View source for CEBURU SYSTEMS, INC. (20240323097). METHOD AND APPARATUS FOR PREDICTING FAILURE IN A NETWORKED ENVIRONMENT simplified abstract

=METHOD AND APPARATUS FOR PREDICTING FAILURE IN A NETWORKED ENVIRONMENT=


==Organization Name==

[[:Category:CEBURU SYSTEMS, INC.|CEBURU SYSTEMS, INC.]]


[[Category:CEBURU SYSTEMS, INC.]]

==Inventor(s)==

[[:Category:Jaseem Masood of Petaluma CA (US)|Jaseem Masood of Petaluma CA (US)]][[Category:Jaseem Masood of Petaluma CA (US)]]

[[:Category:Rehan Khan of SW Ranches FL (US)|Rehan Khan of SW Ranches FL (US)]][[Category:Rehan Khan of SW Ranches FL (US)]]

==METHOD AND APPARATUS FOR PREDICTING FAILURE IN A NETWORKED ENVIRONMENT - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240323097 titled 'METHOD AND APPARATUS FOR PREDICTING FAILURE IN A NETWORKED ENVIRONMENT

The patent application describes a method and apparatus for predicting failure in a customer environment using performance parameters from multiple nodes and an AI engine.

* Receiving performance parameters from multiple nodes in a customer environment
* Generating a node status based on the parameters using an AI engine
* Determining a probability of a performance event for the node occurring in the future
* Predicting a time interval for the occurrence of the performance event based on the probability and parameters

Potential Applications:
- Predictive maintenance in IT systems
- Proactive troubleshooting in network environments
- Enhancing reliability and uptime of critical systems

Problems Solved:
- Identifying potential failures before they occur
- Minimizing downtime and service disruptions
- Improving overall system performance and stability

Benefits:
- Cost savings through proactive maintenance
- Increased efficiency in managing IT infrastructure
- Enhanced customer satisfaction through reliable services

Commercial Applications:
Predictive maintenance software for IT companies to offer proactive support services and improve customer satisfaction.

Questions about the technology:
1. How does the AI engine analyze performance parameters to predict failures?
2. What are the key factors that contribute to the accuracy of failure predictions?

Frequently Updated Research:
Stay updated on advancements in AI algorithms for predictive maintenance in IT systems.


==Original Abstract Submitted==

a method and apparatus for predicting failure in a customer environment includes a method comprising receiving performance parameters from multiple nodes in a customer environment at a performance analysis server (pas), and generating a node status based on the parameters using an ai engine. the node status includes node identifier, performance state of the node, and the parameter causing the state. the method determines a probability of a performance event for the node occurring at a future time interval based on the node status, wherein the performance event includes node failure, service degradation, or negative impact on other nodes. the method further predicts, using the ai engine, a time interval for occurrence of the performance event, based on the probability and the performance parameters.



[[Category:H04L41/16]]
[[Category:H04L43/08]]
[[Category:CPC_H04L41/16]]