Systems and methods provide an assisted slice profile service. A network device receives an order for an enterprise subscription. The network device generates, based on the order, a slicing policy configuration file for user equipment (UE) devices associated with the enterprise subscription. The network device sends the slicing policy configuration file to an enterprise for importation and enforcement by a Mobile Device Management (MDM) system.
A method, a device, and a non-transitory storage medium provide an optimized network function (NF) selection service for roaming. A first network device in a home network receives, from a second network device, a first discovery message, the first discovery message including a visited network identifier for a roaming session. The first network device matches the visited network identifier to a network identifier in a Session Management Function (SMF) profile. The first network device sends, to the second network device, a first identifier for a home SMF for the roaming session based on the visited network identifier.
The present teaching relates to detecting causal reasons for certain action and determining treatments to prevent the action. Information on services to users is collected and used to generate targeted segments of users, each of which corresponds to a level of risk associated with a user action with users estimated at the level of risk. The information is also used to generate causal segments, each of which corresponds to a causal reason that causes the user action. Based on the targeted segments and causal segments, causal reason(s) associated with each user to carry out the user action is estimated. A market action directed to each estimated causal reasons may be automatically recommended and executed to prevent a user to carry out the user action.
A system described herein may maintain a set of backup parameters associated with a blockchain. The set of backup parameters may be associated with a first time, and may include first node configuration information of one or more nodes that maintain the blockchain, and first blockchain state information. The system may identify, at a second time, that the blockchain should be restored using the set of backup parameters associated with the first time, and may output, to the blockchain network, a restoration instruction that indicates the set of backup parameters associated with the first time. The blockchain network may replace second node configuration information of the one or more nodes, associated with the second time, with the first node configuration information associated with the first time, and may replace second blockchain state information, associated with the second time, with the first blockchain state information associated with the first time.
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
5.
SYSTEMS AND METHODS FOR MULTI-SLICE COMMUNICATION SESSIONS IN A WIRELESS NETWORK
A system described herein may receive a request to associate a protocol data unit (“PDU”) session, between a particular User Equipment (“UE”) and a network, with multiple network slices. The system may provide the particular PDU session identifier to a first set of network devices that are associated with the first slice, and to a second set of network devices that are associated with the second slice. The network may route first traffic, received from the UE, to the first set of network devices based on the first traffic including the particular PDU session identifier and the identifier of the first network slice. The network may route second traffic, received from the UE, to the second set of network devices based on the second traffic including the particular PDU session identifier and the identifier of the second network slice.
A network device may receive, from a first radio access network (RAN), a first registration request associated with a first user device, and may determine, for the first registration request, first multimedia priority service (MPS) access indication parameters that are set to true for provision of MPSs to the first user device. The network device may generate a first registration accept message that includes the first MPS access indication parameters, and may provide the first registration accept message, with the first MPS access indication parameters, to the first user device to enable the first user device to utilize one of the MPSs on the first RAN.
A user equipment (UE), comprising at least one processor may be configured to transmit a first attach request to a first wireless network based on a first subscriber identity module. The UE may determine that the first attach request was unsuccessful and may automatically activate a switching application to switch the UE from the first subscriber identity module to a second subscriber identity module based on the determination. The first and second subscriber identity modules are stored on a common universal integrated circuit card (UICC) associated with the UE. The UE may then transmit a second attach request to the first wireless network based on the second subscriber identity module.
A user equipment (UE) may store a default user equipment route selection policy (URSP) and may receive, from a fourth-generation (4G) core network, a default protocol configuration option (PCO) value for a default network slice of the 4G core network. The UE may utilize the default network slice based on the default URSP and the default PCO value and may receive a request to update the default URSP with a new URSP associated with a new network slice of the 4G core network. The UE may update the default URSP with the new URSP to generate an updated URSP and may receive, from the 4G core network, a new PCO value for the new network slice of the 4G core network. The UE may utilize the default network slice and the new network slice based on the updated URSP, the default PCO value, and the new PCO value.
A system described herein may receive, from a policy element of a core of a wireless network, a set of triggers associated with a plurality of radio frequency (“RF”) bands implemented by a radio access network (“RAN”). The system may identify that a User Equipment (“UE”) is connected to the RAN via a first RF band, and may identify a subsequent connection of the UE to the RAN via a second RF band. The system may identify that a particular trigger, of the set of triggers, is satisfied based on the subsequent connection of the UE to the RAN via the second RF band, and may indicate, to the policy element, that the particular trigger has been satisfied. The policy element may output a second set of UE policies for communications between the UE and the core of the wireless network based on the satisfaction of the particular trigger.
A device may receive a usage pattern of a user of a client device, and may receive a request for a user interface from the client device. The device may generate one or more elements of the user interface based on the request, and may prompt a large language model, with the usage pattern, to generate one or more additional elements of the user interface. The device may combine the one or more elements and the one or more additional elements to generate the user interface, and may provide the user interface to the client device.
A device may store a plurality of network identifiers associated with a network, and may allocate, from the plurality of network identifiers, a network identifier to a bootstrap profile of a user equipment upon detection of an attachment of the user equipment to the network. The device may update a network device of the network with the allocated network identifier for the bootstrap profile, and may initiate transmission of the allocated network identifier to the user equipment. The device may quarantine the network identifier associated with the bootstrap profile based on the bootstrap profile being disabled.
An exemplary method includes a computing system receiving, via a user interface, a request from a user for a virtual representation of a device and accessing, based on receiving the request, a virtual model of the device. The method further includes generating, based on imagery of a hand of the user, a virtual model of the hand of the user and providing, via the user interface and based on the virtual model of the device and the virtual model of the hand of the user, an interactive virtual representation of the device as held in the hand of the user.
A device may receive video data identifying videos, and may process the video data with a machine learning model, to determine classifications. The device may generate labels for the videos, and may calculate event severity scores and event severity labels. The device may calculate event severity incoherence scores, and may calculate user feedback scores of users associated with the device. The device may determine reviewer mistrust scores, and may calculate time review scores. The device may calculate reviewer bias scores, and may determine relabeling scores for the videos based on the event severity incoherence scores, the user feedback scores, the reviewer mistrust scores, the time review scores, and the reviewer bias scores. The device may generate new labels for one or more of the videos based on the relabeling scores, and may retrain the machine learning model, with the new labels, to generate a retrained machine learning model.
