The present teaching relates to AI-enabled auto-heal of network cells. Bundled embedding models are obtained, via machine learning, based on historic records representing knowledge on past dynamics of a network. Each of the bundled embedding models captures a respective aspect of the past network dynamics. When temporal data is received with real time observations of the network operation, metrics on the performance thereof, and a point of failure, embeddings of the temporal data relating to the point of failure are derived, based on the bundled embedding models, and used to generate, by time series forecasting, a recommendation on an auto-heal resolution. When performance information of the network associated with the point of failure is received, it is used for online learning of learnable parameters associated with the time series forecasting.
One or more computing devices, systems, and/or methods for mobile device security profiling are provided. A device connected to a communication network is detected. Device profile information associated with the device is used to select a device behavior security model from available device behavior security models. The device behavior security model is provided to the device. The device utilizes the device behavior security model to determine whether the device is exhibiting normal operating behavior or abnormal operating behavior (e.g., an application on the device performing a denial of service attack). If abnormal operating behavior is detected by the device, then the device can perform a remedial action.
In an example, a location associated with a network performance issue may be identified. A plurality of stationary networking devices proximal the location may be identified. Measurement data associated with wireless communication between the plurality of stationary networking devices and wireless communication sites of a telecommunication service provider may be retrieved. The measurement data may be derived from signal measurements performed during a period of time. A network performance profile associated with the network performance issue may be generated based upon the measurement data.
The present teaching relates to a Q&A framework for quality controlling of automatically generated answers via AI. Based on a question on a subject matter received from a user, at least one machine expert is selected to answer the question based on past performances of multiple machine experts for generating respective candidate answers to the question. Each selected machine expert creates a candidate answer based on a reference from a source. Quality assessment is performed with respect to each candidate answer from a respective machine expert and is relied on to determine a candidate answer as the answer to the question. Such determined answer is provided to the user as a response to the question.
In some implementations, a device may receive a request to create an Internet Protocol multimedia subsystem (IMS) data channel for usage by an application that executes on a user equipment (UE). The device may transmit, based on the request, an inquiry as to whether the IMS data channel already exists for the UE. The device may receive a response that indicates that the IMS data channel does not already exist for the UE. The device may forward, based on the response, the request to create the IMS data channel. The device may receive an indication that the IMS data channel has been successfully created. The device may forward the indication that the IMS data channel has been successfully created.
In some implementations, a device may identify a user equipment (UE) route selection policy (URSP) rule to be provisioned for a UE. The device may transmit the URSP rule and an indicator, wherein the indicator indicates a UE behavior with respect to the URSP rule provisioned for the UE by the device and a preconfigured URSP rule stored at the UE.
A method, an end device, and a non-transitory computer-readable storage medium are described in relation to a learning-based network optimization service. The learning-based network optimization service may include an AI/ML model pairing between an end device and a network device based on AI/ML model identifiers that may include or correlate to a radio frequency (RF) modem or chipset identifier of the end device. The AI/ML models may support entity-specific optimizations, use and sub-use cases, and network-side and end device-side key performance indicators.
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
H04L 41/0853 - Retrieval of network configurationTracking network configuration history by actively collecting configuration information or by backing up configuration information
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
H04L 41/5041 - Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
H04W 24/02 - Arrangements for optimising operational condition
H04W 28/18 - Negotiating wireless communication parameters
H04L 41/0823 - Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
H04L 41/5009 - Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
The present teaching relates to adaptive generative AI via feedback. Human evaluators evaluate an answer automatically generated by a machine expert in response to a question based on a reference from a source. The evaluation is relied on to update a fidelity metric for each human evaluator. A cumulative ranking of the answer is determined using the evaluation and the updated fidelity metric of each human evaluator. A fidelity attribute for the machine expert is updated based on the cumulative ranking. Feedback is created based on the answer, the question, the cumulative ranking, and the updated fidelity attribute for adapting the performance of the Q&A system.
A device may include a processor. The processor may be configured to: detect either provisioning or deprovisioning a User Equipment device (UE) with a carrier profile; obtain a first identifier that identifies the UE and a second identifier that identifies the carrier profile; and generate a third identifier based on the first identifier and the second identifier, wherein the third identifier associates the first identifier and the second identifier. The processor may be further configured to either store the third identifier via a network device included in the network; or revoke the third identifier.
A device may receive original data to be deidentified and may select one or more dictionaries, from a plurality of dictionaries, based on the original data. The device may sort the one or more dictionaries based on an output control key to generate one or more sorted dictionaries, and may hash the original data into one or more hash codes. The device may extract a sequence of a quantity of digits or characters, from each of the one or more hash codes, to generate one or more sequences, and may retrieve, from the one or more sorted dictionaries, one or more substitution values corresponding to the one or more sequences. The device may generate deidentified data based on the one or more substitution values, and may perform one or more actions based on the deidentified data.
In some implementations, a device may identify a user equipment (UE) route selection policy (URSP) rule to be provisioned for a UE. The device may transmit the URSP rule and an indicator, wherein the indicator indicates a UE behavior with respect to the URSP rule provisioned for the UE by the device and a preconfigured URSP rule stored at the UE.
Disclosed are systems and methods for a computerized framework that involves a state machine for multi-threaded, multi-step task extraction that is configured to operate by implementing a suite of tables and corresponding views that do not require any updates and/or data copying, which allows for additional states being adopted for disparate processing regimes. The state machine enables insertions of transactional usage records into several tables in a data warehousing environment, where jobs in the table can be configured to identify the job name along with other key information for how to process such job, as well as information related to what kind of identifier (ID) to use and rules for retry and/or error handling. The framework allows multiple threads to work on subsets of data in a single table and update the status of many records while they are in-flight.
In some implementations, a first network device may receive, log data identifying user equipment (UE) mobility information for a set of UEs. The first network device may train a model of UE mobility based on the log data. The first network device may receive based on training the model of UE mobility, UE mobility information for a particular UE. The first network device may analyze, using the model of UE mobility, the UE mobility information for the particular UE to predict a location of the particular UE. The first network device may generate, based on predicting the location of the particular UE, a set of recommended cells for paging the particular UE. The first network device may transmit, to the second network device, information identifying the set of recommended cells.
H04W 68/02 - Arrangements for increasing efficiency of notification or paging channel
H04W 64/00 - Locating users or terminals for network management purposes, e.g. mobility management
H04W 68/06 - User notification, e.g. alerting or paging, for incoming communication, change of service or the like using multi-step notification by changing the notification area
A method, an end device, and a non-transitory computer-readable storage medium are described in relation to a learning-based network optimization service. The learning-based network optimization service may include an AI/ML model pairing between an end device and a network device based on AI/ML model identifiers that may include or correlate to a radio frequency (RF) modem or chipset identifier of the end device. The AI/ML models may support entity-specific optimizations, use and sub-use cases, and network-side and end device-side key performance indicators.
