Abstract: Aspects of the subject disclosure may include, for example, monitoring status of a network configured to provide end-to-end delivery of services to priority user equipment authorized according to a prioritized class of service and other user equipment not authorized for the prioritized class of service. The prioritized class of service extends a reserved network capacity for end-to-end delivery of a service via first domain including first network slices and a second domain including second network slices. A network interruption compromising the reserved capacity is determined based on the monitored status and integration of an alternate group of slices into one of the first network domain, the second network domain, or both to replace the compromised group of slices, is orchestrated. Integration of the alternate group of slices restores reserved network capacity according to the prioritized class of service. Other embodiments are disclosed.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, a processing system that analyses, without user intervention, content of a first message directed from equipment of a sending user to equipment of a receiving user, to identify a task. The processing system determines that the receiving user is unable to perform the task according to biometric data of the receiving user. The biometric data is selected from one of heart rate, blood pressure, respiratory rate, and combinations thereof. According to the content of the first message and the biometric data obtained from the receiving user, the processing system determines that the task should be delegated to another user, and responsive thereto, provides a second message associated with the task to equipment of the other user. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, retrieving call detail record (CDR) data for a plurality of devices, each device of the plurality of devices using a subscriber identity module (SIM) to access a mobility network, identifying data anomalies for the plurality of devices, wherein the identifying the data anomalies is based on the CDR data, wherein the data anomalies may be indicative of inappropriate usage of the mobility network, identifying a device associated with a data anomaly, and initiating a modification of the device to prevent subsequent anomalies. Other embodiments are disclosed.

Abstract: The disclosed technology is directed towards detection of personal threats to a user based on information known to a user's device and other information. A device with a personal threat application program and server communicate information, including data describing the user's current physical situation, e.g., including current location. The server and/or application program obtain data describing environmental conditions around the user's location, e.g., corresponding to a defined personal zone. The personal zone size automatically can vary based on current factors. Based on the situation and environmental data reaching a threshold threat level, a potential threat to the user can be predicted and the prediction, used to take some action, such as to send an alert to the user and/or otherwise output a warning signal. Historical data such as statistics related to the user's current location can be accessed to assist with the prediction.

Abstract: A method performed by a processing system including at least one processor includes grouping a plurality of nodes of a telecommunications network into a plurality of reference groups, based on a plurality of configuration attributes and on a plurality of load, mobility, radio frequency attributes for the plurality of nodes, selecting a first reference group of the plurality of reference groups, where the first reference group includes a subset of the plurality of nodes, selecting a first configuration parameter of the first reference group to be tuned, identifying a first value for the first configuration parameter that is most prevalent among the subset of the plurality of nodes, and setting the first configuration parameter for all nodes in the subset of the plurality of nodes to the first value.

Abstract: Aspects of the subject disclosure may include, for example, identifying a number of radio resources of a mobile device, detecting activity of an application, and determining a data communication requirement of the application. The data communication requirement is compared to a first component carrier capacity and responsive to it exceeding the first component carrier capacity, a number of secondary component carriers providing secondary capacities are identified according to the data communication requirement, wherein a combination of the first capacity and the number of secondary capacities is not less than the data communication requirement. A group of the radio resources is configured according to the number of secondary component carriers, wherein a number of the group of the radio resources does not exceed the maximum number of the radio resources, and wherein the data communication requirement is accommodated by the combination of the first and secondary capacities. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, detecting a first bootstrap profile on a first communication device, and detecting a second bootstrap profile on a second communication device. Further aspects can include determining the first bootstrap profile and the second bootstrap profile are a same bootstrap profile, and providing limited services to the second communication device according to the first communication device and the second communication device having the same bootstrap profile. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, initiating a presence within first metaverse environment, wherein the presence includes a correlation between a physical stimulation of a human interface and first virtual stimuli occurring within the first metaverse environment. A virtual interconnection is initiated between the first metaverse environment and a second metaverse environment including second virtual stimuli occurring within the second metaverse environment. A virtual interaction is determined between the first metaverse environment and the second metaverse environment according to the virtual interconnection and an adjusted virtual stimulus occurring within the first metaverse environment is determined responsive to the virtual interaction between the first metaverse environment and the second metaverse environment.

Abstract: Aspects of the subject disclosure may include, for example, including, in user equipment (UE) capability information, at least a portion of a device identifier of a UE, and causing the UE capability information to be transmitted in resource control messaging. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a system including a cross-segment slice controller (CSSC) configured to interface with a software-defined network (SDN) controller and a software-defined radio (SDR) controller. The SDN controller may be associated with a core network and the SDR controller may be associated with a radio access network (RAN). The system further includes a machine learning (ML) component configured to obtain and analyze data regarding the core network and the RAN, and an intelligent end-to-end (E2E) orchestration platform (IEOP) configured to coordinate with the SDN controller and the SDR controller via the CSSC based on outputs of the ML component to provide dynamic cross-segment network slice management. Other embodiments are disclosed.

