Multi-Level SVM for Wi-Fi Event Classification
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Solution Overview
Problem
Conventional systems face inefficiencies in manually analyzing and scaling large volumes of network event logs from IoT and other components, making it difficult to address Wi-Fi issues effectively.
Innovation Solution
A multi-level machine learning system is implemented to classify Wi-Fi events, using SVMs trained on datasets to predict outcomes and automatically remediate failures, improving scalability and network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis processes are used to examine network event logs, then detailed inspection capability is maintained, but efficiency and scalability deteriorate due to voluminous information volumes
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated machine learning systems. The multi-level SVM framework automatically classifies network events without human intervention, substituting the mechanical manual review process with an automated computational system that handles voluminous log data efficiently.
Solution Approach 2:
The patent introduces an intermediary machine learning classification system between the raw network logs and the analysis outcome. This multi-level SVM framework acts as a mediator that processes, categorizes, and prioritizes events before presentation to administrators, reducing the complexity of direct manual analysis of raw logs.
2Productivity
If conventional computer analysis methods are applied to large volumes of network data, then automated processing is achieved, but scalability deteriorates with increasing data volumes from IoT and network components
Solution Approach 1:
The patent segments the analysis process into multiple hierarchical levels using a multi-level SVM framework. Events are classified in stages from broad categories to specific types, allowing the system to handle increasing data volumes by processing events at appropriate granularity levels rather than requiring uniform deep analysis of all data.
Solution Approach 2:
The patent adds a hierarchical dimension to the analysis process by implementing multi-level classification. Instead of a single flat analysis layer, the system creates multiple classification levels that process events at different granularities, enabling scalability as data volumes grow from IoT and network components.
3Measurement precision
If detailed manual review of network events is performed, then classification accuracy is maintained, but time consumption increases making real-time prediction infeasible
Solution Approach 1:
The patent performs preliminary classification actions through automated multi-level SVM analysis before detailed human review is needed. The system pre-processes and categorizes events, identifying obvious patterns and anomalies that can be handled automatically, reserving detailed analysis only for complex or ambiguous cases that require human judgment.
Solution Approach 2:
The patent implements self-service classification where the machine learning system autonomously performs detailed event analysis and generates predictions without requiring manual intervention for each event. The multi-level SVM framework independently processes events, maintains classification accuracy through trained models, and operates in real-time without human time investment.
Data Source
AI summary
Multi-level machine learning models can be generated from the captured log events. Outcomes are predicted for input events in real-time. The captured log events are received and parsed to expose event outcome data. A first data set is generated by determining whether an outcome associated with the event outcome data was a success or a failure. Responsive to a failed event outcome, a second data set is generated by categorizing the failed event outcome, to train multiple level SVMs for prediction of Wi-Fi input events and automatic remediation of Wi-Fi issues.