Frontend Computer Vision for Real-Time Unscanned Item Detection
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Solution Overview
Problem
Existing methods for identifying unscanned items during retail transactions are inefficient and create additional friction for customers, as they often require random checks at the store exit, leading to long wait times and incomplete item scanning.
Innovation Solution
A system using computer vision object detection and recognition models identifies items in a customer's cart in real-time, mapping scanned items to identified items, and notifies the user of unscanned items before completing the transaction, allowing quick scanning and payment at the point-of-sale.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random item checks are performed at store exit, then unscanned items can be detected, but customer wait time increases and transaction efficiency decreases
Solution Approach 1:
The system performs item identification and scanning verification before the customer reaches the exit point. By using computer vision to capture images of items in the customer's possession during the transaction process, the system identifies and flags unscanned items prior to exit, eliminating the need for post-transaction checking and reducing customer wait time.
Solution Approach 2:
The manual mechanical process of stopping customers at exit and physically checking items is replaced with an automated computer vision system. Cameras capture images, AI algorithms identify items, and the system automatically compares scanned versus unscanned items, substituting human labor and physical inspection with automated optical detection and digital processing.
2Reliability
If all items are manually checked at exit, then complete item verification is achieved, but transaction throughput decreases
Solution Approach 1:
The system enables automatic self-verification of scanned items without requiring staff intervention. The computer vision system autonomously captures images, identifies items, compares them against the scan list, and generates notifications for unscanned items, allowing the verification process to occur automatically without reducing transaction throughput.
Solution Approach 2:
The system provides real-time feedback during the transaction process by notifying customers and staff of unscanned items before completion. This immediate feedback allows for corrective action (scanning missing items) without delaying the transaction, maintaining high throughput while ensuring complete verification.
3Loss of time
If computer vision identification is implemented, then unscanned items are detected earlier, but system complexity increases
Solution Approach 1:
The computer vision system serves multiple functions: capturing images of items, identifying item types, determining item quantity, and generating verification notifications. By consolidating these functions into a single multi-functional system, the patent reduces overall complexity compared to having separate systems for each task.
Solution Approach 2:
The patent introduces a notification system as an intermediary between the computer vision identification and the final transaction completion. This intermediary layer processes the identified items, compares them against scanned items, and communicates results to relevant parties, simplifying the overall system architecture by creating a clear separation of concerns.
Data Source
AI summary
Examples provide a system and method for identifying unscanned items in real-time during a current transaction using computer vision object detection and recognition models. Images of a checkout area are received from camera(s) situated near the point-of-sale (POS). An object detection and recognition model analyze image data to identify items in a cart and/or on the POS conveyor belt. An item identifier (ID) is predicted for each identified item. As each item is scanned at the POS, the scanned item IDs are mapped to the identified item IDs. When a ready-to-pay signal is received, any identified items which fail to map to a scanned item are identified as unscanned items. A notification is sent to a user interface (UI) device associated with the POS. The notification includes an image of each unscanned item and an instruction to scan the items which were inadvertently unscanned prior to completion of the transaction.