In some aspects, the techniques described herein relate to a method including: generating, by a processor, verification data; encoding, by the processor, the verification data within a base avatar using a steganography algorithm to generate an augmented avatar; and transmitting, by the processor, the augmented avatar to a recipient.
A method, a network device, and a non-transitory computer-readable storage medium are described in relation to a hybrid network slicing service. The hybrid network slicing service may enable the initial configuration of a network slice according to network slice requests that may include customized and user-specified network performance criteria. The hybrid network slicing service may enable network slice requests to specify selection of network resources and use/availability based on entity-based criteria including end device and/or application specific associations. The hybrid network slicing service may optimize network slice configurations and generate network slice templates.
H04L 41/122 - Discovery or management of network topologies of virtualised topologies e.g. software-defined networks [SDN] or network function virtualisation [NFV]
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
H04L 41/0895 - Configuration of virtualised networks or elements, e.g. virtualised network function or OpenFlow 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
H04L 43/55 - Testing of service level quality, e.g. simulating service usage
A first User Equipment (“UE”) may communicate with a second UE via a communication link to determine an operational status of the second UE. The second UE may be connected to a network via one or more communication sessions. The first UE may determine, based on the communication link, that the second UE is non-operational, and may output, based on determining that the second UE is non-operational, a request to communicate with the network via the one or more communication sessions associated with the second UE. The network may modify the one or more communication sessions to be associated with the first UE based on the request, and the first UE may communicate with the network via the one or more modified communication sessions.
A device may receive user device application performance logs, radio access network (RAN) performance logs, and multi-access edge computing (MEC) compute logs, and may train a machine learning model with the user device application performance logs, the RAN performance logs, and the MEC compute logs. The device may identify application capacity requirements and MEC compute requirements associated with images of RAN coverage areas of RANs, and may generate labeled and normalized images of RAN coverage areas. The device may receive real time MEC compute data, RAN performance data, and user device application data, and may identify one of the labeled and normalized images of the RAN coverage areas that matches a RAN coverage area of a particular RAN. The device may predict an application capacity and MEC compute requirements for the particular RAN, and may perform one or more actions based on the application capacity and the MEC compute requirements.
A vehicle routing platform may store, in a first data structure, a value at a logical index for each pair of locations of a plurality of pairs of locations. The logical index, for a pair of locations, indicates whether a travel time has been determined. The vehicle routing platform may store, in a second data structure, an offset index for each predetermined logical index of one or more predetermined logical indexes of the first data structure. The offset index, for a predetermined logical index, may be based on a quantity of logical indexes preceding the predetermined logical index in the first data structure. The vehicle routing platform may store, in a third data structure, travel times between pairs of locations of a portion of the plurality of pairs of locations. The vehicle routing platform may determine a travel time, between a pair of locations, using the data structures.
A system described herein may register a particular Service-Based Interface (“SBI”) with a core network (e.g., a Fifth Generation core (“5GC”)). The core network may maintain information associating the system with the particular SBI. The system may request core network information, associated with the core network, from the core network. The core network may provide the requested core network information to the device via the registered particular SBI. The system may provide the core network information, received via the particular SBI, to a radio access network (“RAN”), such as an Open-RAN (“O-RAN”), which may modify RAN configuration parameters based on the provided core network information. The system may also receive requests for RAN information from the core network via the SBI, may obtain the information from one or more elements of the RAN, and may provide the requested RAN information to the core network via the SBI.
A system described herein may register a particular radio access network (“RAN”)-based interface, such as a R1 interface, with a RAN, such as an Open-RAN (“O-RAN”). The system may further be associated with a particular Service-Based Interface (“SBI”) of a core network that includes a plurality of network functions (“NFs”), where each NF is associated with a respective SBI. The system may receive, via the particular SBI, a request for RAN information from one or more NFs of the core network, obtain the RAN information from the RAN via the particular interface, output the requested RAN information to the one or more NFs of the core network via the particular SBI. Associating the system with the SBI may be performed without modifying a Network Repository Function (“NRF”) of the core network.
