Receipt Optical Scanning for Variable-Weight Pricing Accuracy
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Solution Overview
Problem
Current delivery systems lack an efficient means to reconcile price discrepancies between ordered and purchased quantities of items with variable weights, leading to overcharging customers.
Innovation Solution
A delivery system uses machine-learned models to analyze images of receipts to identify items and their actual quantities, enabling accurate pricing based on the actual amounts purchased.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual quantity verification is used for variable weight items, then pricing accuracy is improved, but labor time and operational complexity increase
Solution Approach 1:
The patent replaces manual mechanical verification processes with an automated optical scanning system using cameras and image processing algorithms. The system captures images of items, automatically detects quantities through pattern recognition, and calculates prices, eliminating the need for manual counting and verification while maintaining high precision.
Solution Approach 2:
The system enables self-service automated quantity detection where the optical scanning system independently identifies item quantities without human intervention. The image processing algorithms automatically analyze product images, detect quantities based on visual patterns, and compute pricing, making the verification process autonomous and eliminating labor time requirements.
2Productivity
If automated image processing is implemented, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The optical scanning system is designed as a multi-functional platform that performs multiple tasks: capturing item images, detecting quantities through image analysis, calculating prices based on detected quantities, and integrating with existing delivery management systems. This universal approach consolidates multiple separate functions into a single integrated system, improving operational efficiency while managing complexity through consolidation rather than multiplication of components.
Data Source
AI summary
An online concierge system sends an order associated with a customer to a shopper for fulfillment at a store. The order specifies an ordered amount of an item. The online concierge system receives an image of a receipt for the order from the shopper after fulfillment of the order, applies an image processing algorithm to identify the item in the image of the receipt, and identifies a measured quantity within the image. The measured quantity represents an actual amount of the item purchased at the store. The online concierge system determines a difference between the actual amount and the ordered amount of the item, and determines an amount to be charged or reimbursed to the customer based in part on the difference between the actual amount and the ordered amount of the item.