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Research on the type of technology convergence in the medical device industry based on topic modeling and citation analysis

토픽모델링과 인용 분석에 기반한 의료기기 산업의 기술융합 유형 연구

  • Lee, Seonjae (Department of Industrial Engineering, Ajou University) ;
  • Lee, Sungjoo (Department of Industrial Engineering, Ajou University) ;
  • Seol, Hyeonju (School of Integrated National Security, Chungnam National University)
  • 이선재 (아주대학교 산업공학과) ;
  • 이성주 (아주대학교 산업공학과) ;
  • 설현주 (충남대학교 국가안보융합학부)
  • Received : 2021.05.30
  • Accepted : 2021.07.20
  • Published : 2021.07.28

Abstract

Industrial convergence is manifested in various forms by various drivers, and understanding and categorizing the direction of convergence according to the factors in which the convergence occurs is an essential requirement for the establishment of a company's customized convergence strategy and the government's corporate support policy. In this study, the type of convergence is analyzed from the perspective of knowledge flow between heterogeneous technologies, and for this purpose, the result of topic modeling of the text information of the patent and the citation information of the corresponding patent allocated for each topic are used. The methodology presented through case studies in the medical device field is verified. Through the proposed methodology, companies can predict the flow of convergence and use it as decision-making data to create new business opportunities. It is expected that the government and research institutions will be usefully used as basic data for policy preparation.

4차 산업혁명의 변화 속에서 새로운 성장 동력을 확보하기 위해 융합기술의 중요성이 강조되면서 다양한 형태로의 산업 융합이 이루어지고 있다. 산업 융합은 다수 동인에 의해 여러 형태로 발현되기 때문에 이러한 융합의 특성을 파악하고 흐름을 이해한다면 효과적인 융합 정책을 수립하고 추진할 수 있을 것이다. 이에 본 연구는 특허정보를 활용하여 이종 분야 간 지식의 흐름을 분석하여 기술의 융합 형태를 유형화하고 유형별 특성을 파악하는 것을 목적으로 한다. 이를 위해 첫째, 특허문서의 토픽모델링을 통해 핵심 융합 기술분야를 도출한다. 둘째, 해당 기술분야를 구성하는 이종기술별 특허 건수와 이들 간 특허 인용 분석을 통해 융합과정에서의 지식의 규모와 흐름을 파악한다. 마지막으로, 지식의 규모와 흐름에 따라 융합의 유형을 상생융합, 부분융합, 흡수융합으로 구분하고, 해당 기술분야가 어떠한 유형에 속하는지 판단하고자 한다. 제안된 접근법은 이종 기술간 융합이 활발한 의료기기 산업을 대상으로 사례연구를 수행하여 활용 가능성을 검토하였다. 연구 결과는 향후 기업에서 융합 기반의 신사업 기회 창출이나 정부 등 여러 기관에서 융합을 토대로 한 정책 마련 시 기초자료로서 유용하게 활용될 것으로 기대한다.

Keywords

Acknowledgement

This research was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea in 2018.(NRF-2018S1A5A2A01037180)

