Advertisement

Scientometrics

, Volume 105, Issue 3, pp 1319–1346 | Cite as

Using the comprehensive patent citation network (CPC) to evaluate patent value

  • Guan-Can Yang
  • Gang Li
  • Chun-Ya Li
  • Yun-Hua Zhao
  • Jing Zhang
  • Tong Liu
  • Dar-Zen Chen
  • Mu-Hsuan Huang
Article

Abstracts

Most approaches to patent citation network analysis are based on single-patent direct citation relation, which is an incomplete understanding of the nature of knowledge flow between patent pairs, which are incapable of objectively evaluating patent value. In this paper, four types of patent citation networks (direct citation, indirect citation, coupling and co-citation networks) are combined, filtered and recomposed based on relational algebra. Then, a method based on comprehensive patent citation (CPC) network for patent value evaluation is proposed, and empirical study of optical disk technology related patents has been conducted based on this method. The empirical study was carried out in two steps: observation of network characteristics over the entire process (citation time lag and topological and graphics characteristics), and measurement verification by independent proxies of patent value (patent family and patent duration). Our results show that the CPC network retains the advantages of patent direct citation, and performs better on topological structure, graphics features, centrality distribution, citation lag and sensitivity than a direct citation network; The verified results by the patent family and maintenance show that the proposed method covers more valuable patents than the traditional method.

Keywords

Comprehensive patent citation (CPC) Multiple relationships Patent value evaluation Relational algebra algorithm 

Notes

Acknowledgments

We are indebted to Mao Jin for helpful discussions and several constructive proposals. This research is supported by National Natural Science Foundations of China (NSFC Grant Nos. 71273196; 71403256 and 71303023), and this research was also supported by National Key Technology R&D Program of China (Grant No. 2013BAH21B00).

