At&t Intellectual Property I, LP

At&t Mobility Ii LLC

University of California

Purdue Research Foundation

Matthew Osinski

Zihui Ge

He Yan

Shuai Hao

Jia Wang

Hector Perez

Jason Tipton

Mark Brisbay

Gavin Anderson

Amit Kumar Sheoran

Jennifer Yates

Xiaofeng Shi

Chen Qian

Vijay Gopalakrishnan

Mario Kosseifi

Richard Miller

Hendrik Hofman

Anthony Caracciolo

Giritharan Rana

Kathryn Childs

Joshua Reich

Sonia Fahmy

Heather Magrini

Brandon Hall

Edward Ball

Chunyi Peng

Sumit Maheshwari

Chandra Thompson

Bruno Ribeiro

Benjamin Grizzle

Eric Bonitz

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.

Automated AI/ML event management system

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.

Automatic troubleshooting system for user-level performance degradation in cellular services

Aspects of the subject disclosure may include, for example, detecting an interruption of a supply of operating power to a cell site of a cellular communication network, estimating an estimated time to restoration (ETR) of the supply of operating power to the cell site, wherein the estimating is based on information of an operator of the cellular communication network, determining, based in part on the ETR, to dispatch a portable generator to the cell site to provide a new supply of operating power to the cell site, and initiating a communication to dispatch the portable generator. Other embodiments are disclosed.

Portable generator dispatch recommendation engine

Aspects of the subject disclosure may include, for example, automatically detecting a service issue of a telecommunication system that may impact a customer of a telecommunication service provider of the telecommunication system, predicting, by the processing system, a future customer care interaction by the customer as a result of the service issue, initiating a resolution action of the telecommunication system for the service issue before the customer contacts a customer care agent of the telecommunication service provider, wherein the initiating is responsive to the predicting the future customer care interaction and modifying a component of the telecommunication system to improve efficiency of operation of the telecommunication system by reducing a number of customer care contacts by customers of the telecommunication service provider. Other embodiments are disclosed.

Proactive customer care in a communication system

A method includes collecting operational data from a system, segregating the data into a first component comprising one or more service quality anomalies and a second component comprising one or more network-based events, correlating the first component and the second component to determine whether the one or more network-based events have an impact on service quality, and prioritizing a resolution of the network-based events that have an impact on service quality.

Automatic integrated control loop for fault, performance and service quality management

Systems and methods for providing accurate data about the distribution of UEs after disaster include a movable eNodeB that is moved to at least three locations. The coordinates for the at least three locations are determined and the distance between the eNodeB and a plurality of UEs is measured. The location of each UE relative to the eNodeB is calculated based on the distance between the eNodeB and the plurality of UEs using trilateration. The coordinates for the UEs is then calculated based on the coordinates for the at least three locations and the determined location of the UEs relative to the eNodeB.

Systems and methods for locating user equipment during disasters

A method, computer-readable storage device and apparatus for quantifying a total number of customers impacted by a cellular tower outage are disclosed. For example, the method monitors a plurality of cellular towers, detects a failure of one of the plurality of cellular towers, identifies a subset of the plurality of cellular towers that are included in an affected region, calculates a number of additional customers served by each one of the subset of the plurality of cellular towers that are included in the affected region due to the failure of the one cellular tower, and quantifies the total number of customers impacted by the failure of the one cellular tower by adding the number of additional customers served by the each one of the subset of the plurality of cellular towers that are included in the affected region.

Method and apparatus for quantifying the customer impact of cell tower outages

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Westfield, NJ, United States of America

Matthew Osinski