Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Quantifying the Process of Patent Right Quality Evaluation : Combined Application of AHP, Text Mining and Regression Analysis

특허권리성의 정량적 평가방법에 대한 연구 : AHP, 텍스트 마이닝, 회귀분석의 활용

  • Yoon, Janghyeok (Department of Industrial Engineering, Konkuk University) ;
  • Song, Jaeguk (Department of Economics and Statistics, Korea University) ;
  • Ryu, Tae-Kyu (Korea Institute of Intellectual Property)
  • Received : 2015.03.23
  • Accepted : 2015.04.07
  • Published : 2015.06.30
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Abstract

Technology-oriented national R&D programs produce intellectual property as their final result. Patents, as typical industrial intellectual property, are therefore considered an important factor when evaluating the outcome of R&D programs. Among the main components of patent evaluation, in particular, the patent right quality is a key component constituting patent value, together with marketability and usability. Current approaches for patent right quality evaluation rely mostly on intrinsic knowledge of patent attorneys, and the recent rapid increase of national R&D patents is making expert-based evaluation costly and time-consuming. Therefore, this study defines a hierarchy of patent right quality and then proposes how to quantify the evaluation process of patent right quality by combining text mining and regression analysis. This study will contribute to understanding of the systemic view of the patent right quality evaluation, as well as be an efficient aid for evaluating patents in R&D program assessment processes.

