Cox Communications, Inc.

Anthony Joseph Insinga

Yousef Wasef Nijim

James Alan Strothmann

Michael Paul Overcash

Joshua Paul Bernard Sommer

Jenna Meushaun Taneak Smith

Megan Walls Dover

Carol Jeanette Ansley

Matthew Wayne Chapell

According to aspects disclosed herein, a method of using machine learning as part of creating avatars while watching video content is provided. According to an aspect, a method is configured for receiving a request to create an avatar based on the video content and, in response to receiving the request to create the avatar based on the video content, determining one or more processing resources to use to create the avatar based on an output of a machine learning engine, wherein the one or more processing resources include a cloud-based processing resource, an edge-based processing resource, and a local processing resource. The method includes allocating avatar processing operations to the one or more processing resources to create the avatar based on the output from the machine learning engine. The method further includes creating the avatar using the one or more processing resources, and storing the avatar in an avatar database.

Machine learning and distributed processing for creating avatars while watching video content

Automated communication channel selection is provided. A method, system, and computer readable storage device automatically perform a discovery process of a MoCA® network, to determine a topology of the network, perform a spectrum analysis of a DOCSIS® network of which a portion of the frequency range may be shared, apply one or more business rules to the collected network and spectrum usage data, and determine an optimized channel configuration for transmitting data to end devices in a local network. For example, the channel configuration may be configured for optimal bandwidth allocation, transmission speeds, signal quality, etc., without interferences within the shared frequency spectrum range. A gateway device may include a filter that can be reconfigured as an all-pass filter or as a variable low-pass filter based on the channel configuration determination. The MoCA® network may be reconfigured to operate within a defined frequency range based on the determined channel configuration.

Automated communication channel selection

Aspects of the present disclosure provide a failover service in part by utilizing a smartphone when a business or home modem is inoperable or otherwise unable to provide adequate internet service. Once the smartphone is paired with a communication bridge, the smartphone may be used to provide internet services to wired and wireless devices associated with a first network. The smartphone can utilize its cellular network interface to connect to the internet and provide internet connectivity via the communication bridge to a router or gateway which, in turn, enables devices connected to the router or gateway to access the internet without having to connect directly to a separate network provided by the smartphone. Aspects of the disclosure may rely on machine learning to optimize network and/or device communications, configurations, and/or other failover parameters.

Failover to cellular service for seamless internet connectivity

Aspects of the present disclosure provide for adaptively allocating processing operations according to an edge-only processing mode, a hybrid edge-cloud processing mode, and/or cloud-only processing mode, but are not so limited. In one aspect, the disclosure describes adaptively managing processing of applications or application modules by using machine learning in part to direct how the processing operations are to be performed whether using edge-only resources, hybrid edge-cloud resources, and/or cloud-only resources. Other aspects are described in detail herein.

Adaptive edge processing

Intelligent bandwidth prioritization and allocation are provided. Data about a local network and devices connected to the network may be collected, monitored, measured, and tracked over time for generating a profile about a subscriber. For example, the profile may comprise a data-based representation of the data transmission capabilities and data usages and behaviors associated with the subscriber network that can be used for identifying network performance issues, to determine traffic classification, classification criteria, and priority values for automatically classifying traffic flows, and to determine bandwidth allocation rules, optimizations, and other traffic management instructions that can be automatically implemented by the access point to optimize performance of data flows.

Local network traffic prioritization for improved quality of service

Systems and methods are described for enabling quantum computing at an edge node of a network. For example, a machine learning component residing on each of a plurality of edge nodes of the network may be implemented to distribute application processing by network location and processing type, including distribution among classical processing at a central cloud, classical processing at an edge node, and quantum processing at a quantum edge node including a quantum computing device. By distributing certain applications, such as latency-sensitive applications of a higher order of complexity, to an edge node, and particularly a quantum edge node, latency may be reduced and complex application code may be processed more quicky using quantum computations. For applications to be processed using quantum processing, the machine learning component may further identify qubits for the quantum processing and define containers based on the qubits for deployment by the quantum computing device.

Edge quantum computing

Aspects of the present disclosure provide an agent control system that includes a virtual version of a remote control. In one aspect, the disclosure describes an input history store that stores user input operations corresponding to received user inputs to a remote control located at a subscriber location and an agent controller associated with a service provider that generates a dashboard user interface (UI) that operates in part to display a virtual version of the remote control, one or more of the input operations corresponding to the received user inputs to the remote control at the subscriber location, and/or a recommended action to execute on the virtual version of the remote control. Other aspects are described in detail herein.

Control system that includes a virtual version of a remote control

Automated communication channel selection is provided. A method, system, and computer readable storage device automatically perform a discovery process of a MoCA&#xae; network, to determine a topology of the network, perform a spectrum analysis of a DOCSIS&#xae; network of which a portion of the frequency range may be shared, apply one or more business rules to the collected network and spectrum usage data, and determine an optimized channel configuration for transmitting data to end devices in a local network. For example, the channel configuration may be configured for optimal bandwidth allocation, transmission speeds, signal quality, etc., without interferences within the shared frequency spectrum range. A gateway device may include a filter that can be reconfigured as an all-pass filter or as a variable low-pass filter based on the channel configuration determination. The MoCA&#xae; network may be reconfigured to operate within a defined frequency range based on the determined channel configuration.

Growing community of inventors

Powder Springs, GA, United States of America

Anthony Joseph Insinga