Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Analysis of major research trends in artificial intelligence based on domestic/international patent data

국내외 특허데이터 기반의 인공지능분야 기술동향 분석

  • Received : 2018.04.16
  • Accepted : 2018.06.20
  • Published : 2018.06.28
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Abstract

Recently, the 4th industrial revolution has emerged as the core for enhancing national competitiveness, the development of a technology roadmap to efficiently develop related technologies to realize super intelligence as a main feature of the 4th Industrial Revolution is a major task has been highlighted. The objective of this study is to analyze the domestic and foreign technology level in the artificial intelligence field which is the core technology of the 4th Industrial Revolution era and to present the direction of development based on this. The keyword network analysis and the blank technical analysis based on the IPC classification were performed on the data derived from the keyword search of 'AI (Artificial Intelligence)' among domestic and foreign patent data. As a result, the number of domestic artificial intelligence related technology development was 1.2% compared with developed countries such as USA and Europe. In the major development fields, data recognition technology and digital information transmission technology were relatively insufficient. Through this study, we obtained the blank technology as a result of comparative analysis of domestic artificial intelligence related technologies compared to advanced countries and suggested the direction of domestic artificial intelligence technology development in future.

최근 전 세계적으로 4차 산업혁명이 국가 경쟁력 향상을 위한 핵심으로 부상하면서, 4차 산업혁명의 주요 특징인 초연결과 초지능을 구현하기 위한 관련 기술을 효율적으로 발전시키기 위한 기술 로드맵 개발이 주요 과제로 부각되고 있다. 본 연구에서는 4차 산업혁명 시대의 핵심기술인 '인공지능(Artificial Intelligence)' 분야의 국내외 기술수준에 대한 객관적 비교분석을 통해 선진국 대비 부족하거나 향후 발전 가능성이 높은 세부기술을 도출하고, 이를 토대로 발전 방향성을 제시하였다. 국내외 특허데이터 중 '인공지능' 키워드 검색 결과 도출된 데이터를 대상으로 키워드 네트워크 분석 및 IPC 분류 기준 공백기술 분석을 수행하였다. 분석 결과, 국내 인공지능 관련 기술 개발 건수는 미국, 유럽 등 선진국 대비 1.2% 수준이었으며, 주요 개발 분야의 경우 데이터 인식기술, 디지털 정보 전송기술 등에서 상대적으로 부족한 것으로 나타났다. 본 연구는 선진국 대비 국내 인공지능 관련 기술의 비교분석 수행을 통해 공백기술을 도출하였으며, 향후 이를 활용한 국내 인공지능 기술의 발전 방향성을 제시하였다.

