Abstract: A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an event reporting service. The event reporting service may enable network devices, such as an application function, to subscribe to usage reporting in which notifications for a usage event may be reported periodically or non-singularly. The event reporting service may include data instances in messages exchanged between core network devices that configures the usage and event notification.

Abstract: A Multi-Access Edge Computing (MEC) device, associated with a base station, may include a processor configured to receive a request to send data to a plurality of user equipment (UE) devices. The MEC device may be further configured to determine that the data is to be delivered to the plurality of UE devices by the base station via multicast; determine signal quality values for the plurality of UE devices; select a subset of the plurality of UE devices based on the determined signal quality values; and send, using the base station, the data to the selected subset of the plurality of UE devices via multicast, and delaying or terminating delivery of the requested data to UE devices, of the plurality of UE devices, that are not in the selected subset.

Abstract: The present teaching is directed to network capacity planning based on denoised user clusters and network element clusters. Collected information representing characteristics and activities of users and characteristics and performance of network elements is used to cluster users and network elements to generate initial user clusters and initial network element clusters, each of which is denoised in an iterative process to derive denoised subclusters that have no impure subclusters therein. Network capacity planning is performed based on correlations identified between denoised user subclusters and denoised network element subclusters.

Abstract: Disclosed are systems and methods for a computerized framework enacted by service contact centers that provides a proactive and adaptive response system that accurately identifies security and/or legal concerns of service requests, and enables artificial intelligence/machine learning (AI/ML)-based mechanisms for dynamically addressing the underlying technical and/or service related concerns of such service requests. The disclosed framework can computationally determine how effective service agents have been, and are currently being in curating solutions/responses to each customer service call, which can enable modified functionality for the customer as well as curated services based on how sufficiently handled the service call was responded to by the agent. The disclosed systems and methods provide a generative service call experience that can improve agent performance while reducing the strain on user experience, both during and/or after service calls.

Abstract: The present teaching relates to customer service with AI-based automated auditing on agent fraud. Real-time features of a communication between an agent and a customer are obtained. To detect agent fraud, a batch feature vector is computed based on real-time features extracted from communications involving the agent and accumulated over a batch period. Agent fraud is detected based on a model and the detection result is used to audit the agent for service performance.

Abstract: A system described herein may maintain User Equipment (“UE”) context information associated with one or more communication sessions between a particular UE and a wireless network, and may detect that one or more triggering events have occurred with respect to the particular UE or the one or more communication sessions. The system may output, based on detecting the one or more triggering events, a status update request. The request may include an identifier of the particular UE and/or an identifier of a particular communication session between the particular UE and the wireless network. The system may receive a response to the status update request, and may selectively remove at least a portion of the maintained UE context information, associated with the one or more communication sessions between the particular UE and the wireless network, based on the response to the status update request.

Abstract: A system described herein may identify a chaincode record recorded to a blockchain, and may output chaincode discovery information to a chaincode discovery system. A client device may receive the chaincode discovery information from the chaincode discovery system, and may output a request to invoke the chaincode. The system may obtain verification, from the chaincode discovery system, that the client device is authorized to invoke the chaincode, and may instruct a node that maintains the blockchain to execute the chaincode. The system may receive output values resulting from executing the chaincode and may output, to the client device, the output values resulting from executing the chaincode.

Abstract: In some implementations, a session management function (SMF) may transmit, to a network data analytics function (NWDAF), an analytics message that requests or subscribes to analytics for user equipment (UE) to user plane function (UPF) (UE-to-UPF) latency. The SMF may receive, from the NWDAF, analytics information in response to the analytics message, wherein the analytics information indicates present or future predicted UE-to-UPF latency information for one or more of a downlink direction or an uplink direction. The SMF may transmit a session modify message to modify a protocol data unit (PDU) session, wherein the PDU session is to be modified based on a UPF selection, and the UPF selection is based on the analytics information.

Abstract: A network device may establish an interface with a unified data management (UDM) device, and may utilize the interface to register with the UDM device and to provide, to the UDM device, registration data identifying an association between the network device and a user equipment (UE). The network device may provide policy data to a network data analytics function (NWDAF) that identifies the network device based on the registration data, and may receive, from the NWDAF, analytics calculated based on the policy data. The network device may perform one or more actions based on the analytics.

Abstract: One or more computing devices, systems, and/or methods for upgrading sites of a communication network are provided. Operational information of network functions within a communication network are tracked within a data repository. An upgrade ruleset is defined for identifying sites within a communication network to upgrade. The operational information, the upgrade ruleset, and model rules generated by a model for a set of candidate sites are processed to generate a positive list of sites to upgrade and a negative list of sites to not upgrade. The negative list of sites and criteria used to determine that the negative list of sites are not being upgraded are provided such as to a user. An upgrade process is performed to upgrade the sites within the positive list of sites.

Abstract: In some implementations, a device may provide, as input to a model, input data indicating current value(s) corresponding to key performance indicator(s) (KPI) associated with current calls to a contact center, enterprise event(s), and stability event(s) associated with the contact center. The device may receive, as an output from the machine learning model, predicted value(s) corresponding to the KPI(s). The device may determine predicted rate(s) of change of the KPI(s) based on a comparison of the current and predicted value(s). The device may assign severity level(s) corresponding to the KPI(s) based on the predicted rate(s) of change. The device may determine a service level impact based on the predicted value(s). The device may determine a performance degradation score based on the severity level(s) and the service level impact. The device may determine and automatically apply a strategy based on the performance degradation score.

