17957915. AUTOMATIC TROUBLESHOOTING SYSTEM FOR USER-LEVEL PERFORMANCE DEGRADATION IN CELLULAR SERVICES simplified abstract (The Regents of the University of California)

AUTOMATIC TROUBLESHOOTING SYSTEM FOR USER-LEVEL PERFORMANCE DEGRADATION IN CELLULAR SERVICES

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)

Chen Qian of Scotts Valley CA (US)

Matthew Osinski of Westfield NJ (US)

AUTOMATIC TROUBLESHOOTING SYSTEM FOR USER-LEVEL PERFORMANCE DEGRADATION IN CELLULAR SERVICES - A simplified explanation of the abstract

This abstract first appeared for US patent application 17957915 titled 'AUTOMATIC TROUBLESHOOTING SYSTEM FOR USER-LEVEL PERFORMANCE DEGRADATION IN CELLULAR SERVICES

Simplified Explanation

The patent application abstract describes a method for training machine learning models to predict and identify sources of service degradation in a cellular network.

• Training a cell-level machine learning model to predict the likelihood of a cell site in a cellular network having service issues that impact customers.
• Training a user equipment (UE) level machine learning model using output information from the cell-level model and historical information about UE-level performance metrics.
• Receiving information about service degradation from a customer associated with a UE device on the network.
• Providing the information to the UE-level model to identify the source of the service degradation.

Potential Applications

This technology could be applied in the telecommunications industry to improve network performance and customer satisfaction by quickly identifying and resolving service degradation issues.

Problems Solved

This technology helps in proactively identifying and addressing service degradation issues in a cellular network, leading to improved customer experience and network efficiency.

Benefits

The benefits of this technology include enhanced network performance, increased customer satisfaction, and more efficient troubleshooting of service issues in a cellular network.

Potential Commercial Applications

Potential commercial applications of this technology include telecommunications companies, network equipment manufacturers, and service providers looking to optimize their network performance and customer experience.

Possible Prior Art

One possible prior art in this field could be the use of machine learning models for network optimization and fault detection in telecommunications networks.

What are the specific machine learning algorithms used in training the models described in the patent application?

The patent application abstract does not specify the exact machine learning algorithms used in training the models. It would be helpful to know the specific algorithms to understand the technical approach and potential limitations of the technology.

How does the patent application address privacy concerns related to collecting and analyzing customer data in a cellular network?

The patent application does not mention any specific measures or protocols for addressing privacy concerns related to customer data. Understanding how privacy issues are handled is crucial for ensuring compliance with regulations and protecting customer information.

Original Abstract Submitted

Aspects of the subject disclosure may include, for example, training a cell-level machine learning model to predict a likelihood of a cell site in a cellular network having service issues that impact customers of the cellular network, training a user equipment (UE) level machine learning model using output information from the cell-level machine learning model and historical information about UE-level performance metrics, receiving, from a customer associated with a UE device operating on the cellular network, information about a service degradation experienced by the customer on the UE device, providing the information about the service degradation to the UE-level machine learning model; and receiving, from the UE-level machine learning model, information identifying a source of the service degradation. Other embodiments are disclosed.