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Predicting product development directions for new product planning using patent classification-based link prediction

Author

Listed:
  • Seunghyun Oh

    (Konkuk University)

  • Jaewoong Choi

    (Konkuk University)

  • Namuk Ko

    (Konkuk University)

  • Janghyeok Yoon

    (Konkuk University)

Abstract

Predicting the possible development directions of a product is useful for planning innovative products. Therefore, a systematic approach based on link prediction is proposed in this study to predict possible development directions of a product. In this approach, a target product is represented as a set of cooperative patent classifications (i.e., product CPCs) contained in the patents related to the product, and the new CPCs identified by link prediction are considered possible directions for product development. The approach analyzes co-occurrences of CPCs in the entire the united states patent and trademark office database to construct a universal CPC network, which contains the technological combination records with high potential of success already tried and qualified through patent registration. Next, it constructs a sub-network of the universal network consisting of the product CPCs and their adjacent CPCs (i.e., candidate CPC) and then creates a product-centered network by introducing an artificial product node, which means the target product itself, to the sub-network. Lastly, applying our link prediction approach, this approach calculates the possibility of entering the product CPCs for all candidate CPCs. Consequently, we can discover possible technical elements that can flow into the target product. To show the workings of the approach, this study applies it to a case of smartphones and validates its performance. We expect that this approach can provide hints on a product’s future development directions and assist experts and firms in establishing strategic product planning or identifying the new functional development of products.

Suggested Citation

  • Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03709-w
    DOI: 10.1007/s11192-020-03709-w
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    References listed on IDEAS

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    Cited by:

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    3. Wenbin Zhou & Xuhui Xia & Lei Wang & Zelin Zhang & Baotong Chen, 2022. "A Product Evolution Rules Based Method for Retired Mechanical Product Demand Acquisition," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
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    5. Seol, Youngjin & Lee, Seunghyun & Kim, Cheolhan & Yoon, Janghyeok & Choi, Jaewoong, 2023. "Towards firm-specific technology opportunities: A rule-based machine learning approach to technology portfolio analysis," Journal of Informetrics, Elsevier, vol. 17(4).
    6. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    7. Jing Ma & Yaohui Pan & Chih-Yi Su, 2022. "Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5497-5517, September.
    8. Chie Hoon Song, 2021. "Exploring and Predicting the Knowledge Development in the Field of Energy Storage: Evidence from the Emerging Startup Landscape," Energies, MDPI, vol. 14(18), pages 1-20, September.
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    10. Chie Hoon Song, 2023. "Examining the Patent Landscape of E-Fuel Technology," Energies, MDPI, vol. 16(5), pages 1-19, February.

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