Centurylink Intellectual Property LLC

Level 3 Communications, LLC

Stephen Opferman

Steven M Casey

Kevin Michael McBride

Thomas Schwengler

Felipe Castro

William O'Brien, Jr

Ronald A Lewis

Jeff Leonard

James E Sutherland

William R Benassi

Hunter Rudd

James P Gately

Michael L Elford

Clyde David Cooper, Iii

Michael Paul Winterrowd

Carlos Santiago

Jeffrey Kendall Storey

Pasha G Mohammed

Ron Lewis

Thomas Charles Barnett, Jr

Tom Funk

James D Scheller

Kevin M Mcbride

William R Henning

Todd A Borchert

Rahul Chowdhury

Rick Calvert

Paul Savill

Novel tools and techniques are provided for utilizing blockchain to implement named data networking. In various embodiments, a computing system might determine whether a cache that is communicatively coupled to the computing system contains data that is responsive to a first request received from a user. If so, the computing system might retrieve and send (to the client device) data that is responsive to the received first request. If not, the computing system might send, to a blockchain system, a second request for identifying a blockchain containing a block containing data responsive to the received first request. In response to identifying such a blockchain, the computing system might receive a copy of the identified blockchain; might abstract, from the identified blockchain, the block containing the data responsive to the received first request; might abstract the data from the identified block; and might send the data to the client device.

Utilizing blockchains to implement named data networking

Novel tools and techniques are provided for implementing a telemetry hub, and, more particularly, to methods, systems, and apparatuses for implementing a telemetry hub that obtains sensor data from a plurality of sources and that determines one or more first actions to take in response to receiving the first sensor data. In operation, a telemetry hub might receive first sensor data from one or more sensors. The telemetry hub may determine whether the first sensor data can be trusted and whether the first sensor data is valid. Based on a determination that the first sensor data can be trusted and is valid, the telemetry hub might analyze the first sensor data to determine one or more first actions to take. The telemetry hub might then implement the one or more first actions based at least in part on the analysis of the first sensor data.

Telemetry hub

Novel tools and techniques are provided for implementing off-chain functionality for data contained in blocks of blockchain. In various embodiments, a computing system might assemble data extracted from each block of a first block(s) of a first instance of a blockchain in a generated first file(s), the assembled data being organized to mirror how the data is organized and stored in the first block(s); might store, in a secure database, the first file(s), the first file(s) being configured to be content-searchable, content-sortable, and/or content-filterable; might generate a second instance of the blockchain, which contains one or more second blocks, the one or more second blocks each containing the hash value of each corresponding first block(s), without containing the data contained in each block of the first block(s); and might store, in a distributed peer data storage system of the blockchain system, the generated second instance of the blockchain.

Off-chain functionality for data contained in blocks of blockchain

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

Examples of the present disclosure relate to an AI-supported CDN. In examples, a data processing engine processes log data of a CDN node according to a model to identify an issue. An issue indication is provided to a solution generation engine, which generates a set of solutions to automatically resolve the issue. The set of solutions is provided to a solution implementation engine, which iteratively implements solutions to resolve the issue using solution implementation information associated with a given solution. Thus, the data processing engine need not have knowledge regarding the specific hardware and/or software used within the CDN. Similarly, the solution generation engine need not have knowledge of the structure of the CDN and/or configuration of devices associated with the identified issue, such that the solution implementation engine provides a layer of abstraction between a solution and the implementation-specific details used to implement the solution within the CDN.

AI-supported network techniques

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

Novel tools and techniques are provided for implementing data and source validation for equipment output data and/or for equipment failure predict. In various embodiments, in response to receiving a first request for first data that is output by first equipment, a computing system might retrieve and analyze the first data to determine whether the first data can be trusted. If so, the computing system might send the first data to the requesting device. If not, the computing system might send a second request for identifying a blockchain containing a block containing a copy of the first data. In response to the blockchain system identifying such a blockchain, the computing system might receive the identified blockchain; might abstract the block containing the copy of the first data from the identified blockchain; might abstract the first data from the block; and might send the first data to the requesting device.

