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A Novel Method for Technology Forecasting Based on Patent Documents

  • Joonhyuck Lee
  • Gabjo Kim
  • Dongsik Jang
  • Sangsung Park
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 271)

Abstract

There have been many recent studies on forecasting emerging and vacant technologies. Most of them depend on a qualitative analysis such as Delphi. However, a qualitative analysis consumes too much time and money. To resolve this problem, we propose a quantitative emerging technology forecasting model. In this model, patent data are applied because they include concrete technology information. To apply patent data for a quantitative analysis, we derive a Patent–Keyword matrix using text mining. A principal component analysis is conducted on the Patent–Keyword matrix to reduce its dimensionality and derive a Patent–Principal Component matrix. The patents are also grouped together based on their technology similarities using the K-medoids algorithm. The emerging technology is then determined by considering the patent information of each cluster. In this study, we construct the proposed emerging technology forecasting model using patent data related to IEEE 802.11g and verify its performance.

Keywords

Emerging technology Patent Technology Forecasting 

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References

  1. 1.
    Jun, S.H., Park, S.S., Jang, D.S.: Technology forecasting using matrix map and patent clustering. Industrial Management & Data Systems 112(5), 786–807 (2012)CrossRefGoogle Scholar
  2. 2.
    Kim, Y.S., Park, S.S., Jang, D.S.: Patent data analysis using CLARA algorithm: OLED Technology. The Journal of Korean Institute of Information Technology 10(6), 161–170 (2012)Google Scholar
  3. 3.
    Lee, S., Yoon, B., Park, Y.: An approach to discovering new technology opportunities: Keyword-based patent map approach. Technovation 29(6-7), 481–497 (2009)CrossRefGoogle Scholar
  4. 4.
    Campbell, R.S.: Patent trends as a technological forecasting tool. World Patent Information 5(3), 137–143 (1983)CrossRefGoogle Scholar
  5. 5.
    Lee, J.H., Kim, G.J., Park, S.S., Jang, D.S.: A study on the effect of firm’s patent activity on business performance - Focus on time lag analysis of IT industry. Journal of the Korea Society of Digital Industry and Information Management 9(2), 121–137 (2013)Google Scholar
  6. 6.
    Chen, Y.S., Chang, K.C.: The relationship between a firm’s patent quality and its market value: The case of US pharmaceutical industry. Technological Forecasting and Social Change 77(1), 20–33 (2010)CrossRefGoogle Scholar
  7. 7.
    Lanjouw, J.O., Schankerman, M.: Patent quality and research productivity: Measuring innovation with multiple indicators. The Economic Journal 114(495), 441–465 (2004)CrossRefGoogle Scholar
  8. 8.
    Hair, J.F., Black, B., Babin, B., Anderson, R.E.: Multivariate Data Analysis (1992)Google Scholar
  9. 9.
    Youk, Y.S., Kim, S.H., Joo, Y.H.: Intelligent data reduction algorithm for sensor network based fault diagnostic system. International Journal of Fuzzy Logic and Intelligent Systems 9(4), 301–308 (2009)CrossRefGoogle Scholar
  10. 10.
    Keum, J.S., Lee, H.S., Masafumi, H.: A novel speech/music discrimination using feature dimensionality reduction. International Journal of Fuzzy Logic and Intelligent Systems 10(1), 7–11 (2010)CrossRefGoogle Scholar
  11. 11.
    Jolliffe, I.T.: Principal Component Analysis (2002)Google Scholar
  12. 12.
    Park, W.C.: Data mining concepts and techniques (2003)Google Scholar
  13. 13.
    Uhm, D.H., Jun, S.H., Lee, S.J.: A classification method using data reduction. International Journal of Fuzzy Logic and Intelligent Systems 12(1), 1–5 (2012)CrossRefGoogle Scholar
  14. 14.
    Yi, J.H., Jung, W.K., Park, S.S., Jang, D.S.: The lag analysis on the impact of patents on profitability of firms in software industry at segment level. Journal of the Korea Society of Digital Industry and Information Management 8(2), 199–212 (2012)Google Scholar
  15. 15.
    Organization for Economic Co-operation and Development, Economic Analysis and Statistics Division: OECD Science. Technology and Industry Scoreboard: Towards a Knowledge-based Economy (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Joonhyuck Lee
    • 1
    • 2
  • Gabjo Kim
    • 1
    • 2
  • Dongsik Jang
    • 1
    • 2
  • Sangsung Park
    • 2
  1. 1.Division of Industrial Management EngineeringKorea UniversitySeoulKorea
  2. 2.Graduate School of Management of TechnologyKorea UniversitySeoulKorea

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