Rule-Based Natural Language Processing for Intent Recognition

The patent segments the natural language processing task into distinct components: pattern matching layer and intent classification layer. The pattern matching layer uses rule-based expression patterns to filter and preprocess inputs, while the intent classification layer handles the actual intent recognition. This segmentation allows the system to achieve accurate intent recognition without requiring computationally intensive machine learning models for the entire processing pipeline.

The patent creates simplified copies of natural language expressions in the form of predefined expression patterns. Instead of using complex machine learning models to understand and interpret natural language, the system uses templated pattern copies that represent common expression structures. These pattern copies enable efficient matching and classification while maintaining accuracy for recognized patterns.

The patent performs preliminary action by pre-defining expression patterns and rules during system development rather than during runtime operation. The expression patterns, which capture common natural language structures, are established in advance and stored for efficient matching. This eliminates the need for time-consuming training processes while maintaining high processing accuracy for recognized patterns.

The patent uses simple, lightweight rule-based patterns instead of complex, resource-intensive machine learning models. These pattern rules are computationally inexpensive to store and execute, enabling accurate processing without the computational burden of trained models. The system trades off handling only recognized patterns against avoiding extensive training requirements.