At&t Intellectual Property I, L.p.

Syed Anwar Aftab

Mazin E Gilbert

John F Murray

Rittwik Jana

Manoop Talasila

Shu Shi

Serban Jora

Thomas Kirk

Avi Zahavi

Kazi Farooqui

Sarat Puthenpura

Abbas Munawar Fazal

Sam Houston Parker

David Gerald Belanger

Guy Jacobson

Arthur Martella

Wenjie Zhao

Arun Jotshi

Leimeng Shi

Reuben Klein

Gopalakrishnan Meempat

Mukesh Mantan

Sundar Ramalingam

Sunder Tattavarada

Kenneth G Kristiansen, Jr

Aspects of the subject disclosure may include, for example, a device comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: obtaining a list of a plurality of known cell sites, the list comprising for each known cell site of the plurality of known cell sites a respective set of known geospatial coordinates; obtaining for each set of known geospatial coordinates, a corresponding aerial image; applying each corresponding aerial image to an automated process to generate a model, the model being usable to determine whether a particular test aerial image depicts a cell site; obtaining from a database an identification of an asserted cell site, the identification of the asserted cell site comprising a set of asserted geospatial coordinates; obtaining for the set of asserted geospatial coordinates, a test aerial image; applying the test aerial image to the model to determine whether the test aerial image depicts the asserted cell site, resulting in a determination; and outputting the determination of whether the test aerial image depicts the asserted cell site. Other embodiments are disclosed.

Automation to reduce location error rate in E911 location-based services

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 subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of receiving network configuration data from a plurality of location-based services nodes in a network; transforming the network configuration data into reference audit values; comparing the reference audit values with the network configuration data received from each location-based services node in the plurality of location-based services nodes to generate corrections; and sending the corrections to each location-based services node in the plurality of location-based services nodes responsive to the network configuration data deviating from the reference audit values. Other embodiments are disclosed.

Location-based service network automation platform

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

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

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

Concepts and technologies are disclosed herein for a virtualized services discovery and recommendation engine. A request for a service can be received from a requestor. The request can include feature data that can define two or more features. Two or more features can be determined based on the feature data and requestor data associated with the requestor can be obtained. Two or more services that satisfy the request can be identified and a recommendation that represents the two or more services can be generated. The recommendation can include a data representation of a custom product that can include the two or more services. The recommendation can be tested to determine if the custom product functions, and the recommendation can be provided to the requestor.

Virtualized services discovery and recommendation engine

An automated and model driven mini-cloud deployment method comprising: instantiating a model driven orchestrator; parsing a blueprint to develop a virtual network function topology including: identifying infrastructure components required to establish at least one virtual network function; identifying at least one dependency for the at least one virtual network function; providing a sequence for calling life cycle operations; the model driven orchestrator effectuates the life cycle operations comprising: establishing a uCPE as a tenant within a network registry, configuring the uCPE, and activating the uCPE; and instantiating the at least one virtual network function on the uCPE according to the virtual network function topology.

Growing community of inventors

Budd Lake, NJ, United States of America

Syed Anwar Aftab