Your AI Does Something New.
Let's Make Sure You Own It.
AI patent prosecution is a minefield of eligibility rejections, abstract idea arguments, and rapidly evolving case law. We've prosecuted hundreds of AI patents and know how to get them granted.
AI Patents Are Hard.
On Purpose.
The USPTO has made it clear: you can't patent an abstract idea. And examiners have been trained to treat machine learning models, neural networks, and algorithmic innovations as exactly that. Most AI patent applications face Section 101 rejections.
But "hard" doesn't mean "impossible." It means you need claims drafted by someone who understands both the technology and the legal framework. Someone who knows how to anchor AI innovations in concrete technical improvements, not just business outcomes.
We draft AI patent claims that survive eligibility challenges because we frame them correctly from the start: as technical solutions to technical problems, with specific architectures, training methodologies, and measurable improvements.
AI Technologies We Patent
Deep technical fluency across the AI/ML landscape.
Machine Learning Models
- Novel neural network architectures
- Training and optimization methods
- Transfer learning techniques
- Federated learning systems
- Model compression and distillation
Natural Language Processing
- Large language model innovations
- Retrieval-augmented generation
- Semantic search and embeddings
- Automated document analysis
- Conversational AI systems
Computer Vision
- Object detection and tracking
- Medical image analysis
- Autonomous navigation systems
- Video analytics pipelines
- Generative image models
Autonomous Systems
- Self-driving vehicle architectures
- Robotic control systems
- Drone navigation and coordination
- Sensor fusion methods
- Decision-making frameworks
AI Infrastructure
- Training data pipelines
- MLOps and model deployment
- Hardware acceleration methods
- Edge AI and on-device inference
- Distributed computing for AI
Applied AI
- Cybersecurity threat detection
- Financial modeling and prediction
- Healthcare diagnostics
- Supply chain optimization
- Recommendation systems
How We Win AI Patents
Technical Framing
We draft claims that emphasize the technical improvement, not the business result. A "system that predicts customer churn" gets rejected. A "distributed inference pipeline with adaptive model selection based on input feature dimensionality" gets allowed.
Architecture-First Claims
We anchor independent claims in specific system architectures, data flows, and processing steps. The more structural detail in the claim, the harder it is for an examiner to call it abstract.
Specification Depth
We build specifications with extensive technical detail, pseudocode, architectural diagrams, and experimental results. This gives us ammunition for amendments and arguments during prosecution.
Portfolio Layering
A single patent rarely protects an AI system. We build layered portfolios that cover the model architecture, training pipeline, inference optimization, and application-specific implementations.
Building AI That Matters?
If your AI does something genuinely new, it deserves patent protection that holds up under scrutiny. Let's build a strategy.
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