A device may receive and convert audio data to text data in real-time, and may detect a network fluctuation that causes missing voice packets. The device may process partial text and context of the text data, with a model, to generate a new phrase, and may generate a response phoneme for the new phrase. The device may utilize a text embedding model to generate a text embedding for the response phoneme, and may process the audio data, with the model, to generate a target voice sequence. The device may utilize an audio embedding model to generate an audio embedding for the target voice sequence, and may combine the text embedding and the audio embedding to generate an embedding input vector. The device may process the embedding input vector, with an audio synthesis model, to generate a final voice response, and may provide the audio data and the final voice response.
G10L 13/08 - Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
G10L 15/16 - Speech classification or search using artificial neural networks
G10L 15/187 - Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G10L 19/005 - Correction of errors induced by the transmission channel, if related to the coding algorithm
G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
9.
SYSTEM AND METHOD FOR AUDIOVISUAL CONTENT ANALYSIS ON EDGE DEVICES
Techniques for analyzing audiovisual content, such as streaming content are disclosed. In one embodiment, a method is disclosed comprising obtaining a frame of audiovisual content, using a video decoder to decode compressed model weights of at least one trained model, using the at least one trained model with the decoded weights to analyze the frame and extract content based on the analysis, using the extracted content to make a determination that the audiovisual content comprises a category of content, and causing actionable information to be transmitted to a client device of a user in response to the determination that the audiovisual content comprises the category of content.
H04N 21/434 - Disassembling of a multiplex stream, e.g. demultiplexing audio and video streams or extraction of additional data from a video stream; Remultiplexing of multiplex streams; Extraction or processing of SI; Disassembling of packetised elementary stream
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
A user device may receive an image of a user, and may process the image, with a model, to identify a face of the user and key markers of the face. The user device my process the face and the key markers, with the model, to identify features of the face, and may calculate a z-index representing a distance between the face and a display. The user device may calculate left and right face rotation along an x-axis of the display, and may calculate up and down face rotation along a y-axis of the display, based on the features. The user device may calculate an x-position and a y-position of a cursor on the display based on the left and right face rotation, the up and down face rotation, and the z-index, and may provide the x-position and the y-position of the cursor to the display.
G06F 3/00 - Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
11.
SYSTEMS AND METHODS FOR USING BLOCKCHAIN TO MANAGE SERVICE-LEVEL AGREEMENTS BETWEEN MULTIPLE SERVICE PROVIDERS
In some implementations, a management system may receive service information from a plurality of service providers. A plurality of network devices, of the plurality of service providers, may be blockchain nodes of a blockchain. The service information, of a service provider, may identify network services provided by a network device of the service provider and identifies a location of the network device. The management system may receive, via the blockchain, information regarding a service-level agreement associated with a subset of service providers, of the plurality of service providers, providing network services for an application. The management system may receive, via the blockchain, an approval of the service-level agreement from each service provider of the subset of service providers. The management system may perform an action based on the service-level agreement and based on the network services provided by the subset of service providers for the application.
H04L 41/0686 - Additional information in the notification, e.g. enhancement of specific meta-data
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]
12.
MANAGEMENT OF AUTONOMOUS MOBILE DEVICE DISCONNECTIONS
An autonomous mobile device includes a processor to establish a Transmission Control Protocol Robot Operating System (TCPROS) connection over a Protocol Data Unit (PDU) session between the AMD and a Multi-Access Edge Computing (MEC) device that hosts an AMD controller in a cellular network, from the AMD the AMD controller. The processor may perform a mission in accordance with the mission plan while the AMD is connected to the AMD controller via the TCPROS connection.
