Container Content Identification Using ML Confidence Scoring
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Solution Overview
Problem
Current inventory management systems face inaccuracies due to incomplete initial reporting during inbound processes, leading to inefficiencies and increased operational costs, as manual inspection of containers is often required to verify contents.
Innovation Solution
Implementing a container management system that uses a machine learning system for automated identification of container contents, generating a confidence parameter to determine the accuracy of the identification, and adjusting inventory records accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of containers is performed to verify contents, then measurement precision of inventory data is improved, but productivity decreases and loss of time increases
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated imaging and machine learning system. Cameras capture images of container contents, and ML models automatically identify and classify items, substituting human labor with an automated optical and computational system that achieves both high accuracy and fast processing
Solution Approach 2:
The patent introduces an intermediary system consisting of imaging devices and machine learning algorithms that mediate between the container contents and the inventory management system. This intermediary automatically extracts and verifies inventory information without requiring direct manual inspection
2Reliability
If manual inspection of containers is performed to verify contents, then reliability of inventory data is improved, but loss of time increases
Solution Approach 1:
The automated imaging and ML verification system operates continuously without interruption, processing containers as they arrive without requiring stopping for manual inspection. This maintains continuous inbound workflow while ensuring reliable inventory data through automated verification
Solution Approach 2:
The system replaces time-consuming manual inspection with rapid automated image capture and ML analysis, achieving reliable inventory verification in seconds rather than minutes or hours of manual work
3Productivity
If automated identification system is implemented, then productivity is improved and loss of time is reduced, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional integrated system where cameras serve both security monitoring and inventory identification purposes, ML models perform both item classification and verification of inbound data, reducing the need for separate dedicated devices and lowering overall system complexity
Solution Approach 2:
The machine learning system is trained on historical imaging data to automatically improve its own accuracy over time, and the system self-verifies inventory data without requiring external manual intervention, reducing the need for complex external verification mechanisms
Data Source
AI summary
Disclosed herein are systems and methods for identifying contents of a shipping container that holds a set of unidentified inventory items. To characterize the identity of the unidentified inventory items, one or more scans may be performed on shipping container to obtain container information, which can include a physical attribute of the shipping container, a physical attribute of an item in the shipping, or logistics information relating to the shipping container. Using the container information and stored inventory data, an identity of the unidentified inventory can be characterized, and a confidence value may be determined.