IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v111y2017i2d10.1007_s11192-017-2318-x.html
   My bibliography  Save this article

Evolution monitoring for innovation sources using patent cluster analysis

Author

Listed:
  • Hanlin You

    (National University of Defense Technology)

  • Mengjun Li

    (National University of Defense Technology)

  • Jiang Jiang

    (National University of Defense Technology)

  • Bingfeng Ge

    (National University of Defense Technology)

  • Xueting Zhang

    (National University of Defense Technology)

Abstract

According to the increasing importance of advanced technologies for economy growth and the incremental complexity of research and development management, a novel methodology is proposed in this paper to monitor the evolution trace of innovation sources. This approach focuses on the knowledge-transfer among technologies using patent cluster analysis. More specifically, a citation network model, consisting of patents in “Coherent Light Generators” classification, is established with the data collected from the United States Patent and Trademark Office. In addition, dynamical topological structure is investigated to probe into the overview properties and identify key milestones for the expanding citation network from 1976 to 2014. Next, a novel framework for patent clustering is developed to find out knowledge chunks of which internal knowledge-flows are dense while cut edges are sparse. Community detection algorithms are compared with different assessment indices based on citation network and the selected solution is improved using optimization objectives of cluster analysis. Then, the dynamical structure of the detected knowledge chunks is investigated and the evolution of innovation sources, identified by k-core decomposition, is monitored to unveil the technology development trace. Finally, analysis results are discussed and related conclusions are summarized. This article improves approaches for patent cluster analysis and develops a new follow-up investigation methodology for detected knowledge chunks. It is discovered there are not only scale increases, but also the integration for knowledge chunks during the focal period. Identifying the knowledge chunks which obtain rapid growth in both cluster scale and innovation source is useful to detect technology development opportunities.

Suggested Citation

  • Hanlin You & Mengjun Li & Jiang Jiang & Bingfeng Ge & Xueting Zhang, 2017. "Evolution monitoring for innovation sources using patent cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 693-715, May.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:2:d:10.1007_s11192-017-2318-x
    DOI: 10.1007/s11192-017-2318-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-017-2318-x
    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/s11192-017-2318-x?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. 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.
    2. Liu, Zhong & Huang, Jincai & Cheng, Guangquan, 2016. "Community detection in hypernetwork via Density-Ordered Tree partitionAuthor-Name: Cheng, Qing," Applied Mathematics and Computation, Elsevier, vol. 276(C), pages 384-393.
    3. Hanlin You & Mengjun Li & Keith W. Hipel & Jiang Jiang & Bingfeng Ge & Hante Duan, 2017. "Development trend forecasting for coherent light generator technology based on patent citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 297-315, April.
    4. Pao-Long Chang & Chao-Chan Wu & Hoang-Jyh Leu, 2010. "Using patent analyses to monitor the technological trends in an emerging field of technology: a case of carbon nanotube field emission display," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 5-19, January.
    5. Chen, Duanbing & Fu, Yan & Shang, Mingsheng, 2009. "A fast and efficient heuristic algorithm for detecting community structures in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(13), pages 2741-2749.
    6. Comin, Diego & Mestieri, Martí, 2014. "Technology Diffusion: Measurement, Causes, and Consequences," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 2, chapter 2, pages 565-622, Elsevier.
    7. Andrew Rodriguez & Byunghoon Kim & Mehmet Turkoz & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong, 2015. "New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 565-581, May.
    8. Janghyeok Yoon & Kwangsoo Kim, 2012. "Detecting signals of new technological opportunities using semantic patent analysis and outlier detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 445-461, February.
    9. Chang, Shann-Bin, 2012. "Using patent analysis to establish technological position: Two different strategic approaches," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 3-15.
    10. Yong-Yeol Ahn & James P. Bagrow & Sune Lehmann, 2010. "Link communities reveal multiscale complexity in networks," Nature, Nature, vol. 466(7307), pages 761-764, August.
    11. Péter Érdi & Kinga Makovi & Zoltán Somogyvári & Katherine Strandburg & Jan Tobochnik & Péter Volf & László Zalányi, 2013. "Prediction of emerging technologies based on analysis of the US patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 225-242, April.
    12. Xuezhao Wang & Yajuan Zhao & Rui Liu & Jing Zhang, 2013. "Knowledge-transfer analysis based on co-citation clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 859-869, December.
    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. Angelou, K. & Maragakis, M. & Kosmidis, K. & Argyrakis, P., 2021. "The evolution of triangular research and innovation collaborations in the European area," Journal of Informetrics, Elsevier, vol. 15(3).

    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. Hanlin You & Mengjun Li & Keith W. Hipel & Jiang Jiang & Bingfeng Ge & Hante Duan, 2017. "Development trend forecasting for coherent light generator technology based on patent citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 297-315, April.
    2. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    3. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    4. An, Xin & Li, Jinghong & Xu, Shuo & Chen, Liang & Sun, Wei, 2021. "An improved patent similarity measurement based on entities and semantic relations," Journal of Informetrics, Elsevier, vol. 15(2).
    5. Teng, Hao & Wang, Nan & Zhao, Hongyu & Hu, Yingtong & Jin, Haitao, 2024. "Enhancing semantic text similarity with functional semantic knowledge (FOP) in patents," Journal of Informetrics, Elsevier, vol. 18(1).
    6. Shen, Yung-Chi & Wang, Ming-Yeu & Yang, Ya-Chu, 2020. "Discovering the potential opportunities of scientific advancement and technological innovation: A case study of smart health monitoring technology," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    7. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    8. Byunghoon Kim & Gianluca Gazzola & Jaekyung Yang & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong & Young-Seon Jeong, 2017. "Two-phase edge outlier detection method for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 1-16, October.
    9. 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.
    10. 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.
    11. Xiwen Liu & Xuezhao Wang & Lucheng Lyu & Yanpeng Wang, 2022. "Identifying disruptive technologies by integrating multi-source data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5325-5351, September.
    12. Zehra Taşkın, 2021. "Forecasting the future of library and information science and its sub-fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1527-1551, February.
    13. Xipeng Liu & Xinmiao Li, 2022. "Early Identification of Significant Patents Using Heterogeneous Applicant-Citation Networks Based on the Chinese Green Patent Data," Sustainability, MDPI, vol. 14(21), pages 1-27, October.
    14. Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    15. José Manuel López‐Fernández & Mariluz Maté‐Sánchez‐Val & Francisco Manuel Somohano‐Rodriguez, 2021. "The effect of micro‐territorial networks on industrial small and medium enterprises' innovation: A case study in the Spanish region of Cantabria," Papers in Regional Science, Wiley Blackwell, vol. 100(1), pages 51-77, February.
    16. Li, Junqiu & Wang, Xingyuan & Eustace, Justine, 2013. "Detecting overlapping communities by seed community in weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6125-6134.
    17. Xiao Zhou & Yi Zhang & Alan L. Porter & Ying Guo & Donghua Zhu, 2014. "A patent analysis method to trace technology evolutionary pathways," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 705-721, September.
    18. Porter, Alan L. & Chiavetta, Denise & Newman, Nils C., 2020. "Measuring tech emergence: A contest," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    19. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    20. Moehrle, Martin G. & Caferoglu, Hüseyin, 2019. "Technological speciation as a source for emerging technologies. Using semantic patent analysis for the case of camera technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 776-784.

    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:111:y:2017:i:2:d:10.1007_s11192-017-2318-x. 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.