References

  1. Alcácer, J., & Gittelman, M. (2006). Patent citations as a measure of knowledge flows: The influence of examiner citations. Review of Economics and Statistics, 88(4), 774–779.CrossRefGoogle Scholar
  2. Alcácer, J., Gittelman, M., & Sampat, B. (2009). Applicant and examiner citations in U.S. patents: An overview and analysis. Research Policy, 38(2), 415–427.CrossRefGoogle Scholar
  3. Atallah, G., & Rodríguez, G. (2006). Indirect patent citations. Scientometrics, 67(3), 437–465.CrossRefGoogle Scholar
  4. Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies.Google Scholar
  5. Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389–2404.CrossRefGoogle Scholar
  6. Braam, R. R., Moed, H. F., & van Raan, A. F. J. (1991). Mapping of science by combined co-citation and word analysis. I. Structural aspects. Journal of the American Society for Information Science, 42, 233–266.CrossRefGoogle Scholar
  7. Breschi, S., & Lissoni, F. (2005). Knowledge networks from patent data: Methodological issues and research targets (613-643), Handbook of quantitative science and technology research. Netherlands: Springer.CrossRefGoogle Scholar
  8. Chen, D., Huang, M., Hsieh, H., & Lin, C. (2011). Identifying missing relevant patent citation links by using bibliographic coupling in LED illuminating technology. Journal of Informetrics, 5(3), 400–412.CrossRefGoogle Scholar
  9. de la Potterie, B. V., & van Zeebroeck, N. (2008). A brief history of space and time: The scope-year index as a patent value indicator based on families and renewals. Scientometrics, 75(2), 319–338.CrossRefGoogle Scholar
  10. De Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek (Vol. 27). London UK: Cambridge University Press.CrossRefGoogle Scholar
  11. Dernis, H., & Khan, M. (2004). Triadic patent families methodology. In: OECD Science, Technology and Industry Working Papers. Google Scholar
  12. Emmanuel, D., & Megan, M. (2005). How well do patent citations measure flows of technology? Evidence from French innovation surveys. Economics of Innovation and New Technology, 14(5), 375–393.CrossRefGoogle Scholar
  13. Garfield, E. (1966). Patent citation indexing and notions of novelty similarity and relevance. Journal of Chemical Documentation, 6(2), 536–542.CrossRefGoogle Scholar
  14. Glänzel, W. (2012). Bibliometric methods for detecting and analysing emerging research topics. El Profesional de la Informacion, 2(21), 194–201.CrossRefGoogle Scholar
  15. Glänzel, W., & Czerwon, H. J. (1996). A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level. Scientometrics, 37(2), 195–221.CrossRefGoogle Scholar
  16. Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707.Google Scholar
  17. Hall, B. H., Jaffe, A. B., & Trajtenberg, M. (2001). The NBER patent citation data file: Lessons, insights and methodological tools (No. w8498). National Bureau of Economic Research.Google Scholar
  18. Hall, B. H., Jaffe, A., & Trajtenberg, M. (2005). Market value and patent citations. RAND Journal of Economics, 36(1), 16–38.Google Scholar
  19. Harhoff, D., Narin, F., Scherer, F. M., & Vopel, K. (1999). Citation frequency and the value of patented inventions. The Review of Economics and Statistics, 81(3), 511–515.CrossRefGoogle Scholar
  20. Harhoff, D., Scherer, F. M., & Vopel, K. (2003). Citations, family size, opposition and the value of patent rights. Research Policy, 32(8), 1343–1363.CrossRefGoogle Scholar
  21. Huang, M., Chen, D., & Dong, H. (2011). Identify technology main paths by adding missing citations using bibliographic coupling and co-citation methods in photovoltaics. Paper presented at the Technology Management in the Energy Smart World (PICMET), 2011 Proceedings of PICMET’11.Google Scholar
  22. Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25.CrossRefGoogle Scholar
  23. Lanjouw, J. O., Pakes, A., & Putnam, J. (1998). How to count patents and value intellectual property: The uses of patent renewal and application data. The Journal of Industrial Economics, 46(4), 405–432.CrossRefGoogle Scholar
  24. Lanjouw, J. O., & Schankerman, M. (2004). Patent quality and research productivity: Measuring innovation with multiple indicators*. The Economic Journal, 114(495), 441–465.CrossRefGoogle Scholar
  25. Li, X., Chen, H., Huang, Z., & Roco, M. C. (2007). Patent citation network in nanotechnology (1976-2004). Journal of Nanoparticle Research, 9(3), 337–352.CrossRefGoogle Scholar
  26. Liu, X., Yu, S., Janssens, F., Glänzel, W., Moreau, Y., & De Moor, B. (2010). Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database. Journal of the American Society for Information Science and Technology, 61(6), 1105–1119.Google Scholar
  27. Meyer, M. (2000). What is special about patent citations? differences between scientific and patent citations. Scientometrics, 49(1), 93–123.CrossRefGoogle Scholar
