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특허의 내적.외적 가치산정요인을 고려한 입체적 특허평가모델: 아레니우스 화학반응속도론 기반의 특허수명예측

A three-dimensional patent evaluation model that considers the factors for calculating the internal and external value of a patent: Arrhenius chemical reaction kinetics-based patent lifespan prediction

  • 최용묵 (한양대학교 기술경영전문대학원) ;
  • 이재원 (한국특허정보원) ;
  • 조대명 (한양대학교 기술경영전문대학원)
  • Choi, Yong Muk (Department of Graduate School of Technology & Innovation Management, Hanyang University) ;
  • LEE, JAEWON (Department of IT Development & Support, Korea Institute of Patent Information) ;
  • Cho, Daemyeong (Department of Graduate School of Technology & Innovation Management, Hanyang University)
  • 투고 : 2021.03.06
  • 심사 : 2021.06.20
  • 발행 : 2021.06.28

초록

특허수명은 특허가치를 평가하는 척도로 사용되어 왔다. 본 연구에서는 특허수명을 예측하여 개별특허의 가치를 평가함에 있어, 특허의 내적가치요소와 외적가치요소를 하나의 모델로 평가하기 위하여 화학반응속도 추정식으로 널리 알려진 아레니우스식을 사용한 새로운 평가모델을 제시하였다. 한국의 소멸된 특허데이터를 활용하여 평가모델의 성능을 검증하였으며, 선행연구에서 특허평가모델로 사용되었던 SVM, Logistic reg., ANN 모델과 성능을 비교하였다. 결과적으로, 제안한 평가모델이 다른 모델 보다 정확도가 높았으며, 특허권자의 특성을 고려한 상대체감비용지수 반영 시 여러 평가모델에서 정확도가 상승하는 경향을 보였다. 또한, 특허의 예측수명등급과 특허의 실제수명과는 강한 상관관계가 있었다. 이러한 평가모델은 대량의 특허를 객관적으로 신속하게 평가할 수 있으며 특허의 유지여부에 대한 의사결정 혹은 기술거래나 평가에 활용할 수 있다. 특히, 평가목적에 따라 특허만을 평가하거나 사업화주체나 기술적 특성을 고려한 평가가 필요한 경우에 각각 사용될 수 있다.

This study is a new evaluation using the Arrhenius equation, which is known as the chemical reaction rate estimation equation, to evaluate the intrinsic and extrinsic value elements of patents as a model. The performance of the evaluation model was superior to the SVM, Logistic reg. and ANN models that were used as patent evaluation models in prior studies. In addition, there was a strong correlation between the predicted lifespan of the patent and the actual lifespan of the patent. These evaluation models may be used for evaluation purposes only, or if an evaluation is required, including a commercialization entity or technical characteristics.

