ML Address String Translation to Standardized Formats
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Solution Overview
Problem
Existing address verification and translation systems struggle with processing poorly formatted address strings, including inconsistencies, missing or duplicate substrings, and non-standard formats, which leads to incorrect location identification and revenue loss for businesses.
Innovation Solution
A system that uses machine learning to classify substrings as address or non-address substrings, detects and corrects non-compliant signatures, and translates inconsistent address strings into standardized formats compliant with specific jurisdictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional address verification systems are used to process address strings, then processing speed is maintained, but accuracy of location identification deteriorates due to inconsistent and poorly formatted address strings
Solution Approach 1:
The system segments the address processing task into multiple specialized components: a machine learning model for classification and correction, a format validation module for jurisdiction-specific rules, and a processing queue for batch handling. This segmentation allows each component to focus on specific aspects of address standardization, improving overall accuracy without requiring complete system redesign.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw address strings and the traditional address verification system. This intermediary pre-processes and standardizes addresses before they reach the verification system, improving accuracy while isolating the complexity of ML models from the core verification logic.
2Measurement precision
If machine learning models are integrated to correct address strings, then translation accuracy improves, but processing time increases due to computational requirements
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on extensive address datasets before deployment. During runtime, the pre-trained model quickly classifies and corrects addresses using learned patterns, reducing real-time computational requirements and processing time while maintaining high accuracy.
Solution Approach 2:
The machine learning model enables self-service by automatically detecting and correcting common address formatting errors without requiring manual intervention or complex rule-based systems. The model learns from training data to autonomously standardize addresses, reducing both processing time and operational overhead.
3Quantity of substance
If multiple data sources are integrated to provide comprehensive address coverage, then completeness of address database improves, but consistency of address formats deteriorates
Solution Approach 1:
The patent implements a universal address standardization framework that can process addresses from multiple data sources simultaneously. The system applies jurisdiction-specific formatting rules and a standardized schema to all input addresses, ensuring consistent output formats regardless of source variations. This multi-functional approach maintains database completeness while enforcing format consistency.
Data Source
AI summary
The technology disclosed relates to systems and methods for constructing standardized addresses for a plurality of jurisdictions. The system comprises a trained model configured to process, an input, a string that includes a sequence of substrings. The trained model is configured to produce, as output, respective address component classifications for respective substrings in the sequence of substrings. The trained model is trained on thousands to millions of training examples that map substrings to a plurality of address component types and a plurality of non-address component types.