words that are actually describing a different theme. To address this issue, this research proposes a topic change identification approach based on Latent Dirichlet Allocation to model and analyze topic changes with minimal human intervention. After textual data cleaning, underlying semantic topics hidden in large archives of patent claims are revealed automatically. Concepts are defined by probability distributions over words instead of term frequency, so that polysemy is allowed. A case study using patents published in the United States Patent and Trademark Office (USPTO) from 2009 to 2013 with Australia as their assignee country is presented to demonstrate the validity of the proposed topic change identification approach. The experimental result shows that the proposed approach can be used as an automatic tool to provide machine-identified topic changes for more efficient and effective R&D management assistance.

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http://hdl.handle.net/10453/37291