AI Data Error Detection and Repair Through Confidence-Based Review
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Solution Overview
Problem
Current systems lack an efficient method for detecting and correcting errors in company data, such as outdated client information or incorrect product dimensions, which can lead to operational issues.
Innovation Solution
A system utilizing artificial intelligence (AI) to automate the detection and correction of data errors through a human-in-the-loop process, comprising a connect unit, integrate unit, detect unit, correct unit, and repair unit, with machine learning algorithms for anomaly detection and correction, and a user interface for feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to detect and correct data errors, then data accuracy can be maintained, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical data verification processes with an automated AI-based system that uses machine learning models to detect and correct data errors. The system automatically compares data against learned patterns and external knowledge bases, eliminating the need for human reviewers while maintaining high accuracy through sophisticated anomaly detection algorithms.
Solution Approach 2:
The patent introduces an intermediary AI system that acts as a bridge between raw data and final corrected output. This intermediary layer includes multiple processing stages: initial anomaly detection, confidence scoring, selective human review for low-confidence cases, and automated correction for high-confidence cases. This multi-stage intermediary process enables both speed and accuracy.
2Reliability
If comprehensive data validation is performed on all records, then data quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements partial validation by applying different levels of scrutiny to different data records based on their characteristics and the system's confidence in automated detection. High-confidence automated corrections are applied without human review, while low-confidence cases receive more intensive validation or human review. This selective approach processes more records faster while maintaining quality through focused validation on problematic cases.
3Productivity
If automated AI systems are deployed for data correction, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent segments the data correction system into distinct functional modules: data ingestion module, anomaly detection module, confidence scoring module, human review coordination module, and correction application module. Each module performs a specific function and can be independently trained, deployed, and maintained. This segmentation reduces overall system complexity by creating manageable, specialized components rather than a monolithic complex system.
4Measurement precision
If human review is required for all corrections, then accuracy is maintained, but productivity decreases
Solution Approach 1:
The patent applies partial human review by using confidence scoring to determine which corrections need human verification. Corrections with high confidence scores (above a threshold) are applied automatically without human review, while low-confidence corrections are flagged for human review. This approach maintains accuracy for uncertain cases while achieving high productivity for confident automated corrections, effectively processing far more records than universal human review would allow.
Data Source
AI summary
The following relates generally to detecting and repairing data errors using artificial intelligence (AI). In some embodiments, one or more first processors execute an AI toolkit comprising a plurality of units. The plurality of units may include, for example, a connect unit, an integrate unit, a detect unit, a correct unit, a repair unit, and/or a visualize unit. The AI toolkit may then be deployed to one or more second processors. The one or more second processors may then further execute the AI toolkit and/or run/augment the AI toolkit to detect and/or repair errors in data.