While the use of artificial intelligence (AI) for inventions is the latest industry disruptor tool for patent practitioners, the rapid integration of AI into research, development, and legal workflows has created two fundamentally different challenges for in-house patent counsel. The first concerns whether and how inventions conceived with AI assistance can be patented, if at all. The second concerns the use of AI tools to draft patent applications for human-conceived inventions. Although both sit at the intersection of AI and patent law, they implicate different legal doctrines, carry different risks, and demand different strategic responses. This article examines each in turn, highlighting the divergent legal landscapes and the practical considerations that in-house counsel must navigate.

Patenting AI-Assisted Inventions

The Inventorship Problem

The threshold legal question for AI-assisted inventions is inventorship. Under United States patent law, an "inventor" must be a natural person who conceived of the claimed invention. The Patent Act refers to "individuals," and the Federal Circuit's decision in Thaler v. Vidal (2022) confirmed that an AI system cannot be listed as an inventor on a U.S. patent application. The U.S. Patent and Trademark Office (USPTO) subsequently issued guidance in February 2024 reinforcing that while AI may be used as a tool in the inventive process, each claim must have at least one natural person who made a "significant contribution" to the conception of the invention. However, the USPTO's November 2025 guidance rescinded the 2024 framework and articulated a more demanding standard: conception must be by a natural person who possesses a "definite and permanent idea of the complete and operative invention." Under this revised standard, the inventor must be able to convey enough information that a person of ordinary skill in the art could reduce the invention to practice without extensive research or experimentation.

This evolving framework places in-house counsel in the position of making nuanced factual determinations about where AI assistance ends and human conception begins. If a scientist uses a machine learning model to generate candidate molecular structures, and then selects, modifies, and validates one such structure, the question becomes whether the scientist's contribution rises to the level of conception or whether the scientist merely recognized the output of the machine. The answer is rarely binary, and the consequences of getting it wrong are severe: a patent that names the wrong inventor, or omits a proper one, is potentially unenforceable due to invalidity.

From "Significant Contribution" to "Definite and Permanent Idea"

The USPTO's 2024 guidance on AI-assisted inventions initially drew on the Pannu v. Iolab Corp. framework (155 F.3d 1344 (Fed. Cir. 1998)), which evaluates whether a person has made a contribution to conception that is "not insignificant in quality, when that contribution is measured against the dimension of the full invention." Under that framework, the guidance stated that a natural person must have made “a significant contribution” to at least one claim, and it enumerated several non-exclusive factors, including whether the person constructed the prompt in view of a specific problem, whether the person designed or refined the AI system's training or architecture, and whether the person undertook meaningful evaluation and modification of the AI's output.

The USPTO’s November 2025 guidance, however, abandoned the Pannu factors as the operative standard. The new framework requires that a natural person possess a "definite and permanent idea of the complete and operative invention," which is a formulation rooted in the traditional conception standard rather than a multi-factor contribution analysis. In practical terms, the inventor must hold sufficient understanding of the invention that a person of ordinary skill in the art could reduce it to practice without extensive research or experimentation. This shift raises the bar in cases where AI systems perform increasingly autonomous generative work. The question is no longer whether a human made some significant contribution along the way, but whether a human actually conceived the complete invention in the traditional patent law sense.

As AI capabilities increase — particularly with the emergence of agentic AI and systems capable of greater autonomy requiring less human contribution — patent challengers may argue that issued patents are legally invalid due to the lack of a natural person who possessed a definite and permanent idea of the complete and operative invention at the time of conception. This creates a new vector of invalidity challenge and risk that in-house counsel must assess both prospectively, when deciding whether to file, and retrospectively, when evaluating the enforceability of existing portfolios.

In-house counsel must therefore work closely with inventors and technical staff to document the human contributions at each stage of the inventive process. Maintaining contemporaneous records of how AI tools were used, such as what prompts were given, what outputs were generated, what modifications were made, and why, has become a critical best practice. Under the 2025 USPTO inventorship standard, documentation should specifically demonstrate that the named inventor held a definite and permanent idea of the complete invention, not merely that the inventor provided prompts or made iterative selections from AI-generated outputs. Without such documentation, a patent's validity may be vulnerable to an inventorship challenge years after issuance.

