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

Florian Kreuchauff and Vladimir Korzinov

No 71, Working Paper Series in Economics from Karlsruhe Institute of Technology (KIT), Department of Economics and Management

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%.

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: 2015
New Economics Papers: this item is included in nep-ino, nep-ipr, nep-pr~ and nep-tid
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:kitwps:71

DOI: 10.5445/IR/1000049790

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