AI and the Ownership of Ideas

by

Author: Denis Avetisyan

Artificial intelligence is rapidly reshaping innovation, forcing a critical re-evaluation of intellectual property frameworks worldwide.

This paper presents a comparative analysis of transnational policies surrounding AI-generated works and inventorship, with a focus on the Indian legal landscape.

The rapid advancement of artificial intelligence challenges established intellectual property frameworks, creating uncertainties regarding ownership and incentivizing innovation. This study, ‘Artificial Intelligence and Intellectual Property Rights: Comparative Transnational Policy Analysis’, analyzes these challenges through a comparative lens, focusing on the legal landscapes of India, the US, the UK, and the EU. Preliminary findings reveal significant gaps in Indian law concerning AI-generated works, particularly regarding patentability, trade secret protection, and copyright authorship. Will recalibrating India’s IP jurisprudence, and fostering international harmonization, be sufficient to unlock the full potential of AI-driven innovation while safeguarding intellectual creativity?

The Inevitable Echo: AI and the Crisis of Intellectual Property

Artificial intelligence is no longer a futuristic concept but a present-day engine of innovation, demonstrably accelerating discovery and invention across diverse fields – from pharmaceutical research and materials science to software development and artistic creation. This surge in AI-driven innovation is, however, fundamentally challenging established Intellectual Property Rights frameworks, originally conceived for human authorship. The traditional notions of inventorship and originality are being tested as algorithms increasingly contribute to, or even autonomously generate, novel outputs. Consequently, legal systems worldwide are grappling with questions of ownership, patentability, and the very definition of creative work in an age where machines can demonstrably innovate, necessitating a proactive re-evaluation of IPR to accommodate this rapidly evolving technological landscape.

The escalating volume of content produced by artificial intelligence systems is forcing a critical reassessment of established intellectual property laws. Current legal frameworks, largely constructed around the concept of human authorship, struggle to accommodate outputs generated autonomously by machines. This necessitates a proactive shift in IPR protection, moving beyond solely recognizing human creativity to address the unique challenges posed by AI-driven innovation. Determining ownership, originality, and infringement in the context of AI-generated works requires new definitions and potentially novel legal approaches. Failure to adapt these frameworks risks stifling innovation, creating legal uncertainty, and undermining the incentives for developing and deploying AI technologies – ultimately demanding a forward-looking strategy to safeguard both creators and the public interest.

The engines of artificial intelligence innovation-data mining and web scraping-present a curious challenge to established protections for trade secrets. While these techniques fuel AI’s capacity to learn and create, they simultaneously increase the risk of confidential information being inadvertently-or intentionally-extracted from publicly accessible, yet still proprietary, data sources. This creates a paradoxical situation where the very methods driving progress also erode the foundations of competitive advantage built on confidential business information. Legal frameworks designed to safeguard trade secrets struggle to address the scale and speed at which AI can collect and analyze data, demanding a reassessment of what constitutes permissible access and use in the age of automated information gathering. Consequently, businesses must now navigate a complex landscape where fostering innovation through data utilization clashes with the imperative to protect valuable, confidential knowledge.

The accelerating development of artificial intelligence, and especially generative models, presents a substantial challenge to established legal and ethical norms, demanding swift and considered responses. This paper identifies critical deficiencies within Indian Intellectual Property laws as they pertain to AI-driven creations, noting a significant lag between technological advancement and legal frameworks. Existing statutes, designed for human authorship, struggle to accommodate outputs generated autonomously by algorithms, raising complex questions regarding inventorship, ownership, and liability. The implications extend beyond legal definitions, encompassing ethical considerations related to bias, transparency, and the potential for misuse of AI-generated content, necessitating a proactive and adaptive approach to regulation before these technologies fundamentally reshape the innovation landscape.

The Patent Mirage: Can Machines Truly Invent?

Patent law traditionally protects innovations, but applying these principles to AI presents unique challenges. AI inventions frequently rely on algorithms and mathematical models, which are considered abstract ideas and therefore ineligible for patent protection in many jurisdictions. The difficulty lies in distinguishing between the abstract concept of an algorithm and its practical application to produce a tangible, technical result. Patent offices worldwide are currently grappling with defining the boundaries of patentability for AI, focusing on whether the implementation of an AI algorithm solves a technical problem in a non-obvious manner, and whether the claims are directed to a specific application rather than the underlying abstract idea itself. This assessment is further complicated by the potential for AI algorithms to be implemented in software without a physical component, requiring demonstration of a concrete, technical effect to satisfy patentability requirements.