  28. Narin, F., Noma, E., & Perry, R. (1987). Patents as indicators of corporate technological strength. Research Policy, 16(2–4), 143–155.CrossRefGoogle Scholar
  29. Newman, M. E. J. (2001). Clustering and preferential attachment in growing networks. Physical Review E, 64(2), 25102.CrossRefGoogle Scholar
  30. Newman, M. (2010). Networks: An introduction. Oxford: Oxford University Press.CrossRefGoogle Scholar
  31. OECD. (2009). OECD Patent Statistics Manual. Paris: Organisation for Economic Co-operation and Development.Google Scholar
  32. Pavitt, K. (1988). Uses and abuses of patent statistics. In A. F. J. van Raan (Ed.), Handbook of quantitative studies of science and technology (pp. 509–536). Amsterdam: Elsevier.CrossRefGoogle Scholar
  33. 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. Paper presented at the Judge Institute Working Paper, http://users.ox.ac.uk/~mast0140/EJWP0599.
  34. Reitzig, M. (2003). What determines patent value?: Insights from the semiconductor industry. Research Policy, 32(1), 13–26.CrossRefGoogle Scholar
  35. Sampat, B. (2011, 2011-08-30). USPTO patent and citation data, 2013, from http://thedata.harvard.edu/dvn/dv/boffindata/faces/study/StudyPage.xhtml?globalId=hdl:1902.1/16412.
  36. Schmookler, J. (1966). Invention and economic growth. US: Harvard University Press.CrossRefGoogle Scholar
  37. Schumpeter, J. A. (1939). Business cycles (Vol. 1). London UK: Cambridge University Press.Google Scholar
  38. Sherry, E. F., & Teece, D. J. (2004). Royalties, evolving patent rights, and the value of innovation. Research Policy, 33(2), 179–191.CrossRefGoogle Scholar
  39. Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation, 28(11), 758–775.CrossRefGoogle Scholar
  40. Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.CrossRefGoogle Scholar
  41. Small, H. (1997). Update on science mapping: Creating large document spaces. Scientometrics, 38(2), 275–293.MathSciNetCrossRefGoogle Scholar
  42. Trajtenberg, M. (1990). A penny for your quotes—patent citations and the value of innovations. Rand Journal of Economics, 21(1), 172–187.CrossRefGoogle Scholar
  43. USPTO. (2013). U.S. patent grant maintenance fee events file 2013/08/06, from http://www.google.com/googlebooks/uspto-patents-maintenance-fees.html.
  44. Vinkler, P. (1998). Comparative investigation of frequency and strength of motives toward referencing. The reference threshold model. Scientometrics, 43(1), 107–127.Google Scholar
  45. von Wartburg, I., Teichert, T., & Rost, K. (2005). Inventive progress measured by multi-stage patent citation analysis. Research Policy, 34(10), 1591–1607.CrossRefGoogle Scholar
  46. Walker, R. D. (1995). Patents as scientific and technical literature. Scarecrow Press.Google Scholar
  47. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. London UK: Cambridge University Press.CrossRefGoogle Scholar
  48. Webb, C., Dernis, H., Harhoff, D., & Hoisl, K. (2005). Analysing European and International Patent Citations. OECD Science, Technology and Industry Working Papers.Google Scholar
  49. Wilson, P. (1995). Unused relevant information in research and development. Journal of the American Society for Information Science, 46(1), 45–51.CrossRefGoogle Scholar
  50. Xiaofan, W., Xiang, L., & Guanrong, C. (2012). Network Science: An Introduction. Beijing: Higher Education Press.Google Scholar
  51. Yan, E., & Ding, Y. (2012). Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other. Journal of the American Society for Information Science and Technology, 63(7), 1313–1326.CrossRefGoogle Scholar
  52. Yoon, B., & Park, Y. (2004). A text-mining-based patent network: Analytical tool for high-technology trend. Journal of High Technology Management Research, 15(1), 37–50.MathSciNetCrossRefGoogle Scholar
  53. Zitt, M., & Bassecoulard, E. (1994). Development of a method for detection and trend analysis of research fronts built by lexical or cocitation analysis. Scientometrics, 30(1), 333–351.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2015

Authors and Affiliations

  • Guan-Can Yang
    • 1
  • Gang Li
    • 2
  • Chun-Ya Li
    • 2
  • Yun-Hua Zhao
    • 1
  • Jing Zhang
    • 1
  • Tong Liu
    • 3
  • Dar-Zen Chen
    • 4
  • Mu-Hsuan Huang
    • 5
  1. 1.Institute of Science and Technical Information of ChinaBeijingPeople’s Republic of China
  2. 2.School of Information ManagementWuhan UniversityHubeiPeople’s Republic of China
  3. 3.Beijing Computing CenterBeijingPeople’s Republic of China
  4. 4.Department of Mechanical Engineering and Institute of Industrial EngineeringNational Taiwan UniversityTaipeiTaiwan, ROC
  5. 5.Department of Library and Information ScienceNational Taiwan UniversityTaipeiTaiwan, ROC

Personalised recommendations