Analysis of Research and Development Efficiency of Artificial Intelligence Hardware of Global Companies using Patent Data and Financial data

특허 데이터 및 재무 데이터를 활용한 글로벌 기업의 인공지능 하드웨어 연구개발 효율성 분석

  • Park, Ji Min (Dept of Technology and Business Administration Yonsei University) ;
  • Lee, Bong Gyou (Graduate School of Information, Yonsei University)
  • Received : 2019.12.26
  • Accepted : 2020.02.18
  • Published : 2020.02.29


R&D(Research and Development) efficiency analysis is a very important issue in academia and industry. Although many studies have been conducted to analyze R&D(Research and Development) efficiency since the past, studies that analyzed R&D(Research and Development) efficiency considering both patentability and patent quality efficiency according to the financial performance of a company do not seem to have been actively conducted. In this study, measuring the patent application and patent quality efficiency according to financial performance, patent quality efficiency according to patent application were applied to corporate groups related to artificial intelligence hardware technology defined as GPU(Graphics Processing Unit), FPGA(Field Programmable Gate Array), ASIC(Application Specific Integrated Circuit) and Neuromorphic. We analyze the efficiency empirically and use Data Envelopment Analysis as a measure of efficiency. This study examines which companies group has high R&D(Research and Development) efficiency about artificial intelligence hardware technology.


  1. IITP, ICT R&D Technology Roadmap 2023 - Artificial Intelligence Semiconductor, 2018.
  2. Nature Electronics, Does AI Have a Hardware Problem?, Editorial, 2018.
  3. McKinsey & Company, Artificial-intelligence hardware: New Opportunities for Semiconductor Companies, 2019.
  4. Y.Y. Kor, "Direct and Interaction Effects of Top Management Team and Board Compositions on R&D Investment Strategy," Strategic Management Journal, Vol. 27, No. 11, pp. 1081-1099, 2006.
  5. A. Pessoa, Innovation and Economic Growth: What is the Actual Importance of R&D?, Universidade do Porto, Faculdade de Economia do Porto, 2007.
  6. V. Thomas, S. Sharma, and S.K. Jain, "Using Patents and Publications to Assess R&D Efficiency in the States of the USA," World Patent Information, Vol. 33, No. 1, pp. 4-10, 2011.
  7. R. Smith, "Commentary: The Power of the Unrelenting Impact Factor-is It a Force for Good or Harm?," International Journal of Epidemiology, Vol. 35, No. 5, pp. 1129-1130, 2006.
  8. J.M. Campanario, "Large Increases and Decreases in Journal Impact Factors in Only One Year: The Effect of Journal Self‐ Citations," Journal of the American Society for Information Science and Technology, Vol. 62, No. 2, pp. 230-235, 2011.
  9. S.H. Chang and C.Y. Fan, "Patent Technology Networks and Technology Development Trends of Neuromorphic System," Proceeding of International Conference on Mobile and Wireless Technology, Mobile and Wireless Technology 2018, pp. 287-297, 2018.
  10. M. Shibata, Y. Ohtsuka, M. Takahashi, and K. Okamoto, "Advanced FPGA Technology Trend Based on Patent Analysis with Link Mining," Proceeding of International Conference on Electronics Packaging and iMAPS All Asia Conference, pp.147-151, 2018.
  11. H.S. Jang and S.C. Lee, "A Comparative Analysis of the Change in R&D Efficiency: A Case of R&D Leaders in the Technology Industry," Technology Analysis and Strategic Management, Vol. 28, No. 8, pp. 886-900, 2016.
  12. H.Y. Lee and Y.T. Park, "An International Comparison of R&D Efficiency: DEA Approach," Asian Journal of Technology Innovation, Vol. 13, No. 2, pp. 207-222, 2005.
  13. S.K. Lee, G. Mogi, S.K. Lee, and J.W. Kim, "Econometric Analysis of the R&D Performance in the National Hydrogen Energy Technology Development for Measuring Relative Efficiency: The Fuzzy AHP/DEA Integrated Model Approach," International J ournal of Hydrogen Energy, Vol. 35, pp. 2236-2246, 2010.
  14. H.W. Kim, J.H. Kim, and S.K. Kim, "Measuring the Efficiency of Technology Innovation of the Global Green Car Companies by ANP/ DEA Model," Journal of Technology Innovation, Vol. 20, No. 3, pp. 256-286, 2012.
  15. Y.S. Chen and B.Y. Che, "Patent Indicators as Output Variables of DEA to Evaluate the Efficiency of the Computer Communication Equipment Industry in United States," Applied Economics, Vol. 44, No. 11, pp. 1429-1432, 2012.
  16. S.C. Park, "Patent Application and Trend of Imaging System Applied to Automotive," Korea Multimedia Society, Vol. 14, Issue 1, pp. 11-15, 2010.
  17. H. Ernst, "Patent Information for Strategic Technology Management," World Patent Information, Vol. 25, Issue 3, pp. 233-242, 2003.
  18. AK. Chakrabarti and I. Dror "Technology Transfers and Knowledge Interactions Among Defence Firms in the USA: An Analysis of Patent Citations," International Journal of Technology Management, Vol. 9, Issue 5, pp. 757-770, 1994.
  19. F.M. Tseng, C.H. Hsieh, Y.N. Peng, and Y. W. Chu, "Using Patent Data to Analyze Trends and the Technological Strategies of the Amorphous Silicon Thin-film Solar Cell Industry," Technological Forecasting and Social Change, Vol. 78, Issue 2, pp. 332-345, 2011.
  20. B. Fabry, H. Ernst, J. Langholz, and M. Koster, "Patent Portfolio Analysis as a Useful Tool for Identifying R&D and Business Opportunities- An Empirical Application in the Nutrition and Health Industry," World Patent Information, Vol. 28, Issue 3, pp. 215-225, 2006.
  21. J.H. Kim and Y.J. Han, "Analysis of Huawei's PCT Patent Applications," Journal of the Korea Institute of Information and Communication Engineering, Vol. 19, No. 11, pp. 2507-2517, 2015.
  22. H.R. Greve, "A Behavioral Theory of R&D Expenditures and Innovations: Evidence from Shipbuilding," The Academy of Management Journal, Vol. 46, No. 6, pp. 685-702, 2013.
  23. W.R. Chen and K.D. Miller "Situational and Institutional Determinants of Firms' R&D Search Intensity," Strategic Management Journal, Vol. 28, Issue 4, pp. 369-381, 2007.
  24. J.D. Lee and D.H. Oh, Efficiency Analysis Theory (DEA : Data Envelopment Analysis) , Seoul, IB Book, 2010.
  25. J. Zhu, "Multi-factor Performance Measure Model with an Application to Fortune 500 Companies," European Journal of Operational Research, Vol. 123, Issue 1, pp. 105-124, 2000.
  26. H. Oh, H.J. Lee, and T.W. Chang "A Study on the Technology Convergence of Artificial Intelligence through Patent Analysis," ICIC Express sLetters, Vol. 12, No. 7, pp. 699-706, 2018.