IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v101y2014i1d10.1007_s11192-014-1342-3.html
   My bibliography  Save this article

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

Author

Listed:
  • Bo Wang

    (WISELab, Dalian University of Technology
    Dalian University of Technology
    Drexel University)

  • Shengbo Liu

    (WISELab, Dalian University of Technology
    Dalian University of Technology
    Drexel University)

  • Kun Ding

    (WISELab, Dalian University of Technology
    Dalian University of Technology
    Drexel University)

  • Zeyuan Liu

    (WISELab, Dalian University of Technology
    Dalian University of Technology
    Drexel University)

  • Jing Xu

    (Sichuan University)

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.

Suggested Citation

  • Bo Wang & Shengbo Liu & Kun Ding & Zeyuan Liu & Jing Xu, 2014. "Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 685-704, October.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:1:d:10.1007_s11192-014-1342-3
    DOI: 10.1007/s11192-014-1342-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-014-1342-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-014-1342-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Janghyeok Yoon & Sungchul Choi & Kwangsoo Kim, 2011. "Invention property-function network analysis of patents: a case of silicon-based thin film solar cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(3), pages 687-703, March.
    2. Staša Milojević & Cassidy R. Sugimoto & Erjia Yan & Ying Ding, 2011. "The cognitive structure of Library and Information Science: Analysis of article title words," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(10), pages 1933-1953, October.
    3. Cassidy R. Sugimoto & Daifeng Li & Terrell G. Russell & S. Craig Finlay & Ying Ding, 2011. "The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 185-204, January.
    4. von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
    5. Peter Thompson & Melanie Fox-Kean, 2005. "Patent Citations and the Geography of Knowledge Spillovers: A Reassessment: Reply," American Economic Review, American Economic Association, vol. 95(1), pages 465-466, March.
    6. Peter Thompson & Melanie Fox-Kean, 2005. "Patent Citations and the Geography of Knowledge Spillovers: A Reassessment," American Economic Review, American Economic Association, vol. 95(1), pages 450-460, March.
    7. Masatsura Igami, 2008. "Exploration of the evolution of nanotechnology via mapping of patent applications," Scientometrics, Springer;Akadémiai Kiadó, vol. 77(2), pages 289-308, November.
    8. Cassidy R. Sugimoto & Daifeng Li & Terrell G. Russell & S. Craig Finlay & Ying Ding, 2011. "The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 185-204, January.
    9. Woo Hyoung Lee, 2008. "How to identify emerging research fields using scientometrics: An example in the field of Information Security," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(3), pages 503-525, September.
    10. Shengbo Liu & Chaomei Chen, 2013. "The differences between latent topics in abstracts and citation contexts of citing papers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(3), pages 627-639, March.
    11. Katharina Maria Hofer & Angela Elisabeth Smejkal & F. Zeynep Bilgin & Gerhard A. Wuehrer, 2010. "Conference proceedings as a matter of bibliometric studies: the Academy of International Business 2006–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 845-862, September.
    12. Engelsman, E. C. & van Raan, A. F. J., 1994. "A patent-based cartography of technology," Research Policy, Elsevier, vol. 23(1), pages 1-26, January.
    13. Staša Milojević & Cassidy R. Sugimoto & Erjia Yan & Ying Ding, 2011. "The cognitive structure of Library and Information Science: Analysis of article title words," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(10), pages 1933-1953, October.
    14. Ying Ding, 2011. "Topic-based PageRank on author cocitation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(3), pages 449-466, March.
    15. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    16. Shengbo Liu & Chaomei Chen, 2013. "The differences between latent topics in abstracts and citation contexts of citing papers," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(3), pages 627-639, March.
    17. Xin Ying An & Qing Qiang Wu, 2011. "Co-word analysis of the trends in stem cells field based on subject heading weighting," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 133-144, July.
    18. Loet Leydesdorff, 1997. "Why words and co‐words cannot map the development of the sciences," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 48(5), pages 418-427, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Byeongki Jeong & Janghyeok Yoon, 2017. "Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information," Sustainability, MDPI, vol. 9(4), pages 1-22, March.
    2. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    3. Yuan Zhou & Heng Lin & Yufei Liu & Wei Ding, 2019. "A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 167-185, July.
    4. Xuan Shi & Lingfei Cai & Hongfang Song, 2019. "Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    5. Hong Joo Lee & Hoyeon Oh, 2020. "A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development," Sustainability, MDPI, vol. 12(14), pages 1-15, July.
    6. Jun Gao & Xiang Yu, 2022. "Factors Affecting the Evolution of Technical Cooperation among “Belt and Road Initiative” Countries Based on TERGMs and ERGMs," Sustainability, MDPI, vol. 14(3), pages 1-16, February.
    7. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    8. Shuto Miyashita & Shintaro Sengoku, 2021. "Scientometrics for management of science: collaboration and knowledge structures and complexities in an interdisciplinary research project," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7419-7444, September.
