AI Invention Disclosure – What Engineers Should Prepare (Summary)
AI Patent GeneratorA concise summary of key elements for drafting AI-related invention disclosures, including datasets, model details, training pipeline, and reproducibility requirements.
If your invention involves AI/ML, a high-quality invention disclosure must go beyond high-level architecture and name-dropping models. Capture the end-to-end pipeline so that someone skilled in the art can reproduce it.
Key points to cover:
- Problem statement and technical objective: What does the AI system optimize and why this approach is non-trivial.
- Data assets: source, collection method, labeling standard, class balance, preprocessing, anonymization/compliance.
- Model and features: architecture, custom layers/losses, feature engineering, hyperparameters, and why they matter.
- Training process: pipelines, compute, schedulers, evaluation metrics, ablation studies, baselines.
- Deployment: inference stack, latency/throughput targets, quantization/distillation, online metrics and monitoring.
- Novelty anchors: where your approach differs from prior art and why it yields advantages.