The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

A study on patent evaluation model based on Bayesian approach of the structural equation model

구조방정식 모형의 베이지안 접근법 기반의 특허평가 모델링에 대한 연구

  • Woo, Ho-young (Department of Applied Statistics, Chung-Ang University) ;
  • Kwak, Jungae (Korea Invention Promotion Association) ;
  • Lim, Changwon (Department of Applied Statistics, Chung-Ang University)
  • Received : 2017.09.05
  • Accepted : 2017.11.06
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, the industrial paradigm shift to the fourth industry has already begun, and the importance of patents as intangible intellectual property in the fourth industry era is increasing day by day. Since the technical valuation of a patent is calculated according to the opinion of experts, it is costly and time consuming, and hence, the quality of the patent is judged based on subjective opinions of non-experts. Therefore, it is necessary to develop an objective and rational evaluation system for the qualitative level of patents. In this paper, we classify the valuation of patents into technicality, rights, and usability, and consider the quantitative and objective evaluation modeling of patents using Bayesian structural equation model. In particular, based on the data collected by the Korea Invention Promotion Association, we apply the Bayesian approach, which is capable of stable modeling even under small samples by using prior information, and the structural equation model, which is excellent for modeling and evaluating qualitative performance that is difficult to measure directly, to develop a patent evaluation model.

최근 4차 산업으로의 산업 패러다임의 변화가 이미 시작되었으며, 이러한 4차 산업 시대에 무형 지식재산인 특허의 중요성은 날로 증대되고 있다. 특허의 기술가치평가는 전문가의 의견에 따라서 산정되기 때문에 많은 비용과 시간이 소모되므로 비전문가들의 주관적인 의견에 기인하여 특허의 질적 수준을 판단하게 된다. 따라서 특허의 질적 수준에 대한 객관적이고 합리적인 평가 체계 개발이 필요하다. 본 논문에서는 특허의 가치평가를 기술성, 권리성, 활용성으로 구분하고 베이지안 구조방정식을 사용하여 특허의 정량화되고 객관적인 평가 모델링에 대해 고려하였다. 특히, 한국발명진흥회에서 수집한 자료를 토대로, 직접적으로 측정되기 어려운 질적 성과들을 모형화하고 평가하는데 탁월한 구조방정식과 사전 정보를 활용함으로써 작은 표본 하에서도 안정적인 모형화가 가능한 베이지안 접근법을 함께 적용하여 특허 평가 모형을 개발하였다.

Keywords

References

  1. Bentler, P. M. and Chou, C. (1987). Practical issues in structural modeling, Sociological Methods and Re- search, 16, 78-117. https://doi.org/10.1177/0049124187016001004
  2. Congdon, P. (2001). Bayesian Statistical Modelling, Wiley, New York.
  3. Fong, T. C. and Ho, R. T. (2015). Dimensionality of the 9-item Utrecht Work Engagement Scale revisited: a Bayesian structural equation modeling approach, Journal of Occupational Health, 57, 353-358. https://doi.org/10.1539/joh.15-0057-OA
  4. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004). Bayesian Data Analysis (2nd ed), CRC Press, Florida.
  5. Gelman, A. and Rubin, D. (1992). Inference from iterative simulation using multiple sequences (with discussion), Statistical Science, 7, 457-511. https://doi.org/10.1214/ss/1177011136
  6. Ham, H.-B., Lee, Y.-S., and An C.-H. (2004). An assessment system model for game satisfaction degree to establish game development strategy, Journal of Korea Multimedia Society, 7, 1630-1638.
  7. Hutcheson, G. D. and Sofroniou, N. (1999). The Multivariate Social Scientist: An Introduction to Generalized Linear Models, Sage Publications.
  8. Kang, Y. J. (2008). Understanding and application of Delphi technique, Occasional Task Report, 1-17.
  9. Kass, R. E. and Raftery, A. E. (1995). Bayes factors, Journal of the American Statistical Association, 90, 773-795. https://doi.org/10.1080/01621459.1995.10476572
  10. Kim, B.-K., Cho, H.-J., and Og, J.-Y. (2011a). A study on the technology commercialization process and performance of public research institutes in Korea using the structural equation model, Journal of Korea Technology Innovation Society, 14, 552-577.
  11. Kim, C.-H., Park, S.-P., and Koh, Y.-H. (2011b). Intellectual property strategy, Korea Invention Promotion Association, 15-19.
  12. Kim, J. G., Moon, T. H., and Sohn, S. Y. (2004). Structural equation model for information technologization success index (ITSI) of small and medium enterprise, IE Interfaces, 17, 338-348.
  13. Kim, M., Jeon, M., Sung, K.-I., and Kim, Y.-J. (2016). Bayesian structural equation modeling for analysis of climate effect on whole crop barley yield, Korean Journal of Applied Statistics, 29, 331-344. https://doi.org/10.5351/KJAS.2016.29.2.331
  14. Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed), The Guilford Press, New York.
  15. Korea Institute of Patent Information (2013). Qualitative Evaluation of Patent Utilizing K-PEG Evaluation System, Korea Patent Information Center, Patent Information Promotion Center, Seoul, 1-8.
  16. Korea Institute of Science and Technology Information (2014). Science & Technology Information Analysis for R&D-Value System, Korea Institute of Science and Technology Information Technology Valuation LAB, Daejeon.
  17. Korea Invention Promotion Association (2012). System to Measure, Analyze and Rate patent Technology 3. URL https://smart.kipa.org/kipa2/valmodel/smart process.do?flag=VALMODEL INTRO
  18. Korea Invention Promotion Association (2017). Introduction of Patent Analysis Evaluation System Evaluation Model, URL http://smart.kipa.org/.
  19. Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach, John Wiley & Sons, New York.
  20. Loehlin, J. C. (1992). Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis (2nd ed), Lawrence Erlbaum Associates, New Jersey.
  21. Merkle, E. C. and Rosseel, Y. (2015). blavaan: Bayesian structural equation models via parameter expansion, arXiv preprint arXiv:1511.05604, Retrieved URL https://arxiv.org/abs/1511.05604.
  22. Ministry of Commerce, Industry and Energy (2014). Technical Value Assessment Practical Guide, 27-33.
  23. Muthen, B. and Asparouhov, T. (2012). Bayesian structural equation modeling: a more flexible representation of substantive theory, Psychological Methods, 17, 313-335. https://doi.org/10.1037/a0026802
  24. Park, K. B. (2000). Multivariate Analysis, Hakjisa, Seoul.
  25. Plummer, M. (2015). JAGS Version 4.0.0 User Manual.
  26. Schwarz, G. (1978). Estimating the dimension of a model, Annals of Statistics, 2, 461-464.
  27. Shin, H.-M., Kim, T.-H., Jeong, K.-S., and Won, J.-M. (2009). A development of service quality evaluation models for pedestrians in newtown using structural equation modeling - a case of Bundang New Town -. Seoul Studies, 10, 183-196.
  28. Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002). Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society, Series B, 64, 583-639. https://doi.org/10.1111/1467-9868.00353
  29. Vehtari, A., Gelman, A., and Gabry, J. (2015). Loo: Ecient leave-one-out cross-validation and WAIC for Bayesian models. URL https://github.com/jgabry/loo
  30. Yoo, J. B. and Jung, Y. M. (2010). Analysis of factors affecting patent citation, Journal of the Korean Society for Information Management, 27, 103-118.