DOI QR코드

DOI QR Code

A study of factors affecting citation of patents: Focusing on US automotive patents

특허의 피인용에 영향을 끼치는 요인에 대한 연구: 미국 자동차 특허를 중심으로

  • Ryu, Wonrim (Graduate School of Management of Technology, Korea University) ;
  • Kim, Youngjun (Graduate School of Management of Technology, Korea University)
  • 류원림 (고려대학교 기술경영전문대학원) ;
  • 김영준 (고려대학교 기술경영전문대학원)
  • Received : 2022.02.06
  • Accepted : 2022.03.20
  • Published : 2022.03.28

Abstract

The number of citations in a patent is one of the indicators of the qualitative value of a patent. In this study, negative binomial regression model analysis was performed focusing on 47,354 US patents of 14 global top automotive makers in order to examine the major factors affecting the number of patent citations. As a result of the review, it was found that, elapsed years since filing, the number of patent claims, the number of claim letters, the number of inventors, the number of patent family countries, and the number of patent families, as well as IPC diversity, had a positive and significant effect on the number of citations. The results of this study are expected to provide a basic basis for considering the IPC diversity index together in analyzing and evaluating future patents and establishing strategies for creating excellent patents.

특허의 피인용 수는 특허의 질적 가치와 기업의 가치와 긍정적인 상관관계가 있으며, 특허의 질적 가치를 나타내는 지표 중 하나이다. 본 연구는 특허의 피인용 수에 영향을 끼치는 주요 요인을 살펴보기 위하여, 국제특허분류코드인 IPC가 다양한 분야인 자동차 산업을 대상으로, 글로벌 상위 자동차 업체 14개사의 미국 특허 47,354건에 대하여 음이항 모델 회귀분석을 수행하였다. 분석결과, 출원후 경과년도, 특허의 청구항 수, 특허 패밀리 국가 수, 특허 패밀리 수, IPC 코드의 총 수뿐만 아니라, IPC 다양성 수가 특허의 피인용 수에 긍정적인 유의미한 영향을 미치는 것으로 나타났다. 본 연구의 결과는 향후, 대학, 연구소 및 기업의 기술이전이나 라이선싱을 위한 전략을 세우는 데 있어서, IPC 다양성 지표를 함께 고려할 수 있는 기초적 근거를 제공할 수 있을 것으로 기대된다.

Keywords

References

  1. Ocean Tomo. (2020). Intangible Asset Market Value Study. Oceantomo homepage. https://www.oceantomo.com/intangible-asset-market-value-study
  2. Lin, Bou-Wen, Chen, Chung-Jen & Wu, Hsueh. (2007). Predicting citations to biotechnology patents based on the information from the patent documents. International Journal of Technology Management, 40(1-3). DOI:10.1504/IJTM.2007.013528
  3. H. J. No & H. Lim. (2009). Exploration of Nanobiotechnologies Using Patent Data. The Journal of Intellectual Property, 4(3), 109-129. https://doi.org/10.34122/jip.2009.09.4.3.109
  4. J. B. Yoo & Y. M. Chung. (2010). Analysis of Factors Influencing Patent Citations. Journal of the Korean Society for Information Management, 27(1), 103-118. https://doi.org/10.3743/KOSIM.2010.27.1.103
  5. Reitzig, M. (2004). Improving patent valuations for management purposes: Validating new indicators by analyzing application rationales. Research Policy, 33(6-7), 939-957. https://doi.org/10.1016/j.respol.2004.02.004
  6. K. N. Choo & K. H. Park. (2010). A Study on the Determinants of the Economic Value of Patents Using Renewal Data. Knowledge Management Review, 11(1), 65-81. https://doi.org/10.15813/KMR.2010.11.1.005
  7. Trajtenberg, Manuel. (1990). A Penny for Your Quotes: Patent Citations and the Value of Innovations. RAND Journal of Economics, 21. 172-187. https://doi.org/10.2307/2555502
  8. Hegde Deepak & Sampat Bhaven. (2009). Examiner citations, applicant citations, and the private value of patents. Economics Letters, Elsevier, 105(3), 287-289. https://doi.org/10.1016/j.econlet.2009.08.019
  9. Moore Kimberly A.. (2004). Worthless Patents. SSRN(Online). https://ssrn.com/abstract=566941
  10. Harhoff, D., Scherer, F.M., & Vopel, K. (2003). Citations, Family Size, Opposition and the Value of Patent Rights. Research Policy, 32(8), 1343-1363. https://doi.org/10.1016/S0048-7333(02)00124-5
  11. Lanjouw, Jean & Schankerman, Mark. (2004). Patent Quality and Research Productivity: Measuring Innovation with Multiple Indicators. Economic Journal. 114(495). 441-465. https://doi.org/10.1111/j.1468-0297.2004.00216.x
  12. Manuel Trajtenberg, Rebecca Henderson & Adam Jaffe. (1997). University Versus Corporate Patents: A Window On The Basicness Of Invention. Economics of Innovation and New Technology, 5(1), 19-50. https://doi.org/10.1080/10438599700000006
  13. S. H. Yu. (2004). A Study on the Forecasting Model of Technology Life Cycles by Analysis of US Patent Citation, Journal of Information Management, 35(1), 93-112. https://doi.org/10.1633/JIM.2004.35.1.093
  14. B. U. Yoon. (2005). Methodology for managing technological knowledge and developing new technology using patent analysis. Doctoral dissertation. Seoul University, Seoul.
  15. Karki, M.M. (1997). Patent citation analysis: A policy analysis tool. World Patent Information, 19(14), 269-272. https://doi.org/10.1016/S0172-2190(97)00033-1
  16. Jaffe, A.B., & Trajtenberg, M. (1996). Flows of knowledge from universities and federal laboratories: modeling the flow of patent citations over time and across institutional and geographic boundaries. Proceedings of the National Academy of Sciences of the United States of America, 93(23), 12671-12677. DOI: 10.1073/pnas.93.23.12671
  17. Adam B. Jaffe & Manuel Trajtenberg. (1999). International Knowledge Flows: Evidence From Patent Citations. Economics of Innovation and New Technology, 8(1-2), 105-136. https://doi.org/10.1080/10438599900000006
  18. J. C. Jang & Y. J. Kim. (2021). The Effect of Firm's Technology Convergence on Firm Performance. Journal of Knowledge Management Review, 22(2), 77-93.
  19. Bou-Wen Lin, Chung-Jen Chen & Hsueh-Liang Wu. (2006). Patent portfolio diversity, technology strategy, and firm value. IEEE Transactions on Engineering Management, 53(1), 17-26. https://doi.org/10.1109/TEM.2005.861813
  20. Y. G. Lee, J. D. Lee & Y. I. Song. (2006). An Analysis of Citation Counts of ETRI-Invented US Patents. [ETRI] ETRI Journal, 28(4), 541-544. https://doi.org/10.4218/etrij.06.0206.0019
  21. J. W. Yoon, C. S. Lee & S. J. Lee. (2016). Analysis of Factors Influencing Patent Citations: Focused on Korea Medical Device Patents. Journal of the Korean Society for Information Management, 33(2), 103-133. https://doi.org/10.3743/KOSIM.2016.33.2.103
  22. van Zeebroeck, N. (2007). Patents only live twice: a patent survival analysis in Europe. ULB-Universite Libre de Bruxelles.
  23. Y. G. Lee, Y. I. Song & S. J. Lee. (2007). An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST. Scientometrics, 70(1), 27-39. https://doi.org/10.1007/s11192-007-0102-z