Network Inventory via Visual Component Analysis
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Solution Overview
Problem
Conventional inventory applications for network systems face challenges such as reliance on operational network hardware, complex management interfaces, and vendor-specific protocols, leading to stale and incorrect inventory information, especially during extended downtimes or when human intervention is not feasible.
Innovation Solution
A monitoring system utilizing a processing device with an inventory evaluation module that obtains visual representations of network components, analyzes them using a Machine Learning model to evaluate status, and provides real-time, vendor-agnostic inventory data without requiring operational hardware or human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional inventory applications rely on network hardware management interfaces to communicate inventory information, then inventory data can be obtained through standardized protocols, but the system cannot obtain accurate inventory information when hardware is non-operational, management interfaces are down, or during extended downtimes
Solution Approach 1:
The patent introduces an intermediary imaging system and image processing pipeline between the network hardware and inventory management software. Instead of directly querying hardware management interfaces, the system captures images of physical hardware components (such as LED indicators, component labels, and physical configurations) and processes these images to extract inventory information. This intermediary approach allows the system to obtain inventory data regardless of whether the hardware is operational or the management interface is accessible.
2Adaptability or versatility
If conventional inventory applications use vendor-specific management interfaces and protocols, then communication with specific hardware can be established, but the system complexity increases significantly when normalizing software behavior across multi-vendor equipment
Solution Approach 1:
The patent employs image copying and processing to create a universal interface for inventory management. Instead of implementing multiple vendor-specific protocol handlers, the system captures visual representations (images) of hardware components that can be processed uniformly regardless of vendor or device type. The image processing pipeline extracts relevant inventory information from these visual copies, eliminating the need for complex vendor-specific software adaptations while maintaining broad compatibility.
3Measurement precision
If manual inventory verification is performed to ensure accuracy, then inventory information can be validated, but the productivity and efficiency of inventory management decreases due to human intervention requirements
Solution Approach 1:
The patent implements a self-service inventory management system where the hardware components themselves provide verification information through their physical characteristics visible in images. LED indicators, component labels, and physical configurations captured in images serve as self-verified status information. The automated image processing pipeline extracts and validates inventory data from these self-provided visual cues, eliminating the need for manual verification while maintaining high accuracy and enabling continuous automated monitoring.
Data Source
AI summary
Systems and methods include receiving visual representations of components in network elements in a network; responsive to training a machine learning model to evaluate the visual representations, analyzing the visual representations using the machine learning model to determine a status of the components; and providing the status of the components to an inventory application for any of updating the inventory application and reconciling existing data in the inventory application.