17957956. AUTOMATED AI/ML EVENT MANAGEMENT SYSTEM simplified abstract (AT&T Intellectual Property I, L.P.)
Contents
- 1 AUTOMATED AI/ML EVENT MANAGEMENT SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUTOMATED AI/ML EVENT MANAGEMENT SYSTEM - 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 Original Abstract Submitted
AUTOMATED AI/ML EVENT MANAGEMENT SYSTEM
Organization Name
AT&T Intellectual Property I, L.P.
Inventor(s)
Jia Wang of Basking Ridge NJ (US)
Amit Kumar Sheoran of Raritan NJ (US)
Xiaofeng Shi of Somerville NJ (US)
Matthew Osinski of Westfield NJ (US)
Chen Qian of Scotts Valley CA (US)
AUTOMATED AI/ML EVENT MANAGEMENT SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 17957956 titled 'AUTOMATED AI/ML EVENT MANAGEMENT SYSTEM
Simplified Explanation
The patent application abstract describes a system that uses machine learning to identify events causing service degradation in a cellular network, determine affected customers, recommend proper resources for resolution, and dispatch those resources.
- Machine learning model provides information about event causing service degradation
- Determines event categories associated with the event
- Identifies likely affected customers based on event categories
- Recommends proper resources for resolution based on event categories
- Dispatches proper resources for resolution
Potential Applications
This technology can be applied in various industries such as telecommunications, network management, and customer service to efficiently address service degradation issues and improve customer satisfaction.
Problems Solved
- Quickly identifying and resolving service degradation events - Proactively addressing potential customer impact - Optimizing resource allocation for issue resolution
Benefits
- Improved customer experience - Faster resolution of service degradation events - Efficient resource allocation - Enhanced network performance
Potential Commercial Applications
Optimizing resource allocation in telecommunications networks for faster issue resolution and improved customer satisfaction.
Possible Prior Art
One possible prior art could be the use of machine learning models in network management systems to predict and prevent service degradation events.
Unanswered Questions
How does the machine learning model determine the proper resources for resolution?
The abstract does not provide specific details on how the machine learning model determines the proper resources for resolution based on the event categories. This could involve analyzing historical data, network configurations, or predefined resolution strategies.
What types of events causing service degradation are considered in the system?
The abstract mentions determining event categories associated with the event causing service degradation, but it does not specify the types of events that are considered. It would be interesting to know if the system can handle various types of events such as hardware failures, software glitches, or external interference.
Original Abstract Submitted
Aspects of the subject disclosure may include, for example, receiving, from a machine learning model, information about an event causing a service degradation in a cellular network, wherein the event is external to the cellular network, determining one or more event categories associated with the event causing the service degradation, determining, based on the one or more event categories, likely affected customers, the likely affected customers being likely to experience the service degradation, determining, by the machine learning model, proper resources for resolution of the service degradation, wherein the determining proper resources is based on the one or more event categories, and dispatching the proper resources for resolution of the service degradation. Other embodiments are disclosed.
