Smart Prompt Generator for GPT-Based Network Root-Cause Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud networks with thousands or millions of nodes face challenges in maintaining node health and serviceability, leading to processing delays, increased costs, and customer dissatisfaction due to the inability to effectively detect anomalies and provide timely corrective actions.
Innovation Solution
Implementing a smart prompt generator that uses artificial intelligence to identify network function types and generate customized prompts for a generative pre-trained transformer model to analyze telemetry data, identify root causes of errors, and provide remediation recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring mechanisms are used in cloud networks with thousands or millions of nodes, then the system structure remains simple and easy to operate, but the ability to detect anomalies early and maintain node health deteriorates, leading to processing delays and increased costs
Solution Approach 1:
The patent introduces an AI-based smart prompt generator as an intermediary layer between the complex cloud network infrastructure and the monitoring system. This intermediary automatically collects telemetry data, identifies NF types, extracts relevant information, and generates customized prompts for GPT models, thereby improving anomaly detection capability without requiring direct complex interactions with individual nodes
Solution Approach 2:
The patent replaces traditional manual or rule-based monitoring mechanisms with AI-driven automated analysis. GPT models trained on technical documentation and historical data substitute for conventional monitoring tools, enabling intelligent root cause identification and remediation recommendation without mechanical intervention in each monitoring task
2Measurement precision
If comprehensive telemetry data collection is implemented across all nodes, then the precision of root cause analysis improves, but the quantity of data to be processed increases, leading to longer processing times and higher computational costs
Solution Approach 1:
The patent extracts only the most relevant information from collected telemetry data based on identified NF types. The smart prompt generator selectively processes specific log fields, metrics, and events that are most indicative of the particular NF condition, discarding redundant data while maintaining high diagnostic accuracy
Solution Approach 2:
The patent segments the large volume of telemetry data by NF type and function. Different NF types (AMF, SMF, UPF, etc.) are processed with customized prompts tailored to their specific characteristics, allowing parallel processing of segmented data streams rather than monolithic analysis of all data together
3Adaptability or versatility
If specialized monitoring procedures are developed for each network function type, then the adaptability to different NF types improves, but the complexity of maintaining multiple monitoring procedures increases
Solution Approach 1:
The patent implements a universal smart prompt generator that handles multiple NF types through a single integrated system. The GPT models are trained on diverse technical documentation covering various NF types (AMF, SMF, UPF, NRF, NSSF, etc.), enabling one system to adaptively monitor and diagnose all NF types without requiring separate specialized systems for each
Data Source
AI summary
Conditions are identified in a telecommunications network based on data collected from the telecommunications network. A first artificial intelligence (AI) model is used to identify a network function (NF) type. Based on the NF type, a second AI model is used to generate a prompt for a generative pre-trained transformer (GPT) model. The prompt is input to the GPT model to identify a condition in the telecommunications network.