키워드

참고문헌

  1. Munari, F., & Oriani, R. (Eds.). (2011). The economic valuation of patents: methods and applications. Edward Elgar Publishing.
  2. Bessen, J. (2008). The value of US patents by owner and patent characteristics. Research Policy, 37(5), 932-945. https://doi.org/10.1016/j.respol.2008.02.005
  3. Squicciarini, M., Dernis, H., & Criscuolo, C. (2013). Measuring patent quality: Indicators of technological and economic value.
  4. Choi, Y. M., & Cho, D. (2018). A study on the time-dependent changes of the intensities of factors determining patent lifespan from a biological perspective. World Patent Information, 54, 1-17. https://doi.org/10.1016/j.wpi.2018.05.006
  5. Guellec, D., & de la Potterie, B. V. P. (2000). Applications, grants and the value of patent. Economics letters, 69(1), 109-114. https://doi.org/10.1016/S0165-1765(00)00265-2
  6. Pakes, A., & Schankerman, M. (1984). The rate of obsolescence of patents, research gestation lags, and the private rate of return to research resources. In R&D, patents, and productivity (pp. 73-88). University of Chicago Press.
  7. Pakes, A. (1984). Patents as options: Some estimates of the value of holding European patent stocks (No. w1340). National Bureau of Economic Research.
  8. Narin, F., Noma, E., & Perry, R. (1987). Patents as indicators of corporate technological strength. Research policy, 16(2-4), 143-155. https://doi.org/10.1016/0048-7333(87)90028-X
  9. Harhoff, D., Narin, F., Scherer, F. M., & Vopel, K. (1999). Citation frequency and the value of patented inventions. Review of Economics and statistics, 81(3), 511-515. https://doi.org/10.1162/003465399558265
  10. Yoo, S. H., Lee, Y. H., & Won, D. K. (2006). A study on estimation of technology life span using analysis of patent citation. Journal of the Korean Operations Research and Management Science Society, 31(4), 1-11.
  11. Choi, J., Jeong, B., Yoon, J., Coh, B. Y., & Lee, J. M. (2020). A novel approach to evaluating the business potential of intellectual properties: A machine learning-based predictive analysis of patent lifetime. Computers & Industrial Engineering, 145, 106544. https://doi.org/10.1016/j.cie.2020.106544
  12. KIPI, K-PEG(Korea Patent Evaluation Grading). https://kpeg.pipc.or.kr, (Accessed, 2020.08.10.)
  13. Song, Y., Wen, S., Li, W., Yang, L., &He, Y. (2019). Evaluation of a Patent value based on AHP fuzzy comprehensive evaluation method. Journal of Physics: Conference Series, 1345(2), 022023 https://doi.org/10.1088/1742-6596/1345/2/022023
  14. Cortez, P., & Embrechts, M. J. (2013). Using sensitivity analysis and visualization techniques to open black box data mining models. Information Sciences, 225, 1-17. https://doi.org/10.1016/j.ins.2012.10.039
  15. Jun, S. P., Park, H. W., & Yoo, J. Y. (2012). The development of the method of determining remaining cited-patent life time using the survival curve analysis. Journal of Korea Technology Innovation Society, 15(4), 745-765.
  16. KIAT (2014). Technical Valuation Practice Guide, Ministry of Trade, Industry and Energy
  17. Park, S. T., Leet, S. J., & Kim, Y. K. (2011). Weight Differences of Patent Valuation Factors by Industries. Journal of Digital Convergence, 9(3), 105-116. https://doi.org/10.14400/JDPM.2011.9.3.105
  18. Briand, L., El Emam, K., & Morasca, S. (1996). On the application of measurement theory in software engineering. Empirical Software Engineering, 1(1), 61-88. https://doi.org/10.1007/BF00125812
  19. Korea Patent Office, Korea Patent Law Article 1-29. https://www.law.go.kr, (Accessed, 2020.08.10.)
  20. Trappey, A. J., Trappey, C. V., Govindarajan, U. H., &Sun, J. J. (2019). Patent value analysis using deep learning models-The case of IoT technology mining for the manufacturing industry. IEEE Transactions on Engineering Management.
  21. Kim, Y., Park, S., Lee, J., Jang, D., &Kang, J. (2021). Integrated Survival Model for Predicting Patent Litigation Hazard. Sustainability, 13(4), 1763. https://doi.org/10.3390/su13041763
  22. Dennis D. Crouch, (2012). Patent Maintenance Fees, PatentlyO, https://patentlyo.com/patent/2012/09/patent-maintenance-fees.html (Accessed 2019.08.10.)
