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

Ivan Savin, Ingrid Ott and Chris Konop

Technological Forecasting and Social Change, 2022, vol. 174, issue C

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 represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. 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 as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory.

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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Working Paper: Tracing the evolution of service robotics: Insights from a topic modeling approach (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521007149

DOI: 10.1016/j.techfore.2021.121280

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