Abstract
The patent literature is an important scientific and technological literature, which integrates technical information, market information, and legal information. It is of great significance to expand the bibliometric methods to the measurement of patent literature. This paper takes 4624 NPE (Non Practicing Entities) patents as samples and establishes an inventor coupling network based on two types of feature items, patent literature and Derwent classification codes. We have explored the technical structure of NPE patents. Through centrality analysis, correlation analysis, factor analysis, and visualization analysis, the two coupling analysis methods of Inventor Bibliographic-Patent-Coupling (IBPCA) and Inventor Patent Classification-Coupling (IPPCA) are compared. It is found that inventor centrality analysis, frequency correlation analysis, and cosine similarity measurement all show that IBPCA is correlated with IPPCA; The core technical topics of NPE patents discovered by IBPCA and IPCCA are digital computers, digital telecommunication transmission, and data storage and transmission. However, the two methods differ in factor models fitting analysis and intellectual structure detection. The factor fitting analysis of IPPCA is better than that of IBCCA; IBPCA can detect more topics than IPCCA, and has more advantages in small-scale topic detection; IPCCA is more sensitive to traditional and more stable research topics. Therefore, The combination of the two methods for intellectual structure detection and analysis will be more effective, then more comprehensive and specific conclusions will be obtained.
Similar content being viewed by others
References
Abbas, A., Zhang, L., & Khan, S. U. (2014). A literature review on the state-of-the-art in patent analysis. World Patent Information, 37, 3–13. https://doi.org/10.1016/j.wpi.2013.12.006
Barirani, A., Agard, B., & Beaudry, C. (2013). Discovering and assessing fields of expertise in nanomedicine: A patent co-citation network perspective. Scientometrics, 94(3), 1111–1136. https://doi.org/10.1007/s11192-012-0891-6
Bonino, D., Ciaramella, A., & Corno, F. (2010). Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics. World Patent Information, 32(1), 30–38. https://doi.org/10.1016/j.wpi.2009.05.008
Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389–2404. https://doi.org/10.1002/asi.21419
Chang, S. H., & Fan, C. Y. (2016). Identification of the technology life cycle of telematics: A patent-based analytical perspective. Technological Forecasting and Social Change, 105, 1–10. https://doi.org/10.1016/j.techfore.2016.01.023
Chang, Y. W., Huang, M. H., & Lin, C. W. (2015). Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses. Scientometrics, 105(3), 2071–2087. https://doi.org/10.1007/s11192-015-1762-8
Chen, S. H., Huang, M. H., Chen, D. Z., & Lin, S. Z. (2012). Detecting the temporal gaps of technology fronts: A case study of smart grid field. Technological Forecasting and Social Change, 79(9), 1705–1719. https://doi.org/10.1016/j.techfore.2012.06.005
Chen, Y., & Fang, S. (2011). Methods of social network analysis on patent assignees’ correlation networks. Documentation, Information & Knowledge, 3, 58–66. https://doi.org/10.13366/j.dik.2011.03.015
Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981–1012. https://doi.org/10.1016/j.techfore.2006.04.004
Ferreira, F. A. F. (2018). Mapping the field of arts-based management: Bibliographic coupling and co-citation analyses. Journal of Business Research, 85, 348–357. https://doi.org/10.1016/j.jbusres.2017.03.026
Gmür, M. (2003). Co-citation analysis and the search for invisible colleges: A methodological evaluation. Scientometrics, 57(1), 27–57. https://doi.org/10.1023/A:1023619503005