A video summary device may generate a textual summary of a transcription of a virtual event. The video summary device may generate a phonemic transcription of the textual summary and generate a text embedding based on the phonemic transcription. The video summary device may generate an audio embedding based on a target voice. The video summary device may generate an audio output of the phonemic transcription uttered by the target voice. The audio output may be generated based on the text embedding and the audio embedding. The video summary device may generate an image embedding based on video data of a target user. The image embedding may include information regarding images of facial movements of the target user. The video summary device may generate a video output of different facial movements of the target user uttering the phonemic transcription, based on the text embedding and the image embedding.
A system described herein may monitor, on an ongoing basis, a Key Performance Indicators (“KPIs”) associated with a wireless network. The system may receive, from a particular device, a monitoring policy specifying one or more criteria, and may compare the criteria associated with the monitoring policy to the monitored KPIs. The system may determine, based on the comparing, that the criteria associated with the monitoring policy is satisfied; may determine a particular subset of the monitored KPIs based on the monitoring policy and further based on determining that the criteria associated with the monitoring policy is satisfied; and may output the particular subset of the monitored KPIs to the second device. The system may monitor the wireless network via private interfaces that are not exposed to devices external to the wireless network.
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]
A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an edge cloud management service. The edge cloud management service may automate the provisioning, maintenance, supervision across multi-vendor network devices in a private/enterprise environment. Further, the edge cloud management service may provide abstraction and normalization services across multi-vendor components and enable KPI monitoring, location data, edge discovery metrics, end-to-end latency computation across various cloud service provider technologies.
A network device may receive, for an emergency call, a session initiation protocol (SIP) invite that includes a registered address of a user device that initiated the emergency call, and may determine whether the user device is associated with a private network based on the SIP invite. The network device may extract the registered address of the user device from the SIP invite and based on the user device being associated with the private network, and may provide the registered address to another network device. The other network device may determine a registered geographic location of the user device, based on the registered address, and may provide the registered geographic location to a public safety answering point associated with the emergency call.
A method and system is described that performs dynamic slice management for unmanned aerial vehicles (UAVs). Flight path information associated with a flight path of a UAV is used to determine a predicted optimal application and a predicted optimal network slice for use by the UAV at a location along the flight path. Information associated with the predicted optimal application and the predicted optimal network slice is transmitted to the UAV for use by the UAV at the location. The available network slices to the UAV may be updated based on the determined predicted optimal slice. The UAV may adjust its usage of applications and/or slices based on the information it receives associated with the predicted optimal application and the predicted optimal network slice.
A device may receive network resource model (NRM) data identifying application layer data, user related data, network layer data, and physical layer data associated with a network, and may generate a graphical NRM that is a causal graph representation of the NRM data. The device may merge key performance indicators (KPIs) and network interventions with the graphical NRM, and may generate a hypergraph NRM. The device may determine first embeddings that preserve a structure of the graphical NRM and second embeddings that preserve a structure of the hypergraph NRM, and may train a causal impact model, based on the first embeddings, the second embeddings, a pre-intervention period, and a post-intervention period, to generate learned relationships. The device may retrain the causal impact model based on the learned relationships to generate a trained causal impact model, and may perform one or more actions with the trained causal impact model.
In some aspects, the techniques described herein relate to a method including: receiving, via a network, a message addressed to a mobile device number (MDN); identifying, by the network, a set of hunt group devices associated with the MDN, the set of hunt group devices including at least a first hunt group device and a second hunt group device; transmitting, by the network, the message to each hunt group device in the set of hunt group devices; receiving, by the network, a reply to the message from the first hunt group device; transmitting, by the network, the reply to a sender of the message; and transmitting, by the network, the reply to at least the second hunt group device.
A network device may receive a request for functionality to switch to wideband audio at a user device receiving over-the-top (OTT) video and OTT audio associated with a video conferencing application provided by an application server. The network device may switch from the OTT audio to the wideband audio, for the video conferencing application, based on receiving the request for the functionality. The network device may provide the wideband audio associated with the video conferencing application to the user device to cause the user device to provide the OTT video and the wideband audio independently.
