When Algorithms Discover Genes: Patentability Of AI-Driven CRISPR Technologies And the Human Ingenuity Dilemma

 

Abstract

The convergence of artificial intelligence (AI) and CRISPR-Cas gene-editing technology has taken biomedical research to a new level. Automated systems are now capable of identifying gene targets and mutation pathways with only minimal direct human intervention. While these developments promise significant advancement, they simultaneously expose a doctrinal gap within patent law. Under the Indian Patents Act, 1970, patentability hinges upon the existence of an “inventive step” under Section 2(1)(ja) and an “invention” under Section 2(1)(j), both of which implicitly assume human intellectual contribution. AI-assisted CRISPR discoveries challenge this assumption.
This paper examines whether inventions generated through AI-driven CRISPR platforms satisfy the legal threshold of inventorship and patentability, particularly when human involvement is limited to data input. By analysing comparative jurisprudence from the UK, EU, and the United States, the study highlights the absence of a coherent legal standard for determining “sufficient human contribution” in autonomous scientific discovery. It further engages with the patent disclosure requirements under Section 10 of the Indian Patents Act.

Keywords: Artificial Intelligence and Patent Law, CRISPR-Cas Gene Editing, Inventive Step, Human Contribution Threshold, Biotechnological Inventions, AI-Generated Inventions, Inventorship in Patent Law, Indian Patents Act 1970, Algorithmic Innovation and Patentability Criteria.

I. Introduction

In recent years, artificial intelligence has changed how researchers approach and make discoveries across different fields. In biotechnology particularly, the use of artificial intelligence alongside CRISPR-Cas gene-editing has allowed researchers to study large volumes of genetic data with far greater ease. AI systems can also anticipate how mutations may behave and locate potential treatment targets far more efficiently than before.[1] CRISPR-Cas systems are now increasingly guided by machine-learning models that can independently suggest genetic modifications.[2]

This convergence has accelerated innovation, particularly in areas such as cancer research and drug development.[3] AI-driven CRISPR platforms are capable of identifying gene sequences and editing strategies that might not be immediately apparent to human researchers, given the complexity and scale of genomic data.[4]

This shift creates a basic difficulty for patent law, which has long rested on the idea that inventions arise from human creativity and intellectual effort. Under the Indian Patents Act, 1970, an invention must satisfy the requirement of novelty along with an “inventive step,” defined under Section 2(1)(ja) as involving technical advancement or economic significance arising from human effort.[5] Moreover, patent doctrines such as the “person skilled in the art” standard also presuppose a human inventor capable of exercising judgment and problem-solving skills.[6]

As AI systems increasingly produce solutions on their own in CRISPR-based research, important questions emerge about who qualifies as an inventor and whether such outcomes can be patented. When AI identifies a gene-editing pathway with minimal human intervention, it becomes unclear whether the legal requirement of human contribution is truly satisfied.[7] This uncertainty is not limited to India.

Even where courts abroad have addressed related questions, the law still does not clearly explain how much human involvement is required when AI largely drives the inventive process. If patents are granted too readily, there is a risk of monopolising discoveries produced through algorithms. At the same time, denying protection altogether could reduce incentives to invest in AI-based research systems.

In this context, the present study seeks to examine whether existing patent law principles adequately address AI-driven CRISPR innovations.

II. Understanding AI-Driven CRISPR Technologies

A. Evolution of CRISPR-Cas Gene-Editing Systems
CRISPR-Cas gene-editing technology originated from a natural defence mechanism found in bacteria and archaea, in which clustered regularly interspaced short palindromic repeats (CRISPR) help organisms protect themselves against viral infections.[8] Turning this natural biological system into a programmable gene-editing tool marked a major breakthrough in modern biotechnology. Early CRISPR systems, particularly CRISPR-Cas9, allowed scientists to cut DNA at specific locations with precision, making gene modification faster and more accessible than earlier techniques such as zinc-finger nucleases.[9]

Variants such as Cas12 and Cas13 expanded the system’s capabilities by enabling RNA targeting and improving accuracy.[10] Base editing and prime editing further refined the technology by allowing precise genetic changes without creating double-stranded DNA breaks.[11] These developments have, as a result, greatly expanded the use of CRISPR in medical research.