A device may receive text data associated with a conversation of a user, and may process the text data, with large language models (LLMs), to generate conversation tags. The device may generate user attribute tags based on user data, and may classify the conversation tags and the user attribute tags to generate classified tags. The device may convert the text data and the classified tags to a searchable document, and may process the searchable document and historical tag data, with a statistical model, to identify multiple users that match the user. The device may determine degrees of match between the multiple users and the user, and may identify one of the multiple users based on the degrees of match. The device may utilize the historical tag data associated with the one of the multiple users to generate a response for the user, and may provide the response to the user.
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
A device may determine one or more base stations of a coverage area, and may determine one or more fixed wireless access (FWA) device identifiers in the coverage area. The device may identify a portion of the one or more FWA device identifiers not connected to any of the one or more base stations, as anchor overshoot FWA device identifiers, and may perform one or more actions based on the anchor overshoot FWA device identifiers.
In some implementations, a server may obtain a video recording of a scene captured by a camera onboard a vehicle. The server may perform an object detection that indicates a presence of a traffic light in a frame of the video recording. The server may determine a red light probability that the frame contains at least one relevant red traffic light for the vehicle. The server may calculate a violation score based on the object detection and the red light probability with respect to the frame. The server may determine whether the vehicle is associated with a traffic light violation based on the violation score in relation to a threshold.
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
G08G 1/01 - Detecting movement of traffic to be counted or controlled
G08G 1/017 - Detecting movement of traffic to be counted or controlled identifying vehicles
G08G 1/052 - Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
One or more computing devices, systems, and/or methods for privacy data augmentation are provided. An augmentation pipeline is selected to process data based upon a data type of the data. The augmentation pipeline processes the data to generate information that is input into a machine learning model. The machine learning model processes the information and privacy laws to determine a subset of the data to mask. In this way, the subset of the data is masked to create augmented data that complies with the privacy laws.
One or more computing devices, systems, and/or methods for adaptive API call sequence detection are provided. A series of API calls and gap times between API calls of the series of API calls are recorded. The API calls are received and processed by a production system. The API calls are assigned into API call sequences. An end of an API call sequence is detected based upon a minimum response time and the gap times between the API calls. The API call sequences are utilized to simulate execution of the production system. A configuration is generated and applied to the production system based upon a result of the simulation.
A device may include a processor configured to determine that Quality of Service (QOS) monitoring is to be performed for a communication session; generate a QoS monitoring policy for the communication session; and provide the QoS monitoring policy to a Session Management Function (SMF) associated with the communication session. The processor may be further configured to receive a QoS monitoring report for the communication session from the SMF and perform an action based on the received QoS monitoring report.
A device may comprise a processor. The processor may be configured to: receive, from a User Equipment device (UE), a registration request; obtain Multimedia Priority Service (MPS) policy parameters for the UE in response to the registration request; receive, from the UE, a request to establish a session; set a priority of a message to be sent to establish the session, based on the MPS policy parameters; and send the message to establish the session.
A system described herein may determine, based on attributes of network functions (“NFs”) of a wireless network, a plurality of NF groups, including a particular NF group that includes a particular set of NFs. The system may receive an NF configuration update, and may determine that the NF configuration update is applicable to the particular set of NFs. The system may identify a routing path associated with the particular set of NFs, which may include a sequence of NFs of the particular set of NFs. The system may output, to a first NF of the particular set of NFs, the NF configuration update and information associated with the routing path. The first NF may identify a second NF of the particular set of NFs based on the routing path, and may output the NF configuration update to the second NF based on the routing path.
A device may include a processor configured to detect a Protocol Data Unit (PDU) session associated with a user equipment (UE) device. The processor may be further configured to obtain at least one congestion metric value for a base station associated with the PDU session; determine that the obtained at least one congestion metric value is less than a maximum throughput enforcement threshold; and override a maximum throughput enforcement policy on a User Plane Function (UPF) associated with the UE device, based on determining that the obtained at least one congestion metric value is less than the maximum throughput enforcement threshold.
A system comprises one or more devices. The devices are configured to: receive a context associated with a flow of packets between a User Equipment device (UE) and a network slice in a wireless network; obtain one or more policy rules; apply the policy rules to the context and a model of a transport device to generate or update a map that assigns one or more contexts to queues within the transport device; and send the map to the transport device. The transport device is configured to: adjust parameters of the queues based on the map; shape traffic from each of the queues; schedule packets from the queues; and forward the scheduled packets.
One or more computing devices, systems, and/or methods for visual troubleshooting a network device setup. Images of the network device setup are provided to the system. A GenAI component processes the images to generate one or more device identifying features. The features are further processed to identify the device. The system utilizes hardware-specific information to prompt the GenAI component to answer troubleshooting-related questions concerning the device setup. The images may be pre-processed to include one or more visual guides to assist the GenAI component.
The techniques described herein relate to a method that involves identifying a set of network elements with less than a specified percentage of missing data, removing a percentage of data to create a test data set for each network element, applying various data fill methods to the test data set to generate filled data sets, calculating the percentage of missing data successfully filled and the error between the filled data and the removed data for each data fill method, analyzing the relationships between the percentage of missing data, the percentage of successfully filled data, and the error to generate error curves for each data fill method, and selecting one or more data fill methods to apply to missing data in a network element based on the corresponding error curves.
H04L 43/04 - Processing captured monitoring data, e.g. for logfile generation
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
A first device described herein may maintain a set of slice forwarding rules that include information associating a particular set of traffic attributes with a particular network slice of a wireless network. The first device may receive, via a particular interface of one or more interfaces of the first device, traffic from a second device. The first device may compare attributes of the traffic to the slice forwarding rules. The first device may determine, based on the comparing, that the attributes of the traffic match the particular set of attributes associated with the particular network slice of the wireless network; and may output, via the particular network slice, the traffic to the wireless network. The first device may include a Fixed Wireless Access (“FWA”) device that communicates with a radio access network (“RAN”) of the wireless network.