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
H04L 41/5009 - Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
15.
SYSTEMS AND METHODS FOR SHARING NETWORK SUBSCRIPTIONS BETWEEN USER EQUIPMENT
In some implementations, a device may receive a request to share a network subscription of a first user equipment (UE) with a second UE, wherein the first UE is associated with a first network operator and the second UE is initially associated with a second network operator. The device may verify that the first UE is provisioned with a network subscription sharing capability that allows the first UE to share the network subscription with the second UE. The device may receive a request to register the second UE with the first network operator, wherein the request includes a credential that indicates a user class as a guest user. The device may transmit, based on the network subscription sharing capability of the first UE, a response that accepts a guest registration of the second UE with the first network operator, wherein the response provides a guest network subscription.
In some implementations, a device may receive a request as to whether a task is able to be fulfilled while satisfying one or more conditions, wherein the task is associated with a user equipment (UE) in a wireless network. The device may determine, based on the request, a network congestion state at a location associated with the UE. The device may determine, based on the network congestion state, whether the task is able to be fulfilled while satisfying the one or more conditions. The device provides a response to the request.
A device may receive multiple location estimates, with uncertainties, for the user device based on determining that the uncertainty is greater than or equal to the uncertainty threshold, and may store the location estimate, the multiple location estimates, and the uncertainties in a data structure. The device may identify a greatest uncertainty of the uncertainties, and may remove, from the data structure, location estimates and uncertainties associated with the greatest uncertainty to generate a set of location estimates and a set of uncertainties. The device may calculate a set of weights based on the set of uncertainties, and may calculate a set of weighted location estimates based on the set of weights and the set of location estimates. The device may calculate a weighted average location of the user device based on the set of weighted location estimates, and may provide the weighted average location to the user device.
G01S 5/00 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations using radio waves
H04W 64/00 - Locating users or terminals for network management purposes, e.g. mobility management
In some implementations, a first network device may receive, log data identifying user equipment (UE) mobility information for a set of UEs. The first network device may train a model of UE mobility based on the log data. The first network device may receive based on training the model of UE mobility, UE mobility information for a particular UE. The first network device may analyze, using the model of UE mobility, the UE mobility information for the particular UE to predict a location of the particular UE. The first network device may generate, based on predicting the location of the particular UE, a set of recommended cells for paging the particular UE. The first network device may transmit, to the second network device, information identifying the set of recommended cells.
H04W 68/02 - Arrangements for increasing efficiency of notification or paging channel
H04W 68/04 - User notification, e.g. alerting or paging, for incoming communication, change of service or the like multi-step notification using statistical or historical mobility data
A device may receive, from a robotic device, time-of-flight sensor data that includes a point cloud derived from depth images of an environment with a floor and one or more obstacles, and may shift the depth images by a number of pixels to generate shifted depth images. The device may subtract the shifted depth images from the depth images to generate final images, and may calculate Euclidean distances associated with the final images. The device may generate masks for the final images associated with Euclidean distances that are less than a search radius, and may calculate a covariance matrix based on the masks. The device may calculate eigenvectors for the covariance matrix, and may generate an occupancy grid for the environment based on the eigenvectors for the covariance matrix.
One or more methods and/or systems for generating synthetic data are provided. Profiles are generated for wireless communication sites from first data gathered for a first time period. Measures of similarity of second wireless communication sites to a first wireless communication site are calculated based on the generated profiles. Second wireless communication sites are selected based upon the measures of similarity. Weightings are generated for the selected second wireless communication sites based upon the measures of similarity of the selected second wireless communication sites. Second data is gathered for a second time period from the selected second wireless communication sites. Synthetic data is generated based upon the gathered second data and the generated weightings for the selected second wireless communication sites. The generated synthetic data is for the first wireless communication site for the first time period.
An Internet Protocol (IP) Multimedia Subsystem (IMS) network slice manager and orchestrator receives first performance requirements associated with a first network slice of a mobile network. The IMS network slice manager and orchestrator determines, based on the first performance requirements, first configuration data associated with a first IMS network slice of an IMS network, where the first IMS network slice is linked to the first network slice of the mobile network. The IMS network slice manager and orchestrator configures and provisions, based on the first configuration data, the first IMS network slice in the IMS network.
Disclosed are systems and methods for a cryptographic inventory management (CIM) framework that enables cryptography agility with regards to network security requirements regarding threats from/of quantum computing and artificial intelligence (AI). The disclosed CIM framework can support a mixture of algorithm types, such as, but not limited to, classical, lattice based, code based, isogeny based and the like, which makes switching between cryptographic algorithms efficient, secure and smooth. The disclosed framework can provide a centralized cryptography inventory system (e.g., keys, algorithms, protocols, libraries, crypto-accelerators) that can be compiled, updated and maintained, and can include all the cryptography assets of network functions and support network functions regardless whether they are physical or virtual, quantum vulnerable or not, and the like. The CIM framework can be compiled and implemented as a centralized cryptography system that is built and maintained via automated cryptography assets discovery tools.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
A device may include a processor. The processor may be configured to receive parameters that are associated with a User Equipment device (UE); select, based on the parameters, a connected-mode discontinuous reception (CDRX) timing parameters; and determine a traffic load at the device. When the traffic load at the device is greater than a threshold, the processor may send the CDRX timing parameters to the UE.
A device may receive a query, and may generate an embedding based on the query. The device may determine context for the query based on the embedding and historical data, and may select, based on the context, a base LLM from a plurality of base LLMs. The device may fine-tune the base LLM with LLM fine-tuning data to generate a fine-tuned LLM, and may process the embedding, with the fine-tuned LLM, to identify tasks associated with the query. The device may determine recommended tasks based on the tasks, and may select a task LLM from a plurality of task LLMs. The device may process the tasks and the recommended tasks, with the task LLM, to determine final tasks, and may cause the final tasks to be executed. The device may perform one or more actions based on the final tasks.
One or more methods and/or systems for detecting loading changes are provided. First data is gathered from a wireless cell site. The first data may be indicative of loading of the wireless cell site. Changepoints in the first data may be detected. The first data may be analyzed to obtain indications of causes of the changepoints. The causes may be seasonal events and/or non-seasonal events. A determination of the causes may be made using the indications.
