Verizon Patent and Licensing Inc.

Binaben Dipesh Patel

Bharadwaj Vemuri

Masudur Rahman

Umesh Kumar Gupta

Isaac Alexander Calvo

John C Burlison, Jr

A system described herein may provide for multiple levels of authentication, such that a User Equipment ('UE') may receive secure content from an application server, which may include or may be implemented by a multi-access edge computing (“MEC”) system. As described herein, a user associated with a UE may register the UE and/or a particular application with an authentication system and/or the application server. The registration of the UE and/or the application may establish a “trust” relationship between the authentication system and the UE, such that a user-level authentication performed by the UE, such as biometric authentication, may be accepted by the authentication system and/or the application system as an authentication of the user.

Systems and methods for multi-level authentication

A system described herein may provide for multiple levels of authentication, such that a User Equipment ('UE') may receive secure content from an application server, which may include or may be implemented by a multi-access edge computing ('MEC') system. As described herein, a user associated with a UE may register the UE and/or a particular application with an authentication system and/or the application server. The registration of the UE and/or the application may establish a &#x201c;trust&#x201d; relationship between the authentication system and the UE, such that a user-level authentication performed by the UE, such as biometric authentication, may be accepted by the authentication system and/or the application system as an authentication of the user.

A system described herein may use automated techniques, such as machine learning techniques, to analyze device snapshots from a group of User Equipment ('UEs'), and determine trouble conditions that are experienced by the UEs. The system may identify markers of the trouble conditions based on the snapshots, and may use these markers to predict or identify trouble conditions at other UEs based on snapshots received from the other UEs. Further, once a trouble condition is predicted or identified at the other UEs, the trouble condition may be proactively addressed, without requiring an explicit request from the other UEs to address the trouble condition.

Systems and methods for monitoring and remediating trouble conditions using machine learning techniques

A system described herein improves user equipment ('UE') access to call and data services provided by a network carrier by updating settings on the UE based device data from the UE. The updates may modify the UE hardware configuration, including activating or deactivating different network radios of the UE, enable or disable different call or data services accessed through the different network radios, and/or modify parameters controlling UE network selection and switching (e.g., handoff). In doing so, the system may improve the user experience when accessing wireless services of the network carrier and resolve quality issues whether due to poor signal strength, congested networks, or excessive network switching.

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Franklin Park, NJ, United States of America

Binaben Dipesh Patel