Mobile Receipt OCR Architecture for Secure Transaction Data Aggregation
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Solution Overview
Problem
Consumers face challenges in sharing transaction information with third parties, leading to inefficient data collection and misuse, as traditional methods are prone to errors and privacy concerns.
Innovation Solution
An architecture that uses mobile devices to track and aggregate transaction data from receipts through optical character recognition, allowing customers to maintain private control over their data while enabling secure access for relevant applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If consumers share transaction information with third parties, then ad targeting and feedback can be improved, but privacy risks and data misuse increase
Solution Approach 1:
The patent introduces a mobile device as an intermediary between consumers and third parties. The mobile device captures receipt images, extracts transaction data locally, and shares only processed information with advertisers or feedback systems. This intermediary role protects consumer privacy by preventing direct access to raw transaction data while still enabling useful analytics and targeted advertising.
Solution Approach 2:
The system creates a digital copy of transaction data from physical receipts through image capture and OCR processing. Instead of sharing original receipt documents or requiring manual data entry, the system generates structured data copies that can be safely shared with third parties while the consumer retains the original physical receipt and control over the data.
2Quantity of substance
If manual data entry is used for transaction records, then data can be collected, but errors and inefficiency increase
Solution Approach 1:
The patent replaces the mechanical process of manual data entry with automated optical character recognition (OCR) technology. The mobile device camera captures the receipt image, and software automatically extracts transaction data through pattern recognition and text processing algorithms, eliminating human error and significantly improving both speed and accuracy of data collection.
Solution Approach 2:
The system enables self-service data collection where the consumer simply needs to photograph the receipt with their mobile device. The automated processing handles data extraction, validation, and storage without requiring consumer intervention beyond the initial image capture, making the process both accurate and convenient.
3Object-affected harmful factors
If transaction data is kept private and not shared, then privacy is protected, but data utility and feedback capability are reduced
Solution Approach 1:
The patent extracts only the necessary transaction data elements (such as purchase amount, date, merchant category) from the complete receipt information. This selective extraction allows the system to share minimal necessary data with third parties for analytics and feedback purposes while leaving sensitive personal information private, thus maintaining both privacy protection and data utility.
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
Facilitates efficient and accurate data collection and storage of transaction information, enhancing privacy while providing benefits like improved ad targeting and engagement-based rewards, reducing errors and misuse.
Implementation Method 1
The image can then be translated into text-based data by, e.g., employing optical character recognition techniques
Implementation Method 2
The image can then be translated into text-based data by, e.g., employing optical character recognition techniques
Data Source
AI summary
The claimed subject matter relates to an architecture that can facilitate verification or feedback in association with consumer transactions. In particular the architecture can image a transaction receipt, translate the image into text-based data, and extract relevant transaction data there from. Such relevant transaction data can be saved to a data store maintained or supervised by the customer and/or transmitted to authorized components that can benefit from data related to transaction histories, verification, and/or feedback.