H04L 41/147 - Network analysis or design for predicting network behaviour
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
One or more computing devices, systems, and/or methods for optical network unit testing are provided. An optical network unit is activated to connect through a multi-channel system to a wavelength channel. A command is issued to the optical network unit to restrict activation requests to a set of wavelength channels allowed for activation by the optical network. In response to detecting a wavelength channel switch being performed by the optical network unit, a target wavelength channel to which the optical network unit switched is detected. Operation of the optical network unit is validated based upon the target wavelength channel.
A method, a device, and a non-transitory storage medium provide an enhanced access and mobility policy service. A network device in a visited network receives a roaming request to connect to the visited network for a user equipment (UE) device. The network device determines, based on a first access and mobility (AM) policy association request, whether a home network for the UE device has a subscriber-specific roaming policy. When the home network for the UE device has a subscriber-specific roaming policy the network device sends a second AM policy association request to the home network; receives, from the home network, the AM policy for the roaming UE device; and sends an AM policy association response to the first AM policy association request.
A device may include a processor configured to send a plurality of attach requests to a base station using a radio access technology (RAT) and determine that each of the plurality of attach requests has failed. The processor may be further configured to override a triggering of a backoff timer, wherein the UE device is barred from attaching using the RAT if the backoff timer is triggered and running, in response to determining that each of the plurality of attach requests has failed, and send another attach request to the base station using the RAT during a time period associated with the backoff timer.
A system described herein may serve as an interface between one or more radio units (“RUs”) of a wireless network and one or more Distributed Units (“DUs”) of the RAN. The system may route first traffic, received from a particular Distributed Unit (“DU”) of a wireless network, to a set of radio unit (“RUs”) of the wireless network, wherein the set of RUs includes a first RU and a second RU; determine that the first RU should be removed from the set of RUs; and route second traffic, received from the particular DU and based on determining that the first RU should be removed from the set of RUs, to the second RU and not the first RU.
A method, an end device, and a non-transitory computer-readable storage medium are described in relation to a power control service. The power control service may include identification of an end device that contributes to interference based on positioning information of the end device. The power control service may calculate and transmit a reduced transmit power value to the end device. The power control service may calculate the reduced transmit power value based on an application service used by the end device.
A system described herein may identify one or more Key Performance Indicators (“KPIs”) associated with a first base station. The system may identify, based on the one or more KPIs, an occurrence of a particular event, and select a second base station and a repeater device. The repeater device and the second base station may implement the same set of wireless frequencies. The system may cause the repeater device to repeat wireless signals transmitted to or from the second base station, and may cause the first base station to shut down or to enter a low-power mode. A UE that is wirelessly connected to the first base station prior to the first base station shutting down or entering the low-power mode, may wirelessly connect to the second base station via the repeater device based on the first base station shutting down or entering the low-power mode.
A system described herein may receive an indication that a particular User Equipment (“UE”) has wirelessly connected to a radio access network (“RAN”) of a wireless network. The RAN may be communicatively coupled to a core network of the wireless network. The system may identify that a core bypass policy is associated with the particular UE, and may instruct the RAN to bypass the core network when routing traffic associated with the particular UE. Based on the instruction to bypass the core network, the RAN may route traffic, received from the particular UE, to a Data Network (“DN”) without routing the traffic to the core network.
One or more computing devices, systems, and/or methods for automatically connecting a client device to a remote private network are provided. In an example, a networking device receives, from a client device, a first network-access request indicative of a first service set identifier (SSID). In response to the first network-access request, the networking device (i) establishes an encrypted connection between the networking device and a remote private network, and (ii) provides the client device with access to resources of the remote private network via the encrypted connection.
A device may generate neural network encryption models based on a dataset descriptor, a dataset geometry, and selected neural network types, and may generate obfuscation features based on the dataset descriptor, a noise type, an obfuscation model type, and noise and model parameters. The device may train the neural network encryption models, with a dataset and the obfuscation features, to generate model weights, a latent space, and noising and denoising models, and may generate an intelligent decryption model based on the model weights, the latent space, and the noising and denoising models. The device may receive an encrypted dataset associated with a target environment, and may determine whether the target environment is valid according to immune rules. The device may process, based on determining that the target environment is valid, the encrypted dataset, with the intelligent decryption model, to generate a decrypted dataset.
In some implementations, a mounting assembly may include a chassis having a platform portion, and a spine portion. The mounting assembly may include an end lock having a shaft portion engaged with the spine portion, and a cap portion configured to engage with a lock projection of a camera module, wherein the camera module is to be disposed between the platform portion and the cap portion. The mounting assembly may include a mount configured to mount the mounting assembly. The mounting assembly may include a swing arm pivotably coupled to the chassis by a first hinge, and pivotably coupled to the mount by a second hinge, where a first axis of rotation of the first hinge is orthogonal to a second axis of rotation of the second hinge.
In some implementations, a network device may obtain a message that includes service parameter data associated with an application. The service parameter data may indicate a revalidation time. The network device may generate, based on the service parameter data, a user equipment route selection policy (URSP) rule associated with the application that includes the revalidation time indicated by the service parameter data. The URSP rule may expire at the revalidation time. The network device may transmit the URSP rule for a user equipment (UE) that executes the application. The network device may receive a state indication message associated with the UE that is triggered at the revalidation time.
A device may identify a domain associated with a user, and may identify domain elements in the domain and convert the domain to a knowledge graph with nodes and edges. The device may receive one or more movements of an input associated with the domain, and may predict positions of the input within the knowledge graph based on the one or more movements. The device may identify closest nodes to the positions, and may determine particular domain elements that correspond to the closest nodes. The device may calculate probabilities that the input will traverse the particular domain elements, and may perform one or more actions based on the probabilities that the input will traverse the particular domain elements.
In some implementations, a network device may receive an indication of a first device location at a first time instance and based on a first location source, wherein the first device location is a first location of a device associated with a target asset. The network device may receive, in response to a movement of the device, an indication of a second device location at a second time instance based on a second location source, wherein the second device location is a second location of the device. The network device may generate a time series location history for the device based on the first device location and the second device location. The network device may perform a verification of the target asset based on the time series location history.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
39.
ARTIFICIAL INTELLIGENCE BASED COMMUNICATION SYSTEM FOR NETWORK ISSUE RESOLUTION
One or more computing devices, systems, and/or methods for providing an artificial intelligence based communication system for network issue resolution are provided. Featured related to operation of a communication network are input into one or more models to generate a set of resolution recommendations related to a low signal detected with respect to the communication network. A resolution recommendation is selected and provided as solution content to users of the communication network and/or to agents troubleshooting issues for users. Post recommendation metrics are used to identify a user that has not implemented a resolution action described by the solution content. Accordingly, a follow-up notification is sent to the user as a reminder to perform the resolution action.