Abstract: In some implementations, an agent-assistance platform may receive a first call stream. The agent-assistance platform may identify a first topic of the first call stream. The agent-assistance platform may output, using a recommendation model, one or more recommendations associated with the first topic of the first call stream. The agent-assistance platform may determine whether a recommendation, of the one or more recommendations, is selected for inclusion in the first call stream. The agent-assistance platform may update the recommendation model with information indicating whether the recommendation is selected for inclusion in the first call stream. The agent-assistance platform may receive a second call stream. The agent-assistance platform may identify a second topic of the second call stream, the first topic being the same as the second topic. The agent-assistance platform may selectively output, using the updated recommendation model, the recommendation for the second call stream.

Abstract: An illustrative camera calibration system may identify a common feature present within a scene and visible in each of 1) a first image of the scene captured by a first camera from a first pose represented by a first set of calibration parameters, and 2) a second image of the scene captured by a second camera from a second pose represented by a second set of calibration parameters. The system may estimate a 3D position of the common feature with respect to a global coordinate system associated with the scene and determine that the first set of calibration parameters is more accurate than the second set of calibration parameters. In response to this determination, the system may revise the second set of calibration parameters based on the estimated 3D position of the common feature and the first set of calibration parameters. Corresponding methods and systems are also disclosed.

Abstract: An illustrative volumetric content production system may access first capture data and second capture data. These first capture data may represent an entirety of a capture target and may be captured by a fixed camera system including a first plurality of image capture devices having respective fixed viewpoints with respect to the capture target. The second capture data may represent a portion of the capture target less than the entirety of the capture target and may be captured by a dynamic camera system including a second plurality of image capture devices having respective dynamic viewpoints with respect to the capture target. Based on the first and second capture data, the volumetric content production system may generate a volumetric representation of an object included in the portion of the capture target in accordance with principles described herein. Corresponding methods and systems are also disclosed.

Abstract: A device may receive global metadata terms and aliases, group level metadata terms and aliases, and user level metadata terms and aliases, and may generate global metadata graphs, group level metadata graphs, and user level metadata graphs. The device may train natural language understanding (NLU) models with the global metadata graphs, the group level metadata graphs, and the user level metadata graphs to generate trained NLU models, and may receive a search request from a user. The device may assign a level to the user and an identifier within the level based on a confidence and prior interactions associated with the user, and may select an NLU model from the trained NLU models based on the level and the identifier assigned to the user. The device may process the search request, with the NLU model, to generate search results, and may perform actions based on the search results.

Abstract: A video summary device may determine, based on video data, a plurality of events that occurred during a period of time. The video summary device may determine first measures of relevance of a plurality of portions of the period of time. A first measure of relevance of a portion of time may be determined based on one or more events of the plurality of events. The video summary device may determine second measures of relevance of a plurality of ranges of time between different portions of time of the plurality of portions of the period of time. The second measures of relevance is determined based on the first measures of relevance determined for the different portions of time. The video summary device may determine particular frames of the video data, as a video summary of the video data, based on the second measures of relevance.

Abstract: A device may include a processor configured to maintain a plurality of session management entities (SMEs) for a core network associated with a radio access network (RAN). The processor may be further configured to establish a communication session using a first SME, of the plurality of SMEs; store session data for the communication session in a communication session database accessible by each of the plurality of SMEs; receive, using a second SME of the plurality of SMEs, a message associated with the communication session; retrieve, using the second SME, session data for the communication session from the communication session database, based on receiving the message; and process, using the second SME, the message using the retrieved session data.

Abstract: A device may receive dynamic customer data and static customer data, and may calculate additional customer data based on the dynamic customer data and the static customer data. The device may process the static customer data, the dynamic customer data, and the additional customer data, with a first machine learning model, to determine a next action prediction, and may process the static customer data, the dynamic customer data, and the additional customer data, with a second machine learning model, to determine a next sequence prediction. The device may concatenate the static customer data, the dynamic customer data, the additional customer data, the next action prediction, and the next sequence prediction to generate concatenated data, and may process the concatenated data, with a plurality of machine learning models, to calculate various outputs, and may generate a recommendation for the customer based on the various outputs.

Abstract: In some implementations, a first device may receive, from a second device, network function (NF) configuration information indicating that an NF instance is to be provisioned to support a combination of network slices. The NF configuration information may include first network slice information regarding the first network slice and second network slice information regarding the second network slice. The first device may obtain, from a data structure, first slice configuration information associated with the NF instance supporting the first network slice and second slice configuration information associated with the NF instance supporting the second network slice. The first device may generate configuration parameters to provision the NF instance to support the combination of network slices and provide the configuration parameters to cause the NF instance to be provisioned to support the combination of network slices.

Abstract: Disclosed are embodiments for profiling active workloads in a cloud platform and downstream applications for improving cloud infrastructure based on the profiling. In one embodiment, a method comprises receiving telemetry vectors from a computing device in a cloud platform, each of the telemetry vectors including a plurality of measured values associated with a workload executing on the computing device; clustering the telemetry vectors using an unsupervised learning algorithm, the unsupervised learning algorithm outputting parameters associated with a plurality of clusters; and generating a workload profile from the plurality of clusters by selecting at least one cluster from the plurality of clusters and storing corresponding parameters of the at least one cluster as the workload profile.