IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v102y2015i1d10.1007_s11192-014-1382-8.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-014-1382-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-014-1382-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    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. 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. 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.
    3. 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).
    4. Pengfei Wang & Nguepi Tsafack Elvis & Hua Cheng, 2024. "Structural Characteristics and Evolution of the Dual Network of Patent Technology Collaboration and Innovation in China–Japan–ROK," Sustainability, MDPI, vol. 16(17), pages 1-22, September.
    5. 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.
    6. 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).
    7. Won Sang Lee & So Young Sohn, 2017. "Identifying Emerging Trends of Financial Business Method Patents," Sustainability, MDPI, vol. 9(9), pages 1-21, September.
    8. 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).

    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. 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.
    2. Cammarano, Antonello & Michelino, Francesca & Lamberti, Emilia & Caputo, Mauro, 2017. "Accumulated stock of knowledge and current search practices: The impact on patent quality," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 204-222.
    3. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    4. Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    5. Goio Etxebarria & Mikel Gomez-Uranga & Jon Barrutia, 2012. "Tendencies in scientific output on carbon nanotubes and graphene in global centers of excellence for nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 253-268, April.
    6. Leonid Gokhberg & Irina Kouznetsova, 2009. "Innovation in the Russian Economy: Stagnation before Crisis?," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 3(2), pages 28-46.
    7. Minha Hwang & Bart J. Bronnenberg & Raphael Thomadsen, 2010. "An Empirical Analysis of Assortment Similarities Across U.S. Supermarkets," Marketing Science, INFORMS, vol. 29(5), pages 858-879, 09-10.
    8. 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.
    9. Sara Amoroso & Alex Coad & Nicola Grassano, 2017. "European R&D networks: A snapshot from the 7th EU Framework Programme," JRC Working Papers on Corporate R&D and Innovation JRC107546, Joint Research Centre (Seville site).
    10. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    11. Simen G. Enger & Fulvio Castellacci, 2016. "Who gets Horizon 2020 research grants? Propensity to apply and probability to succeed in a two-step analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1611-1638, December.
    12. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    13. Marco Tortoriello & Ray Reagans & Bill McEvily, 2012. "Bridging the Knowledge Gap: The Influence of Strong Ties, Network Cohesion, and Network Range on the Transfer of Knowledge Between Organizational Units," Organization Science, INFORMS, vol. 23(4), pages 1024-1039, August.
    14. 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.
    15. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    16. Magerman, Tom & Looy, Bart Van & Debackere, Koenraad, 2015. "Does involvement in patenting jeopardize one’s academic footprint? An analysis of patent-paper pairs in biotechnology," Research Policy, Elsevier, vol. 44(9), pages 1702-1713.
    17. 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.
    18. Ying Huang & Donghua Zhu & Yue Qian & Yi Zhang & Alan L. Porter & Yuqin Liu & Ying Guo, 2017. "A hybrid method to trace technology evolution pathways: a case study of 3D printing," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 185-204, April.
    19. Hella Bani Baghdadi & Sami Aouadi, 2018. "Does Patent Performance Promote Relative Technological Performance in Countries Bordering the Mediterranean?," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 9(4), pages 1246-1269, December.
    20. Song, Kisik & Kim, Kyuwoong & Lee, Sungjoo, 2018. "Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 118-132.

    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

    Statistics

    Access and download statistics

    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:spr:scient:v:102:y:2015:i:1:d:10.1007_s11192-014-1382-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.