A device may include a processor configured to receive an indication that a user equipment (UE) device is enabled for performing conditional handovers between different Radio Access Technology (RAT) types. The processor may be further configured to select to prepare a conditional handover for the UE device; select potential target nodes for the conditional handover for the UE device, which include at least one potential target node of a different RAT type than a RAT type currently being used by the UE device to communicate with a base station; generate one or more inter-RAT conditional event criteria for performing an inter-RAT conditional handover by the UE device based on the received indication; and provide conditional handover information to the UE device, which includes information identifying the selected plurality of potential target nodes and information identifying the generated one or more inter-RAT conditional event criteria.
A device may receive data center data associated with a data center, and may receive external environmental data, (e.g., local weather data) associated with the data center. The device may train a machine learning model, with the data center data and the external environmental data, to generate a trained model, and may receive an indication of an environmental condition event associated with a data center. The device may process the indication of the environmental condition event, with the trained model, to identify applications and hardware to shut down to allow particular applications to remain online and to determine whether to move the particular applications to a redundant location. The device may cause the data center to shut down the applications and the hardware and/or to move the particular applications to a redundant location.
H02J 13/00 - Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the networkCircuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
42.
SYSTEMS AND METHODS FOR PROVIDING MULTIFACTOR AUTHENTICATION FOR IMMERSIVE ENVIRONMENTS
A device may receive, from a user device, a custom pose of a user of an immersive environment, and may generate a pose identifier for the user based on the custom pose. The device may receive, from the user device, a custom signature of the user in a three-dimensional space, and may generate a signature identifier for the user based on the custom signature. The device may authenticate the user to access the immersive environment based on at least the pose identifier.
A device may receive key performance indicators (KPIs) associated with a network, and may rank a set of the KPIs associated with anomalous data to generate a list of ranked KPIs. The device may identify a worst performing KPI and a best performing KPI based on the list of ranked KPIs, and may join and filter the KPIs based on the worst performing KPI and the best performing KPI and to generate intermediate KPIs. The device may modify the intermediate KPIs based on customer experience prioritization data and to generate final KPIs, and may associate the final KPIs with automated network events to calculate a network change generated by the automated network events. The device may perform one or more actions based on the network change generated by the automated network events.
H04L 41/082 - Configuration setting characterised by the conditions triggering a change of settings the condition being updates or upgrades of network functionality
A device may include a processor configured to obtain location information for a small cell base station. The processor may be further configured to determine a location index for the small cell base station based on the obtained location information; embed the determined location index in a cell identifier for the small cell base station; assign the cell identifier with the embedded location index to the small cell base station; and provide the assigned cell identifier to the small cell base station.
A device may receive, from a user equipment (UE), a request for an application, and may receive, from the UE, parameters of the application and key performance indicator (KPI) targets for the application. The device may receive, based on the request, KPIs from a core network, a mobile edge computing (MEC) device, and an application server, and may compare the KPIs and the KPI targets for the application. The device may determine, based on comparing the KPIs and the KPI targets, whether to utilize the MEC device or the application server for providing the application to the UE. The device may selectively identify a first user plane function (UPF) associated with the MEC device based on determining to utilize the MEC device, or may identify a second UPF associated with the application server based on determining to utilize the application server.
H04L 67/60 - Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
46.
SYSTEMS AND METHODS FOR PRECISION LOCALIZATION OF SHORT-RANGE WIRELESS BEACONS
A system described herein may determine, based on wireless metrics associated with wireless signals transmitted by a first set of devices, a first reference map associated with a particular space, such as a warehouse, a facility, or the like. The system may determine, based on the first reference map and further based on wireless metrics associated with wireless signals transmitted by the first set of devices and received by a second set of devices, a second reference map associated with the particular space; determine, based on the second reference map and further based on wireless metrics associated with wireless signals transmitted by a third set of devices and received by the second set of devices, locations within the particular space of the third set of devices; and output information indicating the locations, within the particular space, of the third set of devices.
A system described herein may receive wireless metrics and location information associated with a set of first devices over a particular time window. The system may receive a set of wireless metrics associated with a second device over the particular time window. The system may determine measures of similarity between the set of wireless metrics associated with the second device, and the sets of wireless metrics associated with the set of first devices. The system may determine a plurality of weights based on the determined measures of similarity, where each weight is associated with the second device and a respective first device of the set of first devices. The system may apply the weights to locations of corresponding first devices, and determine a location of the second device, relative to one or more of the first devices, based on applying the weights.
A system, a network, an end-to-end network, a method, a non-transitory computer-readable storage medium are described of an intelligent end-to-end architecture for tiers of application services. The system may include a radio access network (RAN) that provides radio access to a core network and mobile edge application service layer networks located at different mobile edges including a far edge, a mid edge, and an edge. The system may include a distributed edge controller configured to determine routing for application service requests, which are sent from end devices, between the mobile edge application service layer networks of the far edge and the mid edge based on one or multiple threshold values.
A system described herein may identify a first network slice with which a communication session, between a User Equipment (“UE”) and a core network, is associated; maintain policy information associating the first network slice with a set of threshold traffic attributes; receive monitored traffic attributes, associated with the communication session; compare the monitored traffic attributes, associated with the communication session, to the set of threshold traffic attributes indicated in the policy information for the first network slice; identify, based on the comparing, a second network slice with which the communication session should be associated; and output an indication to a session control element associated with the core network, wherein the indication includes an identifier of the second network slice, wherein the session control element causes the communication session to be moved from the first network slice to the second network slice based on receiving the indication.
A device may remove protected dimensions from training data to generate modified training data, and may train a first model with the modified training data to generate a first trained model. The device may train a second model with the training data to generate a second trained model, and may utilize the first trained model to generate first predictions based on test data derived from the modified training data. The device may utilize the second trained model to generate second predictions based on the test data, and may determine whether correlations between the first predictions and the second predictions are less than a threshold. The device may selectively determine that the first trained model is bias-reduced based on the correlations being less than the threshold, or determine that the first trained model is biased based on the correlations not being less than the threshold.
A system may maintain subscription information associated with a plurality of User Equipment (“UEs”), which may include priority level information, such as one or more access identities, associating a UE with one or more respective priority levels. The system may receive, from a network function (“NF”) of a wireless network, a request for priority level information associated with the UE, and may identify a particular set of priority levels indicated by the subscription information as being associated with the UE. The system may output the identified particular set of priority levels to the NF. The NF may maintain policy information associating the particular set of priority levels with one or more network parameters and may implement the one or more network parameters when providing service associated with the UE. The system may include a Unified Data Management function (“UDM”) or a Unified Data Repository (“UDR”) of the wireless network.