A User Equipment (“UE”) described herein may connect or request connection to a base station of a wireless network. The base station may implement multiple frequency bands, and connecting or requesting connection to the base station may include connecting or requesting connection via a particular frequency band of the multiple frequency bands. The UE may provide an indication of the particular frequency band to a core of the wireless network. The core may select a set of parameters for communications between the UE and the wireless network based on the indication of the particular frequency band. Additionally, or alternatively, the core may provide an indication of the particular frequency band to a device that provides a service to the UE via the wireless network, and the device may select parameters for the service based on the indication of the particular frequency band.
A system described herein may maintain policy information associating a plurality of network credentials with respective device identifiers of User Equipment (“UEs”) that are authorized to use respective network credentials to access a network. The system may receive network monitoring information indicating that a particular UE, associated with a particular device identifier, is accessing the network using a particular network credential; compare the particular device identifier and the particular network credential to the policy information; determine, based on comparing the particular device identifier and the particular network credential to the policy information, that the particular UE is not authorized to use the particular network credential; and output a notification to the network that the particular UE is not authorized to use the particular network credential to access the network, wherein the network suspends the particular network credential based on the notification.
Systems and methods described herein provide profile-based routing and access control for a management interface of virtual network services with multiple tenants. A network device receives a request from a user device to access a webpage for an assisted network management service, and obtains, in response to the request, a user profile associated with a user of the user device. The network device retrieves an Internet Protocol (IP) address for a customer web server, of multiple customer web servers for the assisted network management service, that corresponds to the user profile. The network device generates, based on the IP address, a routing rule to route the request to the customer web server.
A device may receive dynamic customer data and static customer data, and may calculate additional customer data based on the dynamic customer data and the static customer data. The device may process the static customer data, the dynamic customer data, and the additional customer data, with a first machine learning model, to determine a next action prediction, and may process the static customer data, the dynamic customer data, and the additional customer data, with a second machine learning model, to determine a next sequence prediction. The device may concatenate the static customer data, the dynamic customer data, the additional customer data, the next action prediction, and the next sequence prediction to generate concatenated data, and may process the concatenated data, with a plurality of machine learning models, to calculate various outputs, and may generate a recommendation for the customer based on the various outputs.
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.
A system described herein may provide a technique for receiving a communication session request associated with a particular User Equipment (“UE”). The system may identify locator information associated with an edge discovery system associated with a network that includes a plurality of edge devices, wherein the edge discovery system receives service requests associated with one or more UEs and provides an indication of a respective edge device of the plurality of edge device in response to respective service requests. The system may output, in response to the communication session request, the locator information associated with the edge discovery system, wherein the particular UE communicates with the edge discovery system using the locator information to output a particular service request and receive, from the edge discovery system, an indication of a particular edge device of the plurality of edge devices in response to the particular service request.
A device may receive video data identifying videos associated with one or more unsafe driving events by a driver of a vehicle, and may process the video data, with a machine learning model, to determine classifications for the videos. The device may assign tags to the videos based on the classifications, and may calculate event severity scores based on the classifications. The device may calculate tag scores based on the tags assigned to the videos, and may calculate time-to-contact scores, box cross scores, day/night scores, weather scores, and road condition scores based on the video data. The device may calculate video risk scores for the videos based on the event severity scores, the tag scores, the time-to-contact scores, the box cross scores, the day/night scores, the weather scores, and the road condition scores, and may provide one or more of the video risk scores for display.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/40 - Scenes; Scene-specific elements in video content
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
28.