Abstract: A system includes an orchestrator for a software-defined network and configured to receive a request for operation of the software-defined network, a software-defined network controller in communication with the orchestrator through a northbound application programming interface, at least one network element in communication with the software defined network controller though a southbound application programming interface, and a mutable network element configured to receive the request and instantiate a virtual function within the mutable network element to test the at least one network element in accordance with the request.

Abstract: The described technology is generally directed towards public service answering point (PSAP) call routing issue detection and resolution. A PSAP call routing issue detection tool is disclosed. The PSAP call routing issue detection tool can be configured to identify PSAP call routing problems in a cellular network. The PSAP call routing issue detection tool can present the PSAP call routing problems for further analysis and correction by cellular network engineers. The PSAP call routing issue detection tool can furthermore identify and suggest call routing corrections to address identified PSAP call routing problems. The PSAP call routing issue detection tool can optionally also automatically correct identified PSAP call routing problems according to suggested call routing corrections.

Abstract: Aspects of the subject disclosure may include, for example, a drone service retrieving network state information describing a network state of at least a portion of a communication network, determining an impact of the network state on operation of an unmanned aerial vehicle (UAV), determining operational information for the UAV, and providing the operational information to the UAV. Other embodiments are disclosed.

Abstract: The disclosed technology is directed towards providing guidance, e.g., point-to-point audio guidance, along an evacuation route. The presence of occupant(s) within a structure/area is monitored. If an evacuation event occurs, an evacuation route is determined for each occupant based on their current monitored starting location. Guidance is output, e.g., by various output devices along the evacuation route, which can be the safety monitors that monitor occupant presence, to guide each occupant from point-to-point along the evacuation route. For example, one monitor can be configured to hand off guidance responsibility to a next monitor along the evacuation route. The guidance can be customized for each known occupant based on their recognized features, or can be directed generally to an unknown occupant. Status information of the occupants can be provided to other occupants and to a responding entity. A responding entity can also be given directional guidance.

Abstract: Aspects of the subject disclosure may include, for example, a system comprising a distributed unit (DU) configured to generate sounding reference signal (SRS)-based beams, and a remote unit (RU) communicatively coupled with the DU over a fronthaul, wherein the RU is configured to generate demodulation reference signal (DMRS)-based beams and perform digital beamforming for an uplink using the DMRS-based beams in combination with the SRS-based beams. Other embodiments are disclosed.

Abstract: An example method for controlling customer premises equipment via the cloud or network edge to support advanced network functionality includes detecting a data flow associated with a customer premises equipment device, identifying an application associated with the data flow, monitoring a performance of the application, determining an action to be taken by the customer premises equipment device to optimize the performance of the application, and sending an instruction to the customer premises equipment device to take the action.

Abstract: Aspects of the subject disclosure may include, for example, obtaining input data, processing the input data to generate output data, the output data including an identification of one or more characteristics associated with tethered communications involving at least a first user equipment, and based on the identification of the one or more characteristics, causing an action to be taken. Other embodiments are disclosed.

Abstract: Facilitating selection of the most significant set of categorical features in machine learning is provided herein. Operations of a system include determining a list of unique values of a categorical variable. The operations also include calculating respective mean values, of a target variable, for unique values of the list of unique values of the categorical variable. Further, the operations include sorting the list of unique values by the respective mean values, resulting in a sorted list. The operations also include calculating respective derivatives of the respective mean values in the sorted list considering the respective mean values as a function and a number of the respective mean values in the sorted list as an independent variable. Additionally, the operations include determining a minimum derivative value over the sorted list and outputting the minimum derivative value as a resulting variable significance value.

Abstract: Mitigation of temporal generalization losses a target machine learning model is disclosed. Mitigation can be based on identifying, removing, modifying, transforming, etc., features, explanatory variables, models, etc., that can have an unstable relationship with a target outcome over time. Implementation of a more stable representation can be initiated. Temporal stability measures (TSMs) for one or more model feature(s) can be determined based on one or more variable performance metrics (VPMs). A group of one or more VPMs can be selected based on features of a model in either a development or production environment. Model feature modification can be recommended based on a TSM, which can prune a feature, transform a feature, add a feature, etc. Temporal stability information can be presented, e.g., via a dashboard-type user interface. Models can be updated based on mutations of a model comprising a feature modification(s), including competitive champion/challenger model updating.

Abstract: Machine-learning based user authentication using a mobile device (e.g., using a computerized tool) is enabled. For example, a non-transitory machine-readable medium can comprise executable instructions that, when executed by a processor, facilitate performance of operations, comprising: determining an input received via a mobile device, determining, based on the input and using an authentication model, whether the input threshold matches an input pattern associated with an authorized user profile authorized to access a feature of the mobile device, wherein the input pattern has been determined based on machine learning applied to past inputs at the mobile device other than the input, and wherein the authentication model has been generated based on the machine learning applied to the input pattern, and based on a determination that the input at the mobile device is associated with an authorized user profile, granting access to the feature of the mobile device.