A device may receive a trained model, training data utilized to train the trained model, and a query associated with generating counterfactuals, and may utilize a counterfactual model to generate counterfactuals for the trained model based on the training data and the query. The device may determine contextual parameters associated with ranking the counterfactuals, and may utilize a counterfactual ranking model to calculate a weighted metric based on the query, the counterfactuals, and the contextual parameters. The device may utilize the counterfactual ranking model to apply the weighted metric to the counterfactuals to generate ranked counterfactuals for the trained model, and may perform one or more actions based on the ranked counterfactuals.
A device may receive input data identifying input features associated with a user, and may process the input data, with an initial model, to predict one or more fraud stages for the user. The device may identify one or more sets of models from a plurality of models and based on the one or more fraud stages, and may process the input data, with the one or more sets of models, to determine one or more fraud parameters associated with the user. The device may identify a fraudulent activity associated with the user based on the fraud parameters, and may utilize a large language model to generate steps to resolve the fraudulent activity based on historical fraud resolutions. The device may provide the steps to resolve the fraudulent activity to a representative or the user for implementation.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
Disclosed are systems and methods for a DSCP (Differentiated Services Code Point) marking framework. The disclosed framework provides mechanisms for managing how network requests are handled, on the client and/or server side (e.g., prior to transmission over the network, and/or upon transmission for handling by a server, for example). The disclosed framework provides functionality for dynamically determining and applying DSCP marking that enables networks, and associated network components, to maintain a consistent and reliable network experience by computationally managing how network traffic is managed and transmitted by and between network components.
A network device may receive a subscription request to subscribe to a multimedia priority service (MPS) for a user device, and may generate an MPS profile for the user device based on the subscription request. The network device may store the MPS profile in a data structure, and may receive, from the user device, a request to generate an MPS token for the user device. The network device may retrieve the MPS profile from the data structure based on the request to generate the MPS token, and may generate the MPS token based on the MPS profile. The network device may provide the MPS token to the user device.
A device may include a processor configured to receive a multiple-input and multiple-output (MIMO) rank indicator from a user equipment (UE) device. The processor may be further configured to determine that the received MIMO rank indicator is lower than an expected MIMO rank indicator; determine that the UE device satisfies one or more criteria for a MIMO rank indicator override; and override the MIMO rank indicator for the UE device, in response to determining that the received MIMO rank indicator is lower than the expected MIMO rank indicator and determining that the UE device satisfies the one or more criteria for the MIMO rank indicator override.
Systems and methods for providing centralized authentication storage and management are described. An illustrative method includes a gateway device detecting that a first computing device connected to an on-premises network logs into a cloud-based service by way of the gateway device, stores an authentication token provided to the gateway device by the cloud-based service, and obtains authorization data indicating that a second computing device connected to the on-premises network is authorized to access the cloud-based service. The method may further include a gateway device that shares the authentication token with a second computing device.
In some implementations, a routing device may indicate, to a direct connect gateway in a cloud computing system, a default route, wherein the default route is propagated to a virtual network function (VNF) running in the cloud computing system. The routing device may create a generic routing encapsulation (GRE) tunnel between the VNF and the routing device based on the default route, wherein the GRE tunnel permits a direct peering between the VNF and a private network outside of the cloud computing system. The routing device may perform a routing between the VNF and the private network using the GRE tunnel.
A method, a device, and a non-transitory storage medium provide an enhanced emergency call outage reporting service. A network device receives an emergency call notice message. The emergency call notice message indicates that an emergency call was placed from a user equipment (UE) device using a visited network. The network device compares the emergency call notice message with outage data for a home network and reports, based on the comparing, a network outage associated with the emergency call notice message.
A device may schedule performance of an upload speed test, and may receive, from a speed test server, one or more log files generated by a user device during performance of the upload speed test. The device may generate upload results for the upload speed test based on the one or more log files, and may perform one or more actions based on the one or more log files and the upload results.
A method may include identifying, in a network, a location of a Fifth Generation (5G) node and determining, based on the location of the 5G node and capabilities associated with the 5G node, a number of areas around the 5G node, wherein each area corresponds to a bin. The method may also include identifying, in at least some of the of bins, candidate anchor nodes for the 5G node and ranking the candidate anchor nodes based on parameters associated with the candidate anchor nodes. The method may further include selecting, for the 5G node, an anchor node from the candidate anchor nodes based on the ranking.
A clamp for supporting a wire from a strand includes a first clamp portion having a first inner side and a second clamp portion having a second inner side. An opening sized to receive the strand is defined between the first inner side and the second inner side when the first clamp portion is in proximity of the second clamp portion. The first inner side and the second inner side contact the strand when the strand is received within the opening to connect the clamp to the strand. A support portion is attached to the clamp such that the wire is supported from the strand via the clamp. A fastener cooperates with the clamp and is movable from a first position to a second position to inhibit a relative motion between the strand and the clamp.
A system described herein may receive a plurality of data sets; generate, for each data set, an Eigenvector representing a maximum variance of the data set; generate, for each data set, a projected vector, wherein generating a particular projected vector includes identifying a lowest distance between respective values of the particular data set and the particular Eigenvector, wherein the particular projected vector includes values along the Eigenvector that are each a lowest distance from a corresponding value of the particular data set; compare respective projected vectors, associated with one or more data sets, with one or more other data sets of the plurality of data sets; generate a plurality of clusters based on the comparing, wherein each cluster includes one or more data sets of the plurality of data sets; and train one or more artificial intelligence/machine learning (“AI/ML”) models based on the plurality of clusters.
In some implementations, a platform as a service (PaaS) orchestrator may receive a set of values for a set of parameters of a software component template for a software component. The PaaS orchestrator may generate the software component using the set of values for the set of parameters. The PaaS orchestrator may execute the software component in a sandbox environment. The PaaS orchestrator may validate the software component for deployment. The PaaS orchestrator may deploy the software component in a deployment environment of the PaaS orchestrator. The PaaS orchestrator may detect a network event relating to the deployment environment. The PaaS orchestrator may identify a network management action associated with the network event. The PaaS orchestrator may one or more instructions to cause the network management action to be performed.
G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
65.
SYSTEMS AND METHODS FOR DETECTING AND ADDRESSING APPLICATIONS UTILIZING ADAPTIVE BITRATES WHEN PROVIDING VIDEO TRAFFIC
In some implementations, a network device may receive, from a server, video traffic associated with an application, wherein the application is associated with a video content provider. The network device may detect whether the application utilizes an adaptive bitrate to provide the video traffic, based on a bitrate associated with the video traffic in relation to a maximum bitrate. The network device may assist a remediation based on whether the application utilizes the adaptive bitrate.
