Smart Triaging Assistant Bot for Automated Network Troubleshooting
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Solution Overview
Problem
Current network troubleshooting methods require manual administrative steps, including monitoring, diagnosing, and taking corrective actions, which are time-consuming and inefficient, especially in complex network environments like smart object networks with resource constraints.
Innovation Solution
A smart triaging assistant bot that uses a machine learning model to predict corrective actions based on reported symptoms, trained on historical data from chatbot sessions, to automate the troubleshooting process and provide suggested actions to network administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual monitoring, diagnosis, and corrective actions are performed by network administrators, then troubleshooting can be completed with simple tools and processes, but the time and effort required increases significantly
Solution Approach 1:
A chatbot intermediary is introduced between network administrators and the troubleshooting process. The chatbot automatically performs monitoring, diagnosis, and corrective actions by interacting with network devices through chat interfaces, eliminating the need for administrators to manually execute each troubleshooting step while reducing overall resolution time.
Solution Approach 2:
The system enables self-service troubleshooting where the chatbot autonomously monitors network devices, detects issues, diagnoses problems using machine learning models, and executes corrective actions without requiring continuous human intervention. Administrators only need to initiate troubleshooting through chat commands, and the system handles the entire process automatically.
2Productivity
If automated troubleshooting systems are implemented to reduce manual effort, then productivity increases, but the system complexity and resource requirements increase
Solution Approach 1:
The chatbot system is designed as a universal troubleshooting platform that can handle multiple network devices, various types of issues, and different diagnostic tasks through a single interface. The machine learning model is trained on diverse historical data to generalize across different troubleshooting scenarios, reducing the need for device-specific or issue-specific automated systems.
Solution Approach 2:
Manual mechanical troubleshooting processes are replaced with an intelligent automated system. The chatbot uses natural language processing to communicate with administrators and machine learning models to perform diagnosis and decision-making, substituting human cognitive processes with automated intelligent systems that operate faster and more consistently.
3Measurement precision
If machine learning models are trained on extensive historical data to improve diagnosis accuracy, then measurement precision increases, but the time and computational resources required for training increase
Solution Approach 1:
The machine learning model is trained in advance on extensive historical troubleshooting data before deployment. This preliminary training action prepares the model with learned patterns and knowledge, enabling it to perform rapid and accurate real-time diagnosis during actual troubleshooting operations without requiring additional training time during critical incidents.
Solution Approach 2:
The system continuously collects and processes new troubleshooting data from ongoing operations, enabling continuous improvement of the machine learning model. This continuous learning process maintains and enhances diagnostic accuracy over time without requiring complete retraining, as the system incrementally updates its knowledge base from real-world operations.
Data Source
AI summary
In one embodiment, a server in a network reports one or more symptoms of a monitored device that is malfunctioning to a user interface via a particular chatbot session. The server receives, via the particular chatbot session, a triage request to enter a triage mode regarding the one or more reported symptoms. The server predicts a corrective action using the one or more reported symptoms as input to a machine learning model. The machine learning model is trained using a history of observed symptoms in the network, a history of corrective actions initiated via chatbot sessions and associated with the observed symptoms, and a history of feedback regarding the corrective actions received via the chatbot sessions. The server provides the predicted corrective action to the user interface via the particular chatbot session as a suggested corrective action, in response to the received triage request.