At&t Intellectual Property I, L.p.

Nuance Communications, Inc.

Google Inc.

Other

Facebook, Inc.

Rutgers, the State University of New Jersey

Cerence Operating Company

Mazin E Gilbert

Srinivas Bangalore

David Crawford Gibbon

Behzad Shahraray

John F Murray

Junlan Feng

Zhu Liu

Syed Anwar Aftab

Narendra Kumar Gupta

Rittwik Jana

Michael J Johnston

Andrej Ljolje

Patrick Guy Haffner

Thomas Kirk

Horst Juergen Schroeter

Reuben Klein

Jay Gordon Wilpon

Taniya Mishra

Diamantino Antonio Caseiro

Patrick John Ehlen

Alistair D Conkie

Dilek Zeynep Hakkani-Tur

Gokhan Tur

Dimitrios Dimitriadis

Shu Shi

Serban Jora

Manoop Talasila

Avi Zahavi

Kazi Farooqui

Bernard S Renger

Andrea Basso

Robert Bell

Shona Douglas

Christopher W Rice

Guy Jacobson

Vincent Goffin

Stephan Kanthak

Vijay Gopalakrishnan

Tan Xu

Benjamin J Stern

Ann K Syrdal

Giuseppe Di Fabbrizio

Rajeev Koodli

Donald John Bowen

Gerald Michael Karam

Alicia Abella

Christopher Volinsky

Eric Noel

Juergen Schroeter

Hong Kook Kim

David Eugene Schulz

Robert Elias Schapire

Esther Levin

Arthur Martella

Michael Lederman Littman

Oliver Spatscheck

Kaustubh Joshi

Karrie Jo Hanson

John Mark Mocenigo

David A Kapilow

Varun Gupta

Wen-Ling Hsu

Christopher Rath

Ilya Dan Melamed

Sanjay Krishna

Don Green

Igal Elbaz

Dilek Zeynep Hakkani-Tür

Heather Wighton

Douglas Keith Trotman

Kelley Mantione

Cheryl Brooks

A model-driven system automatically deploys a virtualized service, including multiple service components, on a distributed cloud infrastructure. A master service orchestrator causes a cloud platform orchestrator to retrieve a cloud services archive file, extract a cloud resource configuration template and create cloud resources at appropriate data centers as specified. The master service orchestrator also causes a software defined network controller to retrieve the cloud services archive file, to extract a cloud network configuration template and to configure layer 1 through layer 3 virtual network functions and to set up routes between them. Additionally, the master service orchestrator causes an application controller to retrieve the cloud services archive file, to extract a deployment orchestration plan and to configure and start layer 4 through layer 7 application components and bring them to a state of operational readiness.

Model driven process for automated deployment of domain 2.0 virtualized services and applications on cloud infrastructure

Aspects of the disclosure are directed to a central management plane (CMP) of one or more processors for regulating streams of data from each of a number of network nodes of a distributed network. The one or more processors can train and deploy machine learning models across the network nodes, and the CMP can generate policies for each network node. The generated policies specify how a network node is to transmit data to the platform for further training or retraining of the deployed machine learning models. The CMP generates the policies using metric data characterizing each network node and respective streams of input data, and are generated based on a number of objectives, including model output quality of the deployed models, and operational cost to transmit and process streams of data across the distributed network.

Centralized management of distributed data sources

Devices, computer-readable media and methods are disclosed for establishing a peer-to-peer network for rerouting network traffic of a telecommunication network during a network disruption. For example, a processing system may detect a network disruption between a first device and a second device of the telecommunication network. The processing system may identify a first peering device having a connection to the first device of the telecommunication network, identify a second peering device having a connection to the second device of the telecommunication network, and establish a peer-to-peer network via at least the first peering device and the second peering device, wherein at least one of the first peering device or the second peering device is a mobile endpoint device configured to operate as a virtual network function. The processing system may then route network traffic between the first device and the second device via the peer-to-peer network.

Peer-to-peer network for telecommunication network traffic rerouting

Aspects of the subject disclosure may include, for example, receiving network-related information associated with a first RAN that includes a first RIC, obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed by a second RIC of the second RAN, determining, based on the data relating to the AI model and the network-related information associated with the first RAN, that the AI model can be leveraged by the first RAN to improve network performance of the first RAN, performing synchronization with the AI model synchronization system to obtain the AI model, responsive to the determining that the AI model can be leveraged by the first RAN to improve the network performance of the first RAN, and causing the first RIC to deploy the AI model in the first RAN after the performing the synchronization. Other embodiments are disclosed.

Synchronization of artificial intelligence based microservices

An intelligent agent monitors operation of at least one software virtualized network (VN). Context information associated with the VN is used to analyze a state of the VN. At least one configuration change is caused to the VN in response to analysis of the state of the VN. A change is identified to the state of the VN caused by the configuration change. A determination is made as to whether or not the change to the state of the VN is an improvement to operation of the VN. A response to the determination is made by causing at least one other configuration change to the VN.

Systems and methods to control operation of virtualized networks

A system may allow for generic end user mobile devices to be operated using a virtualized personal device framework. The virtualized personal device framework may provide for a collective intelligence network platform that allows offloading of heavy computing processes onto virtual instance in a cloud network and enables crowd sourcing services for smart cities that may provide smart emergency services in which end-users collaborate.

5G and SDN powered virtualized personal device framework

Aspects of the subject disclosure may include, for example, a service platform that receives a chat request from a client device associated with a user. A chat database of previous chat sessions associated with other users is searched based on the chat request to identify a previous chat session corresponding to the chat request. Chat data corresponding to the previous chat session is retrieved from the chat database and sent to the client device. Other embodiments are disclosed.

Service platform to support automated chat communications and methods for use therewith

A cloud services composition system allows customers to interactively create service constructs from network function virtualization resources. The network function virtualization primitives are modeled using a standard modeling language. An expert system suggests network function virtualization resources for use in the service construct, based on an expert system learning algorithm. The customer uses a graphical user interface to interconnect the resources and create the service construct. The process may involve collaboration with the network provider. The resulting construct is validated for use in a communications network.

Method and apparatus for autonomous services composition

A method may include a processing system having at least one processor for receiving a first machine learning model, the first machine learning model in a first format associated with a first development environment, adapting the first machine learning model to a containerized environment, validating the first machine learning model according to at least one validation criterion associated with a repository, and publishing the first machine learning model to the repository.

Network-based machine learning microservice platform

A system for designing and executing control loops in a cloud environment includes a control platform implemented in the cloud environment having a data collection, analytics and events module, a policy module and an application controller module. The system includes a business process management application coupled to the control platform having a control loop designer module for designing a control loop template and a workflow engine for distributing the control loop template. The business process management application is coupled to the data collection analytics and events module of the control platform as well as the policy module in the application controller module to control platform. The control loop is activated by the control platform.

System and method for designing and executing control loops in a cloud environment

Growing community of inventors

Warren, NJ, United States of America

Mazin E Gilbert