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Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information

Author

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  • Byeongki Jeong

    (Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Janghyeok Yoon

    (Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

Abstract

Augmented reality has recently achieved a rapid growth through its applications in various industries, including education and entertainment. Despite the growing attraction of augmented reality, trend analyses in this emerging technology have relied on qualitative literature review, failing to provide comprehensive competitive intelligence analysis using objective data. Therefore, tracing industrial competition trends in augmented reality will provide technology experts with a better understanding of evolving competition trends and insights for further technology and sustainable business planning. In this paper, we apply a topic modeling approach to 3595 patents related to augmented reality technology to identify technology subjects and their knowledge stocks, thereby analyzing industrial competitive intelligence in light of technology subject and firm levels. As a result, we were able to obtain some findings from an inventional viewpoint: technological development of augmented reality will soon enter a mature stage, technologies of infrastructural requirements have been a focal subject since 2001, and several software firms and camera manufacturing firms have dominated the recent development of augmented reality.

Suggested Citation

  • Byeongki Jeong & Janghyeok Yoon, 2017. "Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information," Sustainability, MDPI, vol. 9(4), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:4:p:497-:d:94091
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    References listed on IDEAS

    as
    1. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    2. Janghyeok Yoon & Hyunseok Park & Kwangsoo Kim, 2013. "Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 313-331, January.
    3. Sungchul Choi & Hyunseok Park, 2016. "Investigation of Strategic Changes Using Patent Co-Inventor Network Analysis: The Case of Samsung Electronics," Sustainability, MDPI, vol. 8(12), pages 1-13, December.
    4. Hidemichi Fujii & Kentaro Yoshida & Ken Sugimura, 2016. "Research and Development Strategy in Biological Technologies: A Patent Data Analysis of Japanese Manufacturing Firms," Sustainability, MDPI, vol. 8(4), pages 1-15, April.
    5. Hu, Albert G. Z. & Jaffe, Adam B., 2003. "Patent citations and international knowledge flow: the cases of Korea and Taiwan," International Journal of Industrial Organization, Elsevier, vol. 21(6), pages 849-880, June.
    6. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    7. Seo, Wonchul & Yoon, Janghyeok & Park, Hyunseok & Coh, Byoung-youl & Lee, Jae-Min & Kwon, Oh-Jin, 2016. "Product opportunity identification based on internal capabilities using text mining and association rule mining," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 94-104.
    8. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Sorace, Stefano & Iazzolino, Gianpaolo, 2017. "Does innovation-orientation lead to retail industry growth? Empirical evidence from patent analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 34(C), pages 88-94.
    9. Bo Wang & Shengbo Liu & Kun Ding & Zeyuan Liu & Jing Xu, 2014. "Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 685-704, October.
    10. Akio Matsumoto & Ugo Merlone & Ferenc Szidarovszky, 2012. "Some notes on applying the Herfindahl--Hirschman Index," Applied Economics Letters, Taylor & Francis Journals, vol. 19(2), pages 181-184, February.
    11. Gao, Lidan & Porter, Alan L. & Wang, Jing & Fang, Shu & Zhang, Xian & Ma, Tingting & Wang, Wenping & Huang, Lu, 2013. "Technology life cycle analysis method based on patent documents," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 398-407.
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    3. Hong-Hua Qiu & Jing Yang, 2018. "An Assessment of Technological Innovation Capabilities of Carbon Capture and Storage Technology Based on Patent Analysis: A Comparative Study between China and the United States," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
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    5. Vasileios Sidiropoulos & Dimitrios Bechtsis & Dimitrios Vlachos, 2021. "An Augmented Reality Symbiosis Software Tool for Sustainable Logistics Activities," Sustainability, MDPI, vol. 13(19), pages 1-14, September.
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    7. Alptekin Durmuşoğlu, 2017. "Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences," Sustainability, MDPI, vol. 9(5), pages 1-10, May.
    8. Francisco Del Cerro Velázquez & Ginés Morales Méndez, 2018. "Augmented Reality and Mobile Devices: A Binominal Methodological Resource for Inclusive Education (SDG 4). An Example in Secondary Education," Sustainability, MDPI, vol. 10(10), pages 1-14, September.
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