ML Diagnostic App Resolves Connectivity Errors
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Solution Overview
Problem
Current diagnostic systems fail to efficiently detect and resolve connectivity issues in real-time within applications, as they lack the capability to automatically classify errors using machine-learning models trained on specific application data, leading to delayed or ineffective problem-solving.
Innovation Solution
A diagnostic application supported by a machine-learning model that monitors target applications, captures live data, and identifies connectivity issues through geospatial, temporal, or geospatial-temporal alignment, providing immediate solutions based on classifications learned from training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional diagnostic systems are used to monitor application connectivity, then the system can detect issues, but it cannot efficiently classify errors or provide automated solutions in real-time
Solution Approach 1:
The system enables self-service by automatically detecting connectivity issues, classifying errors using machine learning, and providing remediation solutions without human intervention. The diagnostic application autonomously monitors application health, identifies problems, and recommends fixes, allowing the system to resolve its own issues and minimize downtime.
Solution Approach 2:
The system changes the parameter of error analysis from traditional rule-based methods to machine learning-based classification. By training models on application-specific data and using multiple ML models for different error types, the system transforms how connectivity issues are detected and classified, enabling faster and more accurate real-time diagnosis.
2Extent of automation
If machine-learning models are implemented for real-time error classification, then automated solution provision is achieved, but system complexity increases
Solution Approach 1:
The diagnostic system is segmented into modular components: a diagnostic application layer that interfaces with the target application, multiple specialized machine learning models for different error types, and a solution provision module. This segmentation allows each component to be independently developed, trained, and maintained, reducing overall system complexity while enabling sophisticated automated classification.
Solution Approach 2:
The diagnostic application serves as an intermediary between the target application and the machine learning models. It collects connectivity data, pre-processes it, and feeds it to appropriate ML models, while also serving as an intermediary between the models and the solution database. This intermediary layer simplifies the integration of complex ML components into the existing application infrastructure.
Data Source
AI summary
A system, methods, and computer-readable media are provided herein for real-time “live” identification of connectivity issues with autonomous predictive solution provision via a diagnostic application supported by a machine-learning model. In aspects, live data in a targeted application is captured in an on-going manner and used by the diagnostic application to automatically identify connectivity issues. When connectivity issues are detected, the live data capture is pushed to the model so that the model can make a predictive classification of the error based on geospatial, temporal, and/or geospatial-temporal alignments in the data. Based on the classification, the model predicts a solution and the diagnostic application provides the solution to the user of the targeted application.