Hasner, C., de Lima, A. A., & Winter, E. (2019). Technology advances in sugarcane propagation: A patent citation study. World Patent Information, 56(9), 16. https://doi.org/10.1016/j.wpi.2018.09.001
Hou, J., Yang, X., & Chen, C. (2018). Emerging trends and new developments in information science: A document co-citation analysis (2009–2016). Scientometrics, 115(2), 869–892. https://doi.org/10.1007/s11192-018-2695-9
Hsiao, T. M., & Chen, K. H. (2020). The dynamics of research subfields for library and information science: An investigation based on word bibliographic coupling. Scientometrics, 125(1), 717–737. https://doi.org/10.1007/s11192-020-03645-9
Huang, M. H., & Chang, C. P. (2014). A comparative study on detecting research fronts in the organic light-emitting diode (OLED) field using bibliographic coupling and co-citation. Scientometrics, 102(3), 2041–2057. https://doi.org/10.1007/s11192-014-1494-1
Huang, M. H., Chiang, L. Y., & Chen, D. Z. (2003a). Constructing a patent citation map using bibliographic coupling: A study of Taiwan’s high-tech companies. Scientometrics, 58(3), 489–506. https://doi.org/10.1023/B:SCIE.0000006876.29052.bf
Huang, Z., Chen, H., Yip, A., Ng, G., Guo, F., Chen, Z. K., & Roco, M. C. (2003b). Longitudinal patent analysis for nanoscale science and engineering: country, institution and technology field. Journal of Nanoparticle Research, 5(3), 333–363. https://doi.org/10.1023/A:1025556800994
Jiang, J., Shi, P., An, B., Yu, J., & Wang, C. (2017). Measuring the social influences of scientist groups based on multiple types of collaboration relations. Information Processing & Management, 53(1), 1–20. https://doi.org/10.1016/j.ipm.2016.06.003
Jun, S., Sung Park, S., & Sik Jang, D. (2012). Technology forecasting using matrix map and patent clustering. Industrial Management & Data Systems, 112(5), 786–807. https://doi.org/10.1108/02635571211232352
Kang, I. S., Na, S. H., Kim, J., & Lee, J. H. (2007). Cluster-based patent retrieval. Information Processing & Management, 43(5), 1173–1182. https://doi.org/10.1016/j.ipm.2006.11.006
Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25. https://doi.org/10.1002/asi.5090140103
Kim, G., & Bae, J. (2017). A novel approach to forecast promising technology through patent analysis. Technological Forecasting and Social Change, 117, 228–237. https://doi.org/10.1016/j.techfore.2016.11.023
Kuan, C. H., Chen, D. Z., & Huang, M. H. (2019). Bibliographically coupled patents: Their temporal pattern and combined relevance. Journal of Informetrics. https://doi.org/10.1016/j.joi.2019.100978
Kuan, C. H., Huang, M. H., & Chen, D. Z. (2018). Missing links: Timing characteristics and their implications for capturing contemporaneous technological developments. Journal of Informetrics, 12(1), 259–270. https://doi.org/10.1016/j.joi.2018.01.005
Kuusi, O., & Meyer, M. (2007). Anticipating technological breakthroughs: Using bibliographic coupling to explore the nanotubes paradigm. Scientometrics, 70(3), 759–777. https://doi.org/10.1007/s11192-007-0311-5
Lai, K. K., & Wu, S. J. (2005). Using the patent co-citation approach to establish a new patent classification system. Information Processing & Management, 41(2), 313–330. https://doi.org/10.1016/j.ipm.2003.11.004
Lee, K., & Lee, J. (2020). National innovation systems, economic complexity, and economic growth: Country panel analysis using the US patent data. Journal of Evolutionary Economics, 30(4), 897–928. https://doi.org/10.1007/s00191-019-00612-3
Leydesdorff, L., Kushnir, D., & Rafols, I. (2012). Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC). Scientometrics, 98(3), 1583–1599. https://doi.org/10.1007/s11192-012-0923-2
