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A patent search strategy based on machine learning for the emerging field of service robotics

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  • Kreuchauff, Florian
  • Korzinov, Vladimir

Abstract

Emerging technologies are in the core focus of supra-national innovation policies. These strongly rely on credible data bases for being effective and efficient. However, since emerging technologies are not yet part of any official industry, patent or trademark classification systems, delineating boundaries to measure their early development stage is a nontrivial task. This paper is aimed to present a methodology to automatically classify patents as concerning service robots. We introduce a synergy of a traditional technology identification process, namely keyword extraction and verification by an expert community, with a machine learning algorithm. The result is a novel possibility to allocate patents which (1) reduces expert bias regarding vested interests on lexical query methods, (2) avoids problems with citational approaches, and (3) facilitates evolutionary changes. Based upon a small core set of worldwide service robotics patent applications we derive apt n-gram frequency vectors and train a support vector machine (SVM), relying only on titles, abstracts and IPC categorization of each document. Altering the utilized Kernel functions and respective parameters we reach a recall level of 83% and precision level of 85%.

Suggested Citation

  • Kreuchauff, Florian & Korzinov, Vladimir, 2015. "A patent search strategy based on machine learning for the emerging field of service robotics," Working Paper Series in Economics 71, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
  • Handle: RePEc:zbw:kitwps:71
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    References listed on IDEAS

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    Cited by:

    1. Borissov, Kirill & Pakhnin, Mikhail & Puppe, Clemens, 2017. "On discounting and voting in a simple growth model," European Economic Review, Elsevier, vol. 94(C), pages 185-204.
    2. Armin Falk & Nora Szech, 2016. "Pleasures of Skill and Moral Conduct," CESifo Working Paper Series 5732, CESifo.
    3. Betz, Frank & Hautsch, Nikolaus & Peltonen, Tuomas A. & Schienle, Melanie, 2016. "Systemic risk spillovers in the European banking and sovereign network," Journal of Financial Stability, Elsevier, vol. 25(C), pages 206-224.
    4. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.

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    More about this item

    Keywords

    Service Robotics; Search Strategy; Patent Query; Data Mining; Machine Learning; Support Vector Machine;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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