Networked Scale Controllers for Precise AI Supply Inventory
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Solution Overview
Problem
Current medical supply inventory management systems lack precision in weight detection, are often shelf-based, lack network connectivity, and fail to optimize supply-related tasks using AI/ML, leading to inefficiencies in labor, supply costs, and delivery speed.
Innovation Solution
A system utilizing a CAN bus scale controller with AI/ML capabilities, enabling IoT communication among multiple scales, detecting precise weights, and optimizing supply delivery through machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional weight transmitters are used for inventory monitoring, then basic weight measurement is achieved, but measurement precision and inventory management capability are insufficient
Solution Approach 1:
The patent combines multiple functions into an integrated scale controller system that merges weight measurement, inventory monitoring, AI/ML processing, and network communication capabilities into a single unified device, thereby improving measurement precision and inventory management while avoiding the need for multiple separate components
Solution Approach 2:
The scale controller is designed as a multi-functional device that can perform weight measurement, inventory tracking, supply monitoring, AI model training, and network communication simultaneously, allowing one device to replace multiple specialized devices and improve overall system efficiency
2Adaptability or versatility
If shelf-based weight sensors are deployed for inventory tracking, then inventory monitoring is enabled, but adaptability to different storage configurations (louver, mobile cart, fixed shelf) is limited
Solution Approach 1:
The scale controller system is designed to work universally across multiple storage configurations including louvers, mobile carts, and fixed shelves through standardized weight detection interfaces and configurable software settings, eliminating the need for configuration-specific hardware
Solution Approach 2:
The system employs dynamic configuration capabilities where the scale controller can adapt its operation mode based on the detected storage type, allowing flexible deployment across different environments without requiring physical reconfiguration or specialized hardware for each storage type
3Productivity
If AI/ML models are trained locally on the scale controller, then supply delivery optimization is achieved, but device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by collecting and preprocessing supply chain data locally on the scale controller before feeding it to AI/ML models, enabling optimized supply delivery predictions while keeping the controller's real-time processing requirements manageable through pre-computed features and historical data caching
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances inventory management by predicting optimized supply techniques, reducing labor and supply costs, and improving delivery speed through AI-driven networked scale controllers.
Implementation Method 1
a first load cell configured to detect a first weight of the first tray holder
Data Source
AI summary
Methods and systems are described for scale-based inventory management and monitoring at a location, such as hospitals. Scales may be configured to receive a tray/holder for a given type of supply item, e.g., bandages or syringes. A weight determined by the scale may be associated with a given quantity of the respective item. Data around supply delivery and restocking is collected and can be used in an AI/ML model to optimize delivery routes, labor options for delivery, delivery speed, or other factors useful in optimizing supply and logistics in any location with logistics challenges.