17957981. AUTOMATED AI/ML MANAGEMENT OF USER EXPERIENCES: SYSTEM AND METHOD simplified abstract (The Regents of the University of California)

AUTOMATED AI/ML MANAGEMENT OF USER EXPERIENCES: SYSTEM AND METHOD

Organization Name

The Regents of the University of California

Inventor(s)

Jia Wang of Basking Ridge NJ (US)

Xiaofeng Shi of Somerville NJ (US)

Amit Kumar Sheoran of Raritan NJ (US)

Matthew Osinski of Westfield NJ (US)

Chen Qian of Scotts Valley CA (US)

AUTOMATED AI/ML MANAGEMENT OF USER EXPERIENCES: SYSTEM AND METHOD - A simplified explanation of the abstract

This abstract first appeared for US patent application 17957981 titled 'AUTOMATED AI/ML MANAGEMENT OF USER EXPERIENCES: SYSTEM AND METHOD

Simplified Explanation

The patent application discusses categorizing users of a cellular network, identifying service degradation, and isolating affected users from the degradation.

• Categorizing users of a cellular network into different user categories.
• Identifying service degradation in the cellular network using a machine learning model.
• Identifying affected users and affected user categories.
• Identifying potentially affected users and categorizing them based on affected user categories.
• Taking action to isolate potentially affected users from the service degradation.

Potential Applications

This technology could be applied in:

• Telecommunications industry for improving network performance.
• Customer service to proactively address service issues.
• Network management to optimize user experience.

Problems Solved

This technology helps in:

• Quickly identifying service degradation in a cellular network.
• Efficiently isolating affected users to prevent widespread impact.
• Enhancing user satisfaction by addressing service issues promptly.

Benefits

The benefits of this technology include:

• Improved network reliability and performance.
• Enhanced user experience and satisfaction.
• Cost savings by minimizing service disruptions.

Potential Commercial Applications

The potential commercial applications of this technology include:

• Telecommunication companies for network optimization.
• Service providers for proactive customer support.
• Network management companies for efficient operations.

Possible Prior Art

One possible prior art could be the use of machine learning models for network optimization in the telecommunications industry. Another could be the categorization of users based on behavior patterns in a network to improve service delivery.

How does the machine learning model differentiate between service degradation and normal fluctuations in network performance?

The machine learning model likely uses historical data and predefined thresholds to distinguish between service degradation and normal variations in network performance.

What measures are taken to ensure the accuracy of categorizing users into different user categories?

The accuracy of categorizing users into different user categories is likely ensured through regular updates of user behavior data and validation against actual user experiences.

Original Abstract Submitted

Aspects of the subject disclosure may include, for example, categorizing users of a cellular network according to a plurality of user categories, identifying, by a machine learning model, a service degradation in the cellular network, identifying at least one affected user, the at least one affected user being affected by the service degradation, identifying one or more affected user categories including the at least one affected user, identifying potentially affected users, the potentially affected users being categorized according to the one or more affected user categories, and taking action to isolate the potentially affected users from the service degradation. Other embodiments are disclosed.