Practical Examples in Life Sciences and Clean/Renewable Energy

The inventorship challenges become concrete when examined in specific industry contexts. In life sciences, for example, consider a pharmaceutical company whose researchers use a deep learning model to screen billions of molecular configurations for a novel kinase inhibitor. The AI system identifies a lead compound with a binding affinity profile that no human researcher had hypothesized. A medicinal chemist then modifies the compound's side chain to improve oral bioavailability and confirms efficacy through in vitro assays. 

Under the 2025 USPTO inventorship standard, the critical question is whether the medicinal chemist possessed a “definite and permanent idea of the complete and operative invention” (i.e., the final compound as claimed) or whether the AI system performed the inventive leap by identifying the core molecular scaffold and binding mechanism. If the claim is directed to the specific compound with the modified side chain, the chemist's contribution may satisfy the inventorship standard. However, if the claim encompasses the broader genus of compounds identified by the AI or is directed to the mechanism of action that the AI discovered, but that was not experimentally confirmed by the chemist, the inventorship analysis becomes far more precarious. In-house counsel at pharmaceutical and biotech companies must work with research teams to structure their AI-assisted discovery workflows so that human scientists are making definitive inventive decisions, not merely curating or optimizing AI outputs, at both the front end (e.g., the point of conception) and the back end (e.g., after the AI output) for each claimed invention. In other words, any AI output should be sandwiched between significant and documented human contribution.

In the clean energy sector, consider a company developing next-generation proton exchange membrane electrolyzers for green hydrogen production. Electrolyzer and fuel cell patents typically involve multiple layers of invention covering different componentry, such as the stack architecture, membrane composition and morphology, flow field geometry, gas diffusion layer design, and system-level integration. Each component of the fuel cell or electrolyzer presents distinct inventorship considerations when AI is involved. 

Suppose the company uses an AI system to simultaneously optimize the interplay between membrane thickness, ion exchange capacity, and mechanical reinforcement structure for a novel composite membrane, while a separate AI model designs the complementary flow field channel geometry to minimize parasitic losses across the bipolar plate. A materials engineer reviews the AI's membrane output, selects a reinforcement architecture based on manufacturing feasibility, and validates mechanical durability under cycling conditions. A mechanical engineer reviews the flow field designs, identifies a channel branching pattern that reduces pressure drop at scale, and integrates it into the company's existing stack architecture.

Under the 2025 USPTO inventorship framework, each layer of the electrolyzer stack may require its own inventorship analysis. For the membrane claims, the question is whether the materials engineer's selection of reinforcement architecture and validation of durability constitutes a definite and permanent idea of the complete membrane invention, or whether the AI's simultaneous optimization of thickness, ion exchange capacity, and reinforcement — features the engineer did not independently conceive — represents the true inventive act. 

For the electrolyzer flow field claims, the question is whether the mechanical engineer's identification of a specific channel branching pattern and integration into an existing stack architecture represents independent conception, or whether the AI generated the core geometric insight and the engineer merely adapted it to practical constraints. The multi-component nature of electrochemical devices means that a single patent application may contain claims directed to the membrane, the electrode assembly, the flow field, and the integrated stack. The inventorship analysis may likely differ for each claim depending on where the AI's contribution was most generative. 

In-house counsel in the hydrogen and electrochemical technology space should advise research teams to document not only what the AI generated for each component but what independent technical judgments the human engineers made at each level of the stack architecture and before and after the AI outputs, that shaped the final claimed inventions beyond mere selection, optimization, or integration of AI outputs.

International Divergence

The inventorship problem is compounded by international inconsistency. While the United States, the European Patent Office, and most other major international patent jurisdictions (except South Africa) have all declined to permit AI systems as named inventors, the doctrinal rationales differ, and the precise boundaries of permissible AI assistance vary. Some jurisdictions offer less guidance than others on how much human involvement is necessary when AI plays a substantial generative role. For in-house counsel managing global patent portfolios, this divergence requires jurisdiction-by-jurisdiction analysis of inventorship standards and may influence decisions about where to file first or how to structure claims to ensure that a natural person's contribution is clearly tied to the inventive concept in a way that will satisfy the legal standards presiding in each respective global jurisdiction.