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
H04N 21/2662 - Controlling the complexity of the video stream, e.g. by scaling the resolution or bitrate of the video stream based on the client capabilities
66.
Systems and Methods for Implementing Contextual Cookies
An illustrative system includes a memory that stores instructions and a processor communicatively coupled to the memory and configured to execute the instructions to perform a process. The process may comprise tracking engagement information of a user while the user views user interface content, determining, based on the engagement information, a gaze point of the user with respect to the user interface content, mapping the gaze point of the user to a user interface element associated with the user interface content, and dynamically generating or updating, based on the engagement information and the mapping of the gaze point of the user to the user interface element, a contextual cookie that is configured to store user intent information associated with the user's engagement with the user interface content.
A device may include a processor. The processor may be configured to, when an Optical Network Unit (ONU) timer reaches an ONU time interval: obtain a value from a history register, in an ONU, which stores a first sum of performance monitoring data at the end of a prior ONU time interval, by issuing a first ONU Control Interface (OMCI) command to the ONU; and store, in a bin accumulation register, the obtained value. Additionally, the processor may be configured to, when a collection timer reaches a collection time interval: obtain a second sum of performance monitoring data collected by the ONU after the prior ONU time interval, by issuing a second OMCI command to the ONU; adjust a result of subtracting a reference value, stored in a reference register, from the second sum; and store the adjusted result.
In an example, a networking apparatus is provided. The networking apparatus includes a set of networking components, a housing assembly configured to house the set of networking components, a set of light sources, and a light guide disposed between the set of light sources and a first housing panel of the housing assembly. The first housing panel includes a first portion having a first thickness and a second portion adjacent the first portion and having a second thickness less than the first thickness. The light guide is configured to direct light from the set of light sources to an inner side of the second portion of the first housing panel to produce a networking icon on an outer side of the second portion of the first housing panel.
H05K 5/00 - Casings, cabinets or drawers for electric apparatus
H04L 43/0811 - Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking connectivity
H05K 5/02 - Casings, cabinets or drawers for electric apparatus Details
69.
SYSTEMS AND METHODS FOR ACCESSING CELLULAR NETWORK VIA WIRELESS LOCAL AREA NETWORK
A device may be hosted by a cellular network. The device may include a processor. The processor may be configured to: receive a request from a User Equipment device (UE) to connect to the cellular network; and determine whether the UE includes a Fifth Generation (5G) Non-Standalone (NSA) device or a 5G Standalone (SA) device. If the UE is determined to include a 5G NSA device, the processor may perform a Fourth Generation (4G) authentication via 4G core network components included in the cellular network; and if the UE is determined to include a 5G SA device, the processor may perform a 5G authentication via 5G core network components included in the cellular network. When the 4G authentication or the 5G authentication is successful, the processor may establish a session with an endpoint in the cellular network.
In some implementations, a network device may transmit user equipment (UE) route selection (URSP) rules during a registration of a UE with a non-standalone (NSA) network, wherein the URSP rules exclude network slicing related rules based on a lack of network slicing support in the NSA network. The network device may transmit, in response to a handover of the UE from the NSA network to a standalone (SA) network that supports network slicing, updated URSP rules during a registration of the UE with the SA network, wherein the updated URSP rules include a flag in each rule that controls the mode of migration of ongoing session(s) at the UE from the NSA network to the SA network during mobility procedures.
A method, a device, and a non-transitory computer-readable storage medium are described in which a small cell aggregation service is provided. The service may include providing a global identifier for small cell devices that may be used for the transmission and reception of messages between small cell devices and core network devices. The service may include an intermediary network device, such as a gateway device or an aggregation device that uses the global identifier to transmit and receive control plane, user plane, or both types of messages, for example.
A method for monitoring an optical distribution network (ODN) in a passive optical network (PON), comprises establishing at least one data channel between an optical line terminal (OLT) and at least one optical network unit (ONU). The at least one ONU receives a live data traffic signal on at least one of the at least one data channel. The at least one ONU monitors the live data traffic signal for one or more abnormalities. The at least one ONU transmits an alert to the OLT when one or more abnormalities is detected.
H04B 10/077 - Arrangements for monitoring or testing transmission systemsArrangements for fault measurement of transmission systems using an in-service signal using a supervisory or additional signal
Disclosed are systems and methods for a computerized, network-based framework that provides enhanced algorithmic mechanisms for improving GSM location determinations for cell tower communications. The disclosed framework's operations can provide improved visit determinations/estimates and increased value for a user's wireless/cellular data, whereby optimized parameters from and/or of the signals associated with a user device (e.g., mobile device) and tower radio communications therebetween can be leveraged to discern the POIs a user has visited and/or is currently visiting. The location tracking of the user, within a predisposed uncertainty sector correlated among the user's device communications with corresponding radio towers, provides network coverage to track specific locations among POIs for which the user has visited and/or is currently located.
A method, a network device, and a non-transitory computer-readable storage medium are described in relation to a data network name (DNN) replacement service. The DNN replacement service may be provided in a roaming context associated with an end device. The DNN replacement service may be implemented by a network device of a home core network of the end device. The network device may obtain a policy pertaining to a session establishment between a visited core network and a home core network. The network device may determine whether DNN replacement is applicable based on the policy. The network device may modify a message to establish the session creation by including a replacement DNN when DNN replacement is applicable.
A method, network device, system, and non-transitory computer-readable storage medium are described in relation to an dynamic user application control service that includes receiving from an application device, a request for access controls associated with a user application and an end device; generating, per the request, policies pertaining to the access controls, wherein the policies include time-based rules, location-based rules, or application type-based rules; obtaining a current location of the end device; comparing, by the network device, a current time with the time-based rules, the current location with the location-based rules, or an application type for the user application with the application-type rules; generating, per the comparing, a policy decision among the policies pertaining to the access controls; and applying, per the policy decision, the time-based rules, the location-based rules, and the application type-based rules for establishment of an application session with the end device.
A system described herein may maintain a set of policies associated with non-Internet Protocol (“IP”) messaging; maintain information associating an IP address of a User Equipment (“UE”) with a non-IP identifier of the UE; receive first traffic, that includes the IP address of the UE as a source of the first traffic, and payload information; identify, using the maintained information associating the IP address of the UE with the non-IP identifier of the UE, the non-IP identifier of the UE based on the IP address of the UE included in the first traffic; identify that the first traffic is associated with a particular policy of the set of policies; and forward second traffic, including the payload information and the non-IP identifier of the UE, to a destination device indicated in the first traffic, where forwarding the second traffic includes implementing the particular policy with respect to the second traffic.
