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Technology Analysis of Global Smart Light Emitting Diode (LED) Development Using Patent Data

Author

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  • Sangsung Park

    (Graduate School of Management of Technology, Korea University, Seoul 136701, Korea)

  • Sunghae Jun

    (Department of Statistics, Cheongju University, Chungbuk 360764, Korea)

Abstract

Technological developments related to smart light emitting diode (LED) systems have progressed rapidly in recent years. In this paper, patent documents related to smart LED technology are collected and analyzed to understand the technology development of smart LED systems. Most previous studies of the technology were dependent on the knowledge and experience of domain experts, using techniques such as Delphi surveys or technology road-mapping. These approaches may be subjective and lack robustness, because the results can vary according to the selected expert groups. We therefore propose a new technology analysis methodology based on statistical modeling to obtain objective and relatively stable results. The proposed method consists of visualization based on Bayesian networks and a linear count model to analyze patent documents related to smart LED technology. Combining these results, a global hierarchical technology structure is created that can enhance the sustainability in smart LED system technology. In order to show how this methodology could be applied to real-world problems, we carry out a case study on the technology analysis of smart LED systems.

Suggested Citation

  • Sangsung Park & Sunghae Jun, 2017. "Technology Analysis of Global Smart Light Emitting Diode (LED) Development Using Patent Data," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:8:p:1363-:d:106736
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    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273.
    2. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    3. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    4. Yongjun Zhu & Erjia Yan & Il-Yeol Song, 2017. "The use of a graph-based system to improve bibliographic information retrieval: System design, implementation, and evaluation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(2), pages 480-490, February.
    5. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    6. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    7. Loet Leydesdorff & Lutz Bornmann, 2012. "Mapping (USPTO) patent data using overlays to Google Maps," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(7), pages 1442-1458, July.
    8. Sangsung Park & Seung-Joo Lee & Sunghae Jun, 2015. "A Network Analysis Model for Selecting Sustainable Technology," Sustainability, MDPI, vol. 7(10), pages 1-16, September.
    9. Keller, Jonas & von der Gracht, Heiko A., 2014. "The influence of information and communication technology (ICT) on future foresight processes — Results from a Delphi survey," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 81-92.
    10. Huang, Lu & Zhang, Yi & Guo, Ying & Zhu, Donghua & Porter, Alan L., 2014. "Four dimensional Science and Technology planning: A new approach based on bibliometrics and technology roadmapping," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 39-48.
    11. Erjia Yan & Ying Ding & Qinghua Zhu, 2010. "Mapping library and information science in China: a coauthorship network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(1), pages 115-131, April.
    12. Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
    13. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
    14. Yi Zhang & Guangquan Zhang & Donghua Zhu & Jie Lu, 2017. "Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(8), pages 1925-1939, August.
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    Cited by:

    1. Choi, Seokkyu & Lee, Hyeonju & Park, Eunjeong & Choi, Sungchul, 2022. "Deep learning for patent landscaping using transformer and graph embedding," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    2. 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.
    3. Muhammad Saidu Aliero & Muhammad Asif & Imran Ghani & Muhammad Fermi Pasha & Seung Ryul Jeong, 2022. "Systematic Review Analysis on Smart Building: Challenges and Opportunities," Sustainability, MDPI, vol. 14(5), pages 1-28, March.
    4. Dae Geon Kim & Sang Ok Choi, 2018. "Impact of Construction IT Technology Convergence Innovation on Business Performance," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    5. Junseok Lee & Ji-Ho Kang & Sunghae Jun & Hyunwoong Lim & Dongsik Jang & Sangsung Park, 2018. "Ensemble Modeling for Sustainable Technology Transfer," Sustainability, MDPI, vol. 10(7), pages 1-15, July.

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