Even with its high level of precision, early CRISPR research depended largely on human expertise to select target genes and anticipate the results of gene editing. Scientists manually analysed genetic sequences and experimental data to determine where and how edits should be made.[12] As genomic data expanded rapidly, this manual approach began to show its limits. The growing complexity of genetic interactions and mutation pathways made it increasingly difficult for researchers to identify the best targets on their own within a reasonable time. As CRISPR systems matured, they increasingly relied on computational tools to enhance accuracy, predict off-target effects, and improve editing efficiency.[13]

B. Role of Artificial Intelligence in Gene Discovery and Editing
Machine-learning tools are now commonly used to forecast mutation effects and evaluate the effectiveness and safety of CRISPR edits. By learning from large genetic datasets and past experiments, these systems can produce insights that would be difficult to obtain through conventional trial-and-error research.

By processing thousands of genetic sequences simultaneously, AI models can suggest gene-editing strategies that are often more successful than those developed through human judgment alone.

Advanced models are capable of identifying previously unknown gene functions and disease-related mutations by detecting patterns within genomic data.[14] Beyond optimisation, then, AI has begun to play a more active role in gene discovery itself. In some cases, AI systems generate hypotheses and propose genetic targets independently.[15] It is increasingly difficult to tell where human input ends and machine invention begins.

C. Distinction Between Human-Guided and Autonomous AI Discoveries
The key issue in deciding whether AI-driven CRISPR work can be patented is identifying this distinction. In human-guided systems, AI functions as a decision-support tool, assisting researchers in analysing data, while humans retain control over problem formulation, target selection, and interpretative judgment.[16]

Autonomous AI discoveries, however, present a fundamentally different scenario. In these systems, AI independently processes genetic data and proposes CRISPR editing strategies without continuous human input.[17] Human involvement is often limited to setting up the system or checking its results after the main invention is already complete.

This matters legally because patents need a clear connection to human creativity. India’s Patents Act, 1970, does not specifically address inventions made entirely by AI, and doctrines such as the “person skilled in the art” presuppose human judgment.

Similar issues are being seen internationally. Courts in the UK, EU, and the US, for example, rejected AI inventorship claims in the DABUS cases, reflecting a broader judicial reluctance to recognise machines as inventors. However, these decisions do not clarify how much human input counts when a system is only partly autonomous.[18]

III. Patentability Framework Under the Indian Patents Act, 1970

A. Meaning of “Invention” under Section 2(1)(j)
Section 2(1)(j) of the Indian Patents Act, 1970 defines an “invention” as a new product or process involving an inventive step and capable of industrial application.[19] Although the provision appears neutral toward technology, it is grounded in the assumption that inventions arise from human thinking and creativity. This understanding is reinforced by the structure of the Act, which consistently links invention to a natural person.[20] AI-driven CRISPR complicates this definition. When AI identifies gene targets or suggests editing methods independently by analysing genomic data, it becomes difficult to trace the invention back to a human mind. People may set up the system or supply the data, but the inventive idea itself often originates with the AI, without human thought at the exact moment of discovery.

Indian patent practice has traditionally focused on tangible human contribution to the inventive concept, rather than mere facilitation. If the human role is limited to enabling AI functionality, it becomes unclear whether the resulting gene-editing method qualifies as an “invention” within the meaning of Section 2(1)(j).

