Postal Address Identification via Statistical Outlier Detection
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Solution Overview
Problem
Current postal address processing systems face challenges in accurately identifying and validating postal addresses, particularly in unstructured and semi-structured data, due to the complexity and variability of address formats, which existing tools struggle to handle effectively.
Innovation Solution
A novel method utilizing a uniform logical model of a postal address that combines frequentist and Bayesian statistical methods to identify components of the address, generating a statistical model based on training data and applying it to candidate objects, with outlier detection to validate the presence of postal addresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large database of known postal addresses is used for validation, then validation accuracy is improved, but the complexity and resource requirements of the system increase
Solution Approach 1:
The patent segments the postal address validation process into two distinct phases: discovery phase (identifying potential addresses using statistical models) and validation phase (confirming addresses using database lookup). This segmentation allows the system to handle complexity by dividing the task into manageable stages, where the statistical model handles the complex discovery phase without requiring the entire large database to be loaded into memory simultaneously.
Solution Approach 2:
The patent introduces a statistical model as an intermediary between the input data and the validation database. This statistical model acts as a mediator that pre-processes and filters candidate addresses before they are submitted to the validation database, reducing the effective search space and allowing accurate validation without directly confronting the full complexity of the large database.
2Reliability
If traditional postal address validation tools are used, then validation capability is provided, but the ability to handle structurally complex and varied address formats is limited
Solution Approach 1:
The patent employs statistical models that can adapt to different address formats by learning from training data. Instead of using rigid rules, the system adjusts its statistical parameters based on the distribution of addresses in the training set, enabling it to handle structural variations and anomalies in address formats while maintaining validation reliability.
Solution Approach 2:
The patent creates a universal statistical model that can handle multiple address types and formats across different geographies. The model is trained on diverse address data and can generalize to handle various structural complexities, making it multi-functional and adaptable to different address formats without requiring separate validation tools for each format type.
3Adaptability or versatility
If statistical models are used to identify address components, then adaptability to various address formats is improved, but the difficulty of detecting and measuring address presence increases
Solution Approach 1:
The patent implements a feedback mechanism where the statistical model continuously refines its predictions based on the validation results. The system uses feedback from the validation phase to adjust and improve the statistical model's detection capabilities, making it easier to detect address presence in future iterations while maintaining adaptability to various formats.
Data Source
AI summary
An apparatus, computer-readable medium, and computer-implemented method for postal address identification, including receiving one or more sequences of one or more tokens corresponding to one or more candidate postal address data objects, computing at least one candidate vector in a vector space, the at least one candidate vector corresponding to at least one candidate postal address data object in the one or more candidate postal address data objects, the vector space describing a universe of postal addresses and being clustered into a plurality of clusters, and determining whether the at least one candidate postal address data object corresponds to a postal address based at least in part on applying one or more outlier detection methods to the at least one candidate vector and one or more clusters in the plurality of clusters.