Hybrid NLP Models for Risk Control Feature Identification

The patent replaces rule-based mechanical systems with machine learning models that automatically learn patterns from data. The ML models substitute manual rule creation and application with automated statistical learning, enabling the system to generalize to new language while maintaining reliability through trained patterns.

The patent transforms the approach from fixed rule parameters to dynamic learned parameters. Instead of manually defined rules, the system uses ML models that adjust parameters based on training data, allowing adaptation to new language contexts while maintaining consistent performance through the learned parameter relationships.

The patent applies preliminary action by pre-training ML models on large datasets of risk control documents before deployment. This preliminary training phase automatically captures domain-specific patterns and rules, eliminating the need for manual rule creation during operation and significantly reducing setup time.

The system enables self-service by allowing the ML models to automatically learn and identify risk control features without human intervention. The models self-adjust parameters and patterns during training, replacing manual rule identification with automated learning that requires minimal human input.

The patent merges multiple ML models into a hybrid architecture that combines the strengths of different approaches. By integrating quality prediction models with feature identification models, the system achieves superior generalization while managing complexity through unified model interactions.

The hybrid ML system provides multi-functionality by simultaneously performing quality prediction, feature identification, and generalization to new language. This universal approach consolidates multiple functions into a coordinated system that handles diverse tasks through integrated model operations.