Proprietary Data Query Transformation for Ambiguous Web Search

The patent introduces a query transformation system as an intermediary between the user's natural language query and the web search engine. This intermediary uses proprietary data and large language models to translate ambiguous natural language queries into precise search queries, thereby maintaining user accessibility while reducing response time by pre-resolving ambiguities.

The system performs preliminary query transformation and context enrichment before the actual web search is executed. By using proprietary data to pre-process and refine the query, the system prepares optimized search parameters in advance, which speeds up the subsequent search execution and reduces overall response time.

Instead of processing every possible ambiguity and edge case in natural language queries through computationally intensive methods, the system applies partial action by using proprietary data to handle the most common query patterns efficiently. The large language models are fine-tuned on proprietary data to provide optimized transformations for typical queries, reducing the need for excessive computational resources while maintaining high accessibility.

The system changes the parameters of query processing by using fine-tuned large language models trained on proprietary data. This parameter change enables more efficient query transformation with lower computational cost compared to using generic, unfine-tuned models, as the proprietary-trained models have optimized their internal parameters for the specific domain, reducing the computing resources needed for query processing.

The fine-tuned large language model serves multiple functions: it transforms queries, enriches context, resolves ambiguities, and optimizes search parameters. By making the model multi-functional, the system improves query accuracy without proportionally increasing complexity, as a single fine-tuned model handles multiple task requirements that would otherwise require separate specialized components.

The system creates a simplified representation or copy of the complex query understanding and resolution process by using the fine-tuned language model to generate transformed queries. This copying approach allows the system to capture the essence of accurate query processing without implementing all the complex underlying mechanisms, thereby improving accuracy while managing system complexity.