Inventory Tracking via Site Image Analysis
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Solution Overview
Problem
The management of device inventories becomes challenging due to improper updates when configuration changes are made, often resulting in devices becoming out of sync with the inventory system, especially due to missing documentation or human errors during physical manipulations by technicians.
Innovation Solution
A system that utilizes captured images of a site to monitor and detect configuration changes, employing AI/ML techniques to identify and map these changes to the corresponding equipment, ensuring accurate updates to the inventory system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual documentation methods are used for device configuration tracking, then implementation simplicity is maintained, but accuracy and reliability of inventory data deteriorate due to human errors and missing documentation
Solution Approach 1:
The patent replaces manual documentation mechanisms with an automated image-based monitoring system. Image capture equipment automatically captures device configurations, and AI/ML models analyze these images to detect changes, substituting the mechanical process of manual documentation with an automated optical and computational system.
Solution Approach 2:
The patent creates visual copies of device configurations through captured images. These image copies serve as records of device states, allowing the system to compare current configurations against historical images to detect changes without requiring manual transcription or documentation.
2Measurement precision
If automated image-based monitoring is implemented, then inventory data accuracy improves, but system complexity and computational requirements worsen
Solution Approach 1:
The patent segments the complex AI/ML processing into distinct functional components: image capture, preprocessing, feature extraction, change detection, and inventory update. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by breaking down the complex processing pipeline into manageable stages.
Solution Approach 2:
The patent introduces an intermediary processing layer between image capture and inventory updating. The AI/ML model acts as an intermediary that transforms raw image data into structured change detection results, which then trigger inventory updates. This intermediary layer simplifies the connection between complex image processing and the inventory management system.
3Reliability
If frequent monitoring is performed to maintain synchronization, then inventory accuracy improves, but time consumption and processing overhead worsen
Solution Approach 1:
The patent implements periodic monitoring through scheduled image captures at defined intervals. This periodic action maintains inventory synchronization by regularly updating the inventory system with current device configurations, while the interval-based approach prevents excessive processing that would occur with continuous monitoring.
Solution Approach 2:
The system performs self-service by automatically detecting changes through image analysis and triggering inventory updates only when necessary. The AI/ML model autonomously identifies configuration changes and initiates updates without requiring manual intervention or continuous processing, reducing time consumption while maintaining synchronization.
Data Source
AI summary
The technologies described herein are generally directed to monitoring the configuration of an inventory of devices using captured images of a site. For example, a method described herein can include identifying a group of equipment installed at locations within a site. The method can further include, based on an image captured at the site by image capture equipment, detecting configuration activity at the site. Further, the method includes, based on analysis of the image, associating, by the tracking equipment, the configuration activity with equipment of the group of equipment.