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Identifying patterns in rare earth element patents based on text and data mining

Author

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  • Yonghan Ju

    (Yonsei University)

  • So Young Sohn

    (Yonsei University)

Abstract

Rare earth elements (REE) are needed to produce many cutting-edge products, and their depletion is a major concern. In this paper, we identify unique characteristics of REE-related patents granted from 1975 to 2013 in five large patent offices around the world. Through topic detection and clustering of patent text, we found that purification processes related to oxides, nitrogen oxide, and exhaust gas were highlighted in the Korean Intellectual Property Office and Japan Patent Office (JPO). Molecular sieve, dispersion, and preparation methods involving yttrium, cerium, methane, zirconium, and ammonia were prominent in the China Patent and Trademark Office (CPTO) in the areas of performing operation and transporting. Quadratic assignment procedure correlation analysis was performed for IPC co-occurrence among REE patents in different offices, and the United States Patent and Trademark Office showed significantly different patterns than the CPTO and JPO. Furthermore, using betweenness centrality as an indicator of technology transition, the manufacture and treatment of nanostructures, nanotechnology for materials and surface science, and electrodes were identified as important REE technologies to be protected in Korea. In Japan, the technological areas identified as important for protection were the apparatuses and processes of manufacturing or assembling devices, compounds of iron, and materials. Our study results offer insights into national strategies for REE-related technologies in each country.

Suggested Citation

  • Yonghan Ju & So Young Sohn, 2015. "Identifying patterns in rare earth element patents based on text and data mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 389-410, January.
  • Handle: RePEc:spr:scient:v:102:y:2015:i:1:d:10.1007_s11192-014-1382-8
    DOI: 10.1007/s11192-014-1382-8
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    1. Stefano Breschi & Lorenzo Cassi & Franco Malerba & Nicholas S. Vonortas, 2009. "Networked research: European policy intervention in ICTs," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00439359, HAL.
    2. Nabeel A. Mancheri, 2012. "Chinese Monopoly in Rare Earth Elements: Supply–Demand and Industrial Applications," China Report, , vol. 48(4), pages 449-468, November.
    3. William Simpson, 2001. "The Quadratic Assignment Procedure (QAP)," North American Stata Users' Group Meetings 2001 1.2, Stata Users Group.
    4. Ronald N. Kostoff & Raymond G. Koytcheff & Clifford G. Y. Lau, 2007. "Global nanotechnology research metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 70(3), pages 565-601, March.
    5. Suzuki, Jun & Kodama, Fumio, 2004. "Technological diversity of persistent innovators in Japan: Two case studies of large Japanese firms," Research Policy, Elsevier, vol. 33(3), pages 531-549, April.
    6. Tom Magerman & Bart Looy & Xiaoyan Song, 2010. "Exploring the feasibility and accuracy of Latent Semantic Analysis based text mining techniques to detect similarity between patent documents and scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 289-306, February.
    7. Alan L. Porter & Alisa Kongthon & Jye-Chyi (JC) Lu, 2002. "Research profiling: Improving the literature review," Scientometrics, Springer;Akadémiai Kiadó, vol. 53(3), pages 351-370, March.
    8. Adams, Stephen, 2010. "The text, the full text and nothing but the text: Part 1 - Standards for creating textual information in patent documents and general search implications," World Patent Information, Elsevier, vol. 32(1), pages 22-29, March.
    9. Chaomei Chen, 2003. "Patents, citations & innovations: A window on the knowledge economy," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(8), pages 802-803, June.
    10. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    11. Mario A. Maggioni & T. Erika Uberti, 2006. "International networks of knowledge flows: an econometric analysis," Papers on Economics and Evolution 2005-19, Philipps University Marburg, Department of Geography.
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    Cited by:

    1. Shu-Hao Chang, 2018. "A pilot study on the connection between scientific fields and patent classification systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 951-970, March.
    2. Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    3. Woo Jin Lee & Won Kyung Lee & So Young Sohn, 2016. "Patent Network Analysis and Quadratic Assignment Procedures to Identify the Convergence of Robot Technologies," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-16, October.
    4. Kim, Tae San & Sohn, So Young, 2020. "Machine-learning-based deep semantic analysis approach for forecasting new technology convergence," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    5. Bo Kyeong Lee & So Young Sohn, 2017. "Exploring the effect of dual use on the value of military technology patents based on the renewal decision," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1203-1227, September.
    6. Won Sang Lee & So Young Sohn, 2017. "Identifying Emerging Trends of Financial Business Method Patents," Sustainability, MDPI, vol. 9(9), pages 1-21, September.
    7. Zhang, Ronda J. & Ye, Fred Y., 2020. "Measuring similarity for clarifying layer difference in multiplex ad hoc duplex information networks," Journal of Informetrics, Elsevier, vol. 14(1).

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    More about this item

    Keywords

    Rare earth elements; Patent; Text mining; Topic detection; Quadratic assignment procedure (QAP); Patent network analysis; Association rule;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • F00 - International Economics - - General - - - General

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