17957956. AUTOMATED AI/ML EVENT MANAGEMENT SYSTEM simplified abstract (THE REGENTS OF THE UNIVERSITY OF CALIFORNIA)

AUTOMATED AI/ML EVENT MANAGEMENT SYSTEM

Organization Name

THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

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 on events causing service degradation
• Determine event categories associated with the service degradation
• Identify likely affected customers based on event categories
• Recommend proper resources for resolution based on event categories
• Dispatch the proper resources for resolution

Potential Applications

This technology could be applied in various industries such as telecommunications, IT services, and network management to efficiently address service degradation issues.

Problems Solved

This technology helps in quickly identifying and resolving service degradation in cellular networks, leading to improved customer satisfaction and network performance.

Benefits

The benefits of this technology include faster resolution of service degradation issues, optimized allocation of resources, and enhanced overall network performance.

Potential Commercial Applications

One potential commercial application of this technology could be in the field of network management software, where it could be integrated to provide real-time monitoring and resolution of service degradation issues.

Possible Prior Art

One possible prior art could be existing network management systems that use rule-based algorithms to detect and resolve service degradation issues in cellular networks.

What are the limitations of the machine learning model used in this system?

The abstract does not provide information on the specific limitations of the machine learning model used in this system. It would be important to know factors such as accuracy, scalability, and adaptability to different network environments.

How does this system handle privacy and data security concerns when dealing with customer information?

The abstract does not address how this system ensures the privacy and security of customer information when identifying likely affected customers. It would be crucial to understand the measures in place to protect sensitive data and comply with regulations such as GDPR.

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.