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Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach

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Abstract

The identification of promising inventions is an important task in technology planning practice. Although several studies have been carried out using patent-based machine learning techniques, none of these have used the quality of knowledge accumulation as an input for identifying promising inventions, and have simply considered the number of backward citations as the link with previous knowledge. The current study therefore aims to fill this research gap by predicting promising inventions with patent-based machine learning, using the quality of knowledge accumulation as an important input variable. Eight criteria and 17 patent indicators are used as input variables, and patent forward citations are employed as the output variable. Six machine learning techniques are tested on 363,620 G06F patents filed between January 1990 and December 2009, and the results show that the quality of knowledge accumulation is the most important variable in predicting emerging inventions.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (NRF-2017R1E1A1A01077324). This work is based on the thesis submitted by Uijun Kwon for a master’s degree at Seoul National University of Science and Technology (SeoulTech), Seoul, Korea, 2018

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  1. Department of Industrial and Information Systems Engineering, Seoul National University of Science and Technology, Seoul, South Korea

    Uijun Kwon & Youngjung Geum

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  1. Uijun Kwon
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Correspondence to Youngjung Geum.

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Kwon, U., Geum, Y. Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach. Scientometrics 125, 1877–1897 (2020). https://doi.org/10.1007/s11192-020-03710-3

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