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Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology

Author

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  • Jason Jihoon Ree

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea)

  • Cheolhyun Jeong

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea)

  • Hyunseok Park

    (Department of Information System, Hanyang University, Seoul 04763, Korea)

  • Kwangsoo Kim

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea)

Abstract

Identification of prevalent problems is an important process of strategic innovation for stakeholders of trending technologies. This paper proposes a systematic and replicable method of patent analysis to identify problems to be solved requisite for sustainable technology planning and development, by implementing the concept of ‘context’ to facilitate problem identification. The main concept of the method entails the importance of the connections between contextual information and problems to provide more focused, relevant, and constructive insights essential for instating goals for research and development activities. These context–problem entities and their entwined connections are discovered using keyword pattern matching, grammar-based text mining, and co-word analysis techniques. The intermediary outputs are then utilized to generate the proposed context–problem network (CP net) for social network, grammar, and quantitative data analysis. For verification, our method was applied to 737 patents in the wireless energy transmission technology domain, successfully yielding CP net data. The detailed analysis of the resulting CP net data delivered meaningful information in the wireless charging technology field: The main contexts, “batteries”, “power transmission coils”, and “cores”, are found to be most relevant to the main problems, “maximizing coupling efficiency”, “minimizing DC signal components”, and “charging batteries”. The results provide a wide range of informative perspectives for individuals, the scientific community, corporate, and market-level stakeholders. Furthermore, the method of this study can be applicable to various technologies since it is independent of specific subject domains. Future research directions aim to improve this method for better quality and modeling of contexts and problems.

Suggested Citation

  • Jason Jihoon Ree & Cheolhyun Jeong & Hyunseok Park & Kwangsoo Kim, 2019. "Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology," Sustainability, MDPI, vol. 11(5), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1484-:d:212819
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    References listed on IDEAS

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    1. Janghyeok Yoon & Sungchul Choi & Kwangsoo Kim, 2011. "Invention property-function network analysis of patents: a case of silicon-based thin film solar cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(3), pages 687-703, March.
    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. Yang, YunYun & Akers, Lucy & Klose, Thomas & Barcelon Yang, Cynthia, 2008. "Text mining and visualization tools - Impressions of emerging capabilities," World Patent Information, Elsevier, vol. 30(4), pages 280-293, December.
    4. Cantner, Uwe & Graf, Holger, 2006. "The network of innovators in Jena: An application of social network analysis," Research Policy, Elsevier, vol. 35(4), pages 463-480, May.
    5. von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
    6. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    7. Joohyung Lim & Sungchul Choi & Chiehyeon Lim & Kwangsoo Kim, 2017. "SAO-Based Semantic Mining of Patents for Semi-Automatic Construction of a Customer Job Map," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
    8. Jongchan Kim & Jaehyun Choi & Sangsung Park & Dongsik Jang, 2018. "Patent Keyword Extraction for Sustainable Technology Management," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    9. Sternitzke, Christian & Bartkowski, Adam & Schramm, Reinhard, 2008. "Visualizing patent statistics by means of social network analysis tools," World Patent Information, Elsevier, vol. 30(2), pages 115-131, June.
    10. 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.
    11. Sangdeok Lee & Yongwoon Cha & Sangwon Han & Changtaek Hyun, 2019. "Application of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects," Sustainability, MDPI, vol. 11(3), pages 1-14, January.
    12. Bangrae Lee & Yong-Il Jeong, 2008. "Mapping Korea’s national R&D domain of robot technology by using the co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 77(1), pages 3-19, October.
    13. Inchae Park & Byungun Yoon, 2018. "Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis," Sustainability, MDPI, vol. 10(11), pages 1-32, November.
    14. Balconi, Margherita & Breschi, Stefano & Lissoni, Francesco, 2004. "Networks of inventors and the role of academia: an exploration of Italian patent data," Research Policy, Elsevier, vol. 33(1), pages 127-145, January.
    15. Sungchul Choi & Janghyeok Yoon & Kwangsoo Kim & Jae Yeol Lee & Cheol-Han Kim, 2011. "SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 863-883, September.
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    1. Andrea Celone & Antonello Cammarano & Mauro Caputo & Francesca Michelino, 2022. "Features of Sustainability-Oriented Innovations: A Content Analysis of Patent Abstracts," Sustainability, MDPI, vol. 14(23), pages 1-16, November.

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