Smart Address Classification Using ML Zone Partitioning

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Solution Overview

Problem

Current methods for determining the shortest route for entity movement in logistics rely heavily on manual classification and sorting based on postal codes, leading to inefficiencies, high processing times, and excessive dependency on skilled personnel, resulting in unsystematic handling and delivery processes.

Innovation Solution

A computer-implemented method that logically partitions geographical regions into dynamic and adaptive logical zones using machine learning algorithms to extract points of interest from address data, reducing the need for manual intervention and improving sorting accuracy and efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If manual classification and sorting based on postal codes is used, then routing paths can be determined, but processing time increases and productivity decreases

Engineering Contradiction:

Improveprocessing timeVSAvoidproductivity of classification and sorting process

Core Design Contradiction:

Loss of timeVSProductivity

Solution Approach 1:

The patent replaces the manual mechanical classification system with an automated image recognition and machine learning system. The system uses optical character recognition (OCR) to extract text from address labels, processes images through neural networks to identify postal codes and routing information, and automatically determines routing paths without human intervention, thereby dramatically reducing processing time and increasing productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service classification and sorting by automatically processing address information without requiring skilled personnel. The machine learning model autonomously extracts postal codes, identifies routing zones, and determines optimal paths, making the classification process independent of human expertise while maintaining high accuracy and speed

Inventive Principle:

Principle #25Self-service

2Reliability

If manual classification by skilled individuals is used, then accurate routing paths can be determined, but dependency on skilled personnel increases

Engineering Contradiction:

Improveaccuracy of routing path determinationVSAvoiddependency on skilled individuals

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The patent replaces human expertise with an automated image recognition system that uses convolutional neural networks and OCR technology to accurately extract and interpret postal codes and routing information from address labels, eliminating dependency on skilled personnel while maintaining or improving accuracy through consistent automated processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where the machine learning model is continuously trained on processed address data, improving its accuracy over time. The system validates extracted postal codes against known databases and uses feedback from routing outcomes to refine its classification algorithms, ensuring reliable and accurate routing path determination without human intervention

Inventive Principle:

Principle #23Feedback

3Productivity

If postal code wise classification and sorting is used, then entities can be routed, but the process consumes a lot of processing time during movement and handling

Engineering Contradiction:

Improvespeed of classification and sortingVSAvoidprocessing time during movement and handling

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary classification and routing determination at the point of origin by automatically processing address labels before entities enter the logistics network. By pre-determining routing paths and postal code classifications using automated image recognition, the system eliminates time-consuming manual sorting during transit and handling, significantly reducing processing time while maintaining high productivity

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11250452B2Method and system for smart address classification

Publication Date: 2022.02.15 NTEX TRANSPORTATION SERVICES PVT LTD

AI summary

The present disclosure provides computer-implemented method and a system for classification and sorting of one or more addresses to increase productivity of classification and sorting process of the one or more addresses. The system logically partitions a geographical region into one or more zones in real-time. The system fetches an address data from an entity of the one or more entities containing destination address. Further, the system extracts one or more points of interests from the fetched address data based on hardware-run machine learning algorithms. Furthermore, the system generates a signal to determine a zone of the one or more zones associated with the entity of the one or more entities. The system logically updates the one or more zones based on the extracted one or more points of interests in real-time.