Keywords

References

  1. M. B. Yoon, J. H. Lee & J. E. Baek. (2016). Topophilia Convergence Science Education for Enhancing Learning Capabilities in the Age of Artificial Intelligence Based on the Case of Challenge Match Lee Sedol and AlphaGo. Journal of the Korea Convergence Society, 7(4), 123-131. https://doi.org/10.15207/JKCS.2016.7.4.123
  2. D. W. Kim & B. J. Kim. (2016). How AlphaGo does Change People's Perception of Introduction of Artificial Intelligence into Intellectual Work. Journal of Cybercommunication Academic Society, 33(4), 107-158.
  3. Bentar Priyopradono, Danny Manongga & Wiranto Herry Utomo. (2013). Spatial Social Network Analysis: Program Pengembangan Usaha Agribisnis Perdesaan(PUAP) or an Exertion Development Program in Supporting the Region Revitalization Development. Social Networking, 2(2), 63-76. https://doi.org/10.4236/sn.2013.22008
  4. S. G. Han. (2016). Main contents of American Artificial Intelligence Report, Seoul : KISA.
  5. Y. D. Yun, Y. W. Yang & H. S. Lim. (2016), A SNS Data-driven Comparative Analysis on Changes of Attitudes toward Artificial Intelligence, Journal of Digital Convergence, 14(12), 173-182. https://doi.org/10.14400/JDC.2016.14.12.173
  6. S. H. Jun. (2013). A Big Data Learning for Patent Analysis, Journal of Korean Institute of Intelligent Systems, 23(5), 406-182. https://doi.org/10.5391/JKIIS.2013.23.5.406
  7. Yuen-Hsien Tseng, Chi-jen Lin & Yu-I Lin. (2007). Text mining techniques for patent analysis. Information Processing & Management, 43, 1216-1247. https://doi.org/10.1016/j.ipm.2006.11.011
  8. P. R. Kim & S. H. Hwang. (2009). A study on the Projection of the IT-based Promising Technologies Utilizing Patent Database. Korea Institute Of Communication Sciences, 34(10), 1021-1030.
  9. H. M. Baek & M. S. Kim. (2013). Technological Convergence Trend through Patent Network Analysis: Focusing on Patent Data in Korea, U.S., Europe, and Japan. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 8(2), 11-19. https://doi.org/10.16972/apjbve.8.2.201306.11
  10. S. H. Jun. (2011). Technology Forecasting of Intelligent Systems using Patent Analysis. Journal of Korean Institute of Intelligent Systems, 21(1), 100-105. https://doi.org/10.5391/JKIIS.2011.21.1.100
  11. J. W. Gu, J. H. Lee, M. S. Chung & J. Y. Lee (2017), Electric Vehicle Technology Trends Forecast Research Using the Paper and Patent Data. Journal of Digital Convergence, 15(2), 165-172. https://doi.org/10.14400/JDC.2017.15.2.165
  12. S. S. Lee. (2013). Network analysis methods, Seoul : Nonhyeong.
  13. J. H. Choi, H. S. Kim & N. G. Im. (2011). Keyword Network Analysis for Technology Forecasting. Korea Intelligent Information Systems Society, 17(4), 227-240.
  14. B. C. Choi, H. M. Baek & M. S. Kim. (2015). Patent Citation Network Analysis as a Measure of Technical Knowledge Diffusion in Korea: Focusing on ICT. Korean Society of Business Venturing, 10(1), 143-151.
  15. J. H. Park & M. Song. (2013). A Study on the Research Trends in Library & Information Science in Korea using Topic Modeling. Journal of Korea Society for Information Management, 30(1), 7-36. https://doi.org/10.3743/KOSIM.2013.30.1.007
  16. B. K. Lee. (2017). Patent Competitiveness and Technology-Industry Linkage Analysis of Artificial Intelligence Technology: Comparative Analysis of Major Advanced Countries. Seoul : KERI
  17. G. H. Jung. (2010). Future Prediction Method Using Text Mining and Network Analysis. Seoul : KISTEP.
  18. J. G. Heo & C. H. Yang. (2013). Applying Network Analysis in Convergent Research Relationships: The case of High-Tech Convergence Technology Development Program. Journal of Korea Technology Innovation Society, 16(4), 883-912.
  19. J. M. Choe. (2016). Investigating Journal Citation Network with Centrality Measures in the Public Administration and Policy Field, Journal of Digital Convergence, 14(9), 301-308. https://doi.org/10.14400/JDC.2016.14.9.301
  20. K. H. Choi, H. H. Oh & H. J. Kwang. (2014). Network analysis using frequency of cross-citation and comparing citation index of accounting journals, Journal of Digital Convergence, 12(2), 143-149. https://doi.org/10.14400/JDC.2014.12.2.143
  21. S. H. Ju. (2016). Analysis on structure of National Innovation System in IT, Journal of Digital Convergence, 14(4), 129-138. https://doi.org/10.14400/JDC.2016.14.4.129
  22. Marc Smith. (2009). Analyzing Social Media Networks: Learning by Doing with NodeXL. Network Analysis with NodeX.
  23. M. S. Chung, S. H. Park, B. H. Chae & J. Y. Lee. (2017). Analysis of major research trends in artificial intelligence through analysis of thesis data. Journal of Digital Convergence, 15(5), 225-233. https://doi.org/10.14400/JDC.2017.15.2.225