Can You Patent AI Inventions in Israel (2026)? Inventorship, Ownership & Filing Strategy for Startups

AI patentability Israel is now one of the most pressing questions facing founders, in-house counsel and inventors building products on machine learning and generative systems. The short answer in 2026 is encouraging: AI-assisted and AI-generated inventions can be patented in Israel provided they meet the statutory requirements, but the way you name inventors, structure ownership and disclose your technology will make or break the application. This guide explains how the Israel Patent Office (ILPO) and the Patents Law, 5727-1967 treat AI-related inventions, where Israeli practice aligns with and diverges from the EPO and USPTO, and the exact filing steps startups should follow.

If you are moving fast toward a first filing, the practical checklists below are designed to be used immediately.

Can AI-generated or AI-assisted inventions be patented in Israel?

The question of AI patentability in Israel starts with a familiar test, not a new one. Israeli patent law does not have a separate regime for artificial intelligence. Instead, an invention that involves AI is assessed under the same substantive conditions that apply to any other invention under the Patents Law, 5727-1967. What matters is whether there is a concrete, reproducible technical contribution, not whether a human or a machine did the heavy lifting during conception.

In practice, this means an AI-related invention is judged on its technical merits: the problem it solves, the way it solves it, and the evidence that the solution works. A machine learning model used to improve the resolution of medical images, a trained system that optimises a manufacturing process, or an algorithm that enables a device to operate more efficiently can all qualify, so long as the claims describe a technical solution to a technical problem and the application enables a skilled person to reproduce it.

Legal standards for patentability in Israel (novelty, inventive step, industrial applicability)

Under the Patents Law, a patentable invention must satisfy several cumulative conditions. It must be new (novel), meaning it is not part of the state of the art anywhere in the world before the filing or priority date. It must involve an inventive step, a non-obvious advance over what a person skilled in the relevant field would readily arrive at. It must be capable of industrial application, meaning it can be made or used in some kind of industry. And it must fall within patentable subject matter rather than an excluded category.

Under the Patents Law, discoveries, scientific theories, mathematical formulae, game rules, mental processes and computer programs “as such” are generally not patentable inventions in themselves; patentability turns on whether a concrete technical process or product is claimed.

For AI inventions, the inventive-step and subject-matter requirements tend to be the decisive battlegrounds. A bare mathematical model or an abstract algorithm, stated at a high level of generality, risks being treated as non-technical. The winning formulation ties the AI to a concrete technical effect: faster processing, reduced memory use, improved sensor accuracy, better control of a physical system, or a measurable improvement in a downstream technical output. When the claim is anchored to that technical effect and the specification proves it, AI patentability in Israel becomes a realistic prospect rather than a gamble.

AI-assisted vs AI-generated inventions, practical examples

It helps to distinguish two scenarios. In an AI-assisted invention, humans define the problem, design the architecture, select and curate the data, tune the system and interpret the results, the AI is a tool. Most real-world AI inventions fall here. Consider a team that designs a neural network to detect anomalies in industrial pumps; the humans conceived the solution and the network is the implementation. In a fully AI-generated invention, the system itself produces an output that the humans did not specifically foresee, for example, a generative model that proposes a novel chemical synthesis route or an unexpected mechanical geometry.

The crucial point for AI patentability in Israel is that this distinction affects inventorship and disclosure, not the threshold patentability of the underlying technical advance. A novel, non-obvious, industrially applicable solution does not become unpatentable merely because a model surfaced it, but you will need a human inventor to name, and you will need to describe the technical contribution with sufficient clarity.

Common examiner objections for AI-related inventions and how to address them

Examiners frequently raise three objections against AI filings: that the claim is directed to non-patentable abstract or mathematical subject matter; that the inventive step is not demonstrated; and that the disclosure is insufficient to enable reproduction. Each has a practical answer. Recast abstract claim language around a concrete technical implementation and effect. Support inventive step with comparative experimental data. And expand the specification with enough architectural and training detail that a skilled reader could rebuild and run the system. These moves mirror the approach examiners at the EPO and USPTO expect for computer-implemented inventions, which gives Israeli applications a well-trodden path to follow.

Inventorship in Israel: can an AI be named as the inventor?

This is the most searched question on AI inventorship in Israel, and the current answer is clear and consistent with the global mainstream: an AI system cannot be named as the inventor on an Israeli patent application. Inventorship is treated as a human attribute. The practical consequence is that every application, including one where a model did substantial conceptual work, must identify one or more natural persons as inventors.

