Merchant Matching Model for Multi-Source Data Identification

The patent introduces a merchant matching model as an intermediary layer between the query system and multiple disparate data structures. This model receives merchant data in various formats, processes it through machine learning algorithms, and outputs standardized matching results. The intermediary handles the complexity of multiple data structures internally while presenting a unified interface to users, thus maintaining data completeness without exposing retrieval complexity.

The system segments the complex task of merchant data retrieval into distinct components: data ingestion from multiple sources, merchant matching model processing, and result output. Each component handles specific aspects of the data retrieval process independently, allowing the system to manage multiple disparate data structures without requiring the entire system to handle all complexity simultaneously.

The merchant matching model is pre-trained using machine learning on comprehensive merchant data from multiple sources. This preliminary training phase allows the model to learn patterns and relationships in the data, enabling it to perform accurate matching without requiring exhaustive queries across all underlying databases during operation. The heavy computational work is done in advance during training, not during runtime queries.

The patent replaces traditional mechanical querying methods (systematic searches across multiple databases) with a machine learning-based matching model. Instead of mechanically querying each database and comparing results, the system uses the trained model to directly determine merchant matches based on input data, significantly reducing computational overhead and network bandwidth consumption during operation.