Family Tree Data Consolidation Using Phonetic Matching
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Solution Overview
Problem
Current systems for managing and consolidating related data records, such as family trees, face challenges in efficiently comparing and merging data from multiple sources, especially when data is inconsistent or entered differently, leading to inaccuracies and incomplete information.
Innovation Solution
A method and system for computerized consolidation of data records that compares and consolidates data records by matching comparative data items, using a combination of phonetic algorithms and weighting factors to determine matches, allowing for the merging of family trees and other related data records across different databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data comparison methods are used to consolidate data records, then the system is simple to implement, but the accuracy of matching inconsistent data from multiple sources deteriorates
Solution Approach 1:
The patent introduces phonetic algorithms as an intermediary mechanism between traditional comparison methods and data matching. This intermediary layer processes data through multiple comparison dimensions (phonetic similarity, weighting factors, confidence scores) to bridge the gap between simple implementation and accurate matching of inconsistent data records.
Solution Approach 2:
The system dynamically adjusts matching parameters including weighting factors for different data fields, confidence score thresholds, and phonetic algorithm sensitivity. By changing these parameters, the system can adapt to different data quality scenarios while maintaining both accuracy and manageable complexity through configurable rather than hard-coded solutions.
2Reliability
If multiple comparison criteria are used to determine data matches, then the reliability of consolidation improves, but the processing time increases
Solution Approach 1:
The patent implements a staged comparison approach where not all comparison criteria are applied uniformly to all data records. Instead, the system applies phonetic algorithms and weighting factors selectively based on initial data quality assessments and confidence scores, performing partial actions only where needed to achieve sufficient reliability without exhaustive processing of every record pair.
Solution Approach 2:
The system uses confidence scores as feedback mechanisms to adjust processing depth. When initial comparisons yield high confidence matches, the system terminates further processing early. When confidence is lower, additional comparison criteria are applied. This feedback-driven approach balances reliability requirements against processing time by adaptively allocating computational resources.
3Measurement precision
If manual verification of data matches is performed, then the precision of matching improves, but the productivity of the consolidation process deteriorates
Solution Approach 1:
The patent implements a self-verification mechanism where the system automatically generates confidence scores and match recommendations without requiring manual intervention. The phonetic algorithms and weighting factor calculations enable the system to self-assess match quality, providing precision comparable to manual verification while maintaining automated processing speed and productivity.
Data Source
AI summary
A method and system are presented for merging a first family tree to a second family tree. The method comprises: providing a plurality of first matrices associated with the first family tree and a plurality of second matrices associated with the second family tree, each matrix comprising attributes on individuals that are linked to one another by a predetermined link; identifying substantially identical first and second matrices in said pluralities of first and second matrices; and generating data indicative of a merge of the family trees to one another if a number of the substantially identical matrices exceeds a predefined number.