DOI QR코드

DOI QR Code

The Major Common Technology Field Analysis of Domestic Mobile Carriers based on Patent Information Data

특허 자료 정보 기반 국내 이동통신 사업자 주요 공통 기술 분야 분석

  • Kim, Jang-Eun (Defense Agency for Technology and Quality(DTaQ)) ;
  • Cho, Yu-Seup (Defense Agency for Technology and Quality(DTaQ)) ;
  • Kim, Young-Rae (Defense Agency for Technology and Quality(DTaQ))
  • Received : 2017.02.03
  • Accepted : 2017.05.12
  • Published : 2017.05.31

Abstract

In order to decide the national technical standards policy for national policy/market economy activities, the people in charge commonly make policy decisions based on the current technology level/concentration/utilization by means of major common technology field analysis using patent data. One possible source of such patent data is the domestic mobile carriers through the Korea Intellectual Property Rights Information System (KIPRIS) of the Korean Intellectual Property Office (KIPO). Using this system, we collected 20,294 patents and 152 International Patent Classification (IPC) types and confirmed KTs (9,738 cases / 47.98%), which perform relatively high technology retention activities compared to other mobile carriers through the KIPRIS of KIPO. Based on these data, we performed three analyses (SNA, PCA, ARIMA) and extracted 30 IPC types from the SNA and 4 IPC types from the PCA. Based on the above analysis results, we confirmed that 4 IPC (H04W, H04B, G06Q, H04L) types are the major common technology field of the domestic mobile carriers. Finally, the number of 4 IPC (H04W, H04B, G06Q, H04L) forecast averages of the ARIMA forecast result is lower than the number of existing time series patent data averages.

Keywords

Technology Analysis;Mobile Carrier;Patent;IPC;SNA;PCA;ARIMA

References

  1. S. C. Bang, "Evolution of Mobile Communication and Core Technologies," KICS Information & Communication Magazine - Open Lecture Series 32(9(Separate issue1)), 2015.
  2. S. E. Lee, easy LTE, HANBIT Academy, 2015.
  3. J. E. Sung, "Analysis of Characteristics and Change Process of Technology Standards Policy in Korea", Seoul Association For Public Administration, 2004.
  4. WIPO, International Patent Classification Version 2016 Guide to the IPC, 2016.
  5. S. Kim, "Basic Study on Patent Statistics and Index Development", Intellectual Property Research Center, 2004.
  6. https://www.itu.int/osg/spu/imt-2000/technology.html
  7. Y. H. Kim et al. Social Network Analysis, 4th edition, PARKYOUNGSA, 2016.
  8. Opsahl, Tore, Filip Agneessens, et al., "Node centrality in weighted networks: Generalizing degree and shortest paths." Social networks 32.3 : pp. 245-251, 2010. DOI: https://doi.org/10.1016/j.socnet.2010.03.006 https://doi.org/10.1016/j.socnet.2010.03.006
  9. Jolliffe, Ian. Principal component analysis. John Wiley & Sons, Ltd, 2002.
  10. Krzanowski, Wojtek. Principles of Multivariate Analysis. Oxford University Press, 1988.
  11. Jackson, J. Edward. A user's guide to principal components. Vol. 587. John Wiley & Sons, 2005.
  12. Kaiser, Henry F. "The application of electronic computers to factor analysis." Educational and psychological measurement, 1960. DOI: https://doi.org/10.1177/001316446002000116 https://doi.org/10.1177/001316446002000116
  13. G. S. Song, "Forecasting Manpower Demand Using ARIMA Model," Journal of Regional Innovation and Human Resources, vol. 2, no. 1, pp. 31-50, 2006.
  14. W. L. Lee, Time Series Analysis and Forecasting, 2nd edition, TAMJIN, 2016.
  15. K. H. Kim, H. S. Kim, "KTX Passenger Demand Forecast with Intervention ARIMA Model," Journal of the Korean Society for Railway, vol. 14, no. 5, pp. 470-476, 2011. DOI: https://doi.org/10.7782/JKSR.2011.14.5.470 https://doi.org/10.7782/JKSR.2011.14.5.470
  16. Tsay and Ruey S., Analysis of financial time series, 3rd edition, John Wiley & Sons, 2010.