Geographical Location Determination via Address Scoring and Prioritization
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Solution Overview
Problem
Accurately determining geographical location information is labor-intensive and costly due to conflicting or outdated addresses, requiring methods to reduce human intervention and enhance verification efficiency.
Innovation Solution
A system and method that compile customer address records from database systems, assign scores based on attributes like recency and source, prioritize records by distance and score, and send subsets to service providers for verification, leveraging digital activity footprints to identify likely accurate addresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical verification by traveling to location is used, then accuracy of geographical location determination is improved, but cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by compiling customer records from multiple database sources and determining scores based on attributes (recency, source reliability, etc.) before physical verification. This pre-processing filters and prioritizes address records, so that when service providers need to verify locations, they already have a prioritized subset of most likely accurate addresses, reducing the time and scope of physical verification needed
Solution Approach 2:
The system introduces an intermediary computational layer that processes and scores address records from multiple databases before sending them to service providers. This intermediary system acts as a mediator between raw data and physical verification, using automated scoring based on recency, source reliability, and other attributes to filter and prioritize records, thereby reducing the burden of physical verification while maintaining accuracy
2Measurement precision
If physical verification by traveling to location is used, then accuracy of geographical location determination is improved, but cost increases significantly
Solution Approach 1:
The system performs preliminary scoring and filtering of address records using automated processes that evaluate recency, source reliability, and other attributes. This pre-processing reduces the number of addresses that require expensive physical verification, thereby lowering overall verification costs while maintaining accuracy for the most promising candidates
Solution Approach 2:
The system introduces an intermediary computational layer that processes and scores address records from multiple databases before sending them to service providers. This intermediary system uses automated scoring based on recency, source reliability, and other attributes to filter and prioritize records, thereby reducing the burden of physical verification while maintaining accuracy
3Reliability
If multiple address records are verified manually, then completeness of verification is improved, but productivity decreases
Solution Approach 1:
The system segments the verification process into distinct phases: (1) automated compilation and scoring of address records from multiple databases, (2) prioritization of records based on scores and recency, and (3) selective verification by service providers. This segmentation allows the system to handle large volumes of records through automated processing while maintaining thoroughness through systematic prioritization
Solution Approach 2:
The system applies partial action by not requiring verification of all address records, but rather focusing verification efforts on the highest-priority subset based on automated scoring. By verifying only the most likely accurate addresses first (partial verification), the system achieves high productivity while maintaining reliability through the scoring filter that identifies the most promising candidates
Data Source
AI summary
Systems and methods for determining accurate addresses for assets are described herein. According to some aspects, a plurality of addresses associated with an asset are compiled from sources such as database systems. For the plurality of address records, scores that correspond to attributes of the address records can be determined. Attributes associated with the address records may include a recency, a source, a date, license plate data, or address type, to name several non-limiting examples. Additionally, a zone of interest for the asset can be determined based on the plurality of address records. The plurality of address records is prioritized based on their distance from the zone of interest and the scores associated with them and, based on the priority, a subset of the plurality of address records is sent to, for example, a service provider.