Statutory language and case law basics

The Patents Law frames the inventor as a person and ties rights such as the right to be named and the right to apply to natural persons and their legal successors. There is no provision contemplating a machine as an inventor, and Israel has limited published decisions directly addressing AI inventorship, so the statutory language and administrative practice are the primary guides. The position is reinforced by the high-profile DABUS litigation abroad, in which applicants sought to name an AI system as inventor. The EPO and the UK courts refused those applications on the basis that an inventor must be a natural person, and the USPTO adopted a comparable stance.

These outcomes signal international alignment, and Israeli practice sits firmly within that consensus.

Practical drafting and declaration approach, naming inventors and statements of contribution

Because AI inventorship in Israel must resolve to human beings, the task is to identify the people whose intellectual contribution produced the claimed invention. Ask who framed the technical problem, who designed or selected the model architecture, who curated and engineered the training data, who set the objectives and constraints, and who recognised and verified the inventive output. Those individuals are candidate inventors. Document their contributions contemporaneously, in lab notebooks, design records, commit histories and internal memos, so that inventorship can be defended if questioned.

Name all genuine joint inventors; over-naming or under-naming can create later validity and ownership problems. Where a model generated an unexpected result, the humans who configured the system and recognised the significance of the output are the proper inventors. A short internal statement of contribution for each named person, prepared at filing, is inexpensive insurance that supports the inventor designation throughout prosecution and any later dispute.

If disputes arise: evidence and proceedings

Inventorship and ownership disputes turn on evidence of who actually contributed. Contemporaneous records, version control logs, dated experiment data and signed agreements are decisive. Startups that maintain a disciplined paper trail from day one are far better placed if a co-founder, former employee or collaborator later asserts a claim.

Ownership and rights, who owns AI inventions in Israel?

Naming an inventor is not the same as owning the patent. Ownership of AI inventions in Israel is governed by a combination of statutory employee-invention rules and contract. For startups, getting the contractual architecture right is often more valuable than any single filing decision, because defective ownership can undermine investment, licensing and exit.

Patents Law rules on employee inventions and assignment

Under the Patents Law, inventions made by an employee in consequence of their service and during the period of service, so-called service inventions, generally belong to the employer, subject to the statute and any agreement. Note that compensation for service inventions is a distinct matter that can be contested before the Compensation and Royalties Committee, and parties frequently address it expressly by contract. This default is powerful, but it should never be relied upon alone. Clear written assignment clauses, confirmation-of-assignment provisions and acknowledgements that the employer owns inventions arising from employment should be built into every employment agreement.

Founders must ensure their own pre-incorporation work and the work of early technical hires is properly assigned to the company; unassigned founder IP is one of the most common and damaging problems uncovered in due diligence.

Developer vs user vs employer, four practical scenarios

• Employee developer. An in-house engineer builds and trains the model. The invention is typically a service invention owned by the employer, but a written assignment clause should confirm this and extend it to improvements and related IP.
• Independent contractor or freelancer. Default ownership does not automatically pass to the hiring company. A written agreement must expressly assign all patent rights in the deliverables and any inventions conceived during the engagement, including background IP needed to practise the invention.
• User of a third-party AI platform. Where the startup uses an external model or API to generate an inventive output, the platform’s terms of service may assert rights over outputs. Review and, where possible, negotiate the licence so that ownership of inventive outputs and the right to file patents rests with the startup.
• Collaborative or university partnership. Joint development typically produces joint ownership unless the agreement states otherwise. Spell out ownership, filing responsibility, cost-sharing and licensing before any joint work begins.

Licensing models, data use and background IP

AI raises a distinction that classic patents rarely did: the difference between the model and its outputs. A startup may own the invention embodied in a patent but still depend on third-party models, pre-trained weights, open-source libraries or licensed datasets to build and operate it. Audit every such dependency for ownership of AI inventions in Israel and downstream freedom to operate. Confirm that training data was lawfully obtained and that its licence permits commercial use and patenting of derived inventions. Where you license in a model or dataset, secure the rights you need; where you license out, define the field of use, carve out background IP and address derived improvements.

These data-rights and background-IP questions are frequently the difference between a clean deal and a stalled one.

