Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology

Bo Wang, Shengbo Liu, Kun Ding, Zeyuan Liu and Jing Xu ()
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Bo Wang: WISELab, Dalian University of Technology
Shengbo Liu: WISELab, Dalian University of Technology
Kun Ding: WISELab, Dalian University of Technology
Zeyuan Liu: WISELab, Dalian University of Technology
Jing Xu: Sichuan University

Scientometrics, 2014, vol. 101, issue 1, No 31, 685-704

Abstract: Abstract An extended latent Dirichlet allocation (LDA) model is presented in this paper for patent competitive intelligence analysis. After part-of-speech tagging and defining the noun phrase extraction rules, technological words have been extracted from patent titles and abstracts. This allows us to go one step further and perform patent analysis at content level. Then LDA model is used for identifying underlying topic structures based on latent relationships of technological words extracted. This helped us to review research hot spots and directions in subclasses of patented technology in a certain field. For the extension of the traditional LDA model, another institution-topic probability level is added to the original LDA model. Direct competing enterprises’ distribution probability and their technological positions are identified in each topic. Then a case study is carried on within one of the core patented technology in next generation telecommunication technology-LTE. This empirical study reveals emerging hot spots of LTE technology, and finds that major companies in this field have been focused on different technological fields with different competitive positions.

Keywords: Noun phrases extraction; Topic model (LDA); Institution-topic model; Content analysis; Long term evolution (LTE) (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (20)

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DOI: 10.1007/s11192-014-1342-3

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