Self-learning barcode suppression for POS scanning efficiency
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Solution Overview
Problem
Point-of-Sale (POS) systems face confusion and operational slowdowns due to the reading of secondary barcodes prior to primary barcodes, leading to incorrect scanning and inefficient processing when secondary barcode data is not needed or desired.
Innovation Solution
Implementing a self-learning suppression system that uses a local database to identify and ignore secondary barcode data, allowing the scanner to focus on reading primary barcodes by determining if the barcode data is stored locally and sending it to a transaction processing system if not, or executing associated functions if stored.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the barcode scanner reads all barcodes including secondary barcodes, then the scanner can capture all possible barcode data, but the POS system experiences confusion and operational slowdowns due to processing unnecessary secondary barcode data
Solution Approach 1:
The system performs preliminary actions by maintaining a database of known secondary barcodes and proactively checking incoming barcode data against this database before full processing occurs. This allows the system to identify and suppress secondary barcodes before they can cause confusion or slowdowns in the POS system, resolving the contradiction by filtering unnecessary data in advance.
2Productivity
If the barcode scanner suppresses secondary barcode data using a local database, then scanning efficiency is improved by avoiding unnecessary processing, but the system complexity increases due to database maintenance and synchronization
Solution Approach 1:
The system implements self-service by automatically learning and updating the local database of secondary barcodes from transaction data without requiring manual configuration or intervention. The scanner autonomously identifies secondary barcodes encountered during normal operation and adds them to the suppression database, reducing system complexity while maintaining high scanning efficiency.
3Measurement precision
If the system learns and stores secondary barcode patterns locally, then accuracy in distinguishing primary from secondary barcodes improves, but memory resources are consumed by the local database
Solution Approach 1:
The system applies partial action by storing only the essential identifying patterns of secondary barcodes in the local database rather than complete barcode data or entire product catalogs. This selective storage approach maintains high identification accuracy while consuming minimal memory resources, resolving the contradiction between precision and resource consumption.
Data Source
AI summary
Various embodiments herein each include at least one of systems, devices, methods, and software for self-learning suppression of secondary barcodes. One such embodiment, in the form of a method, includes receiving barcode data read by a barcode scanning device and determining whether the barcode data is stored in a local database. In such embodiments, when the barcode data is not stored in the local database, the method sends the barcode data to a transaction processing system. Otherwise, when the barcode data is stored in the local database, the method includes executing at least one function associated with the barcode data stored in the local database, such as ignoring the barcode data and causing a barcode reading device to search for another barcode to read.