Artificially Intelligent System and Method for Automatic Generation and Transmission of Digital Receipts
Inventors
Richard Kellerer
Abstract
Systems and methods leverage a multi-stage artificial-intelligence pipeline to convert raw point-of-sale data into bank-grade digital receipts. A convolutional-OCR front end extracts line-item text, which a bidirectional-LSTM classifier normalises and categorises in real time, learning continuously from user feedback. A graph-based anomaly detector flags suspicious spend patterns, while a recommender sub-engine delivers personalised rewards and sustainability insights by fusing purchase context with external carbon-intensity data. The enriched receipt is cryptographically hashed, streamed through an encrypted gateway, and auto-matched to the corresponding payment entry inside the banking core. By driving extraction, classification, enrichment and integrity checks entirely through AI, the system eliminates manual mapping and enables immediate, tamper-evident reconciliation across heterogeneous merchants and payment rails.
CPC Classifications
Filing Date
2025-09-02
Application No.
19316483