Computer-Assisted Image Reconciliation for Discrepancy Resolution
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Solution Overview
Problem
The increased volume of digital image data in various fields, such as medical imaging, satellite data, and package screening, overwhelms human resources, requiring time-consuming and costly secondary reviews to reconcile discrepancies between independent readings, which can be inefficient and complex.
Innovation Solution
A computer-assisted reconciliation technique integrates and resolves discrepancies between multiple independent reads of image data sets, using automated routines and operator interfaces to form a unified data set, allowing for efficient identification and classification of features, and providing tools for reconcilers to address disagreements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple independent reads are performed to ensure accuracy, then reliability of feature identification is improved, but time consumption and resource requirements increase
Solution Approach 1:
The patent segments the reconciliation process by automatically categorizing discrepancies into different types (detection discrepancies, segmentation discrepancies, classification discrepancies) and presenting them in an organized manner. This allows readers to efficiently address specific types of discrepancies rather than reviewing all differences manually, thus maintaining reliability while reducing time consumption.
Solution Approach 2:
The patent introduces an intermediary software system that automatically compares multiple independent reads, identifies discrepancies, and presents them in a structured format. This intermediary tool mediates between the multiple readers and the final reconciliation outcome, enabling accurate comparison without requiring manual review of all data by all readers, thereby reducing time while preserving reliability.
2Reliability
If multiple independent reads are performed to resolve discrepancies, then reliability is improved, but resource requirements (number of readers) increase
Solution Approach 1:
The patent enables the system to perform self-service by automatically comparing multiple independent reads and identifying discrepancies without requiring additional human readers. The software systematically analyzes detection, segmentation, and classification differences between reads, providing automated reconciliation support that reduces dependency on increasing human resources while maintaining reliability through comprehensive comparison.
Solution Approach 2:
The patent introduces an intermediary software system that acts as a virtual reader, automatically comparing multiple independent reads and identifying discrepancies. This intermediary tool performs the comparison function that would otherwise require additional human readers, thereby maintaining reliability through thorough analysis while reducing the need for increased human resources.
3Loss of information
If all data including concurrences are presented for review, then completeness of information is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The patent segments the presentation of reconciliation data by separating concurrences from discrepancies and further categorizing discrepancies into detection, segmentation, and classification types. This segmentation allows the system to maintain completeness of information by preserving all data while reducing interface complexity through organized, hierarchical presentation that guides users through relevant information systematically.
Solution Approach 2:
The patent extracts and separates the essential reconciliation information (discrepancies) from the complete dataset, presenting only the relevant differences that require reader attention. By extracting and highlighting specifically the discordant features while maintaining access to complete data, the system preserves information completeness while simplifying the interface and improving ease of operation.
4Measurement precision
If manual comparison and reconciliation of discrepancies is performed, then accuracy of resolution is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary action by automatically comparing multiple independent reads and pre-identifying all discrepancies before presenting them to readers. The system systematically analyzes detection, segmentation, and classification differences in advance, organizing them by type and location. This preliminary automated comparison preserves accuracy by ensuring thorough analysis while improving productivity by eliminating manual comparison time and presenting ready-analyzed discrepancies for efficient resolution.
Data Source
AI summary
A technique for reconciling two or more reads of an image data set. One or more computer implemented routines is employed to provide computer-assisted reconciliation (CAR) including resolution of discrepancies between the two or more reads. The computer-assisted reconciliation may optimally display the discrepancies, the concurrences and any associated information to a human reconciler, may resolve the discrepancies in a partially automated manner, or may resolve the discrepancies in a fully automated manner. The reconciled data may then be provided to an end user.