A base station determines a latency requirement of a user equipment device (UE), and receives latency measurements associated with multiple neighboring cells or cell sectors having coverage areas within a certain proximity to the base station. The base station measures a latency associated with a first cell or cell sector, having a coverage area generated by the base station, that is currently serving the UE. The base station triggers a mobility event for the UE, based on the UE's latency requirement, the measured latency of the first cell or cell sector, and the latency measurements associated with the multiple neighboring cells or cell sectors, to a selected one of the multiple neighboring cells or cell sectors.
A system described herein may identify traffic associated with a User Equipment (“UE”) that is connected to a first network. The UE may maintain a plurality of UE identities via one or more SIM (“Subscriber Identification Module”) cards, Universal Integrated Circuit Cards (“UICCs”), etc. The system may determine, based on one or more policies, that the traffic is not authorized via the first network. In some situations, the traffic may not be authorized if the first network does not support a type, service, etc. of the traffic. The system may identify a second network, based on the one or more policies, via which the traffic is authorized, and may indicate the second network to the UE. The UE may automatically (e.g., without user intervention) switch to a particular UE identity that is associated with the second network, and may output the traffic via the second network using such UE identity.
Systems and methods described herein enable association of an application with a line of service on Dual SIM Dual Standby (DSDS) devices. A user equipment (UE) device receives a command to execute an application that requires a mobile network connection. The UE device detects multiple available subscriber identity modules (SIMs), wherein each of the multiple available SIMs is associated with a different line of service. The UE device extracts an application identifier for the application, and determines, based on the application identifier, that the application is associated with a designated line of service. The UE devices initiates a session connection request for the application using a SIM, of the multiple available SIMs, associated with the designated line of service.
One or more computing devices, systems, and/or methods for performance indicator acquisition and processing for a communication network are provided. A compiler generates a package with compiler objects specifying how a normalizer is to process performance indicator data from a network element. The package is injected into the normalizer. The normalizer receives incoming performance indicator data from the network element over a message bus. The compiler objects are used to process the incoming performance indicator data to create and persistently store performance indicators records.
One or more computing devices, systems, and/or methods for providing near zero downtime during database upgrades for a computing environment are provided. A first database instance of a first version of a database may be hosted within a computing environment. A second database instance of a second version of the database may be deployed within the computing environment. The database instances are configured with triggers used to propagate data changes amongst the database instances. Requests to the database are routed based on a plurality of routing rules. A first routing rule routes a first portion of the requests to the first database instance and a second portion of the requests to the second database instance. The second database instance is monitored to determine whether to progress to a second routing rule of the plurality of routing rules.
G06F 11/20 - Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements
A device, such as a Subscriber Identification Module (“SIM”) device, may maintain a set of profiles that each include a respective set of authentication credentials for connecting to one or more wireless networks. The device may maintain a set of policies associating respective events with respective profiles of the set of profiles, may receive an indication of a particular event, and may identify, based on the set of policies, that the particular event is associated with a particular profile. The device may select the particular profile as an active profile, and may output a refresh command after selecting the particular profile as the active profile. The device may receive, based on outputting the refresh command, a request for the active profile, and may output, based on the request for the active profile, the set of authentication credentials associated with the selected particular profile.
In some implementations, the techniques described herein relate to a method including: receiving a set of communication events from a user equipment to a base station; obtaining a set of geocoordinates corresponding to the user equipment; calculating an angular mean for the set of geocoordinates; sorting the set of geocoordinates based on distances between the set of geocoordinates and a location of the base station to generate an ordered list of geocoordinates; calculating a k-th order statistic of the ordered list of geocoordinates; generating a predicted location based on the angular mean and the k-th order statistic; and assigning the predicted location as a current location of the user equipment in a cellular core network.
A device may receive channel data associated with digital channels utilized by a customer, and may identify, in the channel data, potential issues and sequential events for the potential issues. The device may generate a causal relationship graph based on the sequential events and the potential issues, and may process the causal relationship graph, with a machine learning model, to identify issues of the customer and events associated with the issues. The device may apply causal inference to identify causal relationships between the issues and the events, and may calculate an inference score for the issues and the events based on the causal relationships. The device may modify, based on the inference score, a customer journey defined by the events to generate a modified customer journey, and may perform one or more actions based on the modified customer journey.
A system described herein may receive traffic from a User Equipment (“UE”) via a radio unit (“RU”), may identify one or more attributes of the traffic, and may select a first Central Unit (“CU”), from a group of CUs that includes the first CU and a second CU, based on the one or more attributes of the traffic. The one or more attributes may include a network slice. The device may further route the traffic to the selected first CU, wherein the CU aggregates received traffic and routes the aggregated traffic to a user plane gateway. The device may include a Distributed Unit (“DU”) of a radio access network (“RAN”) that includes the first and second CUs. The first and second CUs may include a first CU-User Plane (“CU-UP”) and a second CU-UP. The DU and the CUs may be configured by a CU-Control Plane (“CU-CP”) of the RAN.
A device may receive historical input data associated with trips traversed by a plurality of vehicles with vehicle tracking units (VTUs), and may process the historical input data to generate training data. The device may train a neural network model, with the training data, to generate a trained neural network model that provides a latent space representation of vectors, and may cluster the latent space representation of vectors to generate clusters. The device may receive input data associated with a trip traversed by a vehicle of the plurality of vehicles with the VTUs, and may process the input data to generate time series data. The device may compare the time series data and the clusters to determine whether the trip is anomalous or not anomalous, and may perform one or more actions based on the determination of whether the trip is anomalous or not anomalous.
In some implementations, the techniques described herein relate to a method including: receiving a network request from a second network function, the network request including a parameter; validating the parameter upon receipt of the network request to determine a status of the network request, the status indicating a valid or invalid request; processing, by a first network function, a resource managed by the first network function based on the network request and returning a network response when the status indicates a valid request; and denying the network request and returning an error code when the status indicates an invalid request.
In some implementations, a device may receive a multimedia content file. The device may divide the multimedia content file into a set of logical entities. The device may generate a set of embeddings for each logical entity of the set of logical entities. The device may compare groups of logical entities, of the set of logical entities, to generate a similarity metric. The device may selectively merge, based on the similarity metric satisfying a threshold, a pair of logical entities, in a group of logical entities of the groups of logical entities, to generate one or more logical entity sets. The device may process the one or more logical entity sets to generate one or more metadata tags for the one or more logical entity sets. The device may store a metadata file including the one or more metadata tags.
H04N 21/84 - Generation or processing of descriptive data, e.g. content descriptors
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06V 20/40 - ScenesScene-specific elements in video content
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
H04N 21/234 - Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
89.
