T-mobile Usa, Inc.

Tupl, Inc.

Nokia Corporation

Pablo Tapia

Rafael Sanchez-Mejias

Hongliang Zhang

Christopher Harvey Joul

Mark McDiarmid

Mehul Jayant Shah

Brian Allan Olsen

Alexandru Catalin Ionescu

Rafael Guerrero

Kevin Lau

Gamze Seckin

Juan Jose Escobar

Ahmad Arash Obaidi

Jie Hui

Antoine T Tran

Ting Zhang

Sudhanshu Pandey

Sayeedur Rahman

Yasmin Karimli

Jun Liu

Carl Williams

Matti Salmenkaita

Jose Gimenez

Rafael Sanchez Mejias

Ricardo Ferrer

Oscar Martin Vicente

Amarnath Vishvanath Algundgi

Mark Mcdiarmid

Carl Craig Williams

A method and system to automatically analyze, diagnose and repair the state of a network utilizing big data and machine learning techniques. Data from disparate sources related to a first network element is received by a processing layer. Contextual information from a measurements megastore related to the first network element and other network elements is retrieved. The data from the disparate sources and the contextual information is analyzed by an intelligence layer comprising big data and machine learning techniques. Upon determining, by the intelligence layer, that a predetermined condition is met or a predetermined threshold is exceeded, a notification is provided to the first network element. Over time, the intelligence layer adapts to learn based on growing amounts of historical data.

Resource management in a big data environment

A telecommunication device configured to select one of a plurality of network connectivities of the telecommunication device to use for transmission of a network packet is described herein. The telecommunication device may select the network connectivity based on user routing criteria, connectivity metrics, or transmission times. The user routing criteria may be dynamically updated during a communication or connection, affecting selection of the network connectivity for further network packets. The network connectivities may be respectively associated with different network operators. Also, the telecommunication device may select a first network connectivity for transmitting a first network packet and a second network connectivity for transmitting a second network packet. Further, the telecommunication device may select a first network connectivity for uplink communications and a second network connectivity for downlink communications.

Intelligent routing of network packets on telecommunication devices

A virtual marketplace may offer end users the ability to acquire articles (including tools and metadata objects) that are compatible with an enterprise operating system platform. The virtual marketplace may determine one or more articles that are implemented (or installed) on the enterprise operating system platform. The enterprise operating system platform may be provided to the end user by an operator of the marketplace. The articles including articles that may be used for evaluating a performance of an entity. The virtual marketplace may further ascertain data that are processed or generated by the articles that are implemented on the enterprise operating system platform. The virtual marketplace may additionally determine functionalities that are used or provided by the articles. Accordingly, the virtual marketplace may provide a recommendation of associated articles from the virtual marketplace that have at least one dependency relationship with the articles.

Virtual marketplace for distributed tools in an enterprise environment

A method and system of managing a utility system having a plurality of subsystems. A utility management server receives performance measurement data of a subsystem of a first utility system is received, from a first utility monitoring device (UMD). A type of the subsystem and a type of the first UMD is determined. Static rules are applied based on the type of subsystem and the type of UMD. Upon determining that the predetermined condition based on the static rules is not met, identifying a subsystem and its corresponding UMD responsible for a malfunction by retrieving contextual information from the measurements megastore server, analyzing the data from the first UMD and the contextual information, and sending a notification to the UMD having the subsystem that has been identified as being the source of the malfunction.

Systems and methods of utility management

Described herein are one or more self-organizing network (SON) components configured to determine QoS factors associated with QoS classes for a node of a telecommunication network based at least in part on performance metrics, network configuration, or network fault information. The one or more SON components may receive the performance metrics, network configuration, or network fault information from the node, and the performance metrics, network configuration, or network fault information may be associated with communication of traffic of the QoS classes by the node. Upon determining the QoS factors, the one or more SON components may transmit indications of the QoS factors to the node.

Performance-based optimization of QoS factors

An analytic application may automatically determine a root cause of an issue with a wireless carrier network and generate a solution for the root cause. Initially, a data management platform may receive performance data regarding user device and network components of a wireless carrier network from multiple data sources. Subsequently, the analytic application may receive an indication of an issue affecting one or more user devices that are using the wireless carrier network. The analytic application may analyze the performance data using a trained machine learning model to determine a root cause for the issue affecting the one or more user devices. The trained machine learning model may employ multiple types of machine learning algorithms to analyze the performance data. The analytic application may provide the root cause or the solution that resolves the root cause for presentation.