  23. Cornelli, F., & Schankerman, M. (1999). Patent renewals and R&D incentives. The RAND Journal of Economics, 197-213.
  24. Lanjouw, J. O. (1998). Patent protection in the shadow of infringement: Simulation estimations of patent value. The Review of Economic Studies, 65(4), 671-710. https://doi.org/10.1111/1467-937X.00064
  25. Pitkethly, R. (1997). The valuation of patents: a review of patent valuation methods with consideration of option based methods and the potential for further research. Research Papers in Management Studies-University of Cambridge Judge Institute of Management Studies.
  26. Serrano, C. J. (2010). The dynamics of the transfer and renewal of patents. The RAND Journal of Economics, 41(4), 686-708. https://doi.org/10.1111/j.1756-2171.2010.00117.x
  27. Harhoff, D., Hoisl, K., Reichl, B., & de la Potterie, B. V. P. (2009). Patent validation at the country level?the role of fees and translation costs. Research Policy, 38(9), 1423-1437. https://doi.org/10.1016/j.respol.2009.06.014
  28. De Rassenfosse, G., & Jaffe, A. B. (2018). Are patent fees effective at weeding out low-quality patents?. Journal of Economics & Management Strategy, 27(1), 134-148. https://doi.org/10.1111/jems.12219
  29. De Rassenfosse, G., & van Pottelsberghe de la Potterie, B. (2013). The role of fees in patent systems: Theory and evidence. Journal of Economic Surveys, 27(4), 696-716. https://doi.org/10.1111/j.1467-6419.2011.00712.x
  30. Rassenfosse, G. D., & Potterie, B. V. P. D. L. (2012). On the price elasticity of demand for patents. Oxford Bulletin of Economics and Statistics, 74(1), 58-77. https://doi.org/10.1111/j.1468-0084.2011.00638.x
  31. Barney, J. A. (2006). The quality conundrum, Intellectual Asset Management, https://www.oceantomo.com/pdf/studies/IAM_JB_10.2006.pdf, (2019.06.16. accessed)
  32. Lemley, M. A., & Shapiro, C. (2005). Probabilistic patents. Journal of Economic Perspectives, 19(2), 75-98. https://doi.org/10.1257/089533005404865
  33. Hikkerova, L., Kammoun, N., & Lantz, J. S. (2014). Patent life cycle: New evidence. Technological Forecasting and Social Change, 88, 313-324. https://doi.org/10.1016/j.techfore.2013.10.005
  34. Levenspiel, O. (1999). Chemical reaction engineering. John Wiley & Sons.
  35. Cho, D., & Choi, G. (2012). A numeric technology valuation model in conjunction with chemical reaction kinetics. Advanced Science Letters, 8(1), 499-503. https://doi.org/10.1166/asl.2012.2356
  36. Jafari, S. M., Ganje, M., Dehnad, D., Ghanbari, V., & Hajitabar, J. (2017). Arrhenius equation modeling for the shelf life prediction of tomato paste containing a natural preservative. Journal of the Science of Food and Agriculture, 97(15), 5216-5222. https://doi.org/10.1002/jsfa.8404
  37. Woo, C. S., & Park, H. S. (2011). Useful lifetime prediction of rubber component. Engineering Failure Analysis, 18(7), 1645-1651. https://doi.org/10.1016/j.engfailanal.2011.01.003
  38. Brancato, E. L. (1992). Estimation of lifetime expectancies of motors. IEEE Electrical Insulation Magazine, 8(3), 5-13. https://doi.org/10.1109/57.139066
  39. Cooper, M. S. (2005). Investigation of Arrhenius acceleration factor for integrated circuit early life failure region with several failure mechanisms. IEEE Transactions on components and packaging technologies, 28(3), 561-563. https://doi.org/10.1109/TCAPT.2005.848581
  40. Munch, S. B., & Salinas, S. (2009). Latitudinal variation in lifespan within species is explained by the metabolic theory of ecology. Proceedings of the National Academy of Sciences, 106(33), 13860-13864. https://doi.org/10.1073/pnas.0900300106
  41. Gislason, H., Daan, N., Rice, J. C., & Pope, J. G. (2010). Size, growth, temperature and the natural mortality of marine fish. Fish and Fisheries, 11(2), 149-158. https://doi.org/10.1111/j.1467-2979.2009.00350.x
  42. Clark, K. B. (2010). Arrhenius-kinetics evidence for quantum tunneling in microbial "social" decision rates. Communicative & integrative biology, 3(6), 540-544. https://doi.org/10.4161/cib.3.6.12842
