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

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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.

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Acknowledgments

This research is supported by National Natural Science Foundation of China (Grant Number, 61272370), the specialized research fund for doctoral tutor (20110041110034). Thanks to the following experts to help us evaluate our experiment results. Bin Peng from Thomson Reuters, who worked as a patent examiner in State Intellectual Property Office of P.R.China. (bean.peng@thomsonreuters.com). Maoshu Ni, Senior mobile system product manager in Huawei Technology Company. (nimaoshu@huawei.com). Bo Wang, expert of communication technology from Information and Communication Engineering of Dalian University of Technology. (bowang@dlut.edu.cn).

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Authors and Affiliations

  1. WISELab, Dalian University of Technology, Dalian, 116023, China

    Bo Wang, Shengbo Liu, Kun Ding & Zeyuan Liu

  2. Joint-Institute for the Study of Knowledge Visualization and Science Discovery, Dalian University of Technology, Dalian, China

    Bo Wang, Shengbo Liu, Kun Ding & Zeyuan Liu

  3. Joint-Institute for the Study of Knowledge Visualization and Science Discovery, Drexel University, Philadelphia, PA, USA

    Bo Wang, Shengbo Liu, Kun Ding & Zeyuan Liu

  4. Sichuan University, Chengdu, 610065, China

    Jing Xu

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  1. Bo Wang
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  2. Shengbo Liu
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  3. Kun Ding
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  4. Zeyuan Liu
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Correspondence to Jing Xu.

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Wang, B., Liu, S., Ding, K. et al. Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology. Scientometrics 101, 685–704 (2014). https://doi.org/10.1007/s11192-014-1342-3

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