Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Examples of the present disclosure relate to artificial intelligence log processing and CDN optimization. In examples, log data is processed at a node of the CDN rather than transmitting all of the log data for remote processing. The log data may be processed by a model processing engine according to a model, thereby generating model processing results. Model processing results are communicated to a parent node, thereby providing insight into the state of the node without requiring transmission of the full set of log data. Model processing results and associated information may be used to alter the configuration of the CDN. For example, a model processing engine may be added or removed from a node based on a forecasted amount of log data. As another example, edge servers of a node may be added or removed based on expected computing demand.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Novel tools and techniques are provided for implementing error detection in a network, and, more particularly, to methods, systems, and apparatuses for implementing error and/or fault detection in a network and/or media stream and providing options to address the error and/or fault in the network and/or media stream. In various embodiments, a computer might detect an error in a first network and send a notification indicating that the error has occurred. The notification might contain one or more options to address the error in the first network. The computer, a user device, a service provider device, or a content provider device might receive and display the notification containing the one or more options. The computer, the user device, the service provider device, or the content provider device might then select at least one of the one or more options to address the error in the first network.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Novel tools and techniques are provided for implementing error detection in a network, and, more particularly, to methods, systems, and apparatuses for implementing error and/or fault detection in a network and/or media stream and providing options to address the error and/or fault in the network and/or media stream. In various embodiments, a computer might detect an error in a first network and send a notification indicating that the error has occurred. The notification might contain one or more options to address the error in the first network. The computer, a user device, a service provider device, or a content provider device might receive and display the notification containing the one or more options. The computer, the user device, the service provider device, or the content provider device might then select at least one of the one or more options to address the error in the first network.

Abstract: 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.

Abstract: 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.

Abstract: 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.