AI Indicia Data Editing for Barcode Formatting Accuracy
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Solution Overview
Problem
Existing barcode readers face challenges in accurately processing indicia data due to discrepancies between decoded data strings and user requirements, leading to incorrect formatting and manual editing complexities, which is time-consuming and resource-intensive.
Innovation Solution
An AI-based data editing engine that generates predictive indicia data editing models to correct and format decoded data strings, using pattern matching and regular expression algorithms to ensure compliance with user-specific formatting requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional barcode readers decode data strings directly, then the decoding process is simple and fast, but the data formatting accuracy deteriorates due to discrepancies between decoded data and user requirements
Solution Approach 1:
The patent introduces an AI-based data editing engine as an intermediary component between the barcode decoder and the user output. This engine receives decoded data strings, applies predictive editing models to automatically format and correct the data according to user requirements, and outputs the formatted results. The intermediary handles the complexity of data formatting internally, resolving the contradiction between maintaining simple decoding and achieving high formatting accuracy.
Solution Approach 2:
The system implements self-service through automated predictive editing models that learn from user corrections and automatically apply formatting rules without manual intervention. The AI engine autonomously identifies and corrects formatting discrepancies in decoded data, reducing the need for manual editing while improving accuracy. This self-service capability allows the system to maintain simplicity for users while achieving high formatting precision internally.
2Measurement precision
If manual editing is used to correct data formatting discrepancies, then formatting accuracy can be improved, but time consumption and resource usage increase significantly
Solution Approach 1:
The AI-based data editing engine provides self-service by automatically learning from user corrections and applying formatting rules autonomously. The predictive editing models analyze decoded data and automatically generate correctly formatted output without requiring manual intervention for each data item. This automation maintains high formatting accuracy while dramatically improving processing efficiency and reducing resource consumption compared to manual editing methods.
Solution Approach 2:
The system implements feedback mechanisms where user corrections to formatted data are captured and used to refine the predictive editing models. The AI engine learns from these feedback loops, continuously improving its formatting accuracy while maintaining automated operation. This feedback-driven approach ensures high precision is achieved through automation rather than manual processes, preserving productivity while improving accuracy over time.
3Productivity
If AI-based predictive editing models are implemented, then manual editing efforts are reduced and processing efficiency improves, but the device complexity increases
Solution Approach 1:
The AI-based data editing engine serves as a specialized intermediary layer that handles the complexity of predictive editing internally. By isolating the AI processing in a dedicated module, the system can leverage powerful machine learning capabilities without making the entire barcode reading system overly complex. The intermediary manages the AI model training, prediction, and updates, allowing the rest of the system to remain relatively simple while still achieving high productivity through automated formatting.
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.