18459036. MACHINE LEARNING ASSISTED ROOT CAUSE ANALYSIS FOR COMPUTER NETWORKS simplified abstract (Juniper Networks, Inc.)
Contents
- 1 MACHINE LEARNING ASSISTED ROOT CAUSE ANALYSIS FOR COMPUTER NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING ASSISTED ROOT CAUSE ANALYSIS FOR COMPUTER NETWORKS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
MACHINE LEARNING ASSISTED ROOT CAUSE ANALYSIS FOR COMPUTER NETWORKS
Organization Name
Inventor(s)
Ajit Krishna Patankar of Fremont CA (US)
Kihwan Han of Pleasanton CA (US)
Prasad Miriyala of San Jose CA (US)
Mansi Joshi of San Jose CA (US)
Shruti Jadon of San Jose CA (US)
Deepak Kumar Naik of Bangalore (IN)
Maria Charles Maria Selvam of Bangalore (IN)
MACHINE LEARNING ASSISTED ROOT CAUSE ANALYSIS FOR COMPUTER NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18459036 titled 'MACHINE LEARNING ASSISTED ROOT CAUSE ANALYSIS FOR COMPUTER NETWORKS
Simplified Explanation
The abstract describes a system for performing root cause analysis for network devices using artificial intelligence models trained on historical data.
- The system receives telemetry data from network devices.
- It applies an AI anomaly detection model to detect anomalies in the telemetry data.
- It then applies an AI root cause analysis model to determine the root cause of any detected anomalies.
Potential Applications
This technology could be applied in various industries such as telecommunications, IT, and network management to improve troubleshooting and maintenance processes.
Problems Solved
1. Quickly identifying and resolving issues in network devices. 2. Streamlining root cause analysis to minimize downtime and improve overall network performance.
Benefits
1. Increased efficiency in diagnosing and resolving network issues. 2. Enhanced network reliability and performance. 3. Cost savings through proactive maintenance and reduced downtime.
Potential Commercial Applications
Optimizing network operations, improving customer satisfaction, and reducing operational costs in industries reliant on network infrastructure.
Possible Prior Art
One possible prior art could be traditional network monitoring tools that require manual analysis and lack the advanced AI capabilities for automated root cause analysis.
Unanswered Questions
How does the system handle real-time anomalies and root cause analysis?
The article does not specify the real-time capabilities of the system in detecting and analyzing anomalies as they occur.
What is the scalability of the system for large-scale network environments?
The scalability of the system for handling a large number of network devices and data volumes is not discussed in the abstract.
Original Abstract Submitted
An example system for performing root cause analysis for a plurality of network devices includes one or more processors implemented in circuitry and configured to: receive telemetry data from the plurality of network devices; apply an artificial intelligence (AI) anomaly detection model, trained on historical telemetry data to detect anomalies in the historical telemetry data, to the received telemetry data to detect one or more anomalies in the received telemetry data; and apply an AI root cause analysis mode, trained on historical data, to the anomalies to determine a root cause of an issue causing the one or more anomalies.
