IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i5p2424-d504751.html
   My bibliography  Save this article

Identifying Technology Opportunities for Electric Motors of Railway Vehicles with Patent Analysis

Author

Listed:
  • Yunkoo Cho

    (Adam International Patent & Law Office, 1313, 127 Beobwon-ro, Songpa-gu, Seoul 05836, Korea)

  • Young Jae Han

    (Railroad Test & Certification Division, Korea Railroad Research Institute, 176 Cheoldobangmulgwan-ro, Uiwang-si 16105, Gyeonggi-do, Korea)

  • Jumi Hwang

    (Department of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Korea)

  • Jiwon Yu

    (Department of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Korea)

  • Sangbaek Kim

    (Department of Packaging, Yonsei University, 1, Yeonsedae-gil, Heungeop-myeon, Wonju-si 26493, Gangwon-do, Korea)

  • Chulung Lee

    (School of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Korea)

  • Sugil Lee

    (Smart Electrical & Signaling Division, Korea Railroad Research Institute, 176 Cheoldobangmulgwan-ro, Uiwang-si 16105, Gyeonggi-do, Korea)

  • Kyung Pyo Yi

    (Smart Electrical & Signaling Division, Korea Railroad Research Institute, 176 Cheoldobangmulgwan-ro, Uiwang-si 16105, Gyeonggi-do, Korea)

Abstract

An electric motor is a device that changes electrical energy into mechanical energy for railway vehicles. When developing the electric motor, it used to be developed simply for structures or control methods of the motor itself without considering convergence with other devices or technologies. However, as the railway vehicles become more advanced, technology development through convergence with other devices or technologies is spreading. Therefore, based on patent data related to the electric motors applied to the railway vehicles, this research aims to carry out technical forecasting for establishing research and development (R and D) direction for new technologies by predicting vacant technologies from the point of view of technology convergence. In other words, we studied how to find the vacant technologies in a field of convergence technology for the electric motor of the railway vehicles by analyzing the patent data. More specifically, we search the patents data associated with the electric motor of the railway vehicle that contain multiple IPC codes, and use multiple IPC codes to determine the field of convergence technology. In addition, we extract keywords from the patents data related to each of the determined convergence technologies and define the vacant technologies by interpreting the field of convergence technology and the extracted keywords.

