Contextual Address Matching System for Emerging Markets
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Solution Overview
Problem
Traditional consumer data search algorithms fail to accurately match addresses in emerging markets due to varying formats, quality issues, and lack of unique identification, leading to overmatching and undermatching, which affects database record merging and verification processes.
Innovation Solution
A system and method for address matching that involves contextual identification of address parts, normalization, comparison, and scoring, combined with free format token analysis to determine address likeness scores, addressing the formatting and quality issues in consumer data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional consumer data search algorithms are used to match addresses, then the search process is simple and fast, but the matching accuracy deteriorates due to overmatching and undermatching in emerging markets
Solution Approach 1:
The address matching system segments the address comparison process into multiple distinct stages: normalization processing, tokenization, phonetic matching, geographic coordinate extraction, and scoring. Each stage handles specific aspects of address variation, allowing the system to accurately process emerging market addresses while maintaining manageable complexity through modular design
Solution Approach 2:
The patent introduces intermediary components including normalization rules that standardize address formats, tokenization that breaks addresses into comparable units, and phonetic algorithms that serve as mediators between different address representations. These intermediaries enable accurate matching without requiring direct complex comparison of raw address strings
2Adaptability or versatility
If traditional algorithms rely on consistent formatting and discrete fields, then the algorithm design is straightforward, but the system fails to handle varying address formats and quality issues in emerging markets
Solution Approach 1:
The system dynamically adjusts matching parameters based on data quality assessment. When addresses show signs of poor quality or inconsistency, the system modifies weighting factors, adjusts threshold values, and changes the strictness of matching criteria. This allows the system to adapt to varying formats while maintaining reliable results by flexibly responding to data quality conditions
3Reliability
If traditional algorithms are used for address matching, then the processing speed is fast, but false positives and false negatives increase, reducing the usefulness of search results
Solution Approach 1:
The system performs preliminary actions including normalization and tokenization of addresses before the actual matching process. By pre-processing the data to extract key tokens and standardize formats, the system reduces the complexity of the subsequent matching operation, maintaining processing efficiency while improving result reliability through early quality enhancement
Solution Approach 2:
The patent replaces traditional mechanical string comparison methods with phonetic algorithms and geographic coordinate-based matching. This substitution allows the system to quickly assess address similarity through phonetic equivalence and spatial proximity, maintaining high processing speed while significantly reducing false positives and negatives
Data Source
AI summary
A system and method for the matching addresses is provided. Addresses may be received from a search engine or other source for purposes of matching. Address parts in the addresses may be contextually identified. Identified address parts, including their associated data, that have address part types that are alike may be compared to one another and a contextual matching score may be calculated and assigned. A free format token analysis of the addresses may also be performed in parallel with, before, or after, the contextual identification, and a free format matching score may be calculated. An address likeness score may be calculated and assigned based on the contextual matching score and the free format matching score.