Centurylink Intellectual Property LLC

Qwest Communications International Inc.

Verizon Patent and Licensing Inc.

Tivo Corporation

Adeia Media Holdings LLC

Felipe Castro

Steven M Casey

Kevin Michael McBride

Ronnie S Dhaliwal

Thomas Schwengler

Stephen Opferman

Gnanasegeran Selvadurai

Waqar Khan

Misbah Ur Rehman

Jeff Leonard

Bruce A Phillips

John Butala

Amie Elcan

Mayur Kudtarkar

Nikhil Parikh

Charles Ivan Cook

Michael D Sprenger

Hong Choi

Sivanaga Ravi Kumar Chunduru Venkata

Karthik Ramaswamy

Gnanasekaran Swaminathan

Stephen C Opferman

Mayur Kudtakar

Kevin M Mcbride

Novel tools and techniques for providing an in-line AI virtual assistant are provided. A system includes a session border controller coupled to an end-user device, and a session initiation protocol stack. The session initiation protocol stack includes a processor, and non-transitory computer readable media comprising instructions executable by the processor to: receive, from the session border controller, a call invite request; provision, via the AI virtual assistant service, an AI virtual assistant instance in response to the call invite request; and join the AI virtual assistant instance to a call with the end-user device.

In-line, in-call AI virtual assistant for teleconferencing

The present technology relates to improving computing services in a distributed network of remote computing resources, such as edge nodes in an edge compute network. In an aspect, the technology relates to a method that includes aggregating historical request data for a plurality of requests, wherein the aggregated historical request data a time of the request, a location of a device from which the request originated, and/or a type of service being requested. The method also incudes training a machine learning model based on the aggregated historical request data; generating, from the trained machine learning model, a prediction for a type of service to be request; identifying an edge node, from a plurality of edge nodes, based on a physical location of the edge node; and based on predicted service, allocating computing resources for the computing service on the identified edge node.

Predictive resource allocation in an edge computing network utilizing machine learning

Tools are provided for distributing access-restricted content in an internet protocol television ('IPTV') environment based on portable entitlement keys. Such tools can include a decoder, an encoder, and a network entitlement handler. The decoder may be configured to receive a key associated with entitlement information, and transmit the entitlement information over a network. The encoder may be configured to receive content from content providers, and to encode the content to create IP-compatible content, with access restrictions based on entitlement. The network entitlement handler may be configured to receive a request for requested content from the decoder; receive the access-restricted content (including the requested content) from the encoder; and transmit the requested content over the network to the decoder using IP, when the decoder is entitled to receive the requested content.

IPTV follow me content system and method

The present technology relates to improving computing services in a distributed network of remote computing resources, such as edge nodes in an edge compute network. In an aspect, the technology relates to a system that includes a plurality of edge nodes and a beacon. The system performs operations that may include collecting traffic data from the beacon over a period of time, wherein the traffic data includes at least an amount of devices sending probe requests to the beacon; comparing the amount of devices to a predetermined threshold for traffic data; and based on the comparison of the amount of devices to the predetermined threshold for traffic data, generating a recommendation for installation of a new edge node in addition to the plurality of edge nodes.

Predictive resource allocation in an edge computing network utilizing beacons

Novel tools and techniques for an IoT shell are provided. A system includes an internet of things (IoT) device, a database, and a license manager. The database may include one or more sets of authorized licenses, each set of authorized licenses associated with a respective vendor software. The license manager may be in communication with the IoT device and the database, and further include a processor and a non-transitory computer readable medium comprising instructions executable by the processor. The license manager may be configured to receive a request to reserve a license for a first vendor software, determine an availability of the license associated with the first vendor software, register a unique identifier of the IoT device in association with the license, and grant the license to the IoT device.

Methods and systems for the reservation, registration, and granting of device licenses to internet of things devices

A device may receive network data identifying at least one of a signal-to-interference-plus-noise ratio (SINR), a bit error rate (BER), a packet loss, or a frame loss associated with a radio access network (RAN), and may receive model data associated with a plurality of machine learning models. The device may receive inference confidence scores associated with the plurality of machine learning models, and may process the network data, the model data, and the inference confidence scores, with a model, to select a machine learning model from the plurality of machine learning models. The device may cause the selected machine learning model to be implemented in connection with processing images.

Systems and methods for selecting a machine learning model and training images for the machine learning model

Novel tools and techniques are provided for remotely configuring and orchestrating multifunctional cloud devices located on customer premises, in some cases, using a smart cloud adaptive device. In some embodiments, the smart cloud adaptive device, which might have one or more wireless programmable radios configured to communicate with a network termination device, might communicate with a cloud configuration server over a network via the one or more wireless programmable radios (and, in some embodiments, through the network termination device). The smart cloud adaptive device might transmit device identification information associated with a customer and/or service codes indicative of services to be provided to the customer. The smart cloud adaptive device might receive one or more configuration files from the cloud configuration server based on the service codes, and enable functionality among a plurality of functionalities to provision the services, based on the one or more configuration files.

System and method for cloud computing adaptive cloud services

Methods and systems for the reservation and registration of internet of things device licenses from authorized licenses associated with vendor software

Methods and systems for the reservation and registration of internet of things device licenses

Novel tools and techniques are provided for provisioning network services, and, more particularly, to methods, systems, and apparatuses for implementing intelligent network services automation. In various embodiments, a computing system might receive one or more network performance metrics of one or more networks, might receive network usage data associated with the one or more networks, and might analyze, using one or more machine learning techniques, the received one or more network performance metrics and the received network usage data to determine whether the one or more networks can be improved in terms of network efficiency or network operations. Based on a determination that the one or more networks can be improved, the computing system might dynamically reconfigure at least one of one or more network connections within the one or more networks or one or more network resources within the one or more networks.

Method and system for implementing intelligent network services automation

Growing community of inventors

Erie, CO, United States of America

Felipe Castro