Artificial intelligence inventions present distinct patent prosecution challenges. Section 101 subject matter eligibility is the threshold issue: the USPTO and courts have drawn lines around which AI innovations are patentable and which are considered abstract ideas. Prior art in the AI space is dense and fast-moving, with academic publications, open-source implementations, and competitor filings all contributing to a complex landscape. We prosecute AI patents with strategies designed to survive both eligibility challenges and prior art rejections.
What We Patent
Our AI patent prosecution covers machine learning model architectures and training methods, deep neural networks and novel layer configurations, natural language processing systems and methods, computer vision and image recognition, recommendation and personalization engines, generative AI systems and content generation methods, reinforcement learning and autonomous decision-making, data preprocessing and feature engineering methods, AI-driven automation and process optimization, and edge AI and embedded inference systems.
Section 101 Strategy
The patent eligibility landscape for AI inventions has evolved significantly through the Alice/Mayo framework. An AI claim that recites an abstract mathematical concept without more will be rejected under Section 101. The prosecution strategy requires framing the invention in terms of its technical improvement: a specific improvement to a computer's functionality, a particular machine or transformation, a concrete technical solution to a technical problem. We draft claims that anchor the AI innovation in its practical application, tying the algorithm to the specific technical context in which it operates. This approach has proven effective across hundreds of AI patent applications.
Prior Art Challenges
AI prior art comes from multiple sources: issued patents, published applications, academic papers (arXiv, IEEE, ACM), open-source repositories (GitHub, Hugging Face), industry white papers, and product documentation. The volume and velocity of AI publications mean that prior art searches must be comprehensive and the claim differentiation must be precise. We identify the specific technical contribution that distinguishes the invention from the prior art and build the claim set around that contribution.
Portfolio Strategy for AI Companies
AI companies benefit from a layered patent strategy that covers the core model or algorithm, the training methodology, the specific application domain, the data pipeline, and the deployment architecture. This layered approach provides broader protection than a single patent directed to the model alone. For companies whose AI technology spans multiple application domains, the portfolio should include claims tailored to each domain to maximize enforcement options.
Get Started
To discuss patent protection for your AI innovation, schedule a consultation.