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on Intellectual Property Rights |
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Issue of 2022‒05‒09
three papers chosen by Giovanni Ramello Università degli Studi del Piemonte Orientale “Amedeo Avogadro” |
| By: | Arianna Martinelli; Julia Mazzei; Daniele Moschella |
| Abstract: | The recent surge of patent disputes plays an important role in discouraging firms from entering new technology domains (TDs). Using a large-scale dataset combining data from the EPO-PATSTAT database and ORBIS-IP and containing patents applied at EPO between 2000 and 2015, we construct a new measure of litigiousness using patent opposition data. We find that the degree of litigiousness and the density of patent thickets negatively affect the likelihood of firms entering new TDs. Across technologies, the frequency of oppositions discourages firms mostly in high-tech industries. Across firms, the risk of opposition falls disproportionately on small rather than large firms. Finally, for large firms, we observe a sort of learning-by-being-opposed effect. This evidence suggests that litigiousness and hold-up potential discourage firms from entering new TDs, shaping Schumpeterian patterns of innovation characterized by a stable number of large-established firms and a lower degree of turbulence. |
| Keywords: | Patent opposition; Technological entry; Innovation Strategies. |
| Date: | 2022–05–02 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2022/12&r= |
| By: | Kerstin H\"otte; Taheya Tarannum; Vilhelm Verendel; Lauren Bennett |
| Abstract: | Artificial Intelligence (AI) is often defined as the next general purpose technology (GPT) with profound economic and societal consequences. We examine how strongly four patent AI classification methods reproduce the GPT-like features of (1) intrinsic growth, (2) generality, and (3) innovation complementarities. Studying US patents from 1990-2019, we find that the four methods (keywords, scientific citations, WIPO, and USPTO approach) vary in classifying between 3-17% of all patents as AI. The keyword-based approach demonstrates the strongest intrinsic growth and generality despite identifying the smallest set of AI patents. The WIPO and science approaches generate each GPT characteristic less strikingly, whilst the USPTO set with the largest number of patents produces the weakest features. The lack of overlap and heterogeneity between all four approaches emphasises that the evaluation of AI innovation policies may be sensitive to the choice of classification method. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.10304&r= |
| By: | Aurélie Hemonnet-Goujot (CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, AMU IAE - Institut d'Administration des Entreprises (IAE) - Aix-en-Provence - AMU - Aix Marseille Université); Aurélie Kessous (CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon); Fanny Magnoni (CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon) |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03511454&r= |