AI Call Flow Trace Analysis for Telecom Failure Detection
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Solution Overview
Problem
Identifying call flow failures in telecommunications networks is complex and time-consuming, affecting network performance and service quality, due to the large number of interactions and information elements involved in call flows.
Innovation Solution
A system utilizing an AI model that self-learns from successful and failed call flows to build a repository for identifying and predicting call flow failures, processing real-time traces, and applying machine learning algorithms to reduce data processing complexity and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify call flow failures by analyzing all signaling interactions and information elements, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent extracts and analyzes only the most relevant information elements from call flow traces using regular expressions and pattern matching, rather than processing all signaling interactions. This selective extraction maintains failure identification accuracy while dramatically reducing processing time by filtering out unnecessary data early in the analysis pipeline
Solution Approach 2:
The patent segments the call flow analysis into distinct phases: initial trace parsing, selective information element extraction, pattern matching against known failure signatures, and detailed analysis only of suspicious segments. This segmentation allows the system to quickly eliminate normal call flows while applying comprehensive analysis only where needed, resolving the time-accuracy tradeoff
2Reliability
If comprehensive analysis of all call flow parameters is performed, then reliability of failure identification is improved, but device complexity increases
Solution Approach 1:
The patent applies different levels of analysis depth to different parts of the call flow data. Normal segments receive minimal processing with simple pattern matching, while segments matching failure signatures undergo comprehensive parameter analysis. This local quality approach ensures reliable failure identification through thorough analysis of critical segments without applying the same complexity to all data, thus managing overall system complexity
Solution Approach 2:
The patent introduces an intermediary layer of failure signature patterns and regular expressions that mediate between raw call flow traces and comprehensive analysis. This intermediary filters and pre-processes data, identifying suspicious segments that then trigger detailed analysis, thereby reducing the complexity of the overall system while maintaining reliability through multi-stage verification
3Measurement precision
If detailed analysis of hundreds of information elements is performed, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary filtering and pattern matching against known failure signatures before conducting detailed analysis of information elements. By pre-identifying suspicious call flow segments using regex patterns and basic parameter checks, the system prepares targeted analysis lists that maintain high detection precision while improving throughput by avoiding comprehensive analysis of normal call flows
Data Source
AI summary
A system for identifying call flow failures receives a call flow trace including a record of a call connected through multiple segments of a network. The system can identify a set of parameters associated with the multiple segments. The system can validate the call flow trace by matching the set of parameters with existing validation parameters. Responsive to successfully validating the call flow trace, the system can store information extracted from the validated call flow trace. Responsive to not successfully validating the call flow trace, the system can identify a particular segment of the multiple segments of the call associated with the trace that failed the validation and determine whether parameters associated with the particular segment match with existing failure parameters. In response to a determination that the parameters associated with the particular segment match the existing failure parameters, the system can remove information associated with the particular segment.