Data and source validation for equipment output data or equipment failure prediction using blockchains

Examples of the present disclosure relate to content delivery using distributed ledger and AI-based transcoding technologies. In examples, content is received by a content distribution network (CDN) from a content source. The content may be in a lower-quality or different format than is distributed via the CDN. Accordingly, a transcoding model is identified and used to transcode the content. Multiple transcoding models may be used for different content parts to adapt to changing content types (e.g., a sports subpart, an advertising subpart, etc.). Transcoding may occur at edge nodes, such that the original content is transmitted within the CDN, thereby conserving resources. Additionally, transcoded content may be cached, such that the content need not be transcoded in response to every request.

Content delivery using distributed ledger and AI-based transcoding technologies

Examples of the present disclosure relate to the optical identification of telecommunications equipment. In examples, a user interacts with an application to capture image data relating to a device according to instructions presented to the user. The application may further generate metadata, such as user responses to one or more questions. The image data and/or metadata are evaluated using a machine learning model to generate an equipment classification for devices pictured therein. The data may also be used to generate an equipment configuration for the device, as well as an operational state (e.g., based on one or more indicators present on the device, log data, etc.). Accordingly, such information may be used to update a pre-existing inventory record for the device, or generate a new inventory record. In other examples, such information is used to generate one or more predicted issues and associated actions to troubleshoot the device.

Optical identification of telecommunications equipment

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

Examples of the present disclosure relate to a distributed CDN comprised of CDN nodes that each have a compute engine and a data store. Compute engines may be used to execute software associated with computing functionality of the distributed CDN and may be reconfigured according to changing conditions to adapt computing functionality to current or expected demand. Nodes may communicate with one another via an overlay network. A node may determine whether it should process a request or generate a route via the overlay network to the remote node, thereby enabling the remote node to process the request. In another example, a node may receive a request for a compute resource that is not currently provided by the node. The compute engine of the node may be reconfigured to provide the compute resource, thereby enabling the node to service the request rather than redirect the request to a different node.

Distributed content distribution network

Examples of the present disclosure relate to content delivery using distributed ledger and AI-based transcoding technologies. In examples, a content distribution network (CDN) receives content from a content source for distribution to client devices. A content record is generated for the content, which comprises a content hash for the content. The content record may be stored in a distributed ledger. A smart contract associated with the content may be generated, where the smart contract facilitates CDN analytics or accounting for CDN service payments, licensing fees, or royalty payments, among other examples. The smart contract may be associated with the content hash and may be executed based on any of a variety of triggers, such as content playback and/or distribution via the CDN.

Novel tools and techniques for network data plane management are provided. A system includes a host machine that includes a database, processor, and non-transitory computer readable media comprising instructions executable by the processor to obtain, via the database, a network configuration, spawn a container according to the network configuration, wherein the container is configured, based on the network configuration, to be coupled to a network overlay via a network interface, receive, via the network interface, incoming data associated with the container, the incoming data having attached one or more attached network data attributes, and identify, via the database, the attached one or more network data attributes attached to the incoming data as one or more network data attributes of the network data model.

Edge database management of the network data plane

Novel tools and techniques might provide for implementing combined broadband and wireless self-organizing network ('SON') for provisioning of services. In some embodiments, a computing system might receive, from one or more first sensors and one or more second sensors, first operational states of fixed broadband network nodes and second operational states of wireless network nodes, respectively. The computing system might analyze the received first and second operational states, might determine an optimal network pathway and/or an optimal network backhaul pathway, and might establish the optimal network pathway and/or the optimal network backhaul pathway, through a determined combination of fixed and wireless network nodes, thereby implementing the combined broadband and wireless self-organizing network (“SON”) for provisioning of services.

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Denver, CO, United States of America

Stephen Opferman