Tracing the evolution of service robotics: Insights from a topic modeling approach

Ingrid Ott, Ivan Savin and Chris Konop

No 2180, Kiel Working Papers from Kiel Institute for the World Economy (IfW Kiel)

Abstract: Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge, where some topics appear isolated while others are highly interconnected. Furthermore, we propose a novel approach to match the constructed topics to the IFR classification of service robots based on frequency and exclusivity of words overlapping between them. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics and contribute to new practices of utilizing the topic modeling approach.

Keywords: knowledge diffusion; latent Dirichlet allocation; networks; patents; topic matching (search for similar items in EconPapers)
JEL-codes: C11 C15 O33 O34 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-ino
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Journal Article: Tracing the evolution of service robotics: Insights from a topic modeling approach (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:ifwkwp:2180

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