Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Aspects of the subject disclosure may include, for example, using an AI machine to predict one or more traffic patterns, and the comparing the predicted traffic patterns to actual traffic patterns. Recommended travel paths may be modified as a result. AI machines may be feed-forward or recurrent, and may be trained using any suitable algorithm, including deep learning. Other embodiments are disclosed.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed is a web server that includes a headlines module for automatically generating headlines based on data retrieved from a network (e.g., World Wide Web). The web server also includes an interactive agent for generating responses to inquiries relating to the headlines based on the data.

Abstract: 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.

Abstract: Aspects of the subject disclosure may include, for example, obtaining an input, e.g., from a human operator, comprising a request. A number of activities are identified, e.g., by a virtual assistant, based on the request. Performance of the number of activities is facilitated, e.g., by the virtual assistant. A result is determined, e.g., by the virtual assistant, based on the performance of the number of activities, wherein a response to the request is based on the result. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining an input, e.g., from a human operator, comprising a request. A number of activities are identified, e.g., by a virtual assistant, based on the request. Performance of the number of activities is facilitated, e.g., by the virtual assistant. A result is determined, e.g., by the virtual assistant, based on the performance of the number of activities, wherein a response to the request is based on the result. Other embodiments are disclosed.

Abstract: 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.

Abstract: 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.

Abstract: 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.