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Technology Clusters Exploration for Patent Portfolio through Patent Abstract Analysis

Author

Listed:
  • Gabjo Kim

    (Korea Intellectual Property Strategy Agency, Seoul 06132, Korea)

  • Joonhyuck Lee

    (Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea)

  • Dongsik Jang

    (Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea)

  • Sangsung Park

    (Graduate School of Management of Technology, Korea University, Seoul 02841, Korea)

Abstract

This study explores technology clusters through patent analysis. The aim of exploring technology clusters is to grasp competitors’ levels of sustainable research and development (R&D) and establish a sustainable strategy for entering an industry. To achieve this, we first grouped the patent documents with similar technologies by applying affinity propagation (AP) clustering, which is effective while grouping large amounts of data. Next, in order to define the technology clusters, we adopted the term frequency-inverse document frequency (TF-IDF) weight, which lists the terms in order of importance. We collected the patent data of Korean electric car companies from the United States Patent and Trademark Office (USPTO) to verify our proposed methodology. As a result, our proposed methodology presents more detailed information on the Korean electric car industry than previous studies.

Suggested Citation

  • Gabjo Kim & Joonhyuck Lee & Dongsik Jang & Sangsung Park, 2016. "Technology Clusters Exploration for Patent Portfolio through Patent Abstract Analysis," Sustainability, MDPI, vol. 8(12), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1252-:d:84157
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    References listed on IDEAS

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    2. Olof Ejermo, 2009. "Regional Innovation Measured by Patent Data—Does Quality Matter?," Industry and Innovation, Taylor & Francis Journals, vol. 16(2), pages 141-165.
    3. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    4. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
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    Cited by:

    1. Yuanyuan Dong & Zepeng Wei & Tiansen Liu & Xinpeng Xing, 2020. "The Impact of R&D Intensity on the Innovation Performance of Artificial Intelligence Enterprises-Based on the Moderating Effect of Patent Portfolio," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    2. Xiaoli Wang & Yun Liu & Yanbing Ju, 2018. "Sustainable Public Procurement Policies on Promoting Scientific and Technological Innovation in China: Comparisons with the U.S., the UK, Japan, Germany, France, and South Korea," Sustainability, MDPI, vol. 10(7), pages 1-27, June.
    3. Donghyun Choi & Bomi Song, 2018. "Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis," Sustainability, MDPI, vol. 10(8), pages 1-26, August.
    4. I-Cheng Chang & Tai-Kuei Yu & Yu-Jie Chang & Tai-Yi Yu, 2021. "Applying Text Mining, Clustering Analysis, and Latent Dirichlet Allocation Techniques for Topic Classification of Environmental Education Journals," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    5. Dongwook Kim & Sungbum Kim, 2017. "The Role of Mobile Technology in Tourism: Patents, Articles, News, and Mobile Tour App Reviews," Sustainability, MDPI, vol. 9(11), pages 1-45, November.
    6. Jie Hu & Shaobo Li & Jianjun Hu & Guanci Yang, 2018. "A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification," Sustainability, MDPI, vol. 10(1), pages 1-22, January.
    7. Gabriel Marcuzzo Canto Cavalheiro & Mariana Brandao Cavalheiro, 2024. "Cluster Analysis of the Internationalization of Unicorns from Latin America Based on Trademark Registrations," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 1650-1665, March.

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