What to disclose to the Israel Patent Office: best practices for AI patentability in Israel

Disclosure is where many AI applications quietly fail. The Patents Law requires that the specification describe the invention in a manner sufficient for a person skilled in the art to carry it out. For software and machine learning inventions, high-level descriptions that read like marketing copy will not satisfy this requirement. Strong AI patentability in Israel depends on a specification that enables reproduction and substantiates the technical advance.

Enablement and sufficiency: what counts for software and ML inventions

Aim to describe enough that a skilled reader could rebuild and operate the system. This typically includes the model architecture and its key components, the nature and characteristics of the training data (the type, scale, labelling and relevant pre-processing), the training regime and loss objectives, and the critical hyperparameters that make the invention work. You do not always need to disclose every line of production code or the entire dataset, but you must describe representative examples, inputs and outputs, and the steps a practitioner would take to achieve the claimed effect. Vague references to “a neural network” trained on “a dataset” to achieve “better results” invite insufficiency objections.

Balancing disclosure vs trade secrets, practical tips

Patents demand disclosure; trade secrets demand secrecy. The two strategies are in tension, and part of smart filing is deciding what to patent and what to keep confidential. A practical approach is to patent the claimed technical architecture and effect that you need enforceable exclusivity over, while keeping genuinely proprietary assets, such as a unique production dataset, fine-tuning recipes or operational tuning that are hard to reverse-engineer, as trade secrets outside the specification. Describe data by its functional characteristics and representative samples rather than publishing proprietary corpora wholesale. Make these decisions deliberately before filing, because once an application publishes, the disclosed material is public.

Evidence to support inventive step

Examiners and courts respond to data. Build the inventive-step case into the specification with comparative results: benchmarks against prior approaches, ablation studies showing which components drive the improvement, and quantified gains in accuracy, speed, efficiency or another technical metric. This experimental evidence rebuts obviousness arguments and demonstrates that the AI contribution produces a real technical effect rather than a trivial or predictable gain. For many AI filings, the comparative data section is the single most persuasive part of the application.

Filing and prosecution strategy for startups (step-by-step)

With the substantive rules in place, here is a practical, sequenced filing plan tailored to startups pursuing AI patentability in Israel while preserving international options and conserving cash.

Pre-filing checklist and evidence collection

• Capture the invention early with a written disclosure describing the problem, the technical solution, the AI components and the measurable improvement.
• Identify and document the human inventors and their contributions.
• Confirm the chain of ownership: founder assignments, employee clauses, contractor assignments and platform/dataset licences.
• Gather experimental evidence, benchmarks, ablation data and representative inputs and outputs.
• Run a prior-art search covering patents and non-patent literature, including machine learning publications and pre-prints, since the state of the art in AI moves quickly.
• Decide what to patent and what to keep as a trade secret before anything is published or disclosed to third parties without confidentiality.

Claim drafting tips for AI inventions + example claim snippets

Claims should anchor the AI to a concrete technical implementation and effect. Avoid framing a claim as a bare mathematical method or a result divorced from technical means. Instead, recite the system, the data flow and the technical outcome. Useful structural approaches include method claims tied to a technical process, system or apparatus claims reciting the components, and computer-readable medium claims. For example, rather than claiming “a method of predicting failures using a neural network,” claim “a method of controlling an industrial pump, comprising receiving sensor data, processing the sensor data with a trained model having [defined architecture] to generate a failure-risk signal, and adjusting an operating parameter of the pump in response to the signal to reduce downtime.”

Draft a layered claim set: independent claims capturing the broad technical concept, and dependent claims capturing architecture specifics, data-processing steps, training features and alternative implementations. Avoid red-flag phrases that signal abstraction, such as claims reciting only “an algorithm for” a result, “a mathematical model that” outputs a value, or purely mental-step language. Each independent claim should map to a technical effect that the specification demonstrates with data.

Procedural strategies, PCT, priority claims, accelerated prosecution at ILPO

Most startups should file a first application to establish a priority date, then use the 12-month priority window to file internationally. A PCT application filed within 12 months of the priority date preserves the option to enter national or regional phases in major markets, including the US and Europe, while deferring the larger cost of multiple filings. Because the EPO and USPTO apply their own software and AI examination practices, align your disclosure and claim strategy to those standards from the outset so a single well-built specification travels well across jurisdictions.