Patentable Subject Matter and Enablement

Beyond inventorship, AI-assisted inventions may face additional substantive hurdles. If the invention is itself an AI model or algorithm, it must clear the Alice/Mayo framework for patent-eligible subject matter under 35 U.S.C. § 101. Even where the invention is a physical or chemical product identified through AI-driven research, questions may arise about enablement and written description under 35 U.S.C. § 112. If a generative AI system proposes thousands of candidate compounds and the inventor selects one, the specification must still enable a person of ordinary skill in the art to make and use the invention without undue experimentation. The breadth of claims relative to the disclosed embodiments may face heightened scrutiny where the inventive insight originated in a computational process that the specification does not fully describe or support.

AI-Assisted Patent Drafting

A Fundamentally Different Legal Question

Using AI to draft a patent application for a human-conceived invention presents an entirely separate set of legal issues. Here, there is no inventorship question: the inventor is the human who conceived the invention. The AI is merely a tool used by patent counsel or a patent agent to prepare the written description, claims, and/or drawings. The legal risks instead cluster around duties of disclosure, malpractice, privilege, and procedural compliance.

The Duty of Candor and Good Faith

Patent applicants and their representatives owe a duty of candor and good faith to the USPTO under 37 C.F.R. § 1.56. This duty requires full disclosure of information material to patentability and prohibits affirmative misrepresentations. When AI drafting tools generate specification language, there is a risk that the tool may introduce inaccurate technical statements, fabricate prior art references, or generate descriptions that do not faithfully represent the inventor's actual contribution. In other words, the AI is acting as an inventor by drafting invention details that are not originally tied to, described, and/or approved by a human. If such inaccuracies are not caught during review and make their way into the filed application or prosecution history, they could form the basis of an inequitable conduct defense in subsequent litigation or could estop the patentee from claim constructions that the specification's AI-generated language does not support.

In-house counsel must therefore implement rigorous review protocols for AI-generated draft text. Every factual assertion, every cited reference, and every functional description must be verified against the inventor's actual disclosure and the state of the art. The efficiency gains of AI drafting are real, but they cannot come at the cost of the accuracy and fullness of disclosure obligations that attach to patent drafting and prosecution.

Privilege and Confidentiality Concerns

A second major concern is the protection of privileged communications and confidential invention disclosures. Many AI drafting tools, particularly cloud-based large language models, process user inputs on remote servers. If an in-house attorney inputs a confidential invention disclosure into a third-party AI system without adequate contractual protections, the disclosure may jeopardize trade secret protection or waive attorney-client privilege. 

The question of whether inputting privileged information into an AI tool constitutes disclosure to a third party is no longer purely theoretical. In United States v. Heppner, 25-cr-00503-JSR (S.D.N.Y. Feb. 17, 2026), U.S. District Judge Jed S. Rakoff held that when a client voluntarily inputs confidential legal information into a publicly available generative AI tool and later shares the AI-generated materials with legal counsel, those materials are not protected by attorney-client privilege or work-product doctrine. The court reasoned that disclosure to a publicly available AI system constitutes third-party disclosure sufficient to waive privilege. 

While Heppner involved a client's use of a consumer AI tool rather than an attorney's use of an enterprise drafting platform, the decision's reasoning has significant implications for patent practitioners: if inputting confidential invention disclosures into a publicly available AI tool can destroy privilege, in-house counsel must carefully evaluate whether the AI drafting tools they use are sufficiently distinguishable from the consumer-facing platforms at issue in Heppner. The answer may depend on the specific terms of service, data handling policies, contractual restrictions on the provider's use of input data, and whether the tool is deployed in an enterprise environment with adequate confidentiality and training model protections.

In-house counsel should conduct due diligence on the data handling practices of any AI drafting tool before deploying it, negotiate enterprise agreements that restrict the provider's use of input data, and establish internal policies governing what categories of information may be input into such systems. Where the AI tool is hosted internally and no data leaves the organization's control, privilege risks are substantially mitigated, though not completely eliminated.