A system described herein may identify a plurality of configurable parameters associated with Network Functions (“NFs”) of a wireless network; identify a link between a first parameter, associated with a first NF of the plurality of NFs, and a second parameter associated with a second NF of the plurality of NFs; indicate, to first NF and/or the second NF, the link between the first parameter and the second parameter; and output a configuration update to the first NF, where the configuration update includes a particular value for the first parameter associated with the first NF. The second NF may receive an indication of the configuration update and modify, based on receiving the indication of the configuration update and further based on the link between the first parameter and the second parameter, the second parameter based on the particular value.
H04L 41/12 - Discovery or management of network topologies
H04L 41/082 - Configuration setting characterised by the conditions triggering a change of settings the condition being updates or upgrades of network functionality
28.
SYSTEMS AND METHODS FOR LOCALLY TUNED DISTRIBUTED NETWORK FUNCTION CONFIGURATION UPDATES IN A WIRELESS NETWORK
A system described herein may receive a configuration update, which includes a plurality of values that for a plurality of respective parameters based on which Network Functions (“NFs”) of a wireless network operate, and that is provided to multiple NFs of the wireless network. The system may monitor Key Performance Indicators (“KPIs”) associated with a particular NF of the plurality of NFs; receive a set of configuration modification thresholds that include modification thresholds for one or more parameters of the plurality of parameters included in the configuration update; modify at least one parameter, of the plurality of parameters of the configuration update, based on the monitored KPIs associated with the particular NF of the wireless network; and implement, by the particular NF of the wireless network, the modified at least one parameter.
Systems and methods described herein detect user equipment (UE)-based denial-of-service (DoS) or distributed denial-of-service (DDoS) attacks based on energy consumption levels. An energy consumption (EC) DDoS monitoring service, collects, from one or more network devices in a core network, energy consumption data and identifies, based on the energy consumption data, normal energy consumption levels of one or more UE devices or network functions. The EC DDoS monitoring service determines, based on the normal energy consumption levels, energy consumption thresholds indicative of a DoS or DDoS attack.
A device may receive a plurality of documents and a plurality of questions for the plurality of documents, and may determine a plurality of ground truth answers corresponding to the plurality of questions. The device may normalize the plurality of questions to generate a normalized plurality of questions, and may select a set of most frequent questions from the normalized plurality of questions. The device may utilize regular expressions and natural language processing to generate, from the plurality of ground truth answers, a set of answers to the set of most frequent questions, and may dynamically select prompts for LLMs based on the set of most frequent questions and based on context provided to the LLMs. The device may optimize, based on the set of most frequent questions, the set of answers, the prompts, and parameters of configurations for the LLMs, accuracies of the LLMs to generate optimized LLMs.
A device may receive load data identifying a load on a radio access network (RAN), and may select one or more time series forecasting models and a classification model based on seasonality metrics associated with the load data. The device may process the load data, with the one or more time series forecasting models, to forecast a capacity for the RAN, and may process the load data and the capacity, with the classification model, to determine whether the capacity exceeds a capacity threshold. The device may selectively determine that the RAN does not need an upgrade based on determining that the capacity fails to exceed the capacity threshold, or may adjust, based on determining that the capacity exceeds the capacity threshold, the capacity to generate an adjusted capacity. The device may perform one or more actions based on the adjusted capacity.
H04W 24/02 - Arrangements for optimising operational condition
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 43/04 - Processing captured monitoring data, e.g. for logfile generation
H04L 43/0876 - Network utilisation, e.g. volume of load or congestion level
H04L 47/127 - Avoiding congestionRecovering from congestion by using congestion prediction
H04L 47/2441 - Traffic characterised by specific attributes, e.g. priority or QoS relying on flow classification, e.g. using integrated services [IntServ]
32.
SYSTEMS AND METHODS FOR AUTOMATICALLY UPDATING RADIO ACCESS NETWORK CONFIGURATIONS IN CASE OF PREDICTED SERVICE DEGRADATION
A device may receive topology data associated with a plurality of RANs and historical energy consumption data associated with respective radios of the plurality of RANs, and may generate feature data identifying features. The device may process the feature data to generate a trained predictive model, and may receive traffic associated with the plurality of RANs and current energy consumption data associated with the respective radios. The device may create a network data flow graph of the plurality of RANs or the respective radios based on the traffic and the current energy consumption data. The device may process the network data flow graph, with the trained predictive model, to determine energy consumption drops of the respective radios and corresponding KPI degradations of the respective radios. The device may identify a radio with an energy consumption drop above a predefined threshold, and may perform one or more actions for the radio.
Techniques for calibrating base station timing for user equipment (UE) positioning determination are disclosed. In one embodiment, a computerized method is disclosed comprising obtaining signal transmission and reception data samples for a base station and user equipment (UE) pairing, determining an observed downlink communication timing value and an observed uplink communication timing value for the base station and UE pairing using the obtained data samples, determining a true over the air (true OTA) value using the observed downlink and uplink communication timing values, determining a timing correction using the true OTA and one of the observed downlink and uplink communication timing values; and calibrating a timing of the base station using the determined timing correction.
A device may include a processor. The processor may be configured to: receive, from an Application Function (AF), a request for an Authentication and Key Management for Applications (AKMA) Application key; and determine whether a User Equipment device (UE) that sent a session request to the AF is attached to a visiting network or a home network. When the UE is determined to be attached to the visiting network, the processor may be configured to: determine whether to include the AKMA application key in a first reply to the AF; and send the first reply to the AF. When the UE is determined to be attached to the home network, the processor may be configured to: obtain the AKMA application key; and send a second reply that includes the AKMA application key to the AF.
A device may receive video data associated with a vehicle experiencing an event, and may determine object data identifying bounding boxes, tracks, and labels for objects in the video data. The device may calculate sensitivity scores indicating a likelihood that a person inside the vehicle is injured, a likelihood that a person outside the vehicle is injured, a likelihood that an animal is injured, or a dangerousness of the event, and may aggregate the sensitivity scores to generate an aggregated score. The device may horizontally concatenate a subset of frames of the video data to generate an input image, and may generate queries about whether the video data contains graphic content. The device may process the input image and the queries, with a multi-modal large language model, to determine whether the video data contains graphic content, and may perform actions when the video data contains graphic content.
