Self-Scan Product Count Detection for Registration Discrepancies
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Solution Overview
Problem
Existing systems fail to detect discrepancies in the number of products before and after customer registration, particularly in cases of fraudulent acts where customers do not fully scan and register all purchased items.
Innovation Solution
A detection system that includes a first acquisition unit to count products based on customer scanning motions using a scanner, a second acquisition unit to gather registered product counts, and a detection unit to identify discrepancies between the two, with an output unit providing results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If customers perform self-scan registration at cash register terminals, then productivity and ease of operation are improved, but reliability deteriorates due to fraudulent acts where customers fail to register all products
Solution Approach 1:
The patent introduces an image recognition system as an intermediary between the customer's scanning action and the product registration. The system captures images of products on the conveyor belt, recognizes them using AI/ML algorithms, and automatically verifies or supplements the registered products, thereby maintaining self-service efficiency while improving registration reliability
Solution Approach 2:
The system implements feedback by comparing the products detected through image recognition with the products registered by the customer's scanner. When discrepancies are detected (e.g., products on the belt that weren't scanned), the system provides feedback alerts to notify the customer of potential missed items, enabling corrective action before transaction completion
2Measurement precision
If image recognition technology is used to detect products on the conveyor belt, then measurement precision of product count is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical product counting devices with image recognition and AI/ML-based software systems. Instead of using mechanical sensors, weigh cells, or optical counters that require precise physical calibration, the system uses computer vision algorithms to identify and count products based on their visual characteristics, thereby reducing mechanical complexity while maintaining or improving measurement precision
Solution Approach 2:
The image recognition system serves multiple functions simultaneously: it counts products, identifies product types, verifies registration accuracy, and can even determine product orientation or packaging variations. This multi-functionality reduces the need for separate specialized devices, thereby lowering overall system complexity while maintaining high measurement precision
Data Source
AI summary
A detection system according to an aspect of the present disclosure includes: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: acquire a number of one or more products to be registered based on an image in which a customer reads one or more product symbols with a scanner; acquire a number of one or more products registered by the customer; detect discrepancy between the number of the one or more products to be registered and the number of the one or more registered products; and output a detection result.