Section 3(k) of the Indian Patents Act specifically excludes inventions pertaining to mathematical methods or algorithms, presenting a significant hurdle for patenting many Artificial Intelligence (AI) systems. This clause prohibits patents for inventions that are essentially mathematical problems or algorithms, even when implemented with technical effects. Because AI frequently relies on complex mathematical models and algorithmic processes for tasks like machine learning, data analysis, and pattern recognition, applications claiming these core functionalities may be deemed non-patentable under Section 3(k). The practical impact is that simply encoding a mathematical formula or algorithm into a computer program is generally insufficient for patentability; the application must demonstrate a further technical application and concrete, tangible results beyond the abstract mathematical concept itself to overcome this exclusion.

Current patent law assesses inventiveness – whether an invention is non-obvious to a person skilled in the art – and applying this to AI presents challenges. Courts are actively determining the requisite level of human contribution necessary for an AI-driven invention to be considered patentable. Traditional assessments focus on the technical problem solved and whether the solution would have been obvious to an expert, but with AI, the ‘inventive step’ often resides in the algorithm itself or the training data used. The debate centers on whether simply applying a known AI technique to a new problem constitutes sufficient inventiveness, or if modifications to the algorithm or data are needed to demonstrate a non-obvious solution. Several legal cases are currently exploring these issues, particularly regarding the role of the AI system in generating the inventive concept and the degree to which human intervention is necessary to claim inventorship.

Patent applications involving artificial intelligence necessitate a detailed exposition of the relationship between the underlying algorithm, its specific implementation within a system, and the demonstrable technical effects achieved. Current legal frameworks often struggle to differentiate between an abstract idea embodied in an algorithm and a concrete application yielding a tangible, practical result. This paper identifies a critical need for updated legislation to clarify the criteria for patentability in AI, specifically addressing how to assess the inventive step when the primary innovation resides in the algorithm itself, rather than its hardware or software implementation. A successful patent claim requires establishing that the AI system solves a technical problem in a non-obvious manner and delivers a measurable improvement over prior art, a determination complicated by the potential for algorithms to be implemented across diverse technical domains.

The Ghost in the Machine: Authorship and the Algorithm

Historically, copyright law has been predicated on the principle of protecting original works created by human authors. The emergence of Artificial Intelligence (AI) capable of generating content – including text, images, and code – challenges this foundational principle. Current copyright statutes generally do not explicitly address AI-generated works, leading to uncertainty regarding whether such works are eligible for copyright protection and, if so, who should be considered the author and owner. The central issue is whether an AI can be considered an ‘author’ in the legal sense, or if human involvement – such as in the design of the AI, the selection of training data, or the prompting of the AI – is necessary to establish authorship and claim copyright. This ambiguity creates legal complexities regarding infringement, licensing, and the overall protection of AI-generated content.

The question of whether an artificial intelligence system can be legally designated as an ‘author’ under existing copyright law is a significant point of contention. Traditional copyright frameworks are predicated on human authorship, requiring a demonstrable human creator for protection. Granting authorship to an AI would necessitate a re-evaluation of these established principles and potentially involve redefining the concept of ‘originality’. Currently, most jurisdictions require human authorship for copyright to vest; however, the increasing sophistication of AI-generated content challenges this assumption. If AI is not considered an author, the resulting works may fall into the public domain, or ownership may default to the AI’s programmer or owner, creating uncertainty regarding intellectual property rights and incentivizing or disincentivizing further AI development in creative fields.

The establishment of copyright ownership for AI-generated content is significantly impacted by the extent of human contribution. Current legal frameworks generally require human authorship for copyright protection; therefore, the degree to which a human directs and controls the AI’s output is critical. Minimal prompting, such as a simple text instruction, may not be sufficient to establish authorship, as the AI performs the majority of the creative work. Conversely, substantial editing, rearrangement, and creative input applied after the AI generates initial content may qualify as original authorship, granting copyright to the human editor. The threshold for “substantial” input remains undefined and is subject to ongoing legal debate, with differing interpretations impacting the eligibility of AI-assisted works for copyright protection.

Current Indian Intellectual Property (IP) law lacks specific provisions addressing copyright ownership and authorship of AI-generated content, creating legal uncertainty regarding protection and enforcement. This analysis identifies deficiencies in existing legislation concerning the categorization of AI contributions, the definition of ‘originality’ when applied to machine-created works, and the allocation of rights when human input is combined with AI output. Harmonization with international developments, particularly those in jurisdictions like the US and EU, is crucial to establish a predictable legal framework. The paper argues for legislative amendments or judicial precedents to clarify these ambiguities and foster innovation while respecting established copyright principles, ensuring Indian IP law remains relevant in the context of rapidly evolving AI technologies.