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
G06V 20/59 - Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
36.
SYSTEMS AND METHODS FOR DELIVERING SHORT MESSAGE SERVICE MESSAGES USING A SHORT MESSAGE SERVICE CENTER
In some implementations, a device may receive a short message service (SMS) message for delivery to a user equipment (UE). The device may attempt an SMS message delivery to the UE via a first network. The device may determine that the SMS message delivery via the first network has failed. The device may attempt the SMS message delivery via a second network. The device may determine that the SMS message delivery via the second network is successful. The device may set an indicator to skip the first network for a subsequent SMS message for the UE. The device may receive the subsequent SMS message for delivery to the UE. The device may deliver, based on the indicator, the subsequent SMS message to the UE using the second network.
A device may receive network data identifying uplink/downlink packet loss percentage, uplink/downlink jitter, uplink/downlink latency, and uplink/downlink packet throughput associated with a slice of a network, and may set an uplink value for each of the uplink packet loss percentage, the uplink jitter, the uplink latency, and the uplink packet throughput. The device may multiply the uplink values by corresponding uplink weights to calculate an uplink cost, and may set a downlink value for each of the downlink packet loss percentage, the downlink jitter, the downlink latency, and the downlink packet throughput. The device may multiply the downlink values by corresponding downlink weights to calculate a downlink cost, and may calculate a total cost based on the uplink cost and the downlink cost. The device may cause another slice, with the same attributes as the slice, to be instantiated when the total cost satisfies a threshold for a time period.
A device may receive a query from a user device, evaluate the query to generate query evaluation results, and generate an action plan for the query. The device may utilize a tools module to generate environment information, and may utilize a knowledge module to generate knowledge information. The device may utilize a memory module to generate memory information, and may utilize an intuition module to determine logical inferences about the query. The device may process the action plan for the query and the logical inferences about the query, with a large language model, to generate a response to the query, and may determine whether the response answers the query. The device may utilize a reflect module to modify the response and generate a final response based on determining that the response answers the query, and may provide the final response to the user device.
A device may receive video data that includes a text transcript, audio sequences, and image frames, and may detect a network fluctuation. The device may process the text transcript to generate a new phrase, and may generate a response phoneme based on the new phrase. The device may generate a text embedding based on the response phoneme, and may process the audio sequences to generate a target voice sequence. The device may generate an audio embedding based on the target voice sequence, and may process the image frames to generate a target image sequence. The device may generate an image embedding based on the target image sequence, and may combine the embeddings to generate an embedding input vector. The device may generate a final voice response and a final video based on the embedding input vector, and may provide the video data, the final voice response, and the final video.
A method and system are described that receives enhanced cache data associated with operation of a device within a geofence, the enhanced cache data comprising Wi-Fi signal information and an entry count of a power-optimized mode being triggered within the geofence. The enhanced cache data is analyzed to identify the geofence as qualified for Wi-Fi-based triggering based on the entry count exceeding a first threshold. When the geofence is qualified, one or more candidate service set identifiers (SSIDs) are identified based on detection frequency of the one or more candidate SSIDs exceeding a second threshold. A notification is sent to a user device that presents the one or more candidate SSIDs. A selection is received from the user device of at least one SSID from the one or more candidate SSIDs, and the device is configured to use the at least one selected SSID to trigger a geofence event.
A system described herein may receive a request for a blockchain network to perform a particular set of operations, such as executing chaincode recorded to a blockchain associated with the blockchain network. The system may receive Key Performance Indicators (“KPIs”) of nodes of the blockchain network, and may receive a consensus policy associated with the blockchain network. The consensus policy may indicate a particular quantity of result sets used to verify execution of a given operation by the blockchain network. The system may assign different nodes of the blockchain network to perform different portions of the requested set of operations. The assignments may be determined based on the consensus policy and the KPIs of the nodes. The system may aggregate result sets from different nodes in order to generate aggregated result sets, where the quantity of aggregated result sets satisfies the consensus policy.
A network device receives, from a machine learning (ML) engine, predicted future network load conditions associated with UE traffic at nodes, network elements (NEs), and/or network functions (NFs) in a mobile network. The network device applies policies to the predicted future network load conditions to select UEs as candidates for temporary downgrades in mobile network service, and initiates sending of authorization requests to the selected UEs to request authorization for the implementation of a temporary downgrade in mobile network service for each of the selected UEs. The network device causes mobile network service to be downgraded to one or more of the selected UEs, for a temporary time period, based on responses to the authorization requests.
A device may include a processor configured to receive a request to establish a Multimedia Priority Service (MPS) session for a user equipment (UE) device. The processor may be further configured to generate a General Packet Radio Service (GPRS) Tunnelling Protocol (GTP) tunnel in a core network from the device to a gateway associated with a packet data network (PDN); map an MPS priority to data units associated with the MPS session sent via the generated GTP tunnel; generate an Internet Protocol Security (IPSec) tunnel from the device to the UE device through a wireless local area network (WLAN); and prioritize data units associated with the MPS session through the IPSec tunnel based on the MPS priority.
A primary application cloud instance may receive historical usage of an application, may allocate, based on the historical usage, a quantity of cloud resources for enabling the application to be accessed, and may enable the application to be accessed. The primary application cloud instance may provide, to a backup application cloud instance that allocates a minimum quantity of cloud resources for providing a skeletal version of the application, heartbeat and session state information associated with the primary application cloud instance, and the quantity of cloud resources for enabling the application to be accessed. The primary application cloud instance may provide, to the backup application cloud instance, an indication of a failure of the primary application cloud instance, via the heartbeat and session state information, to cause the backup application cloud instance to allocate the quantity of cloud resources and to enable access to the application.
G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
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
45.
SYSTEMS AND METHODS FOR WIRELINE ACCESS TO A WIRELESS CORE NETWORK
A device may securely obtain access to a wireless core network, and may identify a particular endpoint associated with a particular physical interface of a plurality of physical interfaces of the device. The device may request a communication session with the particular endpoint via the wireless core network. The device may receive, via the particular physical interface, a first plurality of analog signals, generate first Internet Protocol (“IP”) traffic based on the first plurality of analog signals received via the particular physical interface, and output the first IP traffic to the particular endpoint via the wireless core network. The device may receive second IP traffic from the particular endpoint via the wireless core network, generate a second plurality of analog signals based on the second IP traffic received from the particular endpoint via the wireless core network, and output the second plurality of analog signals via the particular physical interface.
