Receipt Image Processing for Consumer Inventory Updates
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Solution Overview
Problem
Foodstuff vendors lack access to consumer purchase and usage data due to the anonymous nature of grocery store transactions, preventing them from acquiring specific information on item brands, quantities, prices, and consumer behavior.
Innovation Solution
A computer-executable tool processes receipt images to extract and match textual elements with known products, updating consumer inventories with specific purchase information, and prompts consumers for additional details when necessary to convert non-specific items to specific items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If grocery stores process transactions anonymously to protect consumer privacy, then consumer purchase data remains inaccessible to vendors, but this prevents vendors from gaining valuable consumer behavior insights
Solution Approach 1:
The patent introduces a data intermediary system that sits between the grocery store transaction system and the vendor. This intermediary processes and aggregates purchase data, stripping personally identifiable information while preserving product-level purchase patterns. The intermediary enables information flow to vendors without direct exposure of consumer identities, resolving the contradiction between data accessibility and privacy protection.
Solution Approach 2:
The system creates anonymized copies of purchase data that contain product, quantity, and temporal information without consumer identifying details. These copies are then shared with vendors instead of raw transaction data. This allows vendors to analyze purchase patterns while consumer privacy is maintained through the use of replicated而非original data.
2Measurement precision
If vendors require detailed product information from consumers to improve product tracking, then data accuracy increases, but consumer participation burden increases
Solution Approach 1:
The system enables automatic data capture through consumer-facing applications that scan receipts or utilize mobile device cameras to automatically extract purchase information. Consumers simply point their device at their receipt, and the system automatically processes the data without requiring manual entry. This self-service approach maintains high data accuracy while minimizing consumer effort.
Solution Approach 2:
The patent replaces manual data entry mechanisms with automated optical character recognition (OCR) and image processing technologies. Instead of consumers typing product information, the system uses camera-based scanning and automated text extraction to capture purchase data, significantly reducing the mechanical effort required from consumers while maintaining precision.
3Reliability
If the system processes all receipt data through manual verification to ensure accuracy, then data quality improves, but processing time increases
Solution Approach 1:
The system implements a tiered verification approach where only a subset of data requiring uncertainty undergoes manual review. Automated processing handles the majority of clear, unambiguous cases immediately, while only borderline or complex cases are flagged for human verification. This partial manual action maintains reliability for critical cases while minimizing overall processing time.
Solution Approach 2:
The system incorporates feedback loops where processing results are continuously evaluated and used to improve automated recognition accuracy over time. Common patterns learned from initial processing feed back into the system to refine algorithms, reducing the need for manual verification as the system becomes more accurate. This feedback mechanism progressively improves reliability while maintaining fast processing speeds.
Data Source
AI summary
Systems, methods and computer-readable media are disclosed for capturing purchase information regarding purchased items of a consumer. Upon receiving an image of a receipt (a receipt image) regarding a list of purchased items, receipt text is generated. The receipt text is processed to identify the purchased items in the receipt image. Accordingly, an iteration is begun to iterate through the item blocks of the receipt text. An item block corresponds to a discrete item in the receipt text. The processing comprises extracting textual elements from the item block and matching the textual elements to a known product. Upon matching the textual elements to a known product, the consumer inventory associated with the consumer is updated with regard to the purchase of the known product.