Suggested Citation

  • Yunkoo Cho & Young Jae Han & Jumi Hwang & Jiwon Yu & Sangbaek Kim & Chulung Lee & Sugil Lee & Kyung Pyo Yi, 2021. "Identifying Technology Opportunities for Electric Motors of Railway Vehicles with Patent Analysis," Sustainability, MDPI, vol. 13(5), pages 1-13, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2424-:d:504751
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/5/2424/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/5/2424/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Park, Inchae & Yoon, Byungun, 2018. "Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network," Journal of Informetrics, Elsevier, vol. 12(4), pages 1199-1222.
    2. Finger, Matthias, 2014. "Governance of competition and performance in European railways: An analysis of five cases," Utilities Policy, Elsevier, vol. 31(C), pages 278-288.
    3. Altuntas, Serkan & Dereli, Turkay & Kusiak, Andrew, 2015. "Analysis of patent documents with weighted association rules," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 249-262.
    4. Tijssen, Robert J. W., 1992. "A quantitative assessment of interdisciplinary structures in science and technology: Co-classification analysis of energy research," Research Policy, Elsevier, vol. 21(1), pages 27-44, February.
    5. Kim, Gabjo & Bae, Jinwoo, 2017. "A novel approach to forecast promising technology through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 228-237.
    6. Engelsman, E. C. & van Raan, A. F. J., 1994. "A patent-based cartography of technology," Research Policy, Elsevier, vol. 23(1), pages 1-26, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.
    2. Yong-Jae Lee & Young Jae Han & Sang-Soo Kim & Chulung Lee, 2022. "Patent Data Analytics for Technology Forecasting of the Railway Main Transformer," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    3. Sangyun Han & Soo Kyung Park & Kyu Tae Kwak, 2021. "Workforce Composition of Public R&D and Performance: Evidence from Korean Government-Funded Research Institutes," Sustainability, MDPI, vol. 13(7), pages 1-17, March.
    4. Sick, Nathalie & Bröring, Stefanie, 2022. "Exploring the research landscape of convergence from a TIM perspective: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    5. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    6. Worasak Klongthong & Veera Muangsin & Chupun Gowanit & Nongnuj Muangsin, 2021. "A Patent Analysis to Identify Emergent Topics and Convergence Fields: A Case Study of Chitosan," Sustainability, MDPI, vol. 13(16), pages 1-28, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Youngjung Geum & Moon-Soo Kim & Sungjoo Lee, 2017. "Service Technology: Definition and Characteristics Based on a Patent Database," Service Science, INFORMS, vol. 9(2), pages 147-166, June.
    2. Shugang Li & Ziyi Li & Yixin Tang & Wenjing Zhao & Xiaoqi Kang & Lingling Zheng & Zhaoxu Yu, 2024. "Pioneering Technology Mining Research for New Technology Strategic Planning," Sustainability, MDPI, vol. 16(15), pages 1-26, August.
    3. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    4. Eunsook Jeon & Kyungkook Kim & Hyunjeong Park & Keuntae Cho, 2023. "Global Collaboration in Technology Sectors during the COVID-19 Pandemic: A Patent Review," Sustainability, MDPI, vol. 15(15), pages 1-19, August.
    5. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    6. Youngjae Choi & Sanghyun Park & Sungjoo Lee, 2021. "Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5431-5476, July.
    7. Si Hyung Joo & Yeonbae Kim, 2010. "Measuring relatedness between technological fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(2), pages 435-454, May.
    8. Jumi Hwang & Kyung Hee Kim & Jong Gyu Hwang & Sungchan Jun & Jiwon Yu & Chulung Lee, 2020. "Technological Opportunity Analysis: Assistive Technology for Blind and Visually Impaired People," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    9. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    10. Seongkyoon Jeong & Jong-Chan Kim & Jae Young Choi, 2015. "Technology convergence: What developmental stage are we in?," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 841-871, September.
    11. Derek Bosworth, 1997. "Rivalry and Anticompetitive Practices," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 4(1), pages 97-104.
    12. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    13. Burmaoglu, Serhat & Sartenaer, Olivier & Porter, Alan, 2019. "Conceptual definition of technology emergence: A long journey from philosophy of science to science policy," Technology in Society, Elsevier, vol. 59(C).
    14. Laroche, Florent, 2024. "Goodbye monopoly: The effect of open access passenger rail competition on price and frequency in France on the high-speed paris-Lyon line," Transport Policy, Elsevier, vol. 147(C), pages 12-21.
    15. Sungho Son & Nam-Wook Cho, 2020. "Technology Fusion Characteristics in the Solar Photovoltaic Industry of South Korea: A Patent Network Analysis Using IPC Co-Occurrence," Sustainability, MDPI, vol. 12(21), pages 1-19, October.
    16. Tom Broekel & Lars Mewes, 2017. "Analyzing the impact of R&D policy on regional diversification," Papers in Evolutionary Economic Geography (PEEG) 1726, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Sep 2017.
    17. Nakamura, Eri & Sakai, Hiroki & Shoji, Kenichi, 2018. "Managerial transfers to reduce transaction costs among affiliated firms: Case study of Japanese railway holding companies," Utilities Policy, Elsevier, vol. 53(C), pages 102-110.
    18. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    19. Taeyeoun Roh & Yujin Jeong & Byungun Yoon, 2017. "Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    20. David Rigby, 2012. "The Geography of Knowledge Relatedness and Technological Diversification in U.S. Cities," Papers in Evolutionary Economic Geography (PEEG) 1218, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Oct 2012.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2424-:d:504751. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.