Conversational Network Assistant for Actionable Device Insights
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Solution Overview
Problem
Network administrators face challenges in quickly and deterministically troubleshooting wireless networks, requiring them to manually identify actionable insights from network device details, which can lead to increased downtime and reduced network reliability.
Innovation Solution
A conversational assistant is integrated into a network management system (NMS) to process natural language inputs from administrators, analyzing network data to automatically identify and display relevant device details and insights, reducing the time spent on troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If administrators manually identify actionable insights from network device details, then they can troubleshoot network issues, but it increases the time spent and reduces productivity
Solution Approach 1:
The system enables self-service by automatically analyzing network device details and generating actionable insights without requiring administrator intervention. The NMS processes network data, identifies issues, and presents structured insights to administrators, allowing the system to serve itself in the troubleshooting process.
Solution Approach 2:
The patent replaces the mechanical manual process of analyzing network device details with an automated computational system. The NMS uses processing units to automatically parse network data, identify actionable insights, and present them to administrators, substituting human manual analysis with automated mechanical processing.
2Loss of information
If the dashboard displays all network device details, then administrators have complete information, but it increases complexity and makes it harder to identify relevant insights quickly
Solution Approach 1:
The system extracts only the relevant actionable insights from the complete network device details and presents them to administrators. The NMS processes all network data but selectively outputs only the most important findings, such as performance issues, errors, and anomalies, rather than displaying all raw data.
Solution Approach 2:
The patent segments the comprehensive network device details into distinct actionable insights categories. The NMS divides the complex network data into structured segments such as performance metrics, error logs, and anomaly detections, making it easier for administrators to understand and act upon specific issues.
3Measurement precision
If administrators spend more time identifying relevant network information, then they can make more accurate decisions, but it increases downtime and reduces network reliability
Solution Approach 1:
The system performs preliminary analysis of network device details automatically before administrators need to make decisions. The NMS continuously monitors network data, pre-identifies potential issues, and prepares actionable insights in advance, so that when administrators review the dashboard, the critical information is already organized and highlighted.
Solution Approach 2:
The patent implements feedback mechanisms where the NMS continuously monitors network performance and automatically updates the dashboard with new actionable insights. The system provides real-time feedback to administrators about network status changes, allowing them to respond quickly to emerging issues without manual analysis.
Data Source
AI summary
This disclosure describes a network management system (NMS) configured to determine a particular network device of a plurality of network devices based on a first user input in a conversational assistant. The one or more processors are further configured to identify a set of actionable insights for the particular network device based on network data received from the plurality of network devices and determine a set of views of a dashboard based at least on the set of actionable insights, wherein each view of the set of views displays a portion of the network data received from the plurality of network devices. The one or more processors are further configured to select a view of the set of views of the dashboard based on a second user input in the conversational assistant and cause the dashboard to display the selected view.