Liu, W., Nanetti, A., & Cheong, S. A. (2017). Knowledge evolution in physics research: An analysis of bibliographic coupling networks. PLoS ONE. https://doi.org/10.1371/journal.pone.0184821
Lo, S. C. (2007). Patent coupling analysis of primary organizations in genetic engineering research. Scientometrics, 74(1), 143–151. https://doi.org/10.1007/s11192-008-0110-7
McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41(6), 433–443. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6%3c433::AID-ASI11%3e3.0.CO;2-Q
Narin, F. (1994). Patent bibliometrics. Scientometrics, 30(1), 147–155. https://doi.org/10.1007/BF02017219
Nerur, S. P., Rasheed, A. A., & Natarajan, V. (2008). The intellectual structure of the strategic management field: An author co-citation analysis. Strategic Management Journal, 29(3), 319–336. https://doi.org/10.1002/smj.659
Noh, H., Jo, Y., & Lee, S. (2015). Keyword selection and processing strategy for applying text mining to patent analysis. Expert Systems with Applications, 42(9), 4348–4360. https://doi.org/10.1016/j.eswa.2015.01.050
Noh, H., Song, Y. K., & Lee, S. (2016). Identifying emerging core technologies for the future: Case study of patents published by leading telecommunication organizations. Telecommunications Policy, 40(10), 956–970. https://doi.org/10.1016/j.telpol.2016.04.003
Park, A., Conway, M., & Chen, A. T. (2018a). Examining thematic similarity, difference, and membership in three online mental health communities from reddit: A text mining and visualization approach. Computers in Human Behavior, 78, 98–112. https://doi.org/10.1016/j.chb.2017.09.001
Park, I., Jeong, Y., Yoon, B., & Mortara, L. (2014). Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis. Technology Analysis & Strategic Management, 27(7), 759–781. https://doi.org/10.1080/09537325.2014.971004
Park, I., Jeong, Y., Yoon, B., & Mortara, L. (2015). Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis. Technology Analysis & Strategic Management, 27(7), 759–781. https://doi.org/10.1080/09537325.2014.971004
Park, T. Y., Lim, H., & Ji, I. (2018b). Identifying potential users of technology for technology transfer using patent citation analysis: A case analysis of a Korean research institute. Scientometrics, 116(3), 1541–1558. https://doi.org/10.1007/s11192-018-2792-9
Pénin, J. (2012). Strategic uses of patents in markets for technology: A story of fabless firms, brokers and trolls. Journal of Economic Behavior & Organization, 84(2), 633–641. https://doi.org/10.1016/j.jebo.2012.09.007
Rodriguez, A., Kim, B., Turkoz, M., Lee, J. M., Coh, B. Y., & Jeong, M. K. (2015). New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network. Scientometrics, 103(2), 565–581. https://doi.org/10.1007/s11192-015-1531-8
Shen, J., Gao, J., & Teng, L. (2012). Derwent manual code co-occurrence: A practical method in patent map. Science of Science and Management of S & T, 33(1), 12–16.
Small, H. (1973). Co-citation in the scientific literature a new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(24), 265–269. https://doi.org/10.1002/asi.4630240406
Small, H., & Griffith, B. C. (1974). The structure of scientific literatures I: Identifying and graphing specialties. Science Studies, 4(1), 17–40.
Small, H. G., & Koenig, M. E. D. (1977). Journal clustering using a bibliographic coupling method. Information Processing & Management, 13(5), 277–288. https://doi.org/10.1016/0306-4573(77)90017-6
Song, Y., & Wu, Y. (2014). A comparative study on author bibliographic-coupling analysis and author keyword-coupling analysis based on scientometrics. The Journal of the Library Science in China, 40(1), 25–38.
