Entity Understanding and Resolution System

Tue, 13 Oct 2026 00:00:00 +0000

Conventional entity resolution models, including pretrained sentence transformers, are trained on natural language and struggle with domains like retail receipts, where text is abbreviated, visually structured, and prone to OCR errors. The system disclosed here instead trains a model directly on domain-specific data (e.g., canonical retailer names and their known variants) so it learns the semantic relationships particular to that domain, builds a fast approximate-nearest-neighbor search index over the learned embeddings, and pairs the model with a human-in-the-loop interface so an evaluator can confirm or correct outputs and continuously refine the model over time.

Patent | Alec Stashevsky

Entity Understanding and Resolution System