Triggered Automation Framework for Network Diagnosis
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Solution Overview
Problem
Conventional network troubleshooting methods are inefficient and require extensive manual effort, making it difficult for junior engineers to diagnose and resolve network issues, especially in complex network environments, leading to increased downtime and revenue impact.
Innovation Solution
The Problem Diagnosis Automation System (PDAS) uses Network Intent Clustering (NIC) and Triggered Automation Framework (TAF) to automate repetitive problem diagnosis and enforcement of preventive measures across the network, enabling no-code replication of network designs and logic, and executing automated runbooks based on user-defined conditions and API calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional manual troubleshooting methods are used, then network engineers can diagnose and resolve issues, but the process requires extensive time and effort, especially for junior engineers
Solution Approach 1:
The system enables automated self-diagnosis of network issues through AI-driven analysis of network data, logs, and configurations. The network management system automatically identifies problems, determines root causes, and suggests or implements resolutions without requiring manual intervention from engineers, thereby dramatically improving troubleshooting efficiency and reducing time loss.
Solution Approach 2:
The patent replaces manual mechanical troubleshooting processes with automated electronic systems. AI algorithms analyze network data, replace human engineers' manual diagnosis steps, and automatically execute troubleshooting workflows. This substitution of mechanical human effort with electronic automation systems resolves the contradiction by maintaining diagnostic capability while eliminating time consumption.
2Ease of operation
If manual troubleshooting commands and processes are used for each device, then network issues can be diagnosed, but the complexity increases and knowledge transfer becomes difficult
Solution Approach 1:
The system creates a universal troubleshooting platform that handles multiple device types, network protocols, and issue categories through a single integrated AI system. Instead of requiring engineers to learn device-specific commands and procedures, the unified system automatically adapts to different network configurations and applies appropriate diagnostic workflows, thereby easing operation while managing complexity centrally.
Solution Approach 2:
The AI-driven network management system acts as an intermediary between network issues and engineers. It translates complex device-specific troubleshooting requirements into standardized automated workflows, shielding engineers from underlying complexity while maintaining ease of operation through a consistent user interface and automated processes.
3Reliability
If extensive training is provided to junior engineers on troubleshooting commands and documentation, then their diagnostic capability improves, but the training time and resources increase
Solution Approach 1:
The system provides automated expert-level diagnostic capabilities that do not require extensive training. Junior engineers can leverage the AI-driven system's self-service diagnostic functions, which automatically analyze network issues and provide guidance, eliminating the need for prolonged training periods while maintaining high diagnostic reliability.
Solution Approach 2:
The system captures and replicates expert troubleshooting knowledge within its AI algorithms and automated workflows. Instead of requiring junior engineers to learn and internalize extensive documentation and commands, the system copies expert diagnostic logic into automated processes that guide less experienced engineers through standardized, reliable diagnostic procedures.
4Adaptability or versatility
If traditional network management approaches are used, then basic network functions can be maintained, but the system cannot efficiently handle increasing network complexity
Solution Approach 1:
The system implements dynamic adaptability through AI algorithms that automatically adjust to changing network conditions, configurations, and emerging issues. The network management system evolves its diagnostic and troubleshooting approaches based on real-time data analysis, enabling it to handle increasing network complexity while maintaining or improving management efficiency through intelligent automation.
Data Source
AI summary
Problem Diagnosis Automation System (PDAS) automates the diagnosis of repetitive problems and the enforcement of preventive measures across a network. Automation assets across the network include Network Intent (NI) inside the no-code platform. A Network Intent Cluster (NIC) clones a NI across the network to create a group of NIs (member NIs) with the same design or logic. A subset of Member NIs can be executed according to user-defined conditions based on the member device, the member NI tags, or signature variables. A Triggered Automation Framework (TAF) matches the incoming API calls from a 3rd party system to current incidents and installs the automation (e.g., NI/NIC) to be triggered for each call. It may include: Integrated IT System defining the scope and data of the incoming API calls; Incident Type to match a call to an Incident; and Triggered Diagnosis to define what and how the NIC/NI is executed.