SYSTEMS AND METHODS FOR USING EXTERNAL IDENTIFIERS FOR ROUTING
A device may include a processor. The processor may be configured to receive, via a wireless connection, from an application server that provided a source external identifier to a source User Equipment device (UE), a routing request that includes data and an identifier for a destination UE. The processor may also be configured to look up a target external identifier for the destination UE; and route the data to a target application server that issued the target external identifier.
A system described herein may identify Key Performance Indicators (“KPIs”) associated with User Equipment (“UEs”) that are connected to a radio access network (“RAN”). Each KPI may be associated with wireless communications between a respective UE and the RAN. The system may identify a plurality of service categories associated with the plurality of UEs, where each particular UE is associated with a respective service category. The system may determine, based on the KPIs and the service category associated with each UE of the plurality of UEs, a radio resource partitioning configuration for the RAN. The particular radio resource partitioning configuration may specify that different portions of a set of radio resources, implemented by the RAN, are associated with different network slices. The RAN may implement the radio resource partitioning configuration by sending and receiving traffic, associated with respective network slices, via a corresponding portion of the set of radio resources.
A system described herein may maintain and distribute precision time across diverse locations and network types, including wired and wireless networks. A particular device may receive topology information indicating a particular set of time synchronization nodes, out of a plurality of time synchronization nodes of a time synchronize network; communicate with the particular set of time synchronization nodes on an ongoing basis, wherein the device and the plurality of devices maintain precision time information based on the ongoing communication; receive a request for precision time information from a particular time synchronization client, wherein the particular time synchronization client is assigned to the device based on attributes of the time synchronization client and the device; and output, to the particular time synchronization client and in response to the request, the requested precision time information.
A device may include a processor. The processor may be configured to send first control signals to optical switches to optically connect embedded optical time domain reflectometers (OTDRs), of an optical network, to optical fibers of the optical network; send second control signals to the OTDRs to measure attenuations of optical signals in the optical fibers; receive measurement data from the OTDRs as results of measuring the attenuations of the optical signals in the optical fibers; process the measurement data; and determine whether the optical fibers meet a performance requirement based on the processed measurement data.
G01M 11/00 - Testing of optical apparatusTesting structures by optical methods not otherwise provided for
H04B 10/071 - Arrangements for monitoring or testing transmission systemsArrangements for fault measurement of transmission systems using a reflected signal, e.g. using optical time domain reflectometers [OTDR]
93.
SYSTEMS AND METHODS OF MULTIMODAL USER INTERFACE AUTOMATION
Systems and methods provide user interface (UI) automation for multiple types of devices and applications. A computing device stores an objective for automated manipulation of a UI of a user device and conducts an iterative sequence to achieve the objective. The iterative sequence includes capturing current state information of the UI, wherein the current state information includes text and an image of the UI; sending multimodal input to a generative artificial intelligence (AI) system, wherein the multimodal input includes the image of the UI, a text prompt, and the objective; receiving, from the AI system, a predicted next action for the UI based on the multimodal input; initiating a programmatic action to implement the predicted next action on the UI; and recording the programmatic action. The computing device generates, based on the iterative sequence, a UI automation model associated with achieving the objective on the UI.
G06F 3/023 - Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
In some implementations, a network node a radio access network (RAN) may receive, from a user equipment (UE), an indication of a traffic descriptor associated with an application running on the UE. The network node may allocate, using a RAN scheduling function associated with the network node, resources for the application based on an intra-user flow priority, wherein different applications associated with the UE are allocated with different amounts of resources based on the intra-user flow priority.
In some implementations, a network device may receive a group command associated with data transfers for a plurality of user equipments (UEs). The network device may transmit, based on the group command, a wakeup signal to a subset of UEs, of the plurality of UEs, in a target area, wherein the wakeup signal is associated with a control of data transfers for the subset of UEs based on a number of UEs in the target area and a load condition level associated with the target area.
A method may include receiving, by an access and mobility management function (AMF), a registration request from a user equipment (UE) device. The method may also include obtaining subscriber-related information associated with the UE device, wherein the subscriber-related information includes UE policy association information, and determining, based on the subscriber-related information, whether a UE policy association is enabled for the UE device. The method may further include transmitting, in response to determining that the UE policy association is enabled for the UE device, policy information to the UE device.
H04W 40/22 - Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
97.
SYSTEMS AND METHODS FOR SCHEDULING RESOURCES OF A RADIO ACCESS NETWORK BASED ON PER-USER EQUIPMENT FACTORS
A system described herein may identify a plurality of radio Key Performance Indicators (“KPIs”) associated with a plurality of User Equipment (“UEs”) that are connected to a radio access network (“RAN”). Each radio KPI may be associated with wireless communications between a respective UE and the RAN. The system may identify a plurality of service categories associated with the plurality of UEs, wherein each particular UE is associated with a respective service category. The system may determine, based on the radio KPIs and the service category associated with each UE of the plurality of UEs, a respective priority weight for each UE. The system may schedule traffic, between the RAN and the plurality of UEs, based on the determined priority weights for the plurality of UEs.
H04W 72/566 - Allocation or scheduling criteria for wireless resources based on priority criteria of the information or information source or recipient
A method, a network device, and a non-transitory computer-readable storage medium are described in relation to a coordinated domain-based L4S service. The service may enable end-to-end congestion control based on a predetermined order of domains, which are associated with L4S devices that support a packet flow, for which congestion control responses are to be provided. The service may provide independent feedback loops of state information from each domain and associated L4S device. The feedback loops may operate on different timescales. The service may coordinate congestion control among domains of an end-to-end path that support the packet flow and prevent fragmented or contentious congestion control responses between the domains.
A device may maintain, as a member of a two blockchain networks respective first and second blockchains. The first blockchain may include one or more sets of operations, and the second blockchain may include cross-chain authorization information. The device may identify a request to invoke a particular set of operations included in the first blockchain, where the request includes a set of input parameters and an identifier. The device may determine that the identifier satisfies the cross-chain authorization information included in the second blockchain, and may accordingly communicate with the second blockchain network to execute the particular set of operations. Executing the particular set of operations may include generating a set of output parameters. The device may output the set of output parameters in response to the request.
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
100.
SYSTEMS AND METHODS FOR ADAPTIVE POLICY CONTROL IN CELLULAR NETWORKS
A device may include a processor configured to collect user behavior data associated with a user equipment (UE) device and determine a user behavior pattern for the UE device based on the collected user behavior data. The processor may be further configured to generate a policy in a core network for the UE device based on the determined user behavior pattern for the UE device, wherein the core network manages a communication session for the UE device via a Radio Access Network (RAN), and provide the generated policy to a Policy Control Function (PCF) in the core network to apply to the communication session for the UE device.
patents