B. Inventive Step Requirement under Section 2(1)(ja)
Section 2(1)(ja) defines “inventive step” as a feature involving technical advancement or economic significance that is not obvious to a person skilled in the art. The “person skilled in the art” is a legal fiction representing an average human practitioner with ordinary knowledge and skill.[21]

In AI-driven CRISPR research, this standard falls short. When an AI identifies a gene-editing pathway that no human would reasonably conceive, applying a human-centred non-obviousness test becomes conceptually inconsistent. This raises a critical question: should inventive step be assessed against human capabilities, or against the technological context in which AI operates? If human contribution is minimal, granting patents merely because the AI’s results exceed human expectations risks rewarding the machine’s efficiency rather than genuine human creativity. Conversely, refusing to recognise any inventive step could discourage investment in AI-driven research. The current framework under Section 2(1)(ja) does not resolve this tension.

C. Patent Disclosure Obligations under Section 10
Section 10 of the Indian Patents Act mandates that a patent specification must fully and particularly describe the invention and disclose the best method of performing it.[22] In conventional inventions, compliance with this provision depends on the inventor’s ability to explain the inventive process and technical steps clearly.

AI-driven CRISPR inventions further complicate this requirement. Many AI models used in gene-editing research function as “black boxes,” where the reasoning behind outputs is not fully interpretable even by their developers.[23] While applicants may disclose the algorithmic framework or training methodology, the precise decision-making process that led to a particular gene-editing solution often remains opaque.

This raises concerns about whether such disclosures truly satisfy Section 10. If the invention cannot be meaningfully reproduced without access to proprietary datasets or AI models, the disclosure may be technically complete yet practically insufficient. Indian patent authorities have not yet developed clear standards for evaluating disclosure adequacy in AI-based inventions, which increases the risk of either under-enforcement or excessive secrecy through trade secret protection. Section 10 thus presents a critical yet underexplored challenge in the patenting of AI-driven biotechnological innovations.

IV. The Concept of Inventorship and Human Ingenuity in Patent Law

A. Traditional Understanding of Inventorship
Inventorship in patent law has historically been understood as the act of conceiving the inventive concept through human intellectual effort. An inventor is not merely someone who follows directions or performs routine experiments; rather, it is the person who conceives the central idea that makes the invention new and unique. This understanding is reflected in patent statutes and judicial interpretations across jurisdictions, where inventorship is implicitly linked to natural persons capable of creative reasoning.[24]

Indian courts have repeatedly held that the true inventor is the one who conceives the inventive idea, not merely those who help carry it out. In V.B. Mohammed Ibrahim v. Alfred Schafranek, the Madras High Court held that inventorship depends on contribution to the inventive idea, not on mechanical or administrative involvement.[25] This principle aligns with international patent practice. Where AI independently arrives at a gene-editing solution, human involvement may be limited to providing data or verifying results after the fact. Under traditional standards, such involvement may fall short of inventorship.

B. Human Intellectual Contribution as a Foundational Principle
Human intellectual contribution lies at the heart of patent protection and serves as the moral and legal justification for granting exclusive rights. Patent systems reward inventors for the mental labour involved in solving technical problems, thereby incentivising innovation and public disclosure.[26] This principle assumes that creativity and problem-solving are inherently human attributes.

The Indian Patents Act, 1970, while not explicitly defining “human contribution,” embeds this assumption throughout its provisions. Concepts such as inventive step, disclosure, and the best method requirement presuppose that a human inventor can explain the reasoning behind the invention. Courts have reinforced this view by requiring demonstrable intellectual input rather than mere experimentation or automation.[27] When algorithms handle the creative component of an invention, it becomes unclear whether tasks such as designing the system or organising data amount to sufficient intellectual contribution. If patent law continues to insist that only human creativity counts, many AI-generated biotech discoveries may end up unprotected.

C. The “Person Skilled in the Art” Doctrine and Its Relevance
The “person skilled in the art” is a central doctrinal tool used to assess inventive step and obviousness in patent law. This hypothetical individual represents an average practitioner with ordinary knowledge and technical skill in the relevant field.[28] The doctrine ensures that patents are granted only for advances that exceed routine human expertise.

