2022
Kim, Daria; Alber, Maximilian; Kwok, Man Wai; Mitrovic, Jelena; Ramirez-Atencia, Cristian; Pérez, Jesús Alberto Rodríguez; Zille, Heiner
Clarifying Assumptions About Artificial Intelligence Before Revolutionising Patent Law Journal Article
In: GRUR International, 2022, ISSN: 2632-8623, (ikab174).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, IT Law
@article{10.1093/grurint/ikab174,
title = {Clarifying Assumptions About Artificial Intelligence Before Revolutionising Patent Law},
author = {Daria Kim and Maximilian Alber and Man Wai Kwok and Jelena Mitrovic and Cristian Ramirez-Atencia and Jesús Alberto Rodríguez Pérez and Heiner Zille},
url = {https://doi.org/10.1093/grurint/ikab174},
doi = {10.1093/grurint/ikab174},
issn = {2632-8623},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {GRUR International},
abstract = {This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of ‘artificial ingenuity’. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where ‘intelligent’ systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited.},
note = {ikab174},
keywords = {Artificial Intelligence, IT Law},
pubstate = {published},
tppubtype = {article}
}
This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of ‘artificial ingenuity’. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where ‘intelligent’ systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited.