AI-Based Indicia Data Editing for Barcode Format Accuracy
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Solution Overview
Problem
Existing barcode readers face challenges in accurately processing indicia data due to formatting discrepancies, leading to inefficiencies and manual editing complexities, which consume computing resources and reduce accuracy.
Innovation Solution
An AI-based data editing engine that generates predictive indicia data editing models to automatically adjust decoded data strings, using pattern matching and regular expression algorithms to align with user-specific formatting requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual editing is used to correct formatting discrepancies in decoded data strings, then data processing accuracy can be improved, but computing resource consumption increases and processing efficiency decreases
Solution Approach 1:
The system performs preliminary formatting adjustments by predicting the desired output format based on historical data and patterns before final processing. This preliminary action reduces the need for extensive manual editing later, thereby improving accuracy while reducing computing resource consumption during actual data processing operations
2Measurement precision
If manual editing is performed to align decoded data with user-specific formatting requirements, then data processing accuracy is improved, but processing time increases
Solution Approach 1:
The system enables self-service data formatting by automatically predicting and applying the appropriate output format based on learned patterns from historical data and user preferences. This eliminates the need for time-consuming manual editing while maintaining high accuracy, as the system serves itself by autonomously correcting formatting discrepancies
3Measurement precision
If complex data editing operations are implemented to handle formatting discrepancies, then data processing accuracy improves, but device complexity increases
Solution Approach 1:
The system introduces an intermediary predictive model that acts as a mediator between the decoded data and the final formatted output. This model learns patterns from historical data and user preferences, translating raw decoded strings into properly formatted data without requiring complex manual editing operations, thereby improving accuracy while maintaining system simplicity
Data Source
AI summary
Methods, apparatuses and computer program products for providing artificial-intelligence-based indicia data editing are provided. For example, an example computer-implemented method may include determining, based at least in part on a data processing model associated with a scan setting module, a first decoded data string corresponding to a first indicia; determining, based at least in part on user input data, a first input data string corresponding to the first indicia; generating a predictive indicia data editing model based at least in part on providing the first decoded data string and the first input data string to an artificial intelligence algorithm; and updating the scan setting module based at least in part on the predictive indicia data editing model.