Real-Time Data Classification via Hierarchical Rule Tokenization

The patent transforms static batch-processing classification systems into dynamic real-time processing systems. The data model is executed continuously to classify inquiries as they arrive, rather than waiting for batch processing cycles. This dynamic execution enables the system to maintain high classification accuracy while processing data in real-time, resolving the contradiction between precision and speed.

The patent performs preliminary actions by pre-processing and normalizing training data before model execution, and by caching frequently accessed classification rules and term embeddings. This preparation work is done in advance so that when real-time classification is needed, the system can quickly apply pre-computed transformations and rules without performing heavy computation during the actual classification event, thus maintaining both accuracy and speed.

The patent implements continuous classification by executing the data model in real-time as inquiries arrive, rather than interrupting processing to perform batch operations. The system continuously normalizes incoming inquiries, applies classification rules, and generates results without stopping for batch processing cycles. This continuous operation eliminates the time loss associated with batch processing while maintaining classification accuracy through consistent application of the trained model.

The patent performs preliminary data normalization, term embedding, and rule compilation in advance during system initialization and model training phases. By pre-computing these transformations and storing them in optimized formats, the system eliminates the need to perform these time-consuming operations during actual classification, thus reducing processing delay while maintaining precision.

The patent implements self-service adaptability through the trained data model that automatically learns and adapts to new terms and expressions without requiring manual code modifications. When new inquiries with previously unseen terms are received, the model uses its trained embeddings and classification logic to automatically categorize them appropriately. This self-adapting capability eliminates the need for developers to manually update code for every new term, reducing complexity while improving adaptability.

The patent uses parameter changes in the form of term embeddings and classification thresholds that can be updated without modifying the core system code. By representing terms as vectors in a high-dimensional space and adjusting embedding parameters based on new data, the system adapts to new terminology by changing these parameters rather than restructuring the codebase, thus improving adaptability while maintaining manageable complexity.

The patent segments the classification process into distinct modular components: data normalization, term embedding, rule application, and classification decision-making. Each segment is optimized independently for real-time execution. By dividing the complex process into smaller, manageable segments that can be executed sequentially or in parallel, the system achieves real-time processing speed while maintaining accuracy through careful optimization of each segment.

The patent performs preliminary computations during model training and data preparation phases, including term embeddings, feature extractions, and rule compilations. These pre-computed elements are stored in optimized formats for rapid retrieval during real-time classification. By moving heavy computational work to the preliminary phase, the system achieves fast real-time processing while maintaining high accuracy through the use of pre-validated transformation rules and embeddings.