Generative AI Network Diagnostics for Telecom Log Anomalies
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Solution Overview
Problem
Conventional network diagnostic operations in large telecommunications networks rely on hardcoded, rule-based systems that lack context and require manual intervention, struggling to efficiently identify and manage failures due to the volume and complexity of log information.
Innovation Solution
Implementing generative machine-learning models, such as large language models, trained on network-specific datasets to automatically classify anomalies, generate resolutions, and perform maintenance actions, including service tickets and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based systems are used for network diagnostics, then implementation is straightforward, but the systems lack context and require manual intervention
Solution Approach 1:
The patent replaces traditional rule-based mechanical diagnostic systems with a generative AI model that uses natural language processing to understand and diagnose network issues. The model processes log data and generates diagnostic conclusions in natural language, substituting the rigid rule-matching mechanism with an intelligent system that can comprehend context and nuances in network logs.
Solution Approach 2:
The patent introduces an intermediary layer between raw log data and diagnostic conclusions. The generative AI model acts as a mediator that translates complex log information into understandable diagnostic insights, bridging the gap between raw data and actionable recommendations without requiring manual intervention or rigid rule sets.
2Reliability
If manual intervention is used for network diagnostics, then context can be applied, but the volume and complexity of log information make it challenging to detect and rectify problems
Solution Approach 1:
The patent implements a self-service diagnostic system where the generative AI model autonomously processes network logs, identifies issues, and generates diagnostic conclusions without human intervention. The model reads log data, understands the context, and produces diagnostic insights independently, eliminating the need for manual analysis while maintaining high diagnostic accuracy.
Solution Approach 2:
The patent changes the operational parameters of the diagnostic system by transitioning from rule-based matching to generative language modeling. This parameter change enables the system to handle complex, unstructured log data with contextual understanding, significantly improving both diagnostic accuracy and efficiency by processing large volumes of logs automatically.
3Extent of automation
If generative machine-learning models are implemented, then automated diagnostic operations are achieved, but computational resources are required
Solution Approach 1:
The patent applies preliminary action by pre-training the generative AI model on extensive network log data and diagnostic knowledge before deployment. This pre-training establishes a foundation of contextual understanding that reduces the computational resources needed during actual diagnostic operations, as the model has already learned patterns and relationships from historical data.
Solution Approach 2:
The patent segments the diagnostic process into distinct phases: log data ingestion, model processing, and conclusion generation. This segmentation allows for optimized resource allocation at each stage, enabling the system to manage computational resources more efficiently while maintaining automated diagnostic capabilities.
Data Source
AI summary
In various examples, systems and methods are disclosed relating to automated network infrastructure diagnostic operations using generative artificial intelligence. A system can classify at least one log of a set of logs produced by a network system as corresponding to a network anomaly. Upon classifying the at least one log as corresponding to the network anomaly, the system can generate, using a machine-learning model and the at least one log, a command to produce a message comprising natural language output identifying the network anomaly. The system can cause performance of one or more maintenance actions on the network system based on the message to address the network anomaly.