Artificial Intelligence (AI) is one of the fastest-growing fields in technology, and securing patents for AI innovations has become crucial for companies and inventors. However, AI patents face significantly higher rejection rates than traditional patents. Many AI patent applications are denied due to legal, technical, or procedural issues.
1. Around 50% to 60% of AI-related patent applications face initial rejection by the USPTO
The United States Patent and Trademark Office (USPTO) has strict criteria for AI patents. More than half of AI patent applications are initially rejected due to various reasons, including lack of novelty, obviousness, or unclear claims.
This does not mean all these patents are doomed. Many applicants revise and resubmit their applications to address the examiner’s concerns. The key is anticipating potential objections before filing.
This can be done by conducting thorough prior art searches, drafting detailed claims, and ensuring that the patent application clearly explains the technology and its uniqueness.
Actionable Tip:
Before filing an AI patent, analyze previously granted patents in your field. Look at what was accepted and rejected. This will give you a better understanding of how to structure your application.
2. 35% to 45% of AI patent rejections are due to lack of inventive step or obviousness
Patent examiners often reject AI patents by arguing that the invention is an obvious improvement on existing technology. If the examiner believes that a person skilled in AI could have easily come up with the idea, the patent will be denied.
To avoid this, AI patent applications must show that the invention is not just an incremental improvement but a significant breakthrough. Providing technical proof of unexpected advantages, such as increased accuracy, reduced processing time, or novel training methods, can help convince the examiner.
Actionable Tip:
When drafting your AI patent, emphasize what makes your invention unique compared to existing AI models. Describe technical challenges you overcame and how your approach differs significantly from prior methods.
3. 20% to 30% of AI patent denials are based on insufficient disclosure or lack of clarity
Patent applications must provide enough detail for someone skilled in the field to replicate the invention. Many AI patents fail because they do not fully disclose how the model works, how it was trained, or the data sources used.
AI patent applications must explain algorithms, training processes, and implementation methods in a clear and detailed manner. Simply stating that an invention “uses AI to improve results” is not enough. The application must describe the technical features that make the AI system novel and useful.
Actionable Tip:
Include flowcharts, code snippets, or system architecture diagrams in your application to clarify how the AI works. The more concrete your explanation, the better your chances of approval.
4. The rejection rate for AI patents is 10-15% higher than traditional software patents
AI patent applications face a higher level of scrutiny than general software patents. This is because AI is often viewed as an extension of mathematical models, which are considered abstract ideas under patent law.
Additionally, AI technologies evolve rapidly, making it harder to prove that an invention is truly novel. Examiners tend to be cautious when granting AI patents to avoid issuing overly broad or vague patents.
Actionable Tip:
Make sure your AI patent application highlights a specific, real-world technical improvement rather than just an algorithm. Patents that show tangible, practical applications tend to fare better than those that describe AI in abstract terms.
5. 25% to 40% of AI patent applications are rejected due to ineligible subject matter under 35 U.S.C. §101
35 U.S.C. §101 governs patent-eligible subject matter in the U.S. Many AI patents are rejected because they are classified as abstract ideas without sufficient technical implementation.
To overcome this, patent applications should demonstrate how the AI improves a real-world process or solves a concrete problem. Abstract ideas or generic AI applications will likely be denied under Alice/Mayo legal precedent.
Actionable Tip:
Use specific examples of how your AI is applied to a particular industry (e.g., autonomous driving, medical diagnostics, fraud detection). Showing real-world benefits can help overcome §101 objections.
6. AI patents related to machine learning models face a rejection rate of around 55%
Machine learning patents are among the most difficult to secure. This is because many ML models rely on well-known statistical techniques, which examiners often view as obvious or unpatentable.
To increase the chances of approval, the patent application must highlight unique data preprocessing steps, training methodologies, or novel architectures.
Actionable Tip:
Clearly define how your machine learning model is different from existing models. Providing performance benchmarks and experimental results can help demonstrate its uniqueness.
7. 40% of AI patent rejections are due to lack of novelty
Patent examiners will reject an application if they find prior art (existing patents, papers, or products) that describe the same invention.
To improve the chances of success, conduct a thorough prior art search before filing. This can help identify potential conflicts and allow you to refine your claims accordingly.
Actionable Tip:
Use patent search tools like Google Patents, USPTO Patent Search, or EPO’s Espacenet to review existing patents before drafting your application.
8. Around 70% of initially rejected AI patents eventually get granted after revisions
If your AI patent is rejected, don’t panic. Many applications eventually get approved after modifications.
Patent examiners provide detailed feedback on why a patent was rejected. Responding with well-argued amendments and clarifications can turn an initial rejection into a granted patent.
Actionable Tip:
Work with an experienced patent attorney to draft a strong response to office actions. Examiners appreciate well-structured arguments that directly address their concerns.
9. 30% of AI patent applications are abandoned after a first rejection
Many applicants give up after receiving an initial rejection, assuming that further pursuit is futile. However, as seen above, most AI patents that are eventually granted face at least one rejection.