Applying a human-focused standard to AI-made inventions creates an internal conflict. If an AI-generated gene-editing solution is judged against what a human could conceive, it will often appear non-obvious, potentially leading to overly broad patents. At the same time, this approach overlooks the technological context from which AI innovation actually arises — computing power, not human insight. This mismatch suggests that the “person skilled in the art” doctrine requires reinterpretation in AI-intensive fields.

V. AI-Generated Inventions and the Human Contribution Dilemma

The central legal challenge is determining when human involvement counts as truly inventive. In AI-driven CRISPR research, humans typically create the algorithms, select the training data, and verify the results. Yet new gene-editing solutions can often emerge without direct human input, blurring the line between invention and automation. If humans merely set up the system rather than conceive the inventive idea, it becomes difficult to justify naming them as inventors under current patent law.

This dilemma is not merely theoretical. Pharmaceutical and biotechnology companies increasingly rely on AI platforms to accelerate drug discovery and gene-editing research, as seen in the use of AI-based genomic tools by companies such as Insilico Medicine and in DeepMind’s AlphaFold project.[29] In such settings, attributing inventorship to a human researcher may be more a legal fiction than a reflection of genuine creative contribution. Yet current patent frameworks offer no alternative mechanism for recognising machine-driven invention.

VI. Comparative Jurisprudence on AI Inventorship

A. The DABUS Decisions: UK, EU, and United States
The DABUS cases represent the most significant global judicial engagement with the question of AI inventorship. DABUS (Device for the Autonomous Bootstrapping of Unified Sentience) is an AI system developed by Stephen Thaler, who was named as the inventor in multiple patent applications filed across jurisdictions.[30] These cases involved inventions that the AI reportedly created on its own, without direct human input.

The UK Supreme Court, in Thaler v Comptroller-General of Patents (2023), rejected the application on the ground that the Patents Act 1977 recognises only natural persons as inventors. The Court held that inventorship is a statutory concept tied to human legal personality, and that an AI system cannot own rights or transfer them to an applicant.[31] Similarly, the European Patent Office (EPO), in its 2019 decisions, refused the applications on the basis that the European Patent Convention requires an inventor to be a natural person capable of exercising legal rights.[32]

In the United States, the Court of Appeals for the Federal Circuit in Thaler v Vidal (2022) upheld the USPTO’s refusal, holding that the term “inventor” under the US Patent Act refers exclusively to individuals, as reflected in statutory language and legislative intent.[33] The Court noted that any expansion of inventorship to non-humans would require legislative intervention.

B. Lessons for Indian Patent Law
The DABUS jurisprudence offers important lessons for Indian patent law. Like its UK and US counterparts, the Indian Patents Act, 1970, implicitly assumes human inventorship. Provisions relating to inventive step and assignment presuppose a human agent capable of legal responsibility.[34]

Indian courts have historically adopted a purposive approach to patent interpretation.[35] This suggests that Indian patent law could focus more on actual human contribution rather than adhering strictly to formal rules. The absence of Indian case law on AI inventorship is both a challenge and an opportunity.

One important lesson from other jurisdictions’ experience is that simply holding that AI cannot be an inventor does not resolve the broader question of AI-assisted inventions. Indian patent authorities need clearer standards for assessing human contribution where AI plays a substantial role.

Rather than adopting the DABUS approach wholesale, Indian patent law should articulate its own clear standard for what constitutes meaningful human contribution. Such an approach would preserve the human-centric foundation of patent law while accommodating the realities of AI-driven CRISPR innovation.

VII. Risks of Over-Protection and Under-Protection of AI-Driven CRISPR Inventions

Over-protection arises when patent rights are granted without carefully assessing the extent of human contribution.[36] AI can generate many gene-editing solutions on the basis of computing power rather than human creativity. Granting patents merely because humans played a small role risks protecting the AI’s output rather than genuine human invention.

This can result in broad and overlapping patent claims that restrict access to fundamental gene-editing techniques. The long-standing patent disputes surrounding CRISPR-Cas9 technology illustrate how expansive patent rights can slow downstream research and increase licensing costs for academic institutions and start-ups.[37] This risk becomes even more pronounced with AI.