SYSTEMS AND METHODS FOR ADJUSTING A TRANSCRIPT BASED ON OUTPUT FROM A MACHINE LEARNING MODEL
In some implementations, a transcription system may generate a first transcript based on audio data of a conversation between a first user and a second user. The transcription system may determine, using a first machine learning model of the transcript system, that a portion of the first transcript is incorrect. The transcription system may generate, using a second machine learning model, additional data for transcribing the audio data based on determining that the portion of the first transcript is incorrect. The additional data is generated using a portion of the audio data corresponding to the portion of the first transcript. The transcription system may generate a second transcript based on the audio data and the additional data. The transcription system may provide the second transcript to one or more devices.
A device may receive network data identifying a topology of network devices of a network, quality of service (QoS) rules for user equipment (UEs) and application flows associated with the network, QoS profiles for a radio access network (RAN) of the network, and service data flow (SDF) templates. The device may process the network data, with a model, to calculate a network device service level agreement (SLA) score, for each of the network devices on a sliding window, to generate network device SLA scores, and may aggregate the network device SLA scores to generate an end-to-end SLA score on the sliding window. The device may generate QoS configuration data based on the end-to-end SLA score, and may implement the QoS configuration data.
A system may provide for the design and/or modification of network slices associated with a wireless network. The wireless network may include different slices that are associated different sets of service parameters. Slices may include radio access networks (“RANs”), core networks, or other types of networks, which may include respective sets of network functions (“NFs”), which may perform specific functions with respect to a given RAN and/or core network. Different slices, RANs, core networks, and/or NFs may be associated with particular policies and/or tags which may be specified by one or more users associated with a first access level. One or more users associated with a second access level may configure portions of the wireless network, and the policies and/or tags associated with particular slices, RANs, core networks, or NFs may be automatically implemented by an orchestration system that configures the wireless network based on the provided configuration information.
A method, a device, and a non-transitory storage medium are described in which an integrated access and backhaul service is provided. The integrated access and backhaul service may include an admission control service that manages admission control based on access and backhaul capacities of an integrated access and backhaul network such that access and backhaul capacities at any integrated access and backhaul device are not exceeded. The integrated access and backhaul service may include a capacity allocation service. The capacity allocation service may provision scheduling and resource allocation to support a performance metric and/or other criteria at an integrated access and backhaul device based on access and backhaul demands and capacities.
Systems and methods described herein provide binding support function (BSF) service registration and discovery for environments using oversubscribed network addresses. A network device in a core network receives a registration request from a binding support device. The registration request includes a binding group identifier associated with the binding support device. The network device stores, based on the registration request, a profile for the binding support device that includes the binding group identifier and receives, from a policy control device, a discovery request for a that includes a unique identifier of user equipment (UE) device. The network device provides, to the policy control device, a response to the discovery request that identifies the binding support device.
A user equipment (UE) device may include a processor configured to obtain a configuration specification for at least one discontinuous transmission parameter, wherein the configuration specification includes different values for at least two different Quality of Service (QoS) classes. The processor may be further configured to detect uplink data that is to be sent by the UE device to a device via a base station; determine a QoS class associated with the uplink data; select a value for the at least one discontinuous transmission parameter based on the configuration specification and the determined QoS class associated with the uplink data; and send the uplink data to the base station based on the selected value for the at least one discontinuous transmission parameter.
A method may include receiving a number of images to train a first neural network, masking a portion of each of the images and inputting the masked images to the first neural network. The method may also include generating, by the first neural network, probable pixel values for pixels located in the masked portion of each of the plurality of images, forwarding the images including the probable pixel values to a second neural network and determining, by the second neural network, whether each of the probable pixel values is contextually suitable. The method may further include identifying pixels in each of the plurality of images that are not contextually suitable.
Disclosed are systems and methods for an anomaly detection framework that operates as an executable analysis tool for devices to operate in order to determine whether the device contains an unresponsive touch screen (e.g., defective or malfunctioning touch screen). The disclosed framework can analyze the capacitance capabilities of the touch screen, inclusive of the touch layers associated with the touch screen panel, and determine when a device's touch screen is unresponsive to user provided input, which can be any type of touch or gesture provided on a touch screen.