References

  1. C. Curran & J. Leker. (2011). Patent indicators for monitoring con vergence-examples from NFF and ICT. Technological Forecasting and Social Change, 78(2), 256-273. https://doi.org/10.1016/j.techfore.2010.06.021
  2. Y. R. Cho & E. S. Kim. (2014). A Corporate Strategy on Technological Convergence through Analyzing Patent Networks and Strategic Indicators. The Journal of Intellectual Property, 9(4), 191-221. http://dx.doi.org/10.34122/jip.2014.12.9.4.191
  3. Y. S. Park, W. H. Shon, S. M. Cho & H. J. Lee. (2013). Study for Industrial Convergence Degree Analysis Method based on Industrial Convergence Type. Korean Society for Precision Engineering 2013 Conference, 1289-1290.
  4. K. H. Kim & J. Y. Jung. (2013). A Typology of Industry Convergences Based on Sources for Convergence Industries and Analysis of Critical Success Factors. Journal of the Korean Institute of Industrial Engineers, 39(3), 204-211. http://dx.doi.org/10.7232/JKIIE.2013.39.3.204
  5. H. J. Kang, M. J. Um & D. M. Lim. (2006), A Study on Forecast of the Promising Fusion Technology by US Patent Analysis, Technology Innovation Research, 14(3), 93-116.
  6. F. Hacklin, C. Marxt & F. Fahrni. (2009). Coevolutionary cycles of convergence: an extrapolation from the ICT industry. Technological Forecasting and Social Change, 76(6), 723-736. https://doi.org/10.1016/j.techfore.2009.03.003
  7. K. R. Lee. (2015). Toward a new paradigm of technological innovation: convergence innovation. Asian Journal of Technology Innovation, 23, 1-8. https://doi.org/10.1080/19761597.2015.1019226
  8. K. C. Nam, H. C. Kim & B. S. Kwon. (2014). ICT Convergence Medical Device. The Journal of The Korean Institute of Communication Sciences, 31(12), 44-50.
  9. D. W. Kim. (2021). A Study on the Smart Medical Equipment Management Program (Secure-MEMP) Method Considering Securit. Jouranl of Information and Security, 21(1), 63-72. https://doi.org/10.33778/kcsa.2021.21.1.063
  10. FDA. (2016). Postmarket Management of Cybersecurity in Medical Devices.
  11. Y. J. Geum, M. S. Kim & S. J. Lee. (2016). How industrial convergence happens: A taxonomical approach based on empirical evidences. Technological Forecasting and Social Change, 107, 112-120. https://doi.org/10.1016/j.techfore.2016.03.020
  12. N. Y. Han & J. B. Hong. (2012). Convergence Types of Smal and Medium Companies Understod Through Convergence Research Development. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 7(2), 19-24. http://dx.doi.org/10.16972/apjbve.7.2.201207.19
  13. B. P. Abraham & S. D. Moitra. (2001). Innovation assessment through patent analysis. Technovation, 21(4), 245-252. https://doi.org/10.1016/S0166-4972(00)00040-7
  14. B. G. Jeong, J. W. Kim & J. H. Yun. (2015). Patent-based competitive intelligence analysis of augmented reality technology : Application of topic modeling". The Korean Institute of Industrial Engineers 2015 Conference, 2265-2270.
  15. W. Xing, X. Ye & L. Kui. (2011). Measuring convergence of China's ICT industry : an input-output analysis. Telecommunications Policy, 35(4), 301-313. https://doi.org/10.1016/j.telpol.2011.02.003
  16. H. J. Kang & K. G. Kim. (2007). A Study on Impacts of Industrial Convergence Using Patent Citation. Journal of Technology Innovation, 22(2), 31. https://doi.org/10.14383/SIME.2014.22.2.031
  17. J. H. Yoon & K. S. Kim. (2011). A Study on Interdisciplinary Trends of Technological Convergence Using Patent Information: The Case of Air Pollutant Control Technology. Entrue Journal of Information Technology 10(2), 21.
  18. T. K. Kim, H. R. Choi & H. C. Lee. (2016). A Study on the Research Trends in Fintech using Topic Modeling. Journal of the Korea Academy Industrial Cooperation Society, 17(11), 670-681. https://doi.org/10.5762/KAIS.2016.17.11.670
  19. J. S. Park, S. G. Hong & J. W. Kim. (2017). A Study on Science Technology Trend and Prediction Using Topic Modeling. Journal of the Korea Industrial Information Systems Research, 22(4), 19-28. https://doi.org/10.9723/jksiis.2017.22.4.019
  20. S. H. Park, Y. J. Choi, S. J. Lee & H. J. Seol. (2020). Analysis of Technology Convergence by an Integrated Use of Dynamic Topic Modeling and Network Analysis :ICT-Agritech Case. Journal of the Korean Institute of Industrial Engineers, 46(3), 21-21. DOI : 10.7232/JKIIE.2020.46.3.211
  21. M. Steyvers & T. Griffiths. (2007). Probabilistic Topic Models. Handbook of latent semantic analysis, 427(7), 424-440.
  22. D. M. Blei, A. Y. Ng & M. I. Jordan. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3, 993-1022.
  23. T. Griffiths & M. Steyvers. (2004). Finding Scientific Topics. Proceedings of the National Academy of Sciences, 101(1), 5228-5235. https://doi.org/10.1073/pnas.0307752101
  24. B. K. Jeong, J. W. Kim & J. H. Yoon. (2016). A Semantic Patent Analysis Approach to Identifying Trends of Convergence Technology: Application of Topic Modeling and Cross-impact Analysis, The Journal of Intellectual Property, 1(4), 21-240. http://dx.doi.org/10.34122/jip.2016.12.11.4.211
  25. J. H. Lee, I. S. Lee, K. S. Jung, B. H. Chae & J. Y. Lee. (2017). Patents and Papers Trends of Solar-Photovoltaic(PV) Technology using LDA Algorithm. Journal of Digital Convergence, 15(9), 231-239. http://dx.doi.org/10.14400/JDC.2017.15.9.231
  26. K. B. Kim & K. H. Han. (2020). A Study of the Digital Healthcare Industry in the Fourth Industrial Revolution. Journal of Convergence for Information Technology, 10(3), 7-15. https://doi.org/10.22156/CS4SMB.2020.10.03.007
  27. S. H. Lee & J. Y. Kim. (2020). Artificial intelligence technology trend based on medical big data. The Journal of The Korean Institute of Communication Sciences, 37(9), 85-91.
  28. G. T. Song & S. J. Song. (2018). Recent research trends in smart medical devices and ICT convergence medical industry. The Journal of The Korean Institute of Communication Sciences, 35(6), 50-55.
  29. S. H. Park, Y. M. Yun, H. Y. Kim & J. S. Kim. (2021). Technology Convergence & Trend Analysis of Biohealth Industry in 5 Countries : Using patent co-classification analysis and text mining. Journal of the Korea Convergence Society, 12(4), 9-21. https://doi.org/10.15207/JKCS.2021.12.4.009
  30. S. Rhoades. (1993). The Herfindahl-Hirschman Index. Federal Reserve Bulletin, 79(3), 188-9.
  31. T. K. Ryu et al. (2012). Development of Indicators for IP Competitivenes and Characteristics. Seoul : Korea Institute of Intellectual Property.