Trademark Search, Artificial Intelligence and the Role of the Private Sector
Almost every industry today is confronting the potential role that artificial intelligence and machine learning can play in its future. While there are many, many studies on the role of AI in marketing to the consumer, there is less discussion of the role of AI in creation and selecting a trademark that is both distinctive, recognizable and meaningful to the average consumer. As we argue, given that the role of AI is rapidly increasing in trademark search and similarity areas, lawyers and scholars should be apprised of some of the dramatic implications that its role can produce.We begin, mainly, by proposing, as a general matter, that AI should be of interest to anyone studying trademarks and the role that they play in economic decision-making. By running a series of empirical experiments regarding search, we show how comparative work can help us to assess the efficacy of various trademark search engines, many of which draw on a variety of machine learning methods. Traditional approaches to trademarks, spearheaded by economic approaches, have focused almost exclusively on consumer-based, demand side considerations regarding search. Yet as we show in this paper, these approaches are incomplete because they fail to take into account the substantial costs that are also faced by not just consumers, but trademark applicants as well. In the end, as we show, machine learning techniques will have a transformative effect on the application and interpretation of foundational trademark doctrines, producing significant implications for the trademark ecosystem. In an age where artificial intelligence will increasingly govern the process of trademark selection, we argue that the classic division between consumers and trademark owners is perhaps deserving of an updated, supply-side framework. As we argue, a new framework is needed, one that reflects that putative trademark owners, too, are ALSO consumers in the trademark selection ecosystem, and that this insight has transformative potential for encouraging both innovation and efficiency
Year of publication: |
2021
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Authors: | Katyal, Sonia ; Kesari, Aniket |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Extent: | 1 Online-Ressource (88 p) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | In: Berkeley Technology Law Journal Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 4, 2021 erstellt |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014089757
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