Under-protection, conversely, occurs when AI-generated CRISPR inventions are denied patent protection due to the absence of a clearly identifiable human inventor. Biotech companies increasingly invest in AI platforms to reduce research costs and improve precision in gene editing. If such innovations fall outside patent eligibility, firms may be discouraged from investing in AI-enabled research, or may rely excessively on trade secret protection instead.

This problem is particularly acute in healthcare-related CRISPR applications: over-protection may restrict access to life-saving therapies by enabling monopolistic pricing, while under-protection may delay the development of new treatments due to reduced investment incentives.

VIII. Analysis and Recommendations

The analysis undertaken in this paper demonstrates that existing patent law frameworks, particularly under the Indian Patents Act, 1970, are ill-equipped to address the realities of AI-driven CRISPR inventions.[38] Although the Act remains grounded in human-focused notions of inventorship, modern biotechnology increasingly depends on fully or partly autonomous AI systems. This gap creates legal uncertainty and the risk of both over- and under-protecting important gene-editing technologies. Addressing these issues calls for clear rules and policy updates, not a wholesale overhaul of patent law.

First, there is a pressing need to clarify the threshold of human contribution required for inventorship in AI-assisted inventions. Rather than recognising AI systems as inventors, which would conflict with existing legal structures, patent authorities should adopt a functional human contribution test. This test should require applicants to demonstrate meaningful human involvement at one or more critical stages of the inventive process. Mere ownership of an AI system or passive data input should be explicitly excluded from qualifying as inventorship.

Second, the “person skilled in the art” doctrine should be contextually recalibrated for AI-intensive fields like CRISPR research. Patent examiners should assess inventive step by considering what would be obvious to a skilled human practitioner using ordinary technological tools available at the relevant time, rather than comparing AI-generated outputs directly against unaided human cognition. This adjustment would prevent the automatic satisfaction of non-obviousness merely because AI exceeds human analytical capacity, thereby reducing the risk of overbroad patents based on computational advantage rather than inventive merit.

Third, enhanced disclosure requirements should be introduced for AI-based biotechnological inventions. Patent applicants should be required to disclose not only the result of the invention but also the role played by AI in generating it, including the extent of human control and the functional relationship between training data and output. While full disclosure of proprietary datasets may not always be feasible, applicants should be required to provide sufficient information to enable reproducibility or meaningful scientific understanding. This would strengthen compliance with Section 10 and discourage strategic reliance on trade secrecy to undermine the patent bargain.

Fourth, the Indian Patent Office should consider issuing sector-specific examination guidelines for AI-driven inventions, particularly in biotechnology and pharmaceuticals. Such guidelines would improve consistency in patent examination and align Indian practice with emerging global standards discussed at forums such as the World Intellectual Property Organisation.

Finally, there is a need for institutional capacity-building. Patent examiners must be trained in AI and computational biology to competently assess inventive step. Without technical literacy, even well-designed legal standards risk ineffective implementation.

IX. Conclusion

The growing use of AI in CRISPR-Cas gene-editing research has highlighted a clear gap between modern scientific practice and traditional patent law rules. As AI systems autonomously identify gene targets and therapeutic pathways, the long-standing assumption that inventions necessarily reflect direct human ingenuity becomes increasingly fragile. The Indian Patents Act, 1970, particularly through Sections 2(1)(j) and 2(1)(ja), continues to rely on human-centric concepts of invention and inventive step.

This paper has argued that the absence of a clear legal threshold for human contribution risks two equally problematic outcomes: the over-protection of algorithm-driven discoveries through expansive patent grants, or their complete exclusion from protection due to rigid interpretations of inventorship. Comparative jurisprudence illustrates a global judicial reluctance to recognise a non-human inventor, and also highlights the need for doctrinal recalibration rather than outright rejection.

For patent law to remain relevant, it must recognise genuine human input. Establishing a clear standard for human contribution would preserve the core principles of patent law while adapting it to the realities of AI-powered biotech discovery.

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