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Investigating technology opportunities: the use of SAOx analysis

Author

Listed:
  • Kyuwoong Kim

    (Ajou University)

  • Kyeongmin Park

    (Ajou University)

  • Sungjoo Lee

    (Ajou University)

Abstract

A patent is regarded as one of the most reliable data sources to investigate such opportunities and has been analyzed in numerous ways. The recent trend of patent analysis has focused on the unstructured part of patent information to extract detailed technological information. In particular, information regarding the purpose or effect of technology, which can be pulled from the unstructured part of patent information, is expected to offer useful insights into expanding its application to other areas. Some previous attempts have been made to systematically use this information to identify new technology opportunities, partly due to difficulties in analyzing the unstructured text data in patent documents. To overcome the limitations of previous studies, this study aims to develop a new method, namely Subject–Action–Object–others (SAOx), which enables an in-depth examination of the purpose and effect of the technology in an efficient manner by analyzing “for” and “to” phrases as well as gerund forms for an object element. We also introduce 39 engineering parameters of TRIZ and technology-designative terms of patent documents to define SAO sets and improve information accuracy. The proposed method is applied to human–machine interaction technologies to understand technology trends and explore technology opportunities based on topic modeling. Methodologically, the research findings contribute to patent engineering by extending the range of information extracted from patent information. Practically, the proposed approach will support corporate decision making in R&D investment by providing comprehensive information regarding the purpose or effect of technology in a structured form, fully extracted from patent documents.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:118:y:2019:i:1:d:10.1007_s11192-018-2962-9
    DOI: 10.1007/s11192-018-2962-9
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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. Lee, Jeongjin & Kim, Changseok & Shin, Juneseuk, 2017. "Technology opportunity discovery to R&D planning: Key technological performance analysis," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 53-63.
    4. Kim, Jeeeun & Lee, Sungjoo, 2015. "Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 332-345.
    5. Byeongki Jeong & Janghyeok Yoon, 2017. "Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information," Sustainability, MDPI, vol. 9(4), pages 1-22, March.
    6. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    7. 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.
    8. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    9. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
    10. Guo, Junfang & Wang, Xuefeng & Li, Qianrui & Zhu, Donghua, 2016. "Subject–action–object-based morphology analysis for determining the direction of technological change," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 27-40.
    11. 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.
    12. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    13. Chyi-Kwei Yau & Alan Porter & Nils Newman & Arho Suominen, 2014. "Clustering scientific documents with topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 767-786, September.
    14. Yi Zhang & Xiao Zhou & Alan L. Porter & Jose M. Vicente Gomila, 2014. "How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: “problem & solution” pattern based semantic TRIZ tool and case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1375-1389, November.
    15. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    16. Park, Yongtae & Yoon, Byungun & Lee, Sungjoo, 2005. "The idiosyncrasy and dynamism of technological innovation across industries: patent citation analysis," Technology in Society, Elsevier, vol. 27(4), pages 471-485.
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    Cited by:

    1. Jee, Jeonghun & Park, Sanghyun & Lee, Sungjoo, 2022. "Potential of patent image data as technology intelligence source," Journal of Informetrics, Elsevier, vol. 16(2).
    2. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    3. Lee, MyoungHoon & Kim, Suhyeon & Kim, Hangyeol & Lee, Junghye, 2022. "Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    4. Lijie Feng & Yuxiang Niu & Zhenfeng Liu & Jinfeng Wang & Ke Zhang, 2019. "Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT," Sustainability, MDPI, vol. 12(1), pages 1-35, December.
    5. Shao, Peng & Tan, Runhua & Peng, Qingjin & Liu, Fang & Yang, Wendan, 2024. "Scenario-based anticipatory failure determination and patent technology inspiration for product innovation design," Technological Forecasting and Social Change, Elsevier, vol. 205(C).

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    More about this item

    Keywords

    Technology opportunity; Patent; SAO analysis; Topic modeling;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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