Abstract: A user equipment may generate one or more temperature thresholds based on thermal mitigation guidelines. The one or more temperature thresholds may define a first temperature zone and a second temperature zone. The user equipment may detect a temperature of the user equipment. The user equipment may identify whether the temperature occurs in the first temperature zone or the second temperature zone. The user equipment may send information to a network device to cause one or more network actions to be performed. The one or more network actions may be configured to maintain or reduce the temperature within the first temperature zone or to reduce the temperature to cause the temperature to switch from the second temperature zone to the first temperature zone.

Abstract: A system may receive a first definition for a virtualized instance of a network function. The first definition may include a first set of declarations in a first format that is different than respective formats supported by different virtualized environments. The system may select a first virtualized environment to run the virtualized instance based on requirements specified within the first definition, and may generate a second definition with a second set of declarations that map the first set of declarations from the first format to a second format supported by the first virtualized environment. The system may deploy the virtualized instance to the first virtualized environment using the second set of declarations from the second definition. Deploying the virtualized instance may include configuring its operation based on some of the second set of declarations matching a configuration format supported by the first virtualized environment.

Abstract: A system described herein may use automated techniques to verify network connection integrity and provide visual indicators of connection validity or invalidity. The system may generate a unique identifier related to a user and/or web resource during an initial use. The unique identifier may be utilized to transform a secure image and store the transformed image. Upon subsequent use of the web resource, a visit identifier may be generated and utilized to perform an inverse transformation of the transformed image and display the result, where the resulting image will be the same as the secure image if the visit identifier matches the unique identifier.

Abstract: A method for performing service authorization for private networks based on an enhanced PLMN identifier. The method includes receiving an attach request from a user equipment device (UE) via a private network, where the attach request includes an international mobile subscriber identity value (IMSI). The method further includes determining, based on the IMSI, an organization identifier and a token associated with the private network, where the token is included in an enhanced PLMN for granting the UE access to resources in the private network. The method further includes sending the token to the UE and a network proxy within the private network.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: A device may receive network data identifying a topology of network devices of a network, quality of service (QoS) rules for user equipment (UEs) and application flows associated with the network, QoS profiles for a radio access network (RAN) of the network, and service data flow (SDF) templates. The device may process the network data, with a model, to calculate a network device service level agreement (SLA) score, for each of the network devices on a sliding window, to generate network device SLA scores, and may aggregate the network device SLA scores to generate an end-to-end SLA score on the sliding window. The device may generate QoS configuration data based on the end-to-end SLA score, and may implement the QoS configuration data.

Abstract: Uplink congestion control based on session initiation protocol (SIP) messaging includes receiving SIP messages from user equipment devices (UEs) via established wireless uplink connections; and monitoring a rate of the SIP messages received from the UEs. Uplink congestion control further includes identifying whether the rate of received SIP messages exceeds a first threshold during a first period of time; and sending SIP response messages to the UEs to reduce congestion on the wireless uplink connections upon identifying that the rate of received SIP messages exceeded the first threshold over the first period of time, where the sent SIP response messages instruct the UEs to resend the SIP messages after predetermined time delays.

Abstract: A network device may receive, from a user device, a call associated with a telephone number, where the call is intended for a priority service associated with the telephone number. The network device may identify the call as a priority call based on the telephone number associated with the call, and may determine that a network overload condition exists. The network device may associate a priority tag with the call based on identifying the call as a priority call. The network device may cause the call to be provided preferential treatment, while the call is transmitted for the priority service during the network overload condition, based on the priority tag.

Abstract: In some implementations, a device may obtain load information indicating a load of a first base station, wherein the load is associated with devices connected to the first base station. The device may determine that the load satisfies a load threshold of the first base station. The device may determine that a network repeater is connected to the first base station. The device may determine that a plurality of devices are connected to the network repeater. The device may determine that the network repeater is capable of connecting to a second base station. The device may cause the network repeater to connect to the second base station to reduce the load based on: the load satisfying the load threshold, the plurality of devices being connected to the network repeater, and the network repeater being capable of connecting to the second base station.

Abstract: A device may generate first scores for sentences of text based on a cumulative frequency of words in each sentence, may generate second scores for the sentences based on a cumulative frequency of domain entities in each sentence, and may generate third scores for the sentences based on a sentiment analysis of each sentence. The device may generate a summary of the text, may filter the sentences to extract a first set of sentences, may filter the sentences to extract a second set of sentences, and may filter the sentences to extract a third set of sentences. The device may identify and assign weights to a first group of sentences, a second group of sentences, and a third group of sentences, may generate a ranked list of sentences based on the weighted first group, second group, and third group, and may perform actions based on the final summary.

Abstract: A device may receive device data identifying activation and usage of different types of IoT devices, and may identify a device activation sequence based on the device data. The device may train a model with the device activation sequence, and may generate output data based on training the model with the device activation sequence. The device may calculate weights and a loss function for the output data, and may retrain the model based on the weights and the loss function to generate a retrained model. The device may generate device representations of the different types of IoT devices using the retrained model, and may determine groups of similar IoT devices based on the device representations. The device may generate configuration data for each of the groups of the similar IoT devices, and may cause the different types of IoT devices to be configured based on the configuration data.