Swanson, D. R. (1971). Some unexplained aspects of the cranfield tests of indexing performance factors. The Library Quarterly, 41(3), 223–228. https://doi.org/10.1086/619959
Tseng, Y. H., Lin, C. J., & Lin, Y. I. (2007). Text mining techniques for patent analysis. Information Processing & Management, 43(5), 1216–1247. https://doi.org/10.1016/j.ipm.2006.11.011
Von Wartburg, I., Teichert, T., & Rost, K. (2005). Inventive progress measured by multi-stage patent citation analysis. Research Policy, 34(10), 1591–1607. https://doi.org/10.1016/j.respol.2005.08.001
Wang, J., & Hsu, C. C. (2020). A topic-based patent analytics approach for exploring technological trends in smart manufacturing. Journal of Manufacturing Technology Management, 32(1), 110–135. https://doi.org/10.1108/jmtm-03-2020-0106
Wen, F. (2017). Research on the technology diversity and similarity based on the coupling of derwent patent classification codes. Information Studies: Theory & Application, 40(8), 87–92. https://doi.org/10.16353/j.cnki.1000-7490.2017.08.016
White, H. D. (2003). Author cocitation analysis and Pearson’s r. Journal of the American Society for Information Science and Technology, 54(13), 1250–1259. https://doi.org/10.1002/asi.10325
White, H. D., & Griffith, B. C. (1981). Author cocitation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163–171. https://doi.org/10.1002/asi.4630320302
Yang, S., Han, R., Wolfram, D., & Zhao, Y. (2016). Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis. Journal of Informetrics, 10(1), 132–150. https://doi.org/10.1016/j.joi.2015.12.003
Yeh, H. Y., Sung, Y. S., Yang, H. W., Tsai, W. C., & Chen, D. Z. (2012). The bibliographic coupling approach to filter the cited and uncited patent citations: A case of electric vehicle technology. Scientometrics, 94(1), 75–93. https://doi.org/10.1007/s11192-012-0820-8
Yoon, B., & Magee, C. L. (2018). Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction. Technological Forecasting and Social Change, 132, 105–117. https://doi.org/10.1016/j.techfore.2018.01.019
Zhang, K., Xia, W., Yuan, J., Chen, J., & Geng, Y. (2015). Study on the definition, types and characteristics of NPEs. Science and Technology Management Research, 35(15), 141–146+151.
Zhang, Y., Shang, L., Huang, L., Porter, A. L., Zhang, G., Lu, J., & Zhu, D. (2016). A hybrid similarity measure method for patent portfolio analysis. Journal of Informetrics, 10(4), 1108–1130. https://doi.org/10.1016/j.joi.2016.09.006
Zhao, D., & Strotmann, A. (2008a). Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic-coupling analysis. Journal of the American Society for Information Science and Technology, 59(13), 2070–2086. https://doi.org/10.1002/asi.20910
Zhao, D., & Strotmann, A. (2008b). Information science during the first decade of the web: An enriched author cocitation analysis. Journal of the American Society for Information Science and Technology, 59(6), 916–937. https://doi.org/10.1002/asi.20799
Zhao, D., & Strotmann, A. (2014). The knowledge base and research front of information science 2006–2010: An author cocitation and bibliographic coupling analysis. Journal of the Association for Information Science and Technology, 65(5), 995–1006. https://doi.org/10.1002/asi.23027
Zitt, M., & Bassecoulard, E. (2006). Delineating complex scientific fields by an hybrid lexical-citation method: An application to nanosciences. Information Processing & Management, 42(6), 1513–1531. https://doi.org/10.1016/j.ipm.2006.03.016
Acknowledgements
This study was funded in part by major project of National Social Science Foundation of China (19ZDA348), and Supported by the Fundamental Research Funds for the Provincial Universities of Zhejiang (GK209907299001-201).
Funding
Major project of National Social Science Foundation of China, 19ZDA348, Song Yanhui,Fundamental Research Funds for the Provincial Universities of Zhejiang, GK209907299001-201, Song Yanhui.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yanhui, S., Lixin, L. Inventor bibliographic-patent-coupling analysis and inventor-patent-classification-coupling analysis: a comparative analysis based on NPE. Scientometrics 129, 745–765 (2024). https://doi.org/10.1007/s11192-023-04713-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-023-04713-6