Artificial Intelligence (AI) technologies are rapidly being adopted across a wide range of industries—from manufacturing and healthcare to energy and agriculture. While the accelerated growth of AI creates new business opportunities, it also highlights the importance of developing a well-informed global patent strategy. Major intellectual property offices around the world, such as the European Patent Office (EPO), the Japan Patent Office (JPO), the Korean Intellectual Property Office (KIPO), the China National Intellectual Property Administration (CNIPA), and the United States Patent and Trademark Office (USPTO), each have their own legal frameworks and precedents. Understanding these distinct systems is crucial for anyone seeking to obtain broad and enforceable AI-related patent rights.
Below is an overview of the key considerations for AI patent applications, along with the major guidelines and case examples from these “IP5” offices.
1. Key Considerations for AI Patent Applications
1.1. Characteristics of AI Patents
- Software-Centric Inventions: AI inventions frequently rely on machine learning (ML), deep learning (DL), or neural networks—often perceived as abstract mathematical or statistical methods.
- Computer-Implemented Invention (CII): Many patent offices classify AI applications as CIIs. This classification focuses on whether the invention provides a technical contribution rather than simply presenting an abstract idea.
1.2. Patent Eligibility
- Technical Problem-Solving: An AI algorithm on its own may be considered too abstract for patentability. Applicants must show how their AI invention addresses a specific technical or industrial issue (e.g., automated defect detection in manufacturing, improving production efficiency).
- Industrial Applicability: Demonstrate how the AI solution is practically implemented. A clear real-world context helps prove that the invention goes beyond a mere concept or theory.
1.3. Clarity and Enablement (Written Description)
- Detailed Specification: Include information on model architecture, data types, algorithm design, training methods, and the hardware environment. Such detail helps ensure the invention can be understood and reproduced by a skilled practitioner.
- Risk of Rejection: Insufficiently describing how the AI approach is implemented can lead to rejections for lack of enablement or clarity.
1.4. Novelty and Inventive Step
- Differentiate from Prior Art: Show how your AI-related invention differs from known methods. Merely modifying existing algorithms in a minor way may not be enough to prove inventive step.
- Emphasize Significant Improvements: Focus on tangible enhancements in performance—faster processing speeds, higher accuracy, or solving a problem that was previously intractable using conventional methods.
2. Approaches to Major Patent Offices (IP5)
EPO
JPO
KIPO
CNIPA
USPTO
3. AI Patent Examination Guidelines & Key References
Below is a list of important documents and focal points from each office (presented without a table format):
European Patent Office (EPO)
- Guidelines for Examination in the EPO (most recent edition)
- Case Law of the Boards of Appeal
- Focus: Must show a “technical character” and non-obvious technical effect.
Japan Patent Office (JPO)
- Examination Handbook for Patent and Utility Model (Annex B: AI/IoT)
- AI/IoT Manga Edition
- Focus: Clear problem-solution structure; well-defined technical effect.
Korean Intellectual Property Office (KIPO)
- Patent Examination Guidelines (2021)
- Examination Practice Guide
- Focus: Detailed disclosure, especially regarding AI-specific models, algorithms, and use cases.
China National Intellectual Property Administration (CNIPA)
- Guidelines for Patent Examination (2010)
- Amended Guidelines for Patent Examination (No. 343)
- Focus: Technical feature clarity, industrial applicability, and robust disclosure.
United States Patent and Trademark Office (USPTO)
- Manual of Patent Examining Procedure (MPEP)
- AI Policy Report (2020)
- Focus: Avoidance of “abstract ideas” under 35 U.S.C. § 101; show a genuine technical improvement.
4. Case Studies: From Eligibility to Inventive Step
4.1. Patent Eligibility (EPO and USPTO Examples)
- Eligible Example: An AI-based image recognition system that improves diagnostic accuracy in medical imaging—claims detail a specific neural network architecture and a novel training procedure, resulting in measurable performance gains.
- Ineligible Example: Merely stating a mathematical formula for data processing without explaining how it addresses a specific technical issue.
4.2. Written Description and Enablement (EPO, JPO, KIPO)
- Positive Outcome: Patents that provide diagrams, flowcharts, and parameter details (for example, model layers, data pre-processing, or specialized training steps) meet clarity and enablement requirements.
- Negative Outcome: Applications that simply say, “Use a neural network with standard training” without specifying unique algorithmic aspects or technical benefits may be rejected for insufficient disclosure.
4.3. Novelty and Inventive Step (EPO, JPO, KIPO)
- Granted Patent: Demonstrates a breakthrough in accuracy or a novel data-processing approach that addresses a challenge unsolved by existing methods.
- Rejected Patent: Minor parameter adjustments to standard machine learning models or re-combinations of known methods are typically considered obvious.
5. Strategic Insights and Future Directions
- Emphasize Technical Problem-Solving: Clearly show how the AI invention overcomes a specific industrial or technical challenge.
- Provide Thorough and Specific Disclosures: Specify model architecture, data preprocessing steps, training methodologies, and system design.
- Demonstrate Differentiation: Highlight measurable improvements—such as accuracy or efficiency gains—and illustrate how the approach solves a long-standing problem.
- Stay Informed: Follow updates to examination guidelines from the IP5 offices. Changes in policy or case law can significantly affect patent prosecution strategies.
As AI technologies advance, patent offices will continue refining their examination criteria. By working with seasoned patent professionals and staying up-to-date on evolving guidelines, applicants can secure valuable AI patents and maintain a competitive edge.
Need Assistance with Your AI Patent Strategy?
Navigating AI patent applications across multiple jurisdictions can be challenging. Collaborating with experienced patent attorneys—well-versed in the nuances of each IP5 office—will help you strengthen your application and increase the likelihood of success. Stay proactive in adapting your approach to each region’s evolving standards, and secure robust patent protection for your innovative AI solutions.
