Custodian Data Matching via Non-Phonetic Algorithm Grouping
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Solution Overview
Problem
Existing systems fail to efficiently match different user representations of a person across various computer systems due to varying identifiers and lack of appropriate algorithms, leading to difficulties in identifying custodians' data access and ownership, especially in legal contexts, where manual processes are time-consuming and prone to errors.
Innovation Solution
A computer-implemented method and system that collects and normalizes user representation information from multiple systems, uses non-phonetic algorithms like q-gram and suffix algorithms to group and compare user names, calculates similarity scores, and classifies matches and non-matches, employing dynamic programming and weighting techniques to merge information of matched pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to match user representations across systems, then accuracy can be maintained through human review, but time consumption and operational costs increase significantly
Solution Approach 1:
The patent introduces an intermediary matching system that acts as a mediator between disparate user representation systems. This system collects information sets from multiple sources, normalizes them to a common format, and applies automated matching algorithms to determine correspondences. The intermediary approach maintains accuracy by using structured comparison criteria while eliminating the time consumption of manual review for each case.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. The system uses algorithms to compare user representations, calculate similarity scores, and determine matches automatically. This substitution maintains measurement precision through consistent application of matching criteria while dramatically reducing the time loss associated with manual processes.
2Adaptability or versatility
If multiple identifier formats are maintained across different systems, then system-specific requirements are satisfied, but the complexity of matching and integrating data increases
Solution Approach 1:
The patent segments the matching process into distinct operational stages: collection of information sets from various systems, normalization to a unified format, comparison of normalized data, and determination of matches. This segmentation allows each stage to handle specific aspects of the complexity, making the overall process more manageable while maintaining adaptability to different source systems.
Solution Approach 2:
The normalization step acts as an intermediary transformation layer that converts diverse identifier formats from multiple systems into a unified format suitable for comparison. This intermediary approach preserves system compatibility by accepting various input formats while reducing matching complexity by providing a standardized basis for comparison.
3Reliability
If comprehensive data collection from all systems is performed, then complete custodian identification is achieved, but the volume of data to be processed and matched increases
Solution Approach 1:
The patent segments the large volume of collected data into discrete information sets, each representing a user representation from a specific system. This segmentation allows the system to process and compare individual units systematically, maintaining identification completeness while making the data volume manageable through structured handling.
Solution Approach 2:
The patent extracts only the relevant comparison elements from the comprehensive collected data during the normalization and comparison stages. By taking out and focusing on the essential identifying features needed for matching, the system achieves complete custodian identification without being overwhelmed by the total data volume.
Data Source
AI summary
Provided is a technique for matching different user representations of a person in a plurality of computer systems may be provided. The technique includes collecting information sets about user representations from a plurality of computer systems; normalizing the information sets to a unified format; grouping the information sets in the unified format into indexing buckets based on a user name using a non-phonetic algorithm; determining a similarity score for each pair of information sets in each of the indexing buckets; classifying each information set pair into a set of classes based on the similarity scores, wherein the set of classes comprise at least matches and non-matches; and using a data structure for merging information of information set pairs classified as matches.