Professional Responsibility and Malpractice

Patent practitioners remain professionally responsible for the quality and accuracy of applications they file. The use of AI as a drafting aid does not relieve counsel of the obligation to exercise independent professional judgment. If an AI-generated claim set is overly broad and provokes an avoidable restriction requirement, or if AI-generated specification language creates prosecution history estoppel by unnecessarily narrowing the scope of the disclosure, the practitioner — not the AI tool — bears responsibility. Courts and bar associations have increasingly addressed the obligations of attorneys using AI tools in other litigation contexts, and the principles articulated in those settings apply with equal force to patent prosecution before the USPTO.

For in-house teams, this means that AI drafting tools should be treated as first-draft generators, not as substitutes for skilled patent drafting. The review process must be substantive, not perfunctory, and the attorney or agent of record must be prepared to justify every material choice of lexicography in the application, as if they had drafted it personally. Anything less may be the basis for post-grant patent challenges that the issued patent cannot survive or ethical or malpractice issues that the patent practitioner cannot survive.

USPTO Disclosure Requirements

The USPTO has not yet imposed a blanket requirement to disclose the use of AI in patent drafting, but the landscape is evolving. The Office has solicited public comment on whether such disclosure should be required, and certain other jurisdictions are considering similar measures. Proactive disclosure, even in the absence of a mandate, may reduce litigation risk by forestalling arguments that the applicant concealed material aspects of the application's preparation. Conversely, in the absence of such mandates, proactive disclosure may illicit and/or undue prosecutorial scrutiny from the Examiner around issues of written description, enablement, subject matter eligibility, and/or obviousness. In-house counsel should monitor USPTO developments in this area and be prepared to disclose the use of AI tools for patent drafting if and when such requirements are adopted and/or mandated.

Practical Examples: Life Sciences and Renewable Energy

The drafting-specific risks also manifest differently across industries. In the life sciences, consider a patent attorney using an AI tool to draft a specification for a novel monoclonal antibody therapeutic. The AI generates detailed descriptions of the antibody's complementarity-determining region (CDR) sequences, binding characteristics, and proposed mechanism of action based on the inventor's disclosure. However, the AI tool interpolates pharmacokinetic data from its training data and includes statements about half-life and bioavailability that were never part of the inventor's actual experimental findings. 

If these fabricated data points are not caught during application review and remain in the filed specification, they could create enablement problems. For example, the specification may appear to describe a level of specificity and characterization that the inventor never achieved. This scenario, in and of itself, could support an inequitable conduct argument if a challenger demonstrates that the stated properties were not based on actual experimentation. The duty of candor risk is particularly acute in life sciences patent drafting because AI tools trained on biomedical literature may seamlessly blend real data with hallucinated experimental results in ways that are difficult to detect without deep subject matter expertise. In-house counsel at pharmaceutical and biotech companies should require that every empirical assertion in an AI-generated draft be cross-referenced against the inventor's laboratory notebooks and experimental data before filing.

In the clean energy context, consider a patent agent using an AI drafting tool to prepare an application for a novel electrolyzer stack design that integrates a proprietary membrane architecture with a redesigned bipolar plate and optimized gas diffusion layer. Fuel cell and electrolyzer patents present particular AI drafting challenges because the prior art landscape is dense and highly component-specific, comprising membrane patents, flow field patents, catalyst layer patents, and system integration patents that each draw on overlapping, but distinct bodies of prior art. An AI tool may easily conflate terminology or structural descriptions across these distinct technology layers. 

For example, the AI generates claim language and a detailed written description, but in doing so it incorporates language strikingly similar to published prior art describing a related flow field configuration used in earlier PEM fuel cell stacks. This AI-generated disclosure therefore fails to distinguish the novel interaction between the inventive membrane's ion transport channels and the flow field geometry that constitutes the actual inventive contribution. The AI also describes the gas diffusion layer using parametric language drawn from prior art characterizing conventional carbon fiber substrates, obscuring the inventor's novel approach to porosity gradient engineering.

