ML-Based Network Telemetry for Autonomous Fault Resolution
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Solution Overview
Problem
Conventional network diagnostic solutions require significant human intervention and are inefficient in identifying and resolving data network service interruptions and equipment failures, leading to high costs and customer dissatisfaction due to time-consuming manual processes and limited diagnostic capabilities.
Innovation Solution
A machine learning-based network analytics, troubleshoot, and self-healing holistic telemetry system that utilizes modem-embedded machine analysis of multi-protocol stacks to autonomously identify and locate network problems, generate repair strategies, and perform software-based automated repairs, reducing the need for human diagnosis and intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional network diagnostic tools are used with static time-interval data collection, then data collection is simple and protocol-compliant, but the system cannot provide complete real-time topology health information and requires manual troubleshooting
Solution Approach 1:
The patent implements continuous real-time data collection from customer premises equipment instead of static time-interval sampling. The system continuously monitors network parameters, protocol violations, and equipment status to provide complete topology health information without manual intervention, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The system automatically performs troubleshooting by analyzing collected data to identify network problems and generate repair strategies without requiring manual human intervention. The autonomous diagnostic engine processes data from multiple sources, determines problem causes, and executes repair actions, eliminating the need for manual troubleshooting while maintaining comprehensive monitoring capabilities.
2Productivity
If manualized trial-and-error troubleshooting processes are used, then human expertise can be applied to complex problems, but the process is time-consuming and costly with significant human staff requirements
Solution Approach 1:
The system performs self-diagnosis and self-repair by automatically analyzing network data, identifying problems, and executing repair strategies without human intervention. The autonomous diagnostic engine processes data from customer premises equipment, determines problem causes, and generates repair strategies, significantly improving resolution speed while reducing human staff requirements.
Solution Approach 2:
The system implements feedback loops where collected network data is continuously analyzed to refine diagnostic accuracy and improve troubleshooting effectiveness. The machine learning components learn from historical data and outcomes to enhance future diagnostic performance, creating a self-improving system that increases productivity over time.
3Adaptability or versatility
If separate diagnostic procedures are used for different OSI layers, then specialized tools can be optimized for each layer, but the burden of figuring out which layer is causing problems is time-consuming and manual
Solution Approach 1:
The patent merges diagnostic capabilities across all OSI layers into a single integrated system that simultaneously collects and analyzes data from physical, link, network, transport, and application layers. The unified platform correlates data from multiple sources to identify problems across layer boundaries, eliminating the time-consuming manual process of determining which layer is causing issues while maintaining specialized diagnostic depth.
Solution Approach 2:
The system provides universal diagnostic functionality that can investigate any OSI layer or combination of layers through a single interface. The platform collects diverse data types including physical layer signal quality, link layer protocol violations, network layer routing information, transport layer connection status, and application layer performance metrics, allowing comprehensive multi-layer analysis without requiring multiple specialized tools or manual coordination.
Data Source
AI summary
A novel machine learning-based network analytics, troubleshoot, and self-healing holistic telemetry system is configured to perform modem-embedded machine analysis of multi-protocol stacks (e.g. OSI model stacks) simultaneously from one integrated coherent diagnostic system alone, and identify sources of data network problems autonomously within an entire end-to-end network topology of a network operator, while not necessitating human diagnosis of the data network problems. This system uniquely embeds a smart universal telemetry (SUT) as a quality-of-experience (QoE) parameter collection agent in intermediary transport-level network equipment and each end-user modem, which in turn enables periodic or on-demand collection of robust diagnostic data from all end-user modems and intermediary transport level nodes in a data network. By executing a machine learning (ML)-based artificial intelligence (AI) analytical module in a cloud-computing resource, the system then achieves autonomous identification and source pinpointing of network problems, and in some cases, self-repairs machine-identified data network problems autonomously.