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Potential of patent image data as technology intelligence source

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  • Jee, Jeonghun
  • Park, Sanghyun
  • Lee, Sungjoo

Abstract

With recent advances in natural language processing and data analytics techniques, useful insights can be extracted not only from bibliographic data but also from the descriptive data of patents. Now, those advances have enabled the use of patent image data as a source of technology intelligence in addition to the two conventional types of patent data. Accordingly, this study focuses on the potential of patent image data and proposes an analysis method for investigating product/service/technology structures using block diagram images among the several types of images in patent documents. Using keywords extracted from patent block diagrams, the following four applications were introduced: (1) analysis of technology evolution, (2) in-depth investigation of technological elements, (3) comparative analysis with competitors, and (4) search for similar patents. The research findings of a case study on mobile earphone technology indicate that keywords are closely related to technological elements, and the four applications are found to be feasible. This study is among the first attempts to support technology intelligence using patent image data. It is also expected to be beneficial in subsequent studies and in practice, wherein patent image data convey valuable information regarding inventions.

Suggested Citation

  • Jee, Jeonghun & Park, Sanghyun & Lee, Sungjoo, 2022. "Potential of patent image data as technology intelligence source," Journal of Informetrics, Elsevier, vol. 16(2).
  • Handle: RePEc:eee:infome:v:16:y:2022:i:2:s1751157722000153
    DOI: 10.1016/j.joi.2022.101263
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    References listed on IDEAS

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    1. Yongho Lee & So Young Kim & Inseok Song & Yongtae Park & Juneseuk Shin, 2014. "Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 227-244, July.
    2. 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.
    3. 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.
    4. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    5. Byunghoon Kim & Gianluca Gazzola & Jae-Min Lee & Dohyun Kim & Kanghoe Kim & Myong K. Jeong, 2014. "Inter-cluster connectivity analysis for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1811-1825, March.
    6. von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
    7. Byungun Yoon & Sungjoo Lee & Gwanghee Lee, 2010. "Development and application of a keyword-based knowledge map for effective R&D planning," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 803-820, December.
    8. Vrochidis, Stefanos & Papadopoulos, Symeon & Moumtzidou, Anastasia & Sidiropoulos, Panagiotis & Pianta, Emanuelle & Kompatsiaris, Ioannis, 2010. "Towards content-based patent image retrieval: A framework perspective," World Patent Information, Elsevier, vol. 32(2), pages 94-106, June.
    9. 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).
    10. Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
    11. Huang, Mu-Hsuan & Yang, Hsiao-Wen & Chen, Dar-Zen, 2015. "Increasing science and technology linkage in fuel cells: A cross citation analysis of papers and patents," Journal of Informetrics, Elsevier, vol. 9(2), pages 237-249.
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