If the resulting claims are not sufficiently distinguished from the prior art at each component level, the application may face anticipation or obviousness rejections that could have been avoided with competent and complete human drafting. More critically, because electrochemical device patents often depend on the synergistic interaction between components, such as the membrane's performance under specific flow conditions, or the gas diffusion layer's behavior when paired with a particular catalyst loading, AI-generated specifications that describe each component in generic or prior-art-derived terms may fail to capture the inventive nexus that makes the integrated stack patentable. This creates prosecution history estoppel risks if the applicant is later forced to argue that the invention lies in the combination, having filed a specification that describes each independent element in terms borrowed from the prior art.

Additionally, electrolyzer and fuel cell companies often maintain layered trade secret strategies. For example, the electrochemical membrane composition may be patented while the precise fabrication parameters for the membrane electrode assembly — the hot pressing temperatures, catalyst ink formulations, ionomer-to-carbon ratios, etc. — are maintained as trade secrets. If an in-house attorney inputs the full-stack design package, including proprietary fabrication details, into a cloud-based AI drafting tool to generate a comprehensive specification, and the tool's terms of service permit use of input data for model training, the company may have inadvertently disclosed trade secrets at multiple levels of its technology stack. The damage is compounded in this industry because a competitor with access to both the published patent application and the leaked fabrication parameters would possess substantially all the information needed to replicate the inventive electrolyzer's performance. In-house counsel in the clean energy sectors, such as hydrogen, fuel cell, and electrolyzer, should establish clear protocols delineating which categories of technical information may be input into AI drafting tools. Additionally, patent drafters using AI tools should require component-specific prior art searches to be completed before AI-generated drafts are finalized to ensure that the specification clearly delineates the invention at each technology layer from the existing prior art.

Practical Guidance for In-House Counsel: 3 Tips to Takeaway

1.  Build Institutional Policies

The most effective response to challenges to both AI-assisted inventions and AI-assisted patent drafting for inventions is a comprehensive institutional policy that addresses AI use across the patent and product development lifecycle. Such a policy should distinguish clearly between AI used in the inventive process and AI used in the drafting and prosecution process, because the legal risks and mitigation strategies differ substantially.

For AI-assisted invention, the policy should require contemporaneous documentation of human contributions to both sides of the AI output, establish review procedures for inventorship determinations where AI played a role, and provide guidance on when to seek outside counsel opinions on inventorship. For AI-assisted drafting, the policy should specify approved tools, define acceptable use cases, mandate substantive attorney review, address data security, privacy, and privilege concerns, and establish protocols for monitoring regulatory developments.

2. Training and Awareness

Technical staff who use AI tools in research and development may not appreciate that their use of such tools has legal implications for patent protection. In-house counsel should update and provide regular training to inventors on the laboratory documentation practices that will be needed to support inventorship determinations. Similarly, patent professionals within the legal department who adopt AI drafting tools should be trained on the specific risks of hallucination, inaccuracy, and privilege waiver, and should understand that the standard of care and ethical responsibilities of a USPTO registered patent practitioner has not been relaxed simply because an AI tool assisted in the drafting.

3. Risk Allocation and Vendor Management

Where AI tools are procured from third-party vendors, in-house counsel should negotiate and/or ensure contractual protections addressing data security, confidentiality, intellectual property ownership in the tool's outputs, indemnification for errors, and compliance with applicable legal and ethical standards. The terms of service for consumer-grade AI tools are almost certainly insufficient for patent-sensitive work, and enterprise agreements that offer for protections for client confidentiality and privacy purposes should be negotiated before deployment or utility.

Conclusion

The intersection of artificial intelligence and patent law presents in-house counsel with two related but legally distinct challenges. Patenting AI-assisted inventions raises fundamental questions of inventorship, patentable subject matter eligibilty, and claim validity. These questions address whether a patent can be procured at all. AI-assisted patent drafting, by contrast, raises questions of professional responsibility, candor, privilege, and procedural compliance; these questions address how a patent is obtained and whether it will withstand prosecution or post-grant legal scrutiny. Both demand proactive institutional responses, but the legal doctrines at stake, the risks of failure, and the mitigation strategies are materially different. In-house counsel who conflate the two do so at their peril. Those who address each with tailored policies, regular personnel training, third-party vendor management, and informed judgment will be well positioned to fully harness AI's benefits for inventive concept development and patent protection while managing its legal and ethical risks.