Cloud Cargo Management System Optimizing Container Space
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Solution Overview
Problem
The modern freight shipping industry faces inefficiencies and errors due to human error in managing and tracking commercial items, particularly in warehousing and management facilities, where incorrect processing operations can disrupt the entire supply chain, leading to costly mistakes and slow-downs.
Innovation Solution
A computer-implemented method that collects and groups commercial items based on their physical properties using a cloud-based service, associating each group with a human-recognizable symbol and generating a build plan for efficient storage and shipping, maximizing warehouse space and reducing human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators manually process and track commercial items, then flexibility and adaptability are maintained, but error rates increase and processing speed decreases
Solution Approach 1:
The patent replaces manual human processing with an automated computer vision system that uses cameras, machine learning algorithms, and robotic automation to identify, measure, and sort commercial items. This substitution eliminates human error while maintaining processing speed through automated continuous operation.
Solution Approach 2:
The system enables commercial items to be automatically identified and processed through computer vision technology that reads labels, detects physical properties, and autonomously determines storage locations without requiring human intervention for each individual item processing decision.
2Reliability
If comprehensive manual checking of each package is performed, then error rates decrease, but processing time increases significantly
Solution Approach 1:
The automated vision system operates continuously without interruption, processing multiple items simultaneously in parallel. The system maintains constant monitoring and processing operations, eliminating the start-stop nature of manual checking and significantly reducing total processing time while maintaining high accuracy through continuous automated verification.
Solution Approach 2:
Manual inspection is replaced with automated optical scanning and machine learning algorithms that rapidly analyze package characteristics, dimensions, and labeling in seconds, achieving both speed and accuracy that manual checking cannot provide.
3Reliability
If automated systems are implemented to reduce human error, then processing accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional integrated system where a single vision platform performs multiple tasks including item identification, physical property measurement, label recognition, and storage location determination. This consolidation reduces overall system complexity compared to having separate specialized systems for each function.
Solution Approach 2:
The system uses digital copies and representations of physical items through computer vision to store and process information, replacing the need for complex physical handling and measurement systems. Virtual modeling of items enables accurate processing without complex physical intervention mechanisms.
4Adaptability or versatility
If manual decision-making is used for warehouse operations, then operational flexibility is maintained, but consistency and standardization decrease
Solution Approach 1:
The system incorporates dynamic adaptability through machine learning algorithms that automatically adjust to new item types, packaging formats, and warehouse configurations. The vision system learns from environmental variations and maintains consistent processing standards across different scenarios, combining flexibility with precision.
Solution Approach 2:
The automated system incorporates feedback loops where performance data is continuously analyzed and used to refine processing algorithms. This feedback mechanism ensures consistent operational standards while adapting to changing conditions, maintaining both flexibility and precision through data-driven optimization.
Data Source
AI summary
In an embodiment, the methods and systems disclosed herein utilize a cloud-based service to accept measurements of commercial objects for storage in a database. In an embodiment, commercial objects are automatically grouped based on measured physical characteristics. In an embodiment, a build plan is generated to utilize the maximum amount of space possible in a shipping container based on the grouped commercial objects.