Actionable Tip:
If you receive a rejection, take the time to understand the examiner’s reasoning and revise your application accordingly. Persistence often pays off.
10. AI patents related to deep learning have a rejection rate of 45-50%
Deep learning patents face difficulties because many improvements in neural networks are seen as incremental and predictable.
To succeed, applicants must show how their deep learning model achieves unexpected results or overcomes existing limitations in a novel way.
Actionable Tip:
If your patent involves deep learning, focus on unique data handling techniques, custom loss functions, or hardware optimizations that distinguish it from existing work.
11. AI patent applications mentioning neural networks have an average rejection rate of 50%
Neural networks are one of the most widely used AI techniques, but getting patents approved in this area is difficult. Since neural networks are based on mathematical computations, patent examiners often view them as abstract ideas rather than concrete inventions.
To get past this issue, applicants must describe how their neural network is applied in a novel and technical way. Simply saying that a model is more accurate or performs better is not enough.
Actionable Tip:
Describe specific innovations in the neural network’s architecture, training process, or real-world applications. Avoid broad claims and focus on technical implementations.
12. The rejection rate for AI patents in the healthcare sector is about 55%
Healthcare AI patents are especially challenging because they often involve medical diagnostics or data processing, which can fall under abstract ideas.
Additionally, medical AI applications must meet higher regulatory standards, and patent examiners scrutinize these applications more strictly.
Actionable Tip:
If your AI patent is in the healthcare sector, ensure it demonstrates a tangible improvement in diagnosis, treatment, or patient outcomes. Providing clinical trial results or real-world implementation data can help.
13. AI patents in autonomous driving face a rejection rate of approximately 50-60%
Autonomous vehicle technology is a hot topic in AI, but patenting innovations in this field is difficult. Many self-driving AI patents are rejected due to obviousness or lack of specificity.
Since autonomous driving technology involves multiple sensors, AI models, and decision-making algorithms, patent applications must clearly define what aspect of the technology is novel.
Actionable Tip:
If you’re filing an AI patent for autonomous driving, focus on specific improvements in sensor fusion, decision-making algorithms, or safety mechanisms rather than general claims about self-driving capabilities.
14. The European Patent Office (EPO) rejects around 40% of AI patent applications on first submission
The EPO has different patent laws compared to the USPTO. European patent law is stricter on technical contribution, meaning AI patents must show clear technological advancements beyond just software improvements.
Many AI applications in Europe are rejected because they fail to demonstrate how the invention solves a technical problem in a new way.
Actionable Tip:
If filing an AI patent in Europe, emphasize how the AI system improves hardware efficiency, optimizes system performance, or solves a technical challenge. Abstract AI models are more likely to be rejected.
15. The China National Intellectual Property Administration (CNIPA) rejects AI patents at a rate of 30-40%
China has become one of the leading countries for AI patents, but the CNIPA still enforces strict standards. AI patents in China are more likely to be accepted than in the US or Europe, but many are still rejected due to unclear claims or lack of inventive step.
Actionable Tip:
When applying for an AI patent in China, ensure that your claims are detailed and well-supported with technical evidence. Clear descriptions and examples of real-world applications can help.
16. The average rejection rate for AI patents in financial technology is 50%
AI is transforming finance, from fraud detection to algorithmic trading. However, many AI fintech patents get rejected because they are classified as business method patents, which are harder to patent.
Actionable Tip:
If filing an AI patent in fintech, avoid describing it as a business method. Instead, focus on the technical aspects of the AI system, such as improved security, faster data processing, or novel risk-assessment techniques.
17. AI patents related to natural language processing (NLP) have a rejection rate of 45%
NLP patents face rejection when they do not sufficiently differentiate themselves from existing models. Since many NLP techniques rely on well-established language models, patent examiners often argue that they lack novelty.
Actionable Tip:
Highlight unique aspects of your NLP model, such as new training methods, novel embeddings, or specific domain adaptations that improve accuracy in a particular field.
18. USPTO rejects over 35% of AI patents for failing the Alice/Mayo test on subject matter eligibility
The Alice/Mayo framework is used to determine whether an invention is too abstract to be patented. AI patents frequently fail this test, especially when they describe generic algorithms without technical implementation details.
Actionable Tip:
Describe how your AI interacts with hardware or improves computational efficiency. Showing a real-world application beyond just mathematical models helps pass the Alice/Mayo test.
19. AI patents classified as business method patents face a 60%+ rejection rate
Patents related to business processes using AI are among the hardest to secure. These applications are often rejected for not being technical enough.
Actionable Tip:
Instead of framing your AI patent as a business process, focus on how it improves computing systems, security, or automation in a technical manner.
20. AI-related software patents have a 55-65% rejection rate due to being deemed abstract ideas
Many AI patents get denied because they are categorized as abstract software rather than a concrete invention.
Actionable Tip:
When drafting your patent, focus on technical improvements, such as reducing computational costs, enhancing processing speeds, or optimizing hardware usage.
