Unstructured Address Normalization via NLP for Delivery Routing
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Solution Overview
Problem
In locations with poorly defined addresses and limited geocodes, conventional delivery systems face challenges in providing efficient and reliable delivery services, as they rely on well-defined addresses and geocodes for sorting and routing, which are not applicable in areas where addresses are described relative to landmarks or using descriptive language.
Innovation Solution
The use of natural language processing (NLP) and hybrid machine learning algorithms to process unstructured address data, identifying Normalized Delivery Locations (NDLs) and generating sorting zones, allowing for the efficient routing of packages by converting unstructured addresses into recognizable geographic locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional delivery systems use well-defined addresses and geocodes for sorting and routing, then delivery efficiency and reliability are improved, but the system cannot operate in locations with poorly defined addresses or limited geocodes
Solution Approach 1:
The patent introduces an intermediary processing layer (NLP system and machine learning models) that translates unstructured address descriptions into standardized geocodes and sorting zone identifiers. This intermediary layer enables the conventional delivery system to handle unstructured addresses by converting them into a format the sorting system can process, thus resolving the contradiction between maintaining reliable conventional operations and adapting to new address formats
Solution Approach 2:
The system changes the parameter representation of addresses from structured formats (street names, numbers, landmarks) to standardized internal parameters (geocodes, sorting zone IDs). By transforming the address data into different parameter forms that the sorting system can interpret, the system maintains its conventional reliable operation while gaining the ability to process diverse unstructured address inputs
2Adaptability or versatility
If delivery systems process unstructured address data using NLP and machine learning, then adaptability to various address formats is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the address processing task into distinct modular components: NLP module for text extraction, machine learning module for entity recognition, geocoding module for location mapping, and sorting zone assignment module for routing. This segmentation allows each component to be optimized independently and facilitates easier maintenance and updates, reducing overall system complexity while maintaining high adaptability
Solution Approach 2:
The patent creates a universal processing framework that handles multiple address formats (landmark-based, relative descriptions, informal addresses) through a single integrated system. This multi-functional system processes diverse input types using the same NLP and machine learning pipelines, reducing the need for multiple specialized systems and thereby lowering overall complexity
3Ease of operation
If relative address descriptions are used in locations with poorly defined addresses, then local delivery flexibility is improved, but large scale automated delivery services cannot be implemented
Solution Approach 1:
The patent replaces the mechanical reliance on human local knowledge and manual address interpretation with an automated electronic system using NLP and machine learning. The system electronically processes relative address descriptions and automatically extracts meaningful location information, substituting human cognitive processes with computational algorithms that can handle large volumes of deliveries efficiently while preserving the flexibility of local address descriptions
Data Source
AI summary
Techniques for routing items addressed to an unstructured address are described. One embodiment includes receiving an order for delivery of a first package, the order specifying a first address that does not comply with a defined address format. The first address is processed using one or more hybrid machine learning algorithms to determine a Normalized Delivery Location (NDL) associated with the first address. A sorting zone that encompasses the NDL is determined. The sorting zones correspond to a predefined geographic region. Embodiments facilitate transport of the first package to a physical shipping location within the predefined geographic region.