Can you patent a machine learning model?
Short answer: Sometimes — when claims recite a specific technical training or inference improvement, not “apply ML to predict X.”
ML patents face double scrutiny: Alice (abstract idea) and crowded prior art from academia and Big Tech. Examiners see thousands of “train a neural network on data to classify Y” applications.
Stronger ML claim themes
- Novel architecture or loss function with measured gain
- Data preprocessing that solves a technical bottleneck
- Hardware-aware training under specific constraints
- Federated or privacy-preserving techniques with concrete protocols
Enablement risk
§112 requires enough detail to reproduce results. Vague “any suitable model” language fails. Document datasets, hyperparameters, and evaluation in your disclosure.
Analyze your training pipeline code with Patent PreCheck.