T-mobile Usa, Inc.

T-mobile Innovations LLC

Phi Nguyen

James Ellison

Robert Stamm

Nathaniel Blodgett

Kevin Lau

Kevin Ka-Kin Lau

Joel Werdell

Veeraseshamamba Venigalla

The system receives an indication of an issue associated with a wireless telecommunication network and an indication of an operator capable of resolving the issue. The system sends a notification to an AR/VR device associated with the operator. The notification indicates the issue and prompts the operator to join a virtual collaboration room. The system receives a request to join the virtual collaboration room from the operator based on a gesture or a voice command detected by the AR/VR device. The system creates the virtual collaboration room, via a 5G wireless telecommunication network, including a screen shared among multiple operators. The system records operations performed in the virtual collaboration room until a resolution to the issue has been determined.

Enabling an operator to resolve an issue associated with a 5G wireless telecommunication network using AR glasses

The system receives a request for communication from an originator UE. The request for communication includes a unique identifier of the originator UE and a unique identifier of a receiver UE. The system obtains profile information of the originator UE including a name or a region of the originator UE. The system obtains profile information of the receiver UE including a communication history or calendar entry of the receiver UE. Based on the profile information of the originator UE and the receiver UE, the system determines whether the communication is spam. If the communication is valid, the system routes the communication to the originator UE. If the communication is spam, the system indicates to the receiver UE that there is an incoming communication that is likely spam. The system stores in a database the unique identifier of the originator UE and the determination of whether the communication is spam.

Determining whether an incoming communication is a spam or valid communication

Systems and methods for taking actions in response to a condition or state of a mobile device are disclosed. The system receives interactions between agents and subscribers of a telecommunications service provider relating to use of the mobile device to access a network. The system accesses subscriber data for the subscriber based on the interactions, the subscriber data including a coverage map. The system determines a usage pattern characterizing the use of the mobile device to access the network. And the system uses the interactions, the subscriber data, and the usage pattern to recommend an action to respond to a condition or state associated with the mobile device. The action can include updating the mobile device, changing a device state, or replacing the mobile device. In some implementations, the system trains and uses a machine learning model to recommend the action and/or to identify and access the subscriber data.

Modifying mobile device conditions or states systems and methods

The disclosed system receives a request from a user to interact with an agent of a wireless telecommunication network including a 5G wireless telecommunication network or higher generation wireless telecommunication network. The system determines whether the user is associated with a first AR/VR device including a camera configured to capture an object proximate to the first AR/VR device and a display configured to show a virtual object, which is not part of a surrounding associated with the first AR/VR device. Upon determining that the user is associated with the first AR/VR device, the system creates a high-bandwidth communication channel over the wireless telecommunication network between the first AR/VR device and a second AR/VR device and a virtual room enabling the user and the agent to share visual information over the high-bandwidth communication channel.

Enable interaction between a user and an agent of a 5G wireless telecommunication network using augmented reality glasses

A system, methods, and computer-readable media are provided herein for real-time 'live' identification of connectivity issues with autonomous predictive solution provision via a diagnostic application supported by a machine-learning model. In aspects, live data in a targeted application is captured in an on-going manner and used by the diagnostic application to automatically identify connectivity issues. When connectivity issues are detected, the live data capture is pushed to the model so that the model can make a predictive classification of the error based on geospatial, temporal, and/or geospatial-temporal alignments in the data. Based on the classification, the model predicts a solution and the diagnostic application provides the solution to the user of the targeted application.

Real-time diagnostic monitoring and connectivity issue resolution by a machine-learning data model

Disclosed here is a method to determine a user intent when a user device initiates an interactive voice response (IVR) call with a wireless telecommunication network. A processor can detect the IVR call initiated with the network and determine whether the user device is a member of the network. Upon determining that the user device is a member of the network, the processor can obtain user history including interaction history between the user and the network. Based on the user history, the processor can predict the user intent when the user initiates the IVR call. The processor can detect whether user device is a 5G capable device. Upon the determining that the device is 5G capable and based on the predicted user intent, the processor can suggest to the user an application configured to execute on the user device and configured to address the predicted user intent.

Interactive voice response using intent prediction and, for example a 5G capable device

Disclosed here is a system to automatically predict and resolve issues within a telecommunication network. Initially, the system builds a service registry to store dependence information within the network, which can include software components and hardware components. Various components of the network create logs of their operations. Machine learning models examine the logs and detect any issues. Upon detecting an issue or abnormal event, the system can automatically resolve the issue by determining the most similar issue occurring previously and determining a solution that resolved the previous most similar issue. In addition, the system can propagate the fix to dependent systems and/or notify the dependent systems of the issue.

Predicting and resolving issues within a telecommunication network

Interactive voice response using intent prediction and a 5G capable device

Cellular networks regularly operate with trusted devices, which typically are trusted because of the integration of a SIM card therewith. Untrusted devices typically do not interface with a SIM card, and rely on user input through traditional user interfaces for authentication. Recently, the use of hands-free, always-on digital assistant devices have become more common. Such devices typically have only a voice user interface that may be used by a number of people in close proximity to the device. Particular problems arise in such a scenario when a user wants to access a secure service that requires user authentication. Such problems are addressed with multiple techniques described herein.

Growing community of inventors

Lacey, WA, United States of America

Phi Nguyen