Confidence-Based GUI for Record Matching Validation
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Solution Overview
Problem
Current user interfaces for validating matches between records in computer systems are cumbersome and fail to properly focus user attention on important validation aspects, leading to data integrity issues due to user abandonment or acceptance of incorrect matches.
Innovation Solution
A graphical user interface (GUI) is designed to organize target records into review level categories based on confidence levels, presenting matches in separate sections to simplify user review and validation, with features like table format, pinned fields, and demarcation of differences between records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current user interfaces are used for validating matches, then users can review match results, but the interfaces are crowded and cumbersome causing user fatigue and abandonment
Solution Approach 1:
The patent divides the user interface into multiple organized sections based on confidence levels (e.g., high confidence matches, low confidence matches, partial matches). This segmentation allows users to systematically review matches in a structured manner rather than facing a crowded single-view interface, reducing cognitive load and improving ease of operation.
Solution Approach 2:
The patent introduces an additional organizational dimension by categorizing matches along the confidence level axis. Instead of presenting all matches in a single flat view, the interface adds a hierarchical dimension where matches are grouped and prioritized by confidence scores, transforming the overwhelming 2D clutter into an organized multi-level structure.
2Loss of information
If all match details are displayed in the user interface, then users have complete information for validation, but the interface becomes crowded and difficult to navigate
Solution Approach 1:
The patent segments information display by confidence levels and match types, presenting only relevant match details in each section. High confidence matches show minimal information for quick approval, while low confidence matches display more detailed comparison data. This selective segmentation maintains information completeness while preventing interface crowding.
Solution Approach 2:
The patent applies local quality by providing different levels of detail in different interface sections based on user needs. Critical validation information is prominently displayed in specific areas, while less important details are minimized or hidden. Each section of the interface has optimized information density appropriate to its purpose, improving overall usability without losing essential information.
3Reliability
If users manually review all matches, then data integrity is maintained, but users may abandon the process due to volume and time requirements
Solution Approach 1:
The patent segments the validation workload by confidence levels, allowing users to quickly process high confidence matches with minimal review while allocating more time to low confidence matches that require careful examination. This segmented approach maintains data integrity for all matches while significantly increasing overall validation throughput by reducing time spent on obvious matches.
Solution Approach 2:
The patent enables users to skip or rapidly approve high confidence matches that clearly meet validation criteria, allowing them to rush through the majority of matches that require minimal scrutiny. This selective skipping approach maintains reliability by still requiring thorough review of uncertain matches while dramatically improving productivity by not wasting time on obvious matches.
4Productivity
If computer systems automatically match records, then processing speed increases, but accuracy decreases when records do not match exactly
Solution Approach 1:
The patent applies preliminary action by having the computer system automatically perform initial matching and confidence scoring before user review. This preliminary automated matching rapidly processes large volumes of records to identify potential matches, then presents only the uncertain or low confidence cases to users for validation, maintaining both high processing speed and accurate match determination.
Solution Approach 2:
The patent implements feedback by using user validation results to refine and retrain the automated matching algorithms. User corrections and confirmations provide feedback signals that improve the system's matching accuracy over time, creating a continuous improvement loop that increases both productivity and measurement precision with each iteration.
Data Source
AI summary
A method includes obtaining matches between target records in a target dataset and a reference records in a reference dataset, each match of the matches comprising a corresponding confidence level of the match, categorizing the target records into review level categories according to the corresponding confidence level, and presenting a graphical user interface (GUI). The GUI includes a first section for a first review level category showing a first subset of the target records assigned to the first review level category, the first subset comprising target records related, in the GUI, to at least one matching reference record. The GUI includes a second section for a second review level category, wherein the second section shows a second subset of the target records assigned to the second review level category, the second subset comprising target records related, in the GUI, to at least one matching reference record.