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: 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: 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: 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: 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 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.

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 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.

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 for services to be performed by one or more edge nodes; training a machine learning model based on the aggregated historical request data; generating, from the trained machine learning model, a prediction for an amount of requests for services at the one or more edge nodes; generating a predicted capacity needed to perform the predicted amount of requests; comparing the predicted capacity to a current capacity for the one or mode edge nodes; and based on the comparison, generating a recommendation for an alteration of hardware resources at the one or more edge nodes.

Abstract: Novel tools and techniques are provided for implementing configuration data and settings on customer premises equipment (“CPE”), and, more particularly, to methods, systems, and apparatuses for implementing storage, retrieval, and transfer of configuration data and settings in response to receiving a request to reset the CPE. In operation, the CPE might set one or more configuration settings associated with a user and/or a customer premises on the CPE. Next, the CPE might receive a request to reset the CPE. In response to receiving the request to reset the CPE, the CPE might store one or more configuration settings. The CPE may then reset. In response to resetting the CPE, the CPE might retrieve the one or more configuration settings and configure the CPE with the one or more configuration settings associated with the at least one of the user or the customer premises.

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: 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 receiving, from a mobile computing device, first location data for the mobile computing device at a first time; receiving, from the mobile computing device, second location data for the mobile computing device at a second time; and based on the first location data and the second location data, determining a direction vector for the mobile computing device. The method also includes, based on the direction vector: identifying an edge node from a plurality of edge nodes corresponding to a predicted location of the mobile computing device; and prior to the mobile computing device being in the predicted location, allocating computing resources for the computing service on the identified edge node.

Abstract: Novel tools and techniques are described for auto-summarizing video and/or audio content. In some embodiments, a summary server might retrieve one or more time codes from a time code database, and might analyze the one or more time codes to determine at least one selected time code among the one or more time codes that exceeds a predetermined number of selections. The one or more time codes might correspond to one or more triggers, which might include user-inputted triggers, triggers associated with actions by a live audience, or broadcaster/distributor/producer-provided triggers (i.e., cue tones). The summary server might determine one or more selected segments of video or audio content corresponding to the at least one selected time code, and might create one or more summary tracks, where each of the one or more summary tracks might comprise each of the one or more selected segments of the video or audio content.

Abstract: Systems and methods for associating images with location and/or other information. In some cases, the systems include an image sensor, a location sensor, and a microprocessor. The microprocessor is communicably coupled to a computer readable medium that includes instructions executable by the microprocessor to: receive a location from the location sensor; receive an image from the image sensor; and associate the location with the image. Some of the methods provide for capturing an object image of an object using an image sensor; capturing a location of the image sensor; and associating the location with the object image. Other methods and systems are also disclosed.

Abstract: Novel tools and techniques for entertainment device communication to printing devices are provided. A system includes a printing device and media player coupled to the printing device. The media device may include a processor, and a non-transitory computer readable medium comprising instructions executable by the processor to obtain a content stream including at least one of an audio stream or a video stream, the content stream further including a data stream including a trigger associated with a printable model. The instructions may further be executable to obtain a 3-D model file associated with the printable model responsive to an occurrence of the trigger, transmit at least one of the 3-D model file or printing instructions based on the 3-D model file to the printing device, and print, via the printing device, the printable model based on the 3-D model file.

Abstract: Novel tools and techniques for predictive AI automated cloud service turn-up are provided. A system includes an AI pipeline and service orchestration server coupled to the Ai pipeline. The AI pipeline includes a processor and non-transitory computer readable media comprising instructions executable by the processor to obtain customer usage data associated with a first customer from one or more customer data sources, wherein the customer usage data is indicative of usage patterns of one or more cloud services by the first customer, and generate, via a predictive model, predicted usage data based on the customer usage data, wherein the predicted usage data includes a prediction of an individual cloud service of the one or more cloud services predicted to be used by the first customer. The service orchestration server may be configured to turn-up the individual cloud service based on the predicted usage 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: Novel tools and techniques are provided for invoking virtualized network functions. In some embodiments, a programmable service backbone might comprise at least one virtualized network function, and might provide virtualized network functions required to provision a service offering. In some cases, at least one application programming interface might be configured to invoke the at least one virtualized network function of the programmable service backbone. An application programming interface gateway might be configured to manage access to the at least one application programming interface, and the application programming interface gateway might comprise a security layer.

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.