Image Map Data Entry Error Detection and Correction
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Solution Overview
Problem
User input errors in databases, such as spelling mistakes or additional characters, lead to misinformation, causing errors in transactions and analytics, and existing technologies lack effective methods for automatic detection and correction.
Innovation Solution
A method and system that generate image maps from input strings and predefined strings, using predictive models to determine correlations and modify data entries to match the predefined strings, with the ability to learn from historic data entries and adapt to new correct entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional string comparison methods are used to detect data entry errors, then the system is simple to implement, but it cannot effectively detect semantic similarities or correct misspelled entries
Solution Approach 1:
The patent replaces traditional mechanical string comparison methods with an image processing-based system. Text strings are converted to image maps representing character visual patterns, enabling the system to detect semantic similarities and correct misspelled entries by comparing visual characteristics rather than exact character matches.
Solution Approach 2:
The patent transforms one-dimensional string data into two-dimensional image maps. This dimensional change allows the system to capture spatial relationships between characters and visual patterns, enabling detection of semantic similarities that traditional linear string comparison cannot achieve.
2Productivity
If the system automatically corrects all data entries, then productivity increases, but false corrections may occur reducing reliability
Solution Approach 1:
The system incorporates feedback mechanisms where image map comparisons and classification parameters are used to determine whether corrections should be applied. The predictive model learns from historic data entries to provide feedback on whether automatic corrections are appropriate, balancing processing speed with correction accuracy.
Solution Approach 2:
The patent uses classification parameters derived from image map comparisons to control the correction process. By adjusting these parameters, the system can be configured to be more or less aggressive in applying corrections, allowing optimization of both productivity and reliability based on specific application requirements.
3Measurement precision
If the system uses predefined strings for comparison, then measurement precision improves, but adaptability to new correct entries decreases
Solution Approach 1:
The system performs self-learning by analyzing historic data entries and automatically updating its predictive model. When new correct entries are encountered, the system learns from them and adjusts its classification parameters, enabling it to adapt to evolving data standards without requiring manual reconfiguration of predefined strings.
Solution Approach 2:
The patent transforms the static predefined string approach into a dynamic system. The predictive model continuously evolves by learning from historic data entries, allowing the system to adapt its validation criteria over time. This dynamic approach maintains measurement precision while improving adaptability to new correct entries.
Data Source
AI summary
A method for automatically detecting errors in at least one data entry in a database, the at least one data entry including an input string of characters that do not match at least one predefined string of characters. The method includes generating a first image map; generating at least one classification parameter by comparing the first image map to a second image map, the second image map based at least partially on the predefined string of characters; determining that the input string of characters correlates to the predefined string of characters; and modifying the at least one data entry to match the predefined string of characters in response to determining that the input string of characters correlates to the predefined string of characters. Various other methods and systems for automatically detecting errors in at least one data entry in a database are also disclosed.