Keywords

References

  1. Aiken, L.S., West, S.G., and Pitts, S.C., Multiple linear regression. Handbook of psychology, 2003.
  2. Banwet, D. and Deshmukh, S., Evaluating performance of national R&D organizations using integrated DEAAHP technique. International Journal of Productivity and Performance Management, 2008, Vol. 57, No. 5, pp. 370-388. https://doi.org/10.1108/17410400810881836
  3. Bergmann et al., Evaluating the risk of patent infringement by means of semantic patent analysis : the case of DNA chips, R&D Management, 2008, Vol. 38, No. 5, pp. 550-562. https://doi.org/10.1111/j.1467-9310.2008.00533.x
  4. Berry, M.W., Survey of text mining : clustering, classification, and retrieval, 2004, Springer-Verlag New York Inc.
  5. Bilbao-Osorio, B. and Rodriguez-Pose, A., From R&D to innovation and economic growth in the EU. Growth and Change, 2004, Vol. 35, No. 4, pp. 434-455. https://doi.org/10.1111/j.1468-2257.2004.00256.x
  6. Chiu, Y.-J. and Chen, Y.-W., Using AHP in patent valuation. Mathematical and Computer Modelling, 2007, Vol. 46, No. 7-8, pp. 1054-1062. https://doi.org/10.1016/j.mcm.2007.03.009
  7. Choi et al., Patent function network analysis : A function based approach for analyzing patent information. 2010,
  8. Choi et al., SAO network analysis of patents for technology trends identification : a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells. Scientometrics, 2011, Vol. 88, No. 3, pp. 863-883. https://doi.org/10.1007/s11192-011-0420-z
  9. Chunlin, S. and King-Lien, L., The strategy of designing around existing patents in technology innovation : Case study of critical technology of OTFT. Journal of Chinese Entrepreneurship, 2010, Vol. 2, No. 3, pp. 270-281. https://doi.org/10.1108/17561391011078758
  10. Coccia, M., What is the optimal rate of R&D investment to maximize productivity growth?. Technological Forecasting and Social Change, 2009, Vol. 76, No. 3, pp. 433-446. https://doi.org/10.1016/j.techfore.2008.02.008
  11. Cohen, W.M. and Merrill, S.A., Patents in the knowledge-based economy, 2003, National Academies Press.
  12. Dereli, T. and Durmusoglu, A., A trend-based patent alert system for technology watch. Journal of Scientific and Industrial Research, 2009, Vol. 68, No. 8, p. 674.
  13. Gerdsri, N. and Kocaoglu, D.F., Applying the analytic hierarchy process (AHP) to build a strategic framework for technology roadmapping. Mathematical and Computer Modelling, 2007, Vol. 46, No. 7-8, pp. 1071-1080. https://doi.org/10.1016/j.mcm.2007.03.015
  14. Green, W.H., Econometric analysis. International Edition, 2000.
  15. Gueorguieva et al., The Program Assessment Rating Tool and the Government Performance and Results Act. The American Review of Public Administration, 2009, Vol. 39, No. 3, pp. 225-245. https://doi.org/10.1177/0275074008319218
  16. Hirose, Y. and Hiruma, F., Patent Valuation Model and PatVM, 2005 : Toyo Keizai.
  17. Ho, W., Integrated analytic hierarchy process and its applications-A literature review. European Journal of Operational Research, 2008, Vol. 186, No. 1, pp. 211-228. https://doi.org/10.1016/j.ejor.2007.01.004
  18. Huang, C.C., Chu, P.Y., and Chiang, Y.H., A fuzzy AHP application in government-sponsored R&D project selection. Omega, 2008, Vol. 36, No. 6, pp. 1038-1052. https://doi.org/10.1016/j.omega.2006.05.003
  19. Imoto, S., Yabuuchi, Y., and Watada, J., Fuzzy regression model of R&D project evaluation. Applied Soft Computing, 2008, Vol. 8, No. 3, pp. 1266-1273. https://doi.org/10.1016/j.asoc.2007.02.024
  20. Janasik, N., Honkela, T., and Bruun, H., Text Mining in Qualitative Research. Organizational Research Methods, 2009, Vol. 12, No. 3, p. 436. https://doi.org/10.1177/1094428108317202
  21. Jun, S. and Uhm, D., Technology Forecasting Using Frequency Time Series Model : Bio-Technology Patent Analysis. Journal of Modern Mathematics and Statistics, 2010, Vol. 4, No. 3, pp. 101-104. https://doi.org/10.3923/jmmstat.2010.101.104
  22. KIIP, Development and application of indicators for intellectual property competitiveness, 2010, Korea Institute of Intellectual Property : Seoul.
  23. KIIP, Development of Quantitative Patent Quality Measures for Government Supported Research and Development Programs. 2010, Korea Institute of Intellectual Property : Seoul.
  24. Lee, S.K., Mogi, G., and Kim, J.W., The competitiveness of Korea as a developer of hydrogen energy technology : The AHP approach. Energy policy, 2008, Vol. 36, No. 4, pp. 1284-1291. https://doi.org/10.1016/j.enpol.2007.12.003
  25. Li, Y.R., Wang, L.H., and Hong, C.F., Extracting the significant-rare keywords for patent analysis. Expert Systems with Applications, 2009, Vol. 36, No. 3, pp. 5200-5204. https://doi.org/10.1016/j.eswa.2008.06.131
