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SAO-Based Semantic Mining of Patents for Semi-Automatic Construction of a Customer Job Map

Author

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  • Joohyung Lim

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Gyeongsangbuk-do 37673, Korea)

  • Sungchul Choi

    (Department of Industrial and Management Engineering, Gachon University, Seongnam, Gyeonggi-do 13120, Korea)

  • Chiehyeon Lim

    (School of Management Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea)

  • Kwangsoo Kim

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Gyeongsangbuk-do 37673, Korea)

Abstract

The Outcome-Driven Innovation (ODI) method based on the ‘Jobs-to-be-done’ concept is very useful in the identification of unmet customer needs and has been adopted widely in the industry. The Job Map, a tool of the ODI method, is used to understand customers by defining their behavioral process. Complications must be overcome before the Job Map can be applied to the specific problem in question, such as a time-consuming process, dealing with a large amount of data, and experts’ biased work. To solve these problems, this study develops a patent mining-based method based on the subject-action-object (SAO) structure to support the creation of a Job Map by semi-automatizing data collection and analysis. This effort at better utilizing computers in customer analysis for product design will contribute to expanding computerized methods for solving design and engineering problems in practice.

Suggested Citation

  • Joohyung Lim & Sungchul Choi & Chiehyeon Lim & Kwangsoo Kim, 2017. "SAO-Based Semantic Mining of Patents for Semi-Automatic Construction of a Customer Job Map," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:8:p:1386-:d:107206
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    References listed on IDEAS

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    Cited by:

    1. Chiehyeon Lim & Paul P. Maglio, 2018. "Data-Driven Understanding of Smart Service Systems Through Text Mining," Service Science, INFORMS, vol. 10(2), pages 154-180, June.
    2. Sunghyun Nam & Sejun Yoon & Nagarajan Raghavan & Hyunseok Park, 2021. "Identifying Service Opportunities Based on Outcome-Driven Innovation Framework and Deep Learning: A Case Study of Hotel Service," Sustainability, MDPI, vol. 13(1), pages 1-25, January.
    3. Lim, Chiehyeon & Kim, Ki-Hun & Kim, Min-Jun & Heo, Jun-Yeon & Kim, Kwang-Jae & Maglio, Paul P., 2018. "From data to value: A nine-factor framework for data-based value creation in information-intensive services," International Journal of Information Management, Elsevier, vol. 39(C), pages 121-135.
    4. Junegak Joung & Kiwook Jung & Sanghyun Ko & Kwangsoo Kim, 2018. "Customer Complaints Analysis Using Text Mining and Outcome-Driven Innovation Method for Market-Oriented Product Development," Sustainability, MDPI, vol. 11(1), pages 1-14, December.
    5. Lim, Chiehyeon & Kim, Kwang-Jae, 2018. "Experience Design Board: A tool for visualizing and designing experience-centric service delivery processes," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 142-151.
    6. Jason Jihoon Ree & Cheolhyun Jeong & Hyunseok Park & Kwangsoo Kim, 2019. "Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology," Sustainability, MDPI, vol. 11(5), pages 1-18, March.
    7. Jie Hu & Shaobo Li & Jianjun Hu & Guanci Yang, 2018. "A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification," Sustainability, MDPI, vol. 10(1), pages 1-22, January.
    8. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.

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