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Critically Evaluating Ryan Abbott’s Arguments for Patenting AI Inventions and Naming AI as Inventor
1. Introduction
1.1 Background on AI inventions and patent law
The rapid evolution of artificial intelligence (AI) has led to systems capable of generating technical solutions without direct human formulation. Traditional patent regimes across many jurisdictions rest on the premise that an “inventor” is a human being who engages in mental activity to conceive an invention. This human-centric notion often excludes AI systems from being recognized as inventors, thereby limiting the patentability of AI-derived innovations. Recent developments in machine learning and generative models challenge this anthropocentric framing by producing outputs that may satisfy novelty and utility criteria. The question arises whether patent policy should adapt to include AI-generated inventions under existing doctrines or require legislative revision.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
1.2 Thesis statement: Evaluating Abbott’s arguments
This essay critically examines Ryan Abbott’s position that (a) AI-generated inventions should be patentable and (b) AI systems should be named as inventors. Focusing on three core patent law dimensions—(i) the overarching purposes of patent protection, (ii) moral and legal considerations around inventor attribution, and (iii) the non-obviousness or inventive step requirement—this paper assesses whether Abbott’s rationale aligns with foundational patent objectives or introduces new challenges. By systematically analyzing each dimension, the essay aims to highlight strengths and limitations of Abbott’s approach and propose considerations for future policy design.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
2. Purpose of Patent Law
2.1 Abbott’s rationale: innovation incentives over inventor cognition
Abbott argues that patent law should prioritize the social goal of fostering innovation rather than the cognitive status of the inventor. Under this view, whether an invention springs from a human mind or an AI system is irrelevant so long as the invention advances technology and benefits society. By granting patent rights for AI-derived inventions, the system continues to promote investment and disclosure incentives. Abbott contends that the mental activity requirement—long invoked to ensure that patents reward human ingenuity—has outlived its usefulness in an era where AI can autonomously generate patentable subject matter.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
2.2 Critical evaluation against patent objectives (promote progress, disclose knowledge)
Evaluating Abbott’s rationale against the two central objectives of patent law—promoting technological progress and disclosing knowledge—reveals both promise and complexity. On one hand, recognizing AI outputs could catalyze further research and reduce information asymmetries by forcing AI developers to publish algorithmic advancements. On the other hand, patent offices may struggle to assess inventive contribution absent a human inventor, potentially leading to overbroad or low-quality grants. Moreover, if AI innovators lack incentives to share underlying training data or code, the disclosure function may be undermined. Thus, while Abbott’s focus on incentive alignment resonates with patent theory, practical impediments to quality examination and genuine disclosure persist.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
3. Right of Inventors to Be Named
3.1 Abbott’s view on naming AI as inventor
Abbott advocates naming AI systems as inventors to accurately reflect the source of innovation and ensure transparency in patent records. He suggests that attributing inventorship to AI does not confer moral rights in the human sense but serves an informational function that clarifies who—or what—contributed to the inventive process. Such naming could help allocate economic benefits and litigation responsibilities appropriately among developers, owners, and users of AI technologies. According to Abbott, inventor attribution is about accountability and traceability rather than human dignity or emotional recognition.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
3.2 Analysis of moral rights, legal personhood, and attribution
Granting inventor status to AI systems raises profound questions regarding moral rights and legal personhood. Moral rights doctrines typically protect an author’s personal and reputational link to a creation, something an AI cannot possess. Recognizing AI as an inventor without granting broader personhood risks creating a cosmetic attribution divorced from rights and responsibilities. Conversely, insisting that only natural persons can hold inventorship privileges may incentivize human agents to claim undue credit for AI-generated works. A potential compromise is to treat AI as non-person inventors whose attribution triggers human accountability—developers or users bear ownership and maintenance obligations while AI receives symbolic credit.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
4. Non-Obviousness/Inventive Step Criterion
4.1 Abbott’s position on AI-generated inventive steps
Abbott maintains that the non-obviousness (or inventive step) requirement should be assessed based on the output’s novelty and ingenuity rather than the nature of the inventor. If an AI system produces a solution that would not have been obvious to a person having ordinary skill in the art, it should satisfy the inventive step criterion. Under Abbott’s model, AI-generated designs are tested under existing legal standards without special carve-outs or burdensome proof of human-like creativity. The focus remains on the technical merits of the invention rather than the mechanisms or consciousness behind its conception.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
4.2 Assessment of non-obviousness when AI designs are involved
In practice, assessing inventive step for AI-generated inventions could challenge patent examiners who lack visibility into the AI’s internal reasoning or training corpus. The risk of issuing patents on marginal improvements or known problem framings increases if examiners cannot trace how an AI arrived at its solution. Moreover, reliance on AI may shift the inventive step analysis toward post-hoc rationalization, undermining the rigor of the non-obviousness inquiry. To mitigate these risks, offices could require detailed declarations on algorithmic novelty or maintain specialized AI patent examination units to ensure robust inventive-step assessments.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
5. Conclusion
5.1 Summary of critical insights on Abbott’s stance
Ryan Abbott’s advocacy for patenting AI inventions and naming AI as inventor underscores the need to modernize intellectual property frameworks in light of emergent technologies. His emphasis on social welfare and innovation incentives challenges the traditional mental activity requirement, while his call for accurate inventor attribution enhances transparency. However, this approach must contend with practical examination hurdles, potential disclosure gaps, and unsettling questions of legal personhood and moral rights. Balancing these considerations is essential to preserve core patent objectives while accommodating technological progress.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
5.2 Final evaluation and implications for patent policy
Moving forward, legislatures and patent offices should consider hybrid models that recognize AI contributions without conferring full legal personhood. This could involve naming AI as co-inventor alongside a designated human agent responsible for prosecution and enforcement. Policy reforms might also introduce transparency requirements for AI training data and reasoning processes to safeguard non-obviousness standards. Ultimately, a nuanced framework that aligns Abbott’s innovation-centric rationale with practical safeguards can ensure that patent law continues to drive socially beneficial advancements in the AI era.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
References
No external sources were cited in this paper.