AI Alarm Causation Summarization for Radio Access Networks
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Solution Overview
Problem
Radio access networks generate a high volume of alarms due to failures, which current computing systems struggle to process efficiently, leading to delayed problem correction and diminished network continuity.
Innovation Solution
Implementing artificial intelligence techniques, including vectorization and similarity threshold analysis, to summarize causation of data from network components, reducing data volume and translating it into root cause descriptions for timely problem resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional computing systems process high volume of alarms, then processing completeness is maintained, but processing time increases significantly
Solution Approach 1:
The patent segments the alarm data processing into multiple stages: initial filtering to identify unique alarm patterns, grouping similar alarms together, and then analyzing only the representative samples. This segmentation allows the system to maintain processing completeness while significantly reducing the time required by avoiding redundant analysis of identical alarm instances.
Solution Approach 2:
The patent extracts and removes duplicate alarm entries from the high-volume data stream, keeping only unique alarm patterns for detailed analysis. This extraction process eliminates redundant processing while preserving all essential diagnostic information, thereby maintaining reliability while reducing processing time.
2Measurement precision
If all alarm data elements are retained for analysis, then analysis accuracy is maintained, but data processing complexity increases
Solution Approach 1:
The patent merges multiple identical or similar alarm data elements into single representative entries. By combining duplicate alarms and grouping similar patterns, the system reduces data processing complexity while maintaining analysis accuracy through the preservation of essential alarm characteristics and patterns.
Solution Approach 2:
The patent discards redundant duplicate alarm entries while recovering and preserving the essential diagnostic information through representative sampling. This approach eliminates unnecessary data complexity while maintaining the accuracy needed for effective root cause analysis.
3Measurement precision
If detailed analysis of each alarm element is performed, then diagnostic precision is improved, but processing speed decreases
Solution Approach 1:
The patent performs preliminary filtering and grouping of alarm data before detailed analysis. By pre-processing the data to identify and consolidate unique alarm patterns, the system prepares the information in advance so that subsequent diagnostic analysis can be performed efficiently on reduced datasets without sacrificing precision.
Solution Approach 2:
The patent applies partial analysis to duplicate alarm entries by analyzing only one representative instance from each group of similar alarms. This partial action approach maintains diagnostic precision for identifying root causes while significantly improving processing speed by avoiding redundant detailed analysis of identical alarm patterns.
Data Source
AI summary
Techniques are provided for (x) reducing the amount of data (or elements of a set of data), provided by components of a system, which needs to be processed to more timely deliver information to a system or a user thereof which can promptly take responsive actions, e.g., remedy the underlying problem(s); and (y) translating one or more different types of data (or elements of a set of data) to a root causation description; such root causation description more directly indicates and/or suggests the underlying problem(s) which need to be remedied and optionally solution(s) for remedying the underlying problem(s). Because a device may include components from different vendors, each element may have a different data structure and content(s). Such techniques may be applied to a system comprising at least one radio access network.