21. Over 65% of AI patent applications require at least one amendment to be granted
AI patents rarely get approved on the first try. Most require at least one amendment to address examiner concerns.
Actionable Tip:
Be prepared for office actions and respond strategically. A well-argued response with revised claims and supporting technical details can turn a rejection into an approval.
22. Less than 15% of AI patents get granted without any office action or rejection
It is extremely rare for an AI patent to be granted without at least one rejection or office action.
Actionable Tip:
Expect feedback from the examiner and plan for multiple rounds of revisions. Working with a patent attorney can make this process smoother.
23. USPTO’s AI patent rejection rate has increased by 10-15% since 2015
As AI technology has become more mainstream, patent examiners have become more strict in granting patents.
Actionable Tip:
Keep up with recent patent decisions and adapt your filing strategy accordingly. Look at trends in successful AI patent applications.
24. 50%+ of AI patent applications require multiple office actions before approval
Most AI patents do not get approved in a single response. It often takes multiple rounds of revisions.
Actionable Tip:
Carefully review examiner feedback and revise your claims strategically to increase your chances of approval.
25. The AI patent rejection rate in biotechnology and pharma is about 50%
AI patents in biotech often get rejected due to lack of technical details on how AI improves drug discovery or diagnostics.
Actionable Tip:
If filing in biotech, include experimental data, performance benchmarks, and real-world testing results to strengthen your application.
26. AI patent applications that include explainability techniques have a slightly lower rejection rate of 40%
One of the biggest challenges with AI patents is the black-box nature of many AI models. When patent examiners review AI applications, they often struggle to determine how the AI system actually works.
However, AI patents that include explainability techniques (XAI)—such as methods for interpreting model decisions, feature importance, or transparency mechanisms—tend to have a lower rejection rate. This is because explainable AI systems provide examiners with concrete, understandable details, making it easier to assess novelty and eligibility.
Actionable Tip:
If your AI system includes explainability features, highlight them in your application. Describe how your model provides insights into its decision-making process and why this is an improvement over traditional AI systems.
27. Around 30% of AI patents get rejected because of poor claim drafting
One of the most common and preventable reasons for AI patent rejection is poorly written claims. If your patent claims are too broad, vague, or abstract, your application is likely to be denied.
Many inventors focus on what their AI does instead of how it does it. Examiners reject applications that do not clearly define the technical scope and boundaries of the invention.
Actionable Tip:
Ensure that your patent claims are specific, detailed, and focused on the technical aspects of your AI system. Work with a patent attorney to refine your claims and avoid unnecessary rejections.
28. AI patents using black-box models see a higher rejection rate due to lack of transparency
AI models that operate as black boxes (i.e., their internal decision-making process is not interpretable) have a harder time getting patented. Patent examiners prefer AI innovations that show clear, understandable advancements.
If an AI patent application fails to explain how the model processes data, why it makes certain predictions, or what makes it unique, it is more likely to be denied.
Actionable Tip:
If your AI model is a black box, provide supporting technical explanations—such as model architecture, training process, or feature engineering methods—to give examiners a clearer understanding of your innovation.
29. The AI patent rejection rate in the robotics field is around 45%
AI patents related to robotics and automation face a moderate rejection rate. While AI-driven robots are a hot area for innovation, patents in this field often get rejected due to obviousness, lack of technical details, or failure to describe novel control mechanisms.
To improve your chances of securing a robotics AI patent, focus on the AI-driven functionalities of the robot, not just the hardware. The patent should describe how AI improves robotic decision-making, movement efficiency, or adaptability in ways that traditional control systems cannot.
Actionable Tip:
Clearly describe how AI enhances the robot’s functionality, efficiency, or adaptability compared to non-AI-driven robots. If your robot uses AI for real-time adaptation, reinforcement learning, or human interaction, emphasize these aspects in your patent application.
30. AI patents with hardware integration have a lower rejection rate of about 30-35% compared to purely software-based AI patents
AI patents that combine software and hardware components tend to have a lower rejection rate than purely software-based AI patents. This is because hardware integration makes the invention more tangible and less likely to be classified as an abstract idea.
For example, AI innovations that involve specialized AI chips, edge computing devices, or embedded AI systems often have a smoother path to approval. Examiners see these patents as clear technological advancements rather than just software running on general-purpose computers.
Actionable Tip:
If your AI invention involves specialized hardware, highlight this in your application. Show how the AI system optimizes hardware performance, enables real-time processing, or reduces energy consumption to strengthen your case for patent approval.
wrapping it up
Securing an AI patent is no easy feat, but understanding the common rejection pitfalls and proactively addressing them can significantly improve your chances of success. The data speaks for itself—AI patents face higher rejection rates than traditional software patents, but most of these rejections are avoidable with proper preparation.
Whether it’s overcoming subject matter eligibility issues, proving novelty, or refining your patent claims, the key to success lies in anticipating examiner objections and crafting a strong, well-documented application.
