Synthetic Business Document Generation for Domain-Specific LLM Training

The patent creates synthetic copies of real business documents by extracting key-value pairs and regenerating documents that preserve the statistical properties (spatial distribution, content distribution, key relationships) of original documents. This copying approach generates unlimited domain-specific training data without requiring additional real-world documents, directly resolving the contradiction between needing accurate domain-specific data and facing data scarcity.

The patent transforms the training data generation process by changing from using only real documents to using synthetically generated documents that match the statistical parameters of real documents. By modeling and reproducing key parameters such as spatial distribution of elements, content distribution patterns, and key-value relationships, the system maintains data quality while expanding data quantity through synthetic generation.

The patent performs preliminary analysis of real business documents to extract and model their statistical properties (spatial distribution, content distribution, key relationships) before generating synthetic data. By pre-establishing these models from a small set of real documents, the system eliminates the need for time-consuming collection and preparation of large amounts of real domain-specific data, while still achieving high model performance through realistic synthetic data generation.

Instead of manually collecting and preparing extensive real domain-specific data, the system creates synthetic copies that inherit the statistical characteristics of real documents. This copying mechanism automatically generates large volumes of training data with proper domain-specific properties, dramatically reducing the time and resources required for data preparation while maintaining model performance.