The Looming Convergence: Global Frameworks and National Strategies

The World Intellectual Property Organization (WIPO) serves as a pivotal force in navigating the complex landscape of intellectual property as it applies to artificial intelligence. Recognizing the transformative potential – and inherent challenges – of AI, WIPO actively facilitates international dialogue and collaboration to establish consistent norms and standards for protecting AI-driven innovations. This includes examining the applicability of existing IP frameworks – such as patents, copyrights, and trade secrets – to AI-generated outputs and processes. Through initiatives like the WIPO Conversation on Intellectual Property and AI, the organization fosters a shared understanding of these issues amongst member states, aiming to harmonize legal approaches and encourage cross-border innovation. Ultimately, WIPO’s role extends beyond simply defining ownership; it seeks to build a global ecosystem where AI advancements are both incentivized and responsibly managed through effective intellectual property rights.

The European Union’s AI Act establishes a tiered risk-based framework for artificial intelligence, significantly impacting intellectual property rights across the region and beyond. This legislation categorizes AI systems based on their potential harm – from minimal risk to unacceptable risk – and imposes corresponding obligations on developers and deployers. Crucially, the Act addresses IPR concerns by clarifying ownership and liability for AI-generated outputs, acknowledging the complex interplay between traditional copyright law and machine learning. It proposes transparency requirements for training data, potentially impacting access to and use of copyrighted material. Considered a landmark effort, the EU AI Act is poised to become a global benchmark, influencing the development of AI regulations worldwide and prompting a re-evaluation of intellectual property frameworks to accommodate the unique challenges and opportunities presented by increasingly sophisticated AI systems.

India’s National AI Strategy explicitly frames Intellectual Property Rights as a vital engine for fostering both artificial intelligence innovation and sustained economic growth. The strategy doesn’t treat IPR as a mere legal formality, but rather as a core component of a thriving AI ecosystem. Specific initiatives outlined within the strategy include bolstering the capacity of the Indian patent office to handle AI-related inventions, promoting awareness of IPR among AI developers and researchers, and incentivizing the creation of original AI technologies. Furthermore, the framework acknowledges the need to balance strong IPR protection with the encouragement of open-source AI development, recognizing that both approaches contribute to a dynamic and competitive AI landscape. By prioritizing IPR, the strategy aims to attract investment, stimulate entrepreneurship, and position India as a significant player in the global AI revolution.

The effective integration of global frameworks and national strategies concerning Artificial Intelligence Intellectual Property Rights necessitates a forward-looking, collaborative approach. This paper highlights the critical need for policymakers to move beyond reactive measures and proactively address existing deficiencies within Indian IP laws. Successful implementation demands consistent dialogue and cooperation between governmental bodies, industry leaders developing AI technologies, and legal experts specializing in intellectual property. Identifying and rectifying gaps in current legislation, particularly concerning AI-generated inventions and data ownership, is paramount to fostering innovation and ensuring economic growth. A unified effort, characterized by open communication and shared understanding, will not only strengthen India’s position in the global AI landscape but also provide a robust legal foundation for responsible AI development and deployment.

The pursuit of intellectual property rights in the age of artificial intelligence reveals a fundamental truth about complex systems. This paper, dissecting the transnational policy landscape, demonstrates that attempts to rigidly define inventorship and authorship for AI-generated works are ultimately exercises in attempting to predict the unpredictable. As Edsger W. Dijkstra observed, “It’s always possible to devise a system that doesn’t work.” The very act of establishing these frameworks, while necessary, plants the seeds of future inadequacy, for AI’s evolution will inevitably outpace even the most carefully crafted legislation. Long stability in these legal definitions isn’t a sign of success, but rather a hidden disaster waiting to emerge as AI’s capabilities surpass our current understanding and policy constructs.

What’s Next?

The attempts to map existing intellectual property doctrines onto the emergent landscape of artificial intelligence reveal less about the technology itself, and more about the enduring human need to claim ownership. This paper rightly identifies the friction, particularly within the Indian context, but the true challenge isn’t legislative amendment-it’s accepting that every formalization of “ownership” is merely a temporary dam against the inevitable flow of derivative works. Each patent granted for an AI-assisted invention is a promissory note, payable in future disputes over novelty and non-obviousness.

The pursuit of international harmonization, while laudable, risks becoming an exercise in building sandcastles against the tide. Agreement on definitions of “inventorship” or “authorship” will prove brittle, as AI capabilities continue to outpace legal frameworks. The real innovation will not be in defining who owns the output, but in accepting that the very concept of singular ownership may become increasingly irrelevant.

Ultimately, the field must shift its focus. The question isn’t how to protect AI-generated works, but how to build systems resilient enough to thrive in a world of perfect replication and continuous recombination. Order is just a temporary cache between failures; the future belongs not to those who control intellectual property, but to those who can adapt to its erosion.

Original article: https://arxiv.org/pdf/2601.17892.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-27 16:09