Abstract: In some implementations, a device may receive spatial data corresponding to an interior of a building and objects located in the interior of the building. The device may generate a digital map of the interior of the building based on the spatial data. The device may generate a geographic coordinate map of the interior of the building. The device may receive, from a sensor mounted on an uncrewed aerial vehicle (UAV), sensor data indicating three-dimensional geographic points. The device may compare the sensor data to the geographic coordinate map to localize the UAV on the geographic coordinate map. The device may generate coordinate data indicating geographic coordinates associated with the geographic coordinate map and formatted in a global positioning system coordinate format and a National Marine Electronics Association format. The device may transmit, to a controller of the UAV, at least a subset of the coordinate data.

Abstract: An illustrative communication provider system determines that a first virtual location of a first avatar in a shared virtual space is proximate to a second virtual location of a second avatar in the shared virtual space. The first avatar represents a first user and the second avatar represents a second user located remotely from the first user. The communication provider system detects an availability status of the second user, and, based on the availability status of the second user and the determination that the first virtual location of the first avatar is proximate to the second virtual location of the second avatar, the communication provider system provides audio content for presentation to the second user. The audio content is representative of a spoken communication by the first user. Corresponding methods and systems are also disclosed.

Abstract: A device receives vehicle data from a vehicle telematics device or a client device. The vehicle data includes information relating to a vehicle, a vehicle component, and a sensor associated with the vehicle. The device determines a vehicle profile, and one or more of a driving behavior and a driving location based on the vehicle data. The vehicle profile includes information relating to a condition of the vehicle component. The device determines a wear rate for the vehicle component based on the vehicle profile, and one or more of the driving behavior or the driving location. The device determines a service timeframe for the vehicle component based on the wear rate, the condition of the vehicle component, and a wear threshold. The device generates a recommendation based on the service timeframe, and transmit the recommendation to the client device.

Abstract: A system described herein may provide for the dynamic and secure assignment of lockers that may be used when delivering goods in response to a fulfillment request. In some embodiments, smart tags may be used for packages provided in response to a fulfillment request. A smart locker system of some embodiments may include a set of lockers and one or more scanners that identify goods that have been delivered into a particular locker, such as by scanning smart tags placed on or in packages. The smart locker system may further receive a request from a User Equipment (“UE”), via a contactless tap, to provide the delivered goods, authenticate the UE, locate the particular locker in which the delivered goods are located, and unlock the locker.

Abstract: A system described herein may provide a technique for the augmentation of one or more models based on target distributions of values associated with the models. Such models may be associated with a wireless network, and may associate a set of states to a set of values and/or to a set of actions. Such states may include particular sets of configuration parameters associated with the wireless network, the values may include Key Performance Indicators (“KPIs”) associated with a randomness state model or other metrics associated with the wireless network, and the actions may include one or more actions to perform with respect to the wireless network when given a particular state and/or set of KPIs.

Abstract: One or more computing devices, systems, and/or methods for identifying anomalous behavior of users are provided. In an example, users of a telecommunication service provider may be segmented into a plurality of user segments based upon telecommunication service metrics associated with the users. A machine learning model may be trained using telecommunication service information associated with users of the first user segment to generate a trained machine learning model. Using the trained machine learning model, a forecast of telecommunication service usage associated with a first user segment of the plurality of user segments. A telecommunication service usage metric, associated with a user belonging to the first user segment, may be compared with a range indicated by the forecast. The user may be flagged as having anomalous behavior based upon a determination that one or more telecommunication usage metrics, associated with the user, are outside one or more ranges indicated by the forecast.

Abstract: An illustrative location management system 1) determines base station location data representing locations of first, second, and third first base station facilities, wherein the location of the first base station facility is determined based on user equipment (UE) location data representing at least three different locations of one or more mobile UE devices communicatively coupled to the first based station facility and signal data representing information from signals communicated between the first base station facility and the one or more mobile UE devices by way of the wireless communication network; and 2) determines, based on the base station location data and on signal data representing information from signals communicated between an additional mobile UE device and the first, second, and third base station facilities, a location of the additional mobile UE device.

Abstract: A network device may load, via a boot ROM application, a provider bootloader application from a memory of the network device and may calculate a first hash value based on decrypting a provider bootloader signature with a provider public key. The network device may calculate a second hash value based on the provider bootloader application and may utilize, when the first hash value and the second hash value are equivalent, the provider bootloader application to load an original equipment manufacturer (OEM) bootloader application from the memory. The network device may calculate a third hash value based on decrypting an OEM bootloader signature with one of a plurality of OEM public keys. The network device may calculate a fourth hash value based on the OEM bootloader application. The network device may complete, when the third hash value and the fourth hash value are equivalent, a boot process for the network device.

Abstract: An illustrative 3D modeling system generates a first voxelized representation of an object with respect to a voxel space and a second voxelized representation of the object with respect to the voxel space. Based on a first normal of a first voxel included in the first voxelized representation and a second normal of a second voxel included in the second voxelized representation, the 3D modeling system identifies a mergeable intersection between the first and second voxels. Based on the first and second voxelized representations, the 3D modeling system generates a merged voxelized representation of the object with respect to the voxel space. The merged voxelized representation includes a single merged voxel generated, based on the identified mergeable intersection, to represent both the first and second voxels. Corresponding methods and systems are also disclosed.