This paper investigates the presence of explicit labour-saving heuristics within robotic patents. It analyses innovative actors engaged in robotic technology and their economic environment (identity, location, industry), and identifies the technological fields particularly exposed to labour-saving innovations. It exploits advanced natural language processing and probabilistic topic modelling techniques on the universe of patent applications at the USPTO between 2009 and 2018, matched with ORBIS (Bureau van Dijk) firm-level dataset.The results show that labour-saving patent holders comprise not only robots producers, but also adopters. Consequently, labour-saving robotic patents appear along the entire supply chain. The paper shows that labour-saving innovations challenge manual activities (e.g. in the logistics sector), activities entailing social intelligence (e.g. in the healthcare sector) and cognitive skills (e.g. learning and predicting).

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This paper, relying on a still relatively unexplored long-term dataset on U.S. patenting activity, provides empirical evidence on the history of labor-saving innovations back to early nineteenth century. The identification of mechanization/automation heuristics, retrieved via textual content analysis on current robotic technologies by Montobbio et al. (Robots and the origin of their labour-saving impact, LEM Working Paper Series 2020/03), allows to focus on a limited set of CPC codes where mechanization and automation technologies are more prevalent. We track their time evolution, clustering, eventual emergence of wavy behavior, and their comovements with long-term GDP growth. Our results challenge both the general-purpose technology approach and the strict 50-year Kondratiev cycle, while they provide evidence of the emergence of erratic constellations of heterogeneous technological artefacts, in line with the development-block approach enabled by autocatalytic systems.