Provisioning Flow Troubleshooting With LLM Root Cause Analysis
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Solution Overview
Problem
Existing systems struggle to efficiently troubleshoot failed subscriber segments in telecommunications networks due to errors in the provisioning flow order, leading to service disruptions without effective automated diagnosis.
Innovation Solution
A system utilizing a large language model (LLM) trained on datasets of successful and failed provisioning flows to perform root cause analysis and impact analysis on subscriber segments, identifying errors and their effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to troubleshoot provisioning flow errors, then diagnostic accuracy can be maintained through human expertise, but troubleshooting time and operational efficiency deteriorate due to labor-intensive processes
Solution Approach 1:
The patent replaces manual inspection methods with an automated system comprising a processor and memory that executes instructions to receive provisioning flow data, identify errors, and generate diagnostic reports. This mechanical substitution maintains diagnostic accuracy through systematic analysis while dramatically reducing troubleshooting time by eliminating manual labor.
Solution Approach 2:
The system enables self-service troubleshooting by automatically receiving provisioning flow data from the network, analyzing it to identify errors in provisioning flow order, and generating diagnostic reports without human intervention. This self-service capability resolves the contradiction by providing both accurate diagnostics and time efficiency simultaneously.
2Productivity
If automated troubleshooting systems are implemented to reduce manual inspection time, then troubleshooting efficiency improves, but system complexity increases due to the need for advanced processing capabilities
Solution Approach 1:
The patent implements a universal troubleshooting system that can handle multiple types of provisioning flow errors across different network configurations through a single automated platform. The system receives various provisioning flow data formats, identifies different error types, and generates comprehensive diagnostic reports, thereby achieving high productivity without proportionally increasing complexity through multi-functional integration.
3Reliability
If comprehensive error analysis is performed to identify all provisioning flow errors, then diagnostic completeness improves, but processing complexity and computational resources worsen due to extensive data analysis requirements
Solution Approach 1:
The patent segments the provisioning flow data into discrete elements that can be individually analyzed for errors in provisioning flow order. The system processes the data in structured steps: receiving provisioning flow data, identifying specific errors, and generating diagnostic reports. This segmentation enables comprehensive error analysis while managing processing complexity through systematic breakdown of the analysis task.
Data Source
AI summary
The system receives an indication of a failed subscriber segment of an affected subscriber. A subscriber segment has a provisioning flow order that determines an order in which the services subscribed to by the affected subscriber are provisioned. The failed subscriber segment includes an error in the provisioning flow. The system cleans the coded text of the provisioning flow. Cleaning the coded text of the provisioning flow includes removing predetermined unnecessary characters and transforming the coded text such that the transformed coded text is configured to be inputted into a large language model (LLM). The system inputs the transformed coded text of the provisioning flow into the LLM. The system receives, as an output of the LLM, a root cause analysis of the failed subscriber segment. The root cause analysis determines the error in the provisioning flow order of the failed subscriber segment and identifies the affected subscriber.