A system described herein may maintain information associating a plurality of application servers with corresponding client identifiers; receive a request from a User Equipment (“UE”) to access, by a user of the UE, a service provided by an application server, wherein the request includes a particular client identifier associated with a particular application server; authenticate the user based on received credentialing of the user; provide an access token to the particular application server to obtain a service profile of the user, based on authenticating the user and the client identifier; and provide a service profile associated with the user, based on the access token, to the particular application server, causing the particular application server to execute the service for which the UE requested access.
A device may receive video data and corresponding GPS data and IMU data associated with a vehicle, and may remove video frames from the video data to generate modified video data. The device may select objects and image regions of video frames of the modified video data, and may determine a current speed and a current turn angle of the vehicle based on the GPS data, the IMU data, and the modified video data. The device may mask the objects of the video frames of the modified video data to learn first features, and may mask the image regions of the video frames of the modified video data to learn second features. The device may generate a trained neural network model based on the current speed, the current turn angle, the first features, and the second features, and may implement the trained neural network model in the vehicle.
G01C 21/16 - NavigationNavigational instruments not provided for in groups by using measurement of speed or acceleration executed aboard the object being navigatedDead reckoning by integrating acceleration or speed, i.e. inertial navigation
G01S 19/49 - Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
48.
SYSTEMS AND METHODS FOR A STATIC IP ADDRESS SERVICE
A system may include a device configured to receive a registration request from a Session Management Function (SMF) associated with an Internet Protocol (IP) address range or an IP index and store information associating the SMF with the IP address range or the IP index. The device may be further configured to receive, from a network function (NF), a query for a particular SMF associated with the IP address or the IP index; identify the SMF associated with the IP address or the IP index as the particular SMF; and provide, to the NF, information identifying the SMF based on the received query.
Systems and methods provide a per-session Multimedia Priority Service (MPS) enforcement service. A network device in a wireless core network receives, from a user equipment (UE) device, a registration request including a request for high priority access. The network device obtains subscriber profile data for the UE device, wherein the subscriber profile data includes parameters for granular MPS service priority. The network device provides, to the UE device, a registration response based on the subscriber profile data and receives, from the UE device, a session establishment request to establish a protocol data unit (PDU) session. The network device sends, based on the session establishment request, context data to a session management function (SMF) for the PDU session, wherein the context data includes the parameters for the granular MPS service priority.
A system described herein may implement a first instance of a particular Network Function (“NF”) in a wireless network. The first NF instance may receive an instruction to restore functionality of a second instance of the particular NF, including a plurality of services. The first NF instance may identify state information associated with the second NF instance, which may include information identifying the plurality of services associated with the functionality provided by the second NF instance. The first NF instance may identify a priority associated with each service, and identify a sequence in which to restore each service based on such priorities. The first NF instance may restore each service, of the plurality of services indicated in the state information associated with the second NF instance, in the identified sequence. The first and second NF instances may be instances of the same type of NF.
H04L 41/08 - Configuration management of networks or network elements
H04L 41/40 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using virtualisation of network functions or resources, e.g. SDN or NFV entities
H04W 24/04 - Arrangements for maintaining operational condition
H04W 48/18 - Selecting a network or a communication service
51.
SYSTEMS AND METHODS FOR DYNAMIC BACK-OFF TIMER IN A WIRELESS NETWORK
A device, such as a User Equipment (“UE”), may maintain a set of criteria associated with back-off timer policies. The device may determine that an attempt to communicate with the network are unsuccessful, and may initiate a back-off timer based on determining that the attempt to communicate with the network are unsuccessful. The device may refrain from attempting to communicate with the network after initiating the back-off timer and prior to expiration of the back-off timer. The device may determine, after initiating the back-off timer and prior to expiration of the back-off timer, that criteria of the set of criteria associated with a back-off timer policy have been met, and may modify a duration of the back-off timer based on determining that the one or more criteria have been met. The device may accordingly attempt to communicate with the network after the back-off timer with the modified duration has elapsed.
In some implementations, a device in a first public land mobile network (PLMN) may receive a request for services with a network function producer (NF-P) in the first PLMN, wherein the request for services includes a first token that is associated with a network function consumer (NF-C) in a second PLMN. The device may validate the first token using a signing certificate associated with a signer of the first token. The device may transmit the request for services to the NF-P, wherein the request for services includes a second token that is based on a successful validation of the first token, and a successful validation of the second token enables the NF-P to provide a service to the NF-C based on an authorization of the NF-C during an inter-PLMN roaming.
A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an application differentiated-based L4S service. The service includes receiving feedback information from end devices associated with the application sessions pertaining to the same application and same network. The service further includes calculating a prospective data rate for each of the end devices based on a data rate adjustment value, a weighting value, and a current data rate. The service may further include applying each prospective data rate during each of the application sessions of a prospective time period. The service may include monitoring protocol delays and marking packets with congestion notification when the protocol delays do not satisfy a latency budget.
Systems and methods provide an on-demand embedded subscriber identity module (eSIM) activation function that expedites the activation and provisioning process for user equipment (UE) devices. A network device receives, from a UE device, an activation request for an eSIM and detects, in the activation request, an activation parameter. In response to detecting that the activation parameter includes an on-demand activation indicator, the network device routes the activation request to an on-demand activation function, and initiates provisioning of basic wireless features for the UE device without receiving a provisioning request from the UE device.
A system described herein may receive, from a first User Equipment (“UE”) that is associated with a first network, a request to communicate with a second UE associated with a second network; receive, from the first network, UE information associated with the first UE; identify a particular access policy that is associated with the first UE and the second UE; and selectively grant or deny, based on the particular access policy and the UE information associated with the first UE, the request to communicate with the second UE. The first network may be a home network of the first UE, and the second network may be a home network of the second UE. The UE information may include location information, authentication information, or other monitored information associated with the first UE, as determined or provided by the first network.
