Receipt Verification Using OCR for Purchase Price Discrepancies

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Solution Overview

Problem

Existing online shopping platforms lack personalized shopping trip recommendations and efficient receipt verification systems that consider user preferences, real-time pricing, and inventory data, leading to suboptimal shopping experiences and potential overpayments.

Innovation Solution

A system that utilizes machine learning models to analyze historical shopping data, user preferences, and real-time pricing to recommend optimized shopping trips and sequences of shopping locations, while also employing optical character recognition and price comparison to verify purchase receipts and detect discrepancies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If automated shopping task execution is implemented without personalized recommendations, then system complexity is reduced, but customer satisfaction and shopping optimization deteriorate

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the automated shopping task into multiple independent components: recommendation engine, trip optimization module, receipt verification system, and dispute management. Each component operates independently with specialized functions, allowing the system to provide personalized recommendations without overwhelming complexity. The segmentation enables modular deployment and maintenance while delivering customized shopping experiences.

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If comprehensive price verification and receipt analysis are performed, then payment accuracy is improved, but processing time and computational resources increase

Engineering Contradiction:

Improveprice verification accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary price verification by comparing advertised prices with actual charged prices before the customer receives billing. Receipt analysis is initiated automatically upon receipt submission, and potential discrepancies are identified and flagged in advance. This preliminary action allows most verification tasks to be completed rapidly using pre-established pricing data and automated comparison algorithms, minimizing processing time while maintaining high accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates digital copies of receipts through OCR technology and stores them in structured databases for efficient analysis. Instead of manually analyzing physical receipts, the system works with digital replicas that can be rapidly processed, searched, and compared against pricing data. This copying approach enables parallel processing of multiple receipts and significantly reduces the time required for verification while maintaining accurate price comparison.

Inventive Principle:

Principle #26Copying

3Reliability

If manual receipt verification is used, then system complexity is minimized, but overpayment detection capability and customer protection deteriorate

Engineering Contradiction:

Improveoverpayment detection reliabilityVSAvoidautomation level

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The system implements self-service automated verification where the technology itself performs receipt analysis, price comparison, and discrepancy detection without human intervention. The automated system independently validates purchases against stored pricing data, identifies overcharges, and generates dispute documentation. This self-service approach provides reliable overpayment detection while reducing operational complexity compared to fully manual processes requiring human reviewers for each transaction.

Inventive Principle:

Principle #25Self-service

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Provides personalized shopping experiences with optimized trip recommendations and accurate price verification, enhancing customer satisfaction and reducing overpayments by leveraging machine learning and OCR technology.

Implementation Method 1

extracting one or more line-item purchase information data elements from an image of the purchase receipt... wherein the image of the purchase receipt is a scanned image of the purchase receipt, or a digital image of the purchase receipt generated by using optical character recognition (OCR)

Methodology Applied

Scientific EffectOptical character recognition:

Data Source

PatentUS20260065299A1Executing automated shopping tasks

Publication Date: 2026.03.05 STRETCH COLLECTIVE INC

AI summary

Methods and systems, including computer-readable media, are described for verifying a purchase receipt. A computing system verifies a purchase receipt by implementing a method that includes extracting line-item purchase information data elements from an image of the purchase receipt. The method includes retrieving expected item pricing data and expected tax data from a retailer-specific database indexed by region and data for each item. The method includes identifying a data element having a difference between a unit price represented in the corresponding data element and an expected unit price represented in the corresponding expected item pricing data, in which the difference meets a configurable threshold and corresponds to a purchase price discrepancy. The method includes constructing a discrepancy report having a structured data format, transmitting the discrepancy report to a retailer system over a preferred communication channel determined by a retailer profile associated with the retailer.