Certifying Machine Learning Models via Continuous Simulation

The patent applies preliminary action by conducting continuous testing and verification of machine learning models before deployment in safety-critical applications. The system performs pre-certification testing using simulated hostile attacks and boundary conditions to establish predictability thresholds before the model is deployed, ensuring that the non-deterministic algorithm meets safety requirements in advance.

The patent implements feedback mechanisms by continuously monitoring the machine learning model's performance during operation and using this feedback to update and re-verify the model. The system captures operational data, compares it against verification thresholds, and triggers re-certification when deviations are detected, creating a closed-loop system that maintains predictability despite the algorithm's non-deterministic nature.

The patent applies periodic action by implementing continuous testing and verification at scheduled intervals and triggered events rather than requiring exhaustive testing before every deployment. The system performs periodic certification checks and updates the verification status based on operational experience, reducing overall testing time while maintaining reliability through regular monitoring.

The patent uses preliminary action by establishing verification thresholds and test protocols in advance during the model development phase. By pre-defining acceptance criteria, test scenarios, and verification methods, the system reduces the time required for certification during deployment while ensuring comprehensive reliability assessment.

The patent implements feedback by systematically capturing operational data from deployed machine learning models and using this feedback to trigger verification processes. When operational feedback indicates performance degradation or deviation from expected behavior, the system automatically initiates re-verification, creating a managed adaptation process that balances performance improvement with verification complexity.

The patent applies parameter changes by monitoring specific performance parameters and verification thresholds during model adaptation. The system tracks changes in model behavior against predefined parameters and only triggers full verification when parameter deviations exceed acceptable limits, reducing verification complexity while maintaining model performance through targeted re-certification.