  43. Kovats, R. S., & Hajat, S. (2008). Heat stress and public health: a critical review. Annu. Rev. Public Health, 29, 41-55. https://doi.org/10.1146/annurev.publhealth.29.020907.090843
  44. Mimkes, J. (2003). Concepts of Thermodynamics in Economic Systems I. Economic Growth.
  45. Cimbleris, B. (1998). Economy and thermodynamics. Economy and Energy, 9, 1-9.
  46. Arrhenius, S. (1889). Uber die Dissociationswarme und den Einfluss der Temperatur auf den Dissociationsgrad der Elektrolyte. Zeitschrift fur physikalische Chemie, 4(1), 96-116. https://doi.org/10.1515/zpch-1889-0408
  47. IP5, Statistics Report 2019 (2020), fiveIPoffice
  48. Wikipedia. Ideal gas law. https://en.wikipedia.org/wiki/Ideal_gas_law (Accessed, 2020.04.21.)
  49. N.Gregory Mankiw(2015), Principle of economics, Cengage Learning
  50. Laidler, K. J. (1996). A glossary of terms used in chemical kinetics, including reaction dynamics (IUPAC Recommendations 1996). Pure and applied chemistry, 68(1), 149-192. https://doi.org/10.1351/pac199668010149
  51. Wikipedia. Arrhenius equation. https://en.wikipedia.org/wiki/Arrhenius_equation (Accessed, 2020.04.26.)
  52. Okushima, A., Ueda, R., Ishida, S., Oda, T., Takanashi, C., Watanabe, T., ... &Uenishi, K. (2018). Patent evaluation framework using support vector regression. SPIM Innovation Symposium, 1-18
  53. Jonathan A. Barney (2011). Method and system for rating patents and other intangible assets. PatentRating, USPTO. US7,962,511B2
  54. Schmoch, U. (2008). Concept of a technology classification for country comparisons. Final report to the world intellectual property organisation (wipo), WIPO.
  55. Lanjouw, J. O., & Schankerman, M. (2004). Protecting intellectual property rights: are small firms handicapped?. The journal of law and economics, 47(1), 45-74. https://doi.org/10.1086/380476
  56. Harhoff, D., & Wagner, S. (2009). The duration of patent examination at the European Patent Office. Management Science, 55(12), 1969-1984. https://doi.org/10.1287/mnsc.1090.1069
  57. Frietsch, R., Schmoch, U., Van Looy, B., Walsh, J. P., Devroede, R., Du Plessis, M., ... & Schubert, T. (2010). The value and indicator function of patents (No. 15-2010). Studien zum deutschen Innovationssystem.
  58. Svensson, R. (2012). Commercialization, renewal, and quality of patents. Economics of Innovation and New Technology, 21(2), 175-201. https://doi.org/10.1080/10438599.2011.561996
  59. Serrano, C. J. (2005). The market for intellectual property: Evidence from the transfer of patents. University of Minnesota and Federal Reserve Bank of Minneapolis, mimeo.
  60. 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
  61. van Zeebroeck, N. (2007). Patents only live twice: a patent survival analysis in Europe. ULB-Universite Libre de Bruxelles.
  62. Lanjouw, J. O. (1998). Patent protection in the shadow of infringement: Simulation estimations of patent value. The Review of Economic Studies, 65(4), 671-710. https://doi.org/10.1111/1467-937X.00064
  63. 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
  64. EPO, worldwide bibliographic database (DOCDB). http://www.epo.org. (Accessed, 2018.03.14.)
  65. Kalantar, B., Pradhan, B., Naghibi, S. A., Motevalli, A., &Mansor, S. (2018). Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk, 9(1), 49-69. https://doi.org/10.1080/19475705.2017.1407368
  66. Karsoliya, S. (2012). Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 3(6), 714-717.
  67. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.
  68. Salkind, N. J. (2006). Encyclopedia of measurement and statistics. SAGE publications.
  69. Mukaka, M. M. (2012). A guide to appropriate use of correlation coefficient in medical research. Malawi medical journal, 24(3), 69-71.
  70. Choi, J., Jeong, B., & Yoon, J. (2019). Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications. Technological Forecasting and Social Change, 148, 119737. https://doi.org/10.1016/j.techfore.2019.119737
  71. Park, S. T., & Kim, Y. K. (2012). A Study on patent valuation for the activation of IP finance. Journal of Digital Convergence, 10(11), 315-321. https://doi.org/10.14400/JDPM.2012.10.11.315