Browse > Article

A Technology Landscape of Artificial Intelligence: Technological Structure and Firms' Competitive Advantages  

Lee, Wangjae (서울과학기술대학교 IT정책전문대학원/삼성전자 삼성리서치)
Lee, Hakyeon (서울과학기술대학교 글로벌융합산업공학과)
Publication Information
Journal of Korea Technology Innovation Society / v.22, no.3, 2019 , pp. 340-361 More about this Journal
Abstract
This study analyzes the technological structure of artificial intelligence (AI) and technological capabilities of AI companies based on patent information. 2589 AI patents registered in USPTO from 2007 to 2017 were collected and analyzed by the Latent Dirichlet Allocation (LDA) to derive 20 AI technology topics. Analysis of technology development trends by AI technology reveals that visual understanding, data analysis, motion control, and machine learning are growing, while language understanding and speech technology are sluggish. In addition, we also investigated leading companies in each sub-field of AI as well as core competencies of global IT companies. The findings of this study are expected to be fruitfully used for formulation and implementation of technology strategy of AI companies.
Keywords
Artificial Intelligence (AI); Patent Analysis; Technology Trend; Topic Modeling; Latent Dirichlet Allocation (LDA);
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 강전학.이학연 (2018), "특허 정보를 활용한 클라우드 컴퓨팅 기술 구조 분석", 대한산업공학회지, 44(1): 69-81.   DOI
2 김봉선.김언수 (2014), "특허기술의 특성과 가치의 관계", 전략경영연구, 17(3): 163-181.
3 김태경.최회련.이홍철 (2016), "토픽 모델링을 이용한 핀테크 기술 동향 분석", 한국산학기술학회논문지, 17(11): 670-681.   DOI
4 박재용 (2018), "특허정보를 이용한 인공지능 기술 동향 분석", 한국컴퓨터정보학회논문지, 23(4): 9-16.
5 박주섭.홍순구.김종원 (2017), "토픽모델링을 활용한 과학기술동향 및 예측에 관한 연구", 한국산업정보학회논문지, 22(4): 19-28.   DOI
6 임지연 (2016), "글로벌 인공지능 SW 기술 개발 동향", 한국차세대컴퓨팅학회논문지, 12(4): 33-46.
7 정명석.이주연 (2018), "Latent Dirichlet Allocation (LDA) 모델 기반의 인공지능(A.I.) 기술 관련 연구 활동 및 동향 분석", 한국산업정보학회논문지, 23(3): 87-95.   DOI
8 정보권.이학연 (2016), "국내 산업공학 연구 주제 2001-2015", 대한산업공학회지, 42(6):421-431.   DOI
9 Andrzejewski, D., Mulhern, A., Liblit, B. and Zhu, X. (2007), "Statistical Debugging Using Latent Topic Models", Proceedings of European Conference on Machine Learning, 6-17.
10 Anusuya, M. A. and Katti, S. K. (2009), "Speech Recognition by Machine: A Review", International Journal of Computer Science and Information Security, 6(3): 181-205.
11 Bates, M. (1995), "Models of Natural Language Understanding", PNAS, 92: 9977-9982.
12 Bishop, C. M. (2006), Pattern Recognition and Machine Learning, Springer.
13 Blei, D. M. (2012), "Probabilistic Topic Models", Communications of the ACM, 55(4): 77-84.   DOI
14 Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003), "Latent Dirichlet Allocation", Journal of Machine Learning Research, 3: 993-1022.
15 Britannica (2019), "Encyclopedia Britannica Article: Artificial Intelligence", https://www.britannica.com/technology/artificial-intelligence (20 March 2019).
16 Brynjolfsson, E., Rock, D. and Syverson, C. (2017), "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics", NBER Working Paper.
17 Cincera, M. (1997), "Patents, R&D, and Technological Spillovers at the Firm Level: Some Evidence from Econometric Count Models for Panel Data", Applied Econometrics, 12: 265-280.   DOI
18 Drucker, P. F. (1995), Managing in a Time of Great Change, Truman Talley Books: New York.
19 French, C. S. (1996), Data Processing and Information Technology, Thomson.
20 Fujii, H. and Managi, S. (2018), "Trends and Priority Shifts in Artificial Intelligence Technology Invention: A Global Patent Analysis", Economic Analysis and Policy, 58: 60-69.   DOI
21 Gaikwad, S. K., Gawali, B. W. and Yannawar, P. (2010), "A Review on Speech Recognition Technique", International Journal of Computer Applications, 10(3): 16-24.   DOI
22 Griffiths, T. L. and Steyvers, M. (2004), "Finding Scientific Topics", Proceedings of the National Academy of Sciences, 101: 5228-5235.
23 Lee, H. and Kang, P. (2018), "Identifying Core Topics in Technology and Innovation Management Studies: a Topic Model Approach", Technology Transfer, 43(5): 1291-1317.   DOI
24 Hornik, K. and Grun, B. (2011), "Topicmodels: An R Package for Fitting Topic Models", Journal of Statistical Software, 40(13): 1-30.
25 IITP (2016), ICT Long Term Technology Road Map 2022.
26 Kim, J., Jun, S., Jang, D. and Park, S. (2018), "Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models", Sustainability, 10(1): 155.   DOI
27 Lee, H., Seo, H. and Geum, Y. (2018), "Uncovering the Topic Landscape of Product-Service System Research: from Sustainability to Value Creation", Sustainability, 10: 911.   DOI
28 Maresch, D., Fink, M. and Harms, R. (2016), "When Patents Matter: The Impact of Competition and Patent Age on the Performance Contribution of Intellectual Property Rights Protection", Technovation, 57: 14-20.   DOI
29 Mejia, A. (2019), "Language Understanding", https://azure.microsoft.com/en-us/services/cognitive-services/language-understanding-intelligent-service/ (9 March 2019).
30 Russell, S., Dewey, D. and Tegmark, M. (2015), "Research Priorities for Robust and Beneficial Artificial Intelligence", AI Magazine, 36(4): 105-114.   DOI
31 Samuel, A. L. (1959), "Some Studies in Machine Learning Using the Game of Checkers", IBM Journal of Research and Development, 3(3): 210-229.   DOI
32 Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016), Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann.
33 Szeliski, R. (2010), Computer Vision: Algorithms and Applications, Springer.
34 Winograd, T. (1972), "Understanding Natural Language", Cognitive Psychology, 3(1): l-191.   DOI