Semantic Autofill for Administrative Query Variations

The patent transforms the rigid parameter of exact query string matching into a flexible semantic matching system. By converting query strings into vector representations and calculating cosine similarity, the system changes the matching parameter from exact string equality to a continuous similarity score, enabling adaptation to phrasing variations while maintaining matching accuracy.

The patent replaces the mechanical string comparison mechanism with a machine learning-based semantic understanding system. Instead of using traditional text matching algorithms, the system employs trained models to generate vector representations and compute semantic similarity, substituting rigid mechanical operations with intelligent computational processes.

The patent implements preliminary action by pre-training machine learning models offline and caching vector representations of query strings and response strings. This allows the system to perform complex computational work in advance, so that during actual operation, only lightweight similarity calculations are needed, significantly reducing real-time computing resource consumption while maintaining high accuracy.

The patent applies partial action by implementing a two-stage filtering process. First, a rough filtering stage identifies potentially matching responses using simplified criteria, then a more computationally intensive verification stage confirms the best match. This approach avoids performing full complex computations on all possible responses, reducing overall computing resource usage while maintaining accuracy.

The patent implements universality by designing a multi-functional system where the same machine learning infrastructure serves multiple purposes: generating autofill suggestions, ranking responses by relevance, and adapting to different administrative bodies' query styles. This consolidated approach increases productivity while avoiding the complexity of separate specialized systems for each function.

The patent applies self-service by enabling the system to automatically learn from new query-response pairs and improve its own performance over time. The machine learning models are designed to be retrained on newly accumulated data, allowing the system to self-enhance without requiring manual intervention or complex external management infrastructure, thus improving productivity without proportional increases in system complexity.