In some implementations, a first UE may identify a configuration that includes artificial intelligence or machine learning (AI/ML) model parameters to be used and shared for federated learning. The first UE may generate an AI/ML model based on the configuration, wherein the AI/ML model is based on an anonymization and encryption of one or more information elements (IEs) using policy information. The first UE may secure the AI/ML model. The first UE may establish a circle-of-trust to include the first UE and a second UE. The first UE may transmit the AI/ML model to the second UE based on the second UE being included in the circle-of-trust.
A base station of a mobile network receives a message from a User Equipment device (UE) that is related to providing mobile network service to the UE and identifies a local software and/or hardware configuration of the base station, including identifying installation of a set of possible new or upgraded features of the base station. The base station generates a data identifier that identifies each installed and active one of the set of possible new or upgraded features of the base station, and inserts the data identifier into an information element (IE) of the message. The base station sends the message to a node of the mobile network.
A device may include a first set of wireless communication hardware that implements a first radio access technology (“RAT”), such as a Fifth Generation (“5G”) RAT, and a second set of wireless communication hardware that implements a second RAT, such as a Long-Term Evolution (“LTE”) RAT. The device may detect a wireless broadcast, via the first set of wireless communication hardware, that has been transmitted by a first base station, and may connect, based on a connection request that includes a Reduced Capability (“RedCap”) indication, to the first base station, which may implement radio parameters based on the RedCap indication. The device may detect a second wireless broadcast, via the second set of wireless communication hardware, that has been transmitted by a second base station, and may connect to the second base station. The second base station may implement radio resource parameters for the device based on network-maintained information.
H04W 76/16 - Setup of multiple wireless link connections involving different core network technologies, e.g. a packet-switched [PS] bearer in combination with a circuit-switched [CS] bearer
H04L 5/00 - Arrangements affording multiple use of the transmission path
59.
SYSTEMS AND METHODS FOR TRANSMITTING PERIODIC DISCONNECT PEER REQUESTS BASED ON GRACEFUL CONNECTION CYCLING
In some implementations, a network device may establish a connection between a client and a server instance associated with a service. The network device may identify that a timer for transmitting a periodic disconnect peer request has expired. The network device may transmit the periodic disconnect peer request to the client based on an expiry of the timer, wherein the periodic disconnect peer request includes a value for graceful connection cycling, and the graceful connection cycling is to be applied to the connection based on the periodic disconnect peer request.
A system comprises one or more devices that implement network functions (NFs). The NFs comprise a network data analytics function (NWDAF) configured to provide first analytics to a policy control function (PCF). In addition, the NFs also comprise the PCF configured to: receive the first analytics from the NWDAF; generate a first policy rule based on the first analytics; and forward the generated first policy rule to a network component to process the first policy rule at the network component.
A device described herein may detect entry into a coverage area of a first frequency band provided by a network device and transmit by the device, capability information to the network device, the capability information indicating that the device is a power sensitive device or a narrow band device. The device then receiving an indication of a quantity of repetitions to extend uplink coverage for the device, based on the capability information; and enabling by the device, the quantity of repetitions for uplink transmissions to extend uplink coverage.
A system described herein, which may be implemented by a Network Slice Access Control Function (“NSACF”) of a wireless network, may monitor analytics information with respect to a plurality of network slices of a wireless network. The analytics information may be received from a Network Data Analytics Function (“NWDAF”) of the wireless network. The system may determine, based on the monitored analytics information, a capacity threshold for at least a particular network slice. The system may receive a request for access to the particular network slice; determine, based on the capacity threshold for the particular network slice, whether to accept or deny the request; and output, in response to the request an indication of whether the request is accepted or denied. The indication may be provided to a network function of the wireless network or to an external device via a Network Exposure Function (“NEF”).
Disclosed are systems and methods for a location estimation accuracy framework that operates on and/or in connection with a cellular network(s) to determine and estimate the location of user equipment (e.g., mobile devices) within and/or across cellular networks. The disclosed framework can execute operations that leverage a combination of Cell-ID information, GPS technology, timing measurements, and network-based positioning techniques to deliver accurate location information, enabling a wide range of location-based services and applications. The framework leverages determine path loss values for direct and/or indirect paths between UE and cell sites to determine locations of the UE, for which network services can be based and/or provided.
H04L 43/0811 - Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking connectivity
H04L 5/00 - Arrangements affording multiple use of the transmission path
In some implementations, a base station (BS) operating in a wired simulation framework may identify a packet that is received from a mobile terminal (MT) in the wired simulation framework or is to be transmitted to the MT. The BS may map a mobile terminal identifier (MTID) associated with the packet to a radio network temporary identifier (RNTI) associated with the packet, or vice versa, using one of a first table or a second table, respectively. The BS may decode or transmit the packet based on the mapping.
In some implementations, a network device may receive a key performance indicator (KPI) metric associated with a media resource function, wherein the media resource function is associated with a virtualized telephone application service. The network device may compare the KPI metric to a threshold. The network device may generate an internal event based on the KPI metric satisfying the threshold. The network device may correlate the internal event and an external event, wherein the external event is based on external system formulated data, and correlation results include an indication of non-matching data or matched data between the internal event and the external event. The network device may identify a blocked voice traffic flow based on the correlation results. The network device may transmit a notification that indicates the blocked voice traffic flow, wherein the notification includes a recommendation to resolve the blocked voice traffic flow.
H04L 47/12 - Avoiding congestionRecovering from congestion
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]
In some implementations, the techniques described herein relate to a method including: receiving a natural language question from a user; determining, using a large language model, whether the natural language question is a transactional question or an informational question; generating, using a first generative artificial intelligence (AI) model, a first response to the natural language question when the natural language question is an informational question; generating, using a transaction generative AI model, a second response to the natural language question when the natural language question is a transactional question; generating, using a sentiment-based response generator, a third response based on one of the first response or the second response and a sentiment of the natural language question; and presenting the third response to the user.
A system may comprise a network device and a communication device. The communication device may be configured to: receive a request from a communication device to subscribe to a blocking list; subscribe the communication device to the blocking list; and send a network identifier that is associated with the blocking list. The communication device may be configured to: receive the network identifier from the network device; download the blocking list from a location identified by the network identifier; and block voice calls or messages that originate from a source identified by the blocking list.