Automatic customer complaint resolution

A set of key performance indicators (KPIs) is calculated for a first set of user devices based on device performance data of the first set of more user devices or network performance data of a communication network that provides communication services to the first set of user devices. The device performance data has at least one of temporal or geographical commonality with the network performance data. A machine learning (ML) model is then trained based on training data, in which the training data includes the network performance data and the set of KPIs calculated for the first set of user devices. The ML model is applied to additional network performance data of the communication network to extrapolate one or more extrapolated KPIs for the first set of user devices or a second set of user devices.

Communication network quality of experience extrapolation and diagnosis

A distributed tool virtual marketplace may offer end users the ability to acquire tools that are compatible with an enterprise operating system platform. The virtual marketplace may determine one or more tools that are implemented on the enterprise operating system platform. The enterprise operating system platform may be provided to the end user by an operator of the marketplace. The tools including a tool that may be used for evaluating a performance of an entity. The virtual marketplace may further ascertain data that are processed or generated by the tools that are implemented on the enterprise operating system platform. The virtual marketplace may additionally determine functionalities that are used or provided by the tools. Accordingly, the distributed tool virtual marketplace may provide a recommendation of associated tools from the distributed tool virtual marketplace that have at least one dependency relationship with the tools.

A method and system of managing fixed line network elements. Data from disparate sources is received by a processing layer of a monitoring server via a wireline communication network. The intelligence layer determines whether a first node from the disparate nodes is new or pre-existing. Based on a determination by the intelligence layer whether a node is new or pre-existing, different static rules are applied to the received data from the first node. Contextual information is retrieved from the measurements megastore related to the first node. A root cause of a malfunction of the first node is determined. A notification is generated based on the root cause of the malfunction.

Fixed line resource management

A network fix application may automatically determine a root cause of an issue with a wireless carrier network and generate a network fix prioritization to implement a solution for the root cause before receiving a customer or network trouble ticket. Initially, a data adaptor platform may receive performance data regarding user device and network components of a wireless carrier network from multiple data sources. The network fix application may analyze the performance data using a trained machine learning model to predict a root cause for the issue affecting the one or more user devices based on the symptoms indicated in the performance data. Additionally, the network fix application may analyze the performance data using another trained machine learning model to provide a network fix prioritization to implement a resolution for each predicted root cause in the most optimal order.

Artificial intelligence-based network advisor

A communication analytics engine that executes in conjunction with a data collection platform may provide a unified and scalable solution for call data aggregation and processing. A data collection platform may establish a communication connection with a wireless carrier network. The data collection platform may collect call data of multiple user devices via the communication connection, in which the multiple user devices may use the wireless carrier network to initiate and receive calls to one or more additional devices. The data collection platform may convert the call data into a format that is readable by the communication analytics engine. The communication analytics engine may analyze the call data to generate analytic results that includes one or more key performance indicators (KPIs).

Wireless communication data analysis and reporting

A performance management engine may be implemented to continuously detecting entity performance issues. The performance management engine may calculate one or more key performance indicators (KPIs) that measure performance of an entity. The performance management engine may further receive one or more additional sources of data regarding the entity via a data management platform that interfaces with multiple data sources. The performance management engine may aggregate the KPIs and the one or more additional sources of data into datasets according to one or more grouping parameters. The data in one or more datasets may be analyzed by the performance management engine to generate one or more comprehensive performance indicators. The comprehensive performance indicators are then provided for display on a user device. Each KPI or comprehensive performance indicator measures performance of a device, a component, a node, or a service of the entity.

Distributed multi-data source performance management

A system and method that facilitates mobile device selection among available qualities of service is described herein. A mobile device presents a user interface to enable selection of change to a current quality of service on the mobile device. The user interface further enables selection of one or more parameters associated with the selected change to the current quality of service. In response to the selection, the mobile device transmits to the telecommunications network the selected change in quality of service. The mobile device then receives the selected change in quality of service if the telecommunications network is able to accommodate the selected change.

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Snoqualmie, WA, United States of America

Pablo Tapia