  26. Liang, W.Y., The analytic hierarchy process in project evaluation : an R&D case study in Taiwan. Benchmarking : An International Journal, 2003, Vol. 10, No. 5, pp. 445-456. https://doi.org/10.1108/14635770310495492
  27. Magerman, T., B. Van Looy, and X. Song, Exploring the feasibility and accuracy of Latent Semantic Analysis based text mining techniques to detect similarity between patent documents and scientific publications. Scientometrics, 2010, Vol. 82, No. 2, pp. 289-306. https://doi.org/10.1007/s11192-009-0046-6
  28. Merito, M., Giannangeli, S., and Bonaccorsi, A., Do incentives to industrial R&D enhance research productivity and firm growth? Evidence from the Italian case. International Journal of Technology Management, 2010, Vol. 49, No. 1, pp. 25-48. https://doi.org/10.1504/IJTM.2010.029409
  29. OECD, The knowledge-based economy, 1996, OECD : Paris.
  30. Oostdijk, N., Verberne, S., and Koster, C., Constructing a broad-coverage lexicon for text mining in the patent domain, 2010.
  31. Ortega, J.L., Collaboration patterns in patent networks and their relationship with the transfer of technology : the case study of the CSIC patents. Scientometrics, 2011, Vol. 87, No. 3, pp. 657-666. https://doi.org/10.1007/s11192-011-0363-4
  32. Parent, O. and LeSage, J. P., Determinants of knowledge production and their effects on regional economic growth. Journal of Regional Science, 2011.
  33. Park, H., Yoon, J., and Kim, K., Identifying patent infringement using SAO based semantic technological similarities, Scientometrics, 2012, Vol. 90, No. 2, pp. 515-529. https://doi.org/10.1007/s11192-011-0522-7
  34. Piras, G., Postiglione, P., and Aroca, P., Specialization, R&D and productivity growth: evidence from EU regions. The Annals of Regional Science, 2010, pp. 1-17.
  35. RIPIS, R&D Intellectual Property Information System. 2011 [cited 2011 Dec 21]; Available from : rndip.or.kr.
  36. Rodriguez, J.C. and Gomez, M., Innovation Trends in NAFTA Countries : An Econometric Analysis of Patent Applications. Journal of Technology Management and Innovation, 2011, Vol. 6, No. 3, pp. 116-125. https://doi.org/10.4067/S0718-27242011000300009
  37. Rogers, G.M. and Spencer, D., Report of the Advisory Committee for GPRA Performance Assessment FY 2007. Washington, DC : NSF-07-207, 2007.
  38. Saaty, T.L. and Vargas, L.G., The seven pillars of the analytic hierarchy process. Models, Methods, Concepts and Applications of the Analytic Hierarchy Process, 2001, pp. 27-46.
  39. Saaty, T.L., How to make a decision : the analytic hierarchy process. European journal of operational research, 1990, Vol. 48, No. 1, pp. 9-26. https://doi.org/10.1016/0377-2217(90)90057-I
  40. Seol, H., Lee, S., and Kim, C., Identifying new business areas using patent information : A DEA and text mining approach. Expert Systems with Applications, 2011, Vol. 38, No. 4, pp. 2933-2941. https://doi.org/10.1016/j.eswa.2010.06.083
  41. Srivastava, A. and Sahami, M., Text mining : Classification, clustering, and applications, 2009, Chapman and Hall/CRC.
  42. Stam, E. and Wennberg, K., The roles of R&D in new firm growth. Small Business Economics, 2009, Vol. 33, No. 1, pp. 77-89. https://doi.org/10.1007/s11187-009-9183-9
  43. Tikoria, J., Banwet, D., and Deshmukh, S., Performance measurement of national R&D organisations using analytic hierarchy process : a case of India. International Journal of Innovation and Regional Development, 2009, Vol. 1, No. 3, pp. 276-300. https://doi.org/10.1504/IJIRD.2009.021847
  44. Wang, J., Lin, W., and Huang, Y.H., A performanceoriented risk management framework for innovative R&D projects. Technovation, 2010, Vol. 30, No. 11-12, pp. 601-611. https://doi.org/10.1016/j.technovation.2010.07.003
  45. Wei, C.C., Chien, C.F., and Wang, M.J.J., An AHP-based approach to ERP system selection. International Journal of Production Economics, 2005, Vol. 96, No. 1, pp. 47-62. https://doi.org/10.1016/j.ijpe.2004.03.004
  46. Yamada, Y., Kato, K., and Hirokawa, S., Text Mining for Analysis of Interviews and Questionnaires, 2011.
  47. Yang et al., Enhancing patent landscape analysis with visualization output. World Patent Information, 2010, Vol. 32, No. 3, pp. 203-220. https://doi.org/10.1016/j.wpi.2009.12.006
  48. Yoon, J. and K. Kim, An Automated Method for Identifying TRIZ Trends from Patents. Expert Systems with Applications, 2011, Vol. 38, No. 12, pp. 15540-15548. https://doi.org/10.1016/j.eswa.2011.06.005
  49. Yoon, J. and Kim, K., Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics, 2011, Vol. 88, No. 1, pp. 213-228. https://doi.org/10.1007/s11192-011-0383-0

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