    9. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.
    10. Hansu Hwang & SeJin An & Eunchang Lee & Suhyeon Han & Cheon-hwan Lee, 2021. "Cross-Societal Analysis of Climate Change Awareness and Its Relation to SDG 13: A Knowledge Synthesis from Text Mining," Sustainability, MDPI, vol. 13(10), pages 1-21, May.
    11. Takano, Yasutomo & Mejia, Cristian & Kajikawa, Yuya, 2016. "Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies," Journal of Informetrics, Elsevier, vol. 10(4), pages 967-980.
    12. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    13. Heng Xu & Menglu Zhang & Jun Zeng & Huihui Hao & Hao-Chiang Koong Lin & Mengyun Xiao, 2022. "Use of Latent Dirichlet Allocation and Structural Equation Modeling in Determining the Factors for Continuance Intention of Knowledge Payment Platform," Sustainability, MDPI, vol. 14(15), pages 1-25, July.
    14. Yawen Qin & Xiaozhen Qin & Haohui Chen & Xun Li & Wei Lang, 2021. "Measuring cognitive proximity using semantic analysis: A case study of China's ICT industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6059-6084, July.
    15. Han, Xiaotong & Zhu, Donghua & Lei, Ming & Daim, Tugrul, 2021. "R&D trend analysis based on patent mining: An integrated use of patent applications and invalidation data," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    16. Jinlou Zhao & Hongyu Gao & Yongli Li & Jiaguo Liu, 2017. "Which factors affect the duration of hot topics on social media platforms?," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 2395-2407, September.
    17. Chung, Jaemin & Ko, Namuk & Kim, Hyeonsu & Yoon, Janghyeok, 2021. "Inventor profile mining approach for prospective human resource scouting," Journal of Informetrics, Elsevier, vol. 15(1).
    18. Kim, Min Sung & Kim, Junghwan & Kim, Seongcheol, 2023. "Korea's leadership in 5G and beyond: Footprints and futures," Telecommunications Policy, Elsevier, vol. 47(8).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chaoqun Ni & Cassidy R. Sugimoto & Blaise Cronin, 2013. "Visualizing and comparing four facets of scholarly communication: producers, artifacts, concepts, and gatekeepers," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1161-1173, March.
    2. Yi Bu & Binglu Wang & Win-bin Huang & Shangkun Che & Yong Huang, 2018. "Using the appearance of citations in full text on author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 275-289, July.
    3. Erjia Yan, 2014. "Topic-based Pagerank: toward a topic-level scientific evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 407-437, August.
    4. Qian-Jin Zong & Hong-Zhou Shen & Qin-Jian Yuan & Xiao-Wei Hu & Zhi-Ping Hou & Shun-Guo Deng, 2013. "Doctoral dissertations of Library and Information Science in China: A co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 781-799, February.
    5. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    6. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    7. Yu-Wei Chang, 2018. "Examining interdisciplinarity of library and information science (LIS) based on LIS articles contributed by non-LIS authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1589-1613, September.
    8. Pin Li & Guoli Yang & Chuanqi Wang, 2019. "Visual topical analysis of library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1753-1791, December.
    9. Yosuke Miyata & Emi Ishita & Fang Yang & Michimasa Yamamoto & Azusa Iwase & Keiko Kurata, 2020. "Knowledge structure transition in library and information science: topic modeling and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 665-687, October.
    10. Carlos G. Figuerola & Francisco Javier García Marco & María Pinto, 2017. "Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1507-1535, September.
    11. David Rigby, 2012. "The Geography of Knowledge Relatedness and Technological Diversification in U.S. Cities," Papers in Evolutionary Economic Geography (PEEG) 1218, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Oct 2012.
    12. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    13. Choe, Hochull & Lee, Duk Hee & Seo, Il Won & Kim, Hee Dae, 2013. "Patent citation network analysis for the domain of organic photovoltaic cells: Country, institution, and technology field," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 492-505.
    14. Adam B. Jaffe & Gaétan de Rassenfosse, 2017. "Patent citation data in social science research: Overview and best practices," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(6), pages 1360-1374, June.
    15. Beibei Hu & Xianlei Dong & Chenwei Zhang & Timothy D. Bowman & Ying Ding & Staša Milojević & Chaoqun Ni & Erjia Yan & Vincent Larivière, 2015. "A lead-lag analysis of the topic evolution patterns for preprints and publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2643-2656, December.
    16. Yan, Erjia, 2014. "Research dynamics: Measuring the continuity and popularity of research topics," Journal of Informetrics, Elsevier, vol. 8(1), pages 98-110.
    17. Katia Angue & Cécile Ayerbe & Liliana Mitkova, 2014. "A method using two dimensions of the patent classification for measuring the technological proximity: an application in identifying a potential R&D partner in biotechnology," The Journal of Technology Transfer, Springer, vol. 39(5), pages 716-747, October.
    18. Kim, Min Sung & Kim, Junghwan & Kim, Seongcheol, 2023. "Korea's leadership in 5G and beyond: Footprints and futures," Telecommunications Policy, Elsevier, vol. 47(8).
    19. Rons, Nadine, 2018. "Bibliometric approximation of a scientific specialty by combining key sources, title words, authors and references," Journal of Informetrics, Elsevier, vol. 12(1), pages 113-132.
    20. Yuan Zhou & Heng Lin & Yufei Liu & Wei Ding, 2019. "A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 167-185, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:101:y:2014:i:1:d:10.1007_s11192-014-1342-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.