Dynamic Electronic Record Matching Through Staged Attribute Comparison
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Solution Overview
Problem
Existing systems face inefficiencies in processing large volumes of electronic data records, requiring numerous comparisons to identify duplicates or corresponding records, which is time-consuming and computationally expensive, and necessitate repeating the process if comparison criteria are modified.
Innovation Solution
A system and method that efficiently determines, matches, and links electronic data records by selecting attributes for comparison, scoring them against thresholds, and forming loosely connected super entities, allowing flexible modification of criteria without re-comparing all records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If N2 comparisons are performed to identify duplicate or corresponding records in large volumes of electronic records, then complete matching accuracy is achieved, but processing time and computational cost increase significantly
Solution Approach 1:
The patent segments the matching process into multiple stages: first comparing a subset of key attributes to generate candidate matches, then performing more comprehensive comparisons only on those candidates. This hierarchical segmentation reduces the total number of comparisons from N2 to a manageable level while maintaining matching accuracy through progressive filtering.
Solution Approach 2:
The patent performs preliminary comparisons on selected attributes before conducting full record matching. By pre-comparing specific attributes and establishing candidate sets, the system prepares data in advance to avoid performing complete N2 comparisons, thereby reducing processing time while preserving matching precision through subsequent verification steps.
2Measurement precision
If N2 comparisons are performed to identify duplicate or corresponding records, then all matching records are identified, but computational resources are excessively consumed
Solution Approach 1:
The matching process is divided into segmentation steps where different attribute sets are compared at different stages. Critical attributes are compared first to create candidate sets, reducing the scope of subsequent comparisons. This segmentation maintains matching completeness by ensuring all candidates are identified while reducing computational cost through progressive filtering.
Solution Approach 2:
The patent applies partial action by comparing only essential attributes initially rather than all attributes for every record pair. This partial comparison approach identifies candidate matches with sufficient accuracy to proceed to more detailed verification only when necessary, thereby reducing overall computational cost while maintaining matching completeness.
3Adaptability or versatility
If the criterion for linking electronic records is modified, then the matching process can be adapted to new requirements, but the time-consuming comparison process must be repeated
Solution Approach 1:
The patent performs preliminary comparisons and stores intermediate results that can be reused when matching criteria are modified. By pre-comparing attributes and caching results, the system enables rapid re-evaluation under different criteria without repeating all N2 comparisons, thus maintaining criteria flexibility while minimizing re-computation time.
Solution Approach 2:
The patent separates the matching process into parameter-independent preprocessing steps and parameter-dependent evaluation steps. By changing only the evaluation parameters (criteria) while reusing precomputed data structures and intermediate results, the system adapts to new requirements efficiently without reperforming the entire comparison process.
4Measurement precision
If all attributes are compared for every record pair, then comprehensive matching accuracy is achieved, but processing efficiency decreases
Solution Approach 1:
The patent segments attribute comparison into hierarchical levels: critical attributes are compared first for all candidate pairs, then additional attributes are compared only for pairs that meet initial thresholds. This segmentation maintains matching accuracy by ensuring comprehensive verification of promising candidates while improving processing efficiency through progressive filtering.
Solution Approach 2:
The patent applies different comparison depths to different record pairs based on their similarity in critical attributes. Pairs showing strong initial matches receive more thorough attribute comparison, while pairs with weak initial matches receive minimal or no further comparison. This local quality approach maintains accuracy for true matches while improving overall processing efficiency.
Data Source
AI summary
A system for searching and/or linking corresponding electronic data records includes: choosing, for a target electronic data record, a group of candidate electronic data record(s) for comparison; selecting a first set of one or more attributes for comparison between the target record and the group of candidate record(s), and choosing a threshold for the comparisons. The system further includes comparing attribute(s) of the target electronic data record to attribute(s) of the group of candidate records; and scoring the comparison of the attribute(s) of the target electronic data record to the attribute(s) of the candidate records. The system further can include configuring a sieve to apply to electronic records, wherein the sieve has a second set of attribute(s) and a corresponding threshold for each of the second set of attribute(s), and applying the sieve to one or more electronic data records to identify records.