Where speed matters, for example, to support fundraising or to deter a fast-moving competitor, the ILPO offers accelerated examination options, and Israel participates in Patent Prosecution Highway (PPH) arrangements with a number of offices, which can bring an application to grant more quickly than the standard track. Discuss eligibility and timing with your patent attorney early, because the evidence and claim quality needed to survive faster examination are the same, only on a compressed timeline. Responding to office actions for AI inventions is often most effective when supported by expert declarations and fresh comparative data that address the examiner’s specific objection rather than generic argument.

Comparative table, Israel vs EPO vs USPTO on AI inventorship and patentability

The practical takeaway is reassuring: the three systems are closely aligned on the key questions. Because Israel, the EPO and the USPTO all require a human inventor and all reward applications that tie AI to a demonstrated technical effect, a single, well-constructed Israeli application built to these shared standards can serve as a strong foundation for international filing.

Practical examples and short case studies for startups

The following anonymised, hypothetical scenarios illustrate how the principles above come together.

• Generative model proposing a chemical synthesis route. A biotech startup uses a generative model to suggest a novel, more efficient synthesis pathway. Patentability: the novel pathway and its technical advantage (higher yield, fewer steps) can be patentable if non-obvious and reproducible. Inventorship: name the scientists who designed the model, curated the chemical data and verified the route. Ownership: ensure employee and contractor assignments cover both the model and the discovered process. Claim tip: claim the synthesis method and its technical parameters, not the model as an abstract tool.
• Software optimising medical imaging. A health-tech company trains a model that improves the resolution and diagnostic quality of imaging output. Patentability: strong, because the technical effect, better image quality for a defined technical purpose, is concrete and measurable. Inventorship: the engineers who designed the architecture and training regime. Ownership: confirm dataset licences permit commercial use and patenting. Claim tip: tie the claim to the imaging apparatus or process and the quantified improvement.
• Model-generated mechanical design. A hardware startup uses generative design to produce a lighter, stronger component geometry. Patentability: the novel structure and its functional advantage can be protected. Inventorship: name the engineers who set constraints, objectives and verified the design. Ownership: check the generative-design platform’s terms on output rights. Claim tip: claim the component by its structural features and performance, supported by comparative test data.

Next steps for founders and counsel (checklist and templates)

To move from strategy to action on AI patentability in Israel, founders should take the following immediate steps:

• Create a written invention disclosure for each candidate invention and store it securely with a date.
• Audit and fix the ownership chain: founder assignments, employee IP clauses, contractor assignments and platform and dataset licences.
• Identify and document human inventors and their contributions.
• Collect comparative experimental evidence to support inventive step.
• Decide, invention by invention, what to patent and what to keep as a trade secret.
• Engage a patent attorney early to assess patentability, draft robust claims and map an international filing plan.

Before a first meeting with counsel, prepare the invention disclosure, your experimental data, a list of inventors, copies of relevant contracts and licences, and a short note on commercial priorities and target markets. This preparation shortens the path to a strong first filing. For deeper dives, see our planned guides on how to draft claims for AI-assisted inventions in Israel, ownership, licensing and data-use agreements for AI models in Israel, and accelerated prosecution for AI inventions at the Israel Patent Office.

Conclusion and resources

AI patentability in Israel is well within reach for startups that approach it methodically. The substantive test is the familiar one, novelty, inventive step and industrial application, and AI-related inventions succeed when they are framed around a concrete technical effect, supported by comparative evidence and described in enough detail to enable reproduction. Remember the two defining rules for 2026: a human must be named as inventor, and ownership depends on disciplined contracts layered on top of the statutory employee-invention regime. Fix your ownership chain early, document your inventors and evidence, decide deliberately what to patent and what to keep secret, and build a single strong specification that travels across Israel, the EPO and the USPTO. For tailored help, consult an Intellectual Property lawyer in Israel, hire a specialist and review the author profile for Jeremy Ben David, Israel Patent Attorney. Further cluster guides, including AI patentability in other jurisdictions and ownership, licensing and data-use agreements for AI models in Israel, expand on the strategies above.

Sources

1. Israel Patent Office (Ministry of Justice)
2. Patents Law, 5727-1967 (Israel), WIPO Lex
3. European Patent Office, Guidelines for Examination
4. United States Patent and Trademark Office, Artificial Intelligence initiative
5. WIPO, Artificial Intelligence and Intellectual Property
6. The Israel Bar Association