A device may receive data identifying danger zones for traffic signals associated with a vehicle, and may identify a set of danger zones for the vehicle. The device may retrieve a current location, direction, and speed of the vehicle based on determining that the vehicle has not reached a point of no return with respect to the set of danger zones. The device may identify a danger zone for the vehicle based on the current location, direction, and speed of the vehicle, and may process a video frame, with a model and based on determining that the vehicle has reached a point of no return with respect to the danger zone, to determine whether a traffic signal in the danger zone indicates proceed, stop, or yield. The device may perform one or more actions based on determining whether the traffic signal in the danger zone indicates proceed, stop, or yield.
G08G 1/01 - Detecting movement of traffic to be counted or controlled
G06Q 10/1093 - Calendar-based scheduling for persons or groups
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
69.
METHOD AND SYSTEM FOR AN ENERGY CONSUMPTION CONTROL SERVICE
A method, network device, system, and non-transitory computer-readable storage medium are described in relation to an energy consumption control service that includes receiving, from an end device, a registration request; retrieving, responsive to the registration request, subscription information including energy consumption control service criteria; selecting, using the energy consumption control service criteria, network components based on energy consumption profiles associated with the network components; and provisioning an application session for the end device according to the energy consumption control service criteria.
A user device may receive a user interface that includes content, and may provide the user interface for display to a user of the user device. The user device may receive a user interaction with the user interface, and may calculate, based on the user interaction, gaze data identifying a gaze of the user, a dwell time of the gaze, and an eye behavior of the user relative to the content. The user device may generate intent data based on the gaze data, and may provide the intent data and one or more prompts to a large language model (LLM) system. The user device may receive one or more responses from the LLM system based on providing the intent data and the one or more prompts to the LLM system.
A method may include providing a network data analytics function (NWDAF) in a network and providing, a non-third generation partnership project (3GPP) interworking function (N3IWF) in the network. The method may also include subscribing, by the N3IWF, to the NWDAF, and obtaining, by the NWDAF and from the N3IWF, data associated with processing performed by the N3IWF.
A system described herein may maintain information indicating groups of wireless trip devices. The system may maintain information associating each wireless trip device, of a plurality of wireless trip devices, with respective edge computing devices. The system may receive a wireless alert from a particular wireless trip device, which indicates an electrical fault condition. The system may identify a particular group of wireless trip devices with which the wireless alert is associated, and may identify a particular set of edge computing devices that are associated with respective wireless trip devices of the group of wireless trip devices. The system may output, to each edge computing device of the identified particular set of edge computing devices, a notification based on the wireless alert, and each edge computing device may wirelessly communicate respective wireless trip devices based on the wireless alert received from the particular wireless trip device.
The present teaching relates to personalized network update. Information on users' network activities is collected and analyzed to identify indirect and direct relations between each user and others. Each user's network influence is determined and represented based on indirect relation embeddings and direct relation embeddings, obtained to characterize the respective indirect and direct relations. A personalized priority for each user is predicted based on the user's representation. A network update schedule is determined based on users' personalized priorities so that network update is conducted in a personalized manner.
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
74.
SYSTEMS AND METHODS FOR PROVIDING RELIABLE AND LOW LATENCY VOICE CONTROL OF EXTENDED REALITY AND INTERNET OF THINGS DEVICES
A radio access network (RAN) may receive, from a user device, a video frame, a voice command, and a gesture command associated with an application, and may encode the voice command, the video frame, and the gesture command to generate a data frame. The RAN may determine whether the data frame satisfies a plurality of thresholds associated with a respective plurality of parameters. The RAN may selectively provide the data frame to an application system based on determining that the data frame satisfies the plurality of thresholds, or may adjust one or more of the respective plurality of parameters based on determining that the data frame fails to satisfy at least one of the plurality of thresholds, and provide the data frame to the application system after adjusting the one or more of the respective plurality of parameters.
In some implementations, a device may detect a voice call involving a user. The device may identify a usage of user-specific language or vocabulary based on a usage pattern. The device may generate, based on the user-specific language or vocabulary, one or more replacement words to replace words spoken by the user during the voice call. The device may generate a random value to be applied to the voice call to create voice obfuscation for the voice call, wherein the random value is used to obfuscate one or more voice characteristics of the voice call. The device may communicate encoded data associated with the voice call, wherein the encoded data is in accordance with the voice obfuscation.
In some implementations, a device may receive event information associated with a virtual radio access network (vRAN) communication failure, identifying, by the device and based on the event information, that a communication interface between a vRAN network function (NF) and a Core network NF has failed. The device may initiate a reestablishment of the communication interface. In a radio access network (RAN) and Core domain, interfaces across various NFs may be key for effective end-to-end communications. Monitoring and identifying communication failures across the RAN and Core with specific event information may be critical. An ability to consider appropriate factors for communication failures may be enabled to dynamically reestablish the communication interface.
H04L 43/0811 - Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking connectivity
H04W 92/00 - Interfaces specially adapted for wireless communication networks
77.
SYSTEMS AND METHODS FOR PREVENTING USER DEVICE PINGING IN ASYNCHRONOUS COMMUNICATION MODE
A network device may receive a context creation request based on a protocol data unit (PDU) session establishment request for establishing a PDU session with a user device, and may generate a policy context request based on receiving the context creation request. The network device may provide the policy context request to a policy control function (PCF), and may receive, from the PCF and based on the policy context request, policy rules for the PDU session. The network device may receive, from the PCF, a terminate PDU session notification indicating that a binding support function (BSF) is unreachable, and may generate, based on the terminate PDU session notification, a PDU session release command that includes a cause code indicating an issue with the BSF and an instruction to not reinitiate the PDU session. The network device may cause the PDU session release command to be provided to the user device.
In some implementations, a policy control function (PCF) device may receive a PCF device key uniquely associated with a user equipment (UE). The PCF device may generate an integrity key and an encryption key based on the PCF device key and an identifier of the PCF device. The PCF device may generate, based on the integrity key, integrity data associated with policy information related to the UE. The PCF device may encrypt, based on the encryption key, the policy information to generate encrypted policy information. The PCF device may send, for the UE, a UE policy message indicating the integrity data, the encrypted policy information, and the identifier of the PCF device.
