Screenshot Analysis for Enterprise Client Error Detection
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Solution Overview
Problem
Existing methods are inefficient in remotely detecting application malfunctions on client devices deployed in enterprise facilities, as they require manual intervention and are costly to maintain with increasing device numbers.
Innovation Solution
A client management server analyzes screenshots from client devices using a classification model to differentiate between normal and error states, performing remedial actions such as restarts and updating the model based on feedback to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used to detect application malfunctions, then detection accuracy can be maintained, but cost and time consumption increase significantly with increasing device numbers
Solution Approach 1:
The system enables self-service by having client devices automatically capture screenshots and transmit them to the server for analysis. The classification model automatically detects error states without requiring manual intervention, and remedial actions are executed autonomously. This transforms the manual detection process into an automated self-service system that maintains accuracy while reducing time consumption.
Solution Approach 2:
The patent replaces the mechanical manual inspection process with an automated electronic system. Screenshots are captured and transmitted electronically, processed by a classification model using machine learning algorithms, and analyzed by a server. This substitution of mechanical manual detection with electronic automation enables scalable reliable detection without increasing time consumption.
2Loss of time
If automated detection systems are deployed to reduce manual intervention, then time consumption decreases, but system complexity increases
Solution Approach 1:
The system extracts only the essential visual information from the entire device state by capturing screenshots of specific content areas. Instead of monitoring all device parameters, the system focuses on extracting visual error indicators through screenshot analysis. This extraction approach simplifies the detection system while maintaining effectiveness in identifying application malfunctions.
Solution Approach 2:
The classification model serves as an intermediary between the raw screenshot data and the final error detection decision. This intermediary component processes the visual information and translates it into actionable error state classifications, simplifying the overall system architecture. The model acts as a mediator that bridges the gap between simple screenshot capture and complex error analysis.
3Reliability
If the classification model is trained with comprehensive error states, then detection accuracy improves, but training data requirements and model complexity increase
Solution Approach 1:
The system employs partial action by training the classification model on a representative sample of error states rather than requiring comprehensive coverage of all possible errors. The model is trained to recognize common error patterns and visual indicators, which provides sufficient detection accuracy for practical purposes without requiring exhaustive training data. This partial training approach reduces model complexity while maintaining effective detection capabilities.
Data Source
AI summary
A client management server manages client devices deployed throughout an enterprise facility that may be used for various applications such as wayfinders and internal and external meeting room computers. The client management receives screenshots from the client devices and applies a classifier to classify an operating state of each device as corresponding to a normal operating state or an error state. If the operating state is classified as corresponding to the error state, a remedial action is triggered. The classifier may be a machine trained model that is trained using supervised or unsupervised learning, including by simulating errors on the clients.