Currently, robots, Artificial Intelligence and machine learning systems (hereinafter referred to collectively as “AI” or “AI systems”) can create inventions, which, had they been created by humans, would be eligible for patent protection. This study addresses the patentability of these inventions created by AI systems. We argue that traditional patent law has become outdated, inapplicable and irrelevant with respect to inventions created by AI systems. We call on policy makers to rethink current patent law governing AI systems and replace it with tools more applicable to the new (3A) era of advanced automated and autonomous AI systems. Our argument is based on three pillars: the features of AI systems, the Multiplayer Model and the irrelevance of theoretical justifications concerning intellectual property. In order to fully convey the ability of AI systems to create inventions, the article explains, for one the first times in the legal literature, what AI systems are, how they work and what makes them (so) intelligent. This understanding is crucial to any further discourse about AI systems. We identify eight crucial features of AI systems they are: (1) creative; (2) unpredictable; (3) independent and autonomous; (4) rational; (5) evolving; (6) capable of data collection and communication; (7) efficient and accurate; and they (8) freely choose among alternative options. We argue that, due to these features, AI systems are capable of independently developing inventions which, had they been created by humans, would be patentable (and able to registered as patents). The traditional approach to patent law in which policy makers seek to identify the human inventor behind the patent is, therefore, no longer relevant. We are facing a new era of machines “acting” independently, with no human being behind the inventive act itself. The second pillar of our argument is the Multiplayer Model, which characterizes the long process through which inventions are created by AI systems. The Multiplayer Model, which is also almost absent in the current legal publications, describes the multiple participants and stakeholders, both overlapping and independent, involved in the process, including software programmers, data and feedback suppliers, trainers, system owners and operators, employers, the public and the government. The model conveys that the efforts of traditional patent law to identify a single inventor of these products and processes are no longer applicable. The third pillar of our argument is the irrelevancy of theoretical justifications such as personality and inventiveness/efficiency to inventions created by AI systems. In contrast to other scholars, we argue that traditional patent law is irrelevant and inapplicable to these situations, that these inventions should not be patentable at all and that other tools can achieve the same ends while promoting innovation and public disclosure. These other, non-patent incentives include commercial tools such as electronic and cyber controls over inventions, first-mover market advantages and license agreements. This proposal serves a gatekeeping function and is superior to a revision of the non-obviousness standard used by other scholars to afford patent protection to inventions by AI systems. In maintaining the traditional patents system by hunting for a “real” human inventor, policy makers exhibit a misunderstanding of advanced technology and AI systems features. We conclude with a discussion of the implications of our analysis for different legal regimes, such as tort, contracts and even criminal law.
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Oct 30, 2024
Lisette Põld 
Just Published
Open Access