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
One or more computing devices, systems, and/or methods for providing adaptive explainability for machine learning models are provided. A knowledge structure, representing entities with nodes and relationships between entities as edges between the nodes, is processed to create knowledge system entity embeddings. A dimensionality of the knowledge system entity embeddings is reduced to create dimensional embeddings. The dimensional embeddings and relationships are processed using an optimal transport plan to generate feedback. The feedback is used to modify the knowledge structure for generating adaptive explainability information that explains predictions generated by the machine learning models.
A method, a device, and a non-transitory storage medium provide an inference model monitoring service. A network device stores a first inference model and a second inference model that is a derivative of the first inference model. The network device performs a weight comparison of connection weights of the second inference model against connection weights of the first inference model and assigns a weight vulnerability indicator. The network device performs a manifold comparison of a reduced dimensional manifold of the second inference model to a reduced dimensional manifold of the first inference model and assigns a manifold vulnerability indicator. The network device generates a vulnerability status of the second inference model based on the different indicators and performs a remedial action for the second inference model.
In some implementations, the techniques described herein relate to a method including: receiving input features associated with a cellular network that includes key performance indicators and metrics collected from computing devices in the cellular network; preprocessing the input features to generate input features; providing the input features to a machine learning model to generate a predicted user experience classification, wherein the machine learning model is trained using a dataset including a plurality of sets of input features and corresponding user experience labels; and performing one or more actions based on the predicted user experience classification, wherein performing one or more actions includes identifying a root cause of a user experience issue and generating a recommendation to modify the cellular network to address the root cause.
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
82.
METHOD AND SYSTEM FOR DYNAMIC EXTENDED DISCONTINUOUS RECEPTION SERVICE
A method, an end device, and a non-transitory computer-readable storage medium are described in relation to a dynamic eDRX service. The dynamic eDRX service may dynamically update eDRX cycle parameters based on network information. The network information may relate to a congestion state, a data usage value, or another criteria. The dynamic eDRX service may transmit the updated eDRX cycle parameters to an end device of relevance.
A device may receive time series data associated with a mobile network, and may process the time series data, with a bidirectional-long short-term memory (Bi-LSTM) model, to generate a preliminary mobile network traffic prediction. The device may apply an adaptive waterwheel plant optimization model, to the preliminary mobile network traffic prediction, to train the Bi-LSTM model and to generate a trained Bi-LSTM model. The device may process the preliminary mobile network traffic prediction, with the trained Bi-LSTM model, to generate a final mobile network traffic prediction, and may perform one or more actions based on the final mobile network traffic prediction.
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
84.
DATA AUGMENTATION USING MACHINE TRANSLATION CAPABILITIES OF LANGUAGE MODELS
Disclosed are embodiments for improving training data for machine learning (ML) models. In an embodiment, a method is disclosed where an augmentation engine receives a seed example, the seed example stored in a seed training data set; generates an encoded seed example of the seed example using an encoder; inputs the encoded seed example into a machine learning model and receives a candidate example generated by the machine learning model; determines that the candidate example is similar to the encoded seed example; and augments the seed training data set with the candidate example.
A method, a device, and a non-transitory storage medium are described in which a radio access network (RAN) key performance indicator geographic normalization, decluster, and selection service is provided. The service may include automated new RAN device design, apportioning of geographic agnostic metrics associated with a RAN device to geo-bins, analyzing candidate geo-bins and associated candidate new RAN devices builds based on objective criteria, and iteratively calculating and ranking solutions that optimally satisfy the objective criteria. The service may also provide radio frequency propagation modeling for existing and new RAN device builds.
A device includes a processor. The processor may be configured to: commission a fabric device over a Bluetooth® Low Energy (BLE) connection between the device and the fabric device; and send a code to a router on a wireless local area network (WLAN) to enable the router to commission the fabric device for a fabric on the WLAN.
A system described herein may maintain a local copy of a blockchain that is associated with a blockchain network, receive a first request to perform a blockchain operation with respect to the blockchain, perform the requested blockchain operation with respect to the local copy of the blockchain to generate a first result, and output the first result in response to the first request. The system may output a second request to the blockchain network to perform the blockchain operation, receive a second result from the blockchain network in response to the second request, wherein the second result is generated by the blockchain network based on the blockchain network performing the blockchain operation with respect to the blockchain, wherein the second result is received from the blockchain network after outputting the first result in response to the first request, and maintain information indicating whether the first result matches the second result.
H04L 67/1095 - Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
88.
SYSTEMS AND METHODS FOR SCHEDULING AND PROVIDING OPTIMAL DOMAIN RESOURCES TO CONSUMERS
A device may receive a resource request for a domain with resource types, and may assign a classification to the resource request. The device may identify a queue position for the resource request, and may determine resource parameters for the resource request based on resource characteristics. The device may process the resource parameters and optimized parameter values, with a resource optimizer model, to generate optimized resource parameters. The device may process the resource types of the domain, with one or more resource scheduling models and based on the optimized resource parameters, to generate a consumption schedule for the resource types, and may service the resource request based on the consumption schedule for the resource types.
A device may receive data identifying characteristics, location dependent characteristics, and a network service associated with target consumers, such as households or businesses, and may process the data, with one or more propensity models, to determine propensities of the target consumers to utilize the network service. The device may process the data and the propensities, with a state transition model, to calculate probabilities that the target households will utilize the network service, and may determine utilization states of the target consumers over time based on the probabilities that the target consumers will utilize the network service. The device may aggregate the utilization states of the target consumers to determine penetration rates for the network service, and may perform one or more actions based on the penetration rates for the network service.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
G06Q 30/0202 - Market predictions or forecasting for commercial activities
Embodiments described herein allow reduced latency and computational costs by identifying a slice associated with a packet and determining an action to be performed with respect to the packet without having to perform deep packet inspection on the packet. An aspect of the present disclosure is a method comprising receiving a packet including a slice identifier corresponding to a slice of a network, the slice identifier having a plurality of bits; determining a bit value for each bit in a subset of the plurality of bits; and performing an action with respect to the packet based on the bit value of at least one bit in the subset.
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.
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.
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.
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.
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.
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.
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 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.
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.
patents
