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

Florian Kreuchauff () and Vladimir Korzinov ()
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Florian Kreuchauff: Geschäftsstelle Expertenkommission Forschung und Innovation (EFI) c/o SV Gemeinnützige Gesellschaft für Wissenschaftsstatistik mbH
Vladimir Korzinov: Karlsruhe Institute of Technology

Scientometrics, 2017, vol. 111, issue 2, No 9, 743-772

Abstract: Abstract Emerging technologies are often not part of any official industry, patent or trademark classification systems. Thus, delineating boundaries to measure their early development stage is a nontrivial task. This paper is aimed to present a methodology to automatically classify patents 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 citation 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, 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%.

Keywords: Service robotics; Search strategy; Patent query; Data mining; Machine learning; Support vector machine (search for similar items in EconPapers)
JEL-codes: C02 C18 C45 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11192-017-2268-3

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