A method may include establishing, by a first service provider and via an application programming interface (API), an agreement with a second entity. The agreement may identify services that can be modified without submission of a new order. The method may also include receiving a first message requesting a change associated with a service and determining, by the first service provider, whether the requested change corresponds to a change that is permitted based on the agreement and whether resources are available in the first network to implement the requested change. The method may further include determining whether the requested change is accepted, sending a second message to the second entity that indicates whether the change is accepted and in response to determining that the change is accepted, automatically implementing the change to the service in the first network.
H04L 67/51 - Discovery or management thereof, e.g. service location protocol [SLP] or web services
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
37.
IDENTIFYING SEARCH RESULTS USING DEEP QUERY UNDERSTANDING
An improved search engine is disclosed. The search engine receives search queries from client devices and inputs these queries into a first neural network (an action understanding model) that includes an action embedding layer. The action embedding layer can be a word embedding layer constructed using action terms. The action understanding model outputs a filter match associated with a type of filter and, in some scenarios, an action-condition pair. The action-condition pair includes an action associated with the type of filter and a condition comprising an adaptive value associated with the action. Based on the filter and, if present, action-condition pair(s), the embodiments generate a structured query and issue the structured query to a data repository (e.g., database). The search engine then returns a search results page responsive to the search query that includes the results returned by the data repository in response to the structured query.
A system described herein may provide for an interface, such as a midhaul interface, between one or more radio elements of a radio access network (“RAN”), such as a Distributed Unit (“DU”) and/or a radio unit (“RU”), and higher layer functions of the RAN, such a Central Unit (“CU”). The interface may provide for the DU to be communicatively coupled to multiple CUs, which may be associated with multiple distinct core networks. The system may route uplink traffic, received from User Equipment (“UEs”) via the RU and the DU, to an appropriate CU. The system may resolve conflicting instructions, for the DU, received from different CUs. The resolution may be performed based on relative priority levels of the CUs or other factors. The system may provide for granular monitoring of traffic parameters on a per-CU basis, thus preserving the privacy of different core networks.
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
40.
SYSTEMS AND METHODS FOR MONITORING NETWORK DEVICES BASED ON PREDICTIVE ANALYSIS
A device may receive network data for network devices of an IMS network, and may determine that the network devices are active. The device may identify a set of network devices associated with the network data satisfying an upper threshold, and may determine that the network data of the set of network devices indicates healthy redundant network devices and satisfies a lower redundancy threshold. The device may determine that network data of the healthy redundant network devices satisfies an upper redundancy threshold, and may calculate a variance for the network data of the set of network devices. The device may analyze the network data of the set of network devices based on the variance satisfying a variance threshold, and may identify a first increase or a second increase in traffic for a particular network device. The device may perform actions based on identifying the first increase or the second increase.
A device may receive real time audio data associated with a call between an agent and a customer, and may receive customer data identifying historical interactions with the customer. The device may receive chat data associated with the customer or interactive voice response (IVR) data associated with the customer, and may generate, based on the real time audio data, transcript data identifying a real time transcript of the call with the customer. The device may process the real time audio data, the customer data, the chat data or the IVR data, and the transcript data, with a machine learning model, to determine a customer intent and one or more actions to perform based on the customer intent; and may perform the one or more actions.
A first network device may receive, from an application server, a first request for a dedicated bearer session for an application with a quality of service (QoS) and for a notification of an idle session for the application. The first network device may generate, based on the first request, a second request to be notified regarding expiration of an idle session timer for the application, and may provide the second request to a second network device. The first network device may receive, from the second network device and based on the second request, a notification of expiration of the idle session timer for the application, and may provide, to the application server, the notification of expiration of the idle session timer for the application.
One or more computing devices, systems, and/or methods for managing security associated with applications are provided. In an example, service allocation information associated with a plurality of private network sites of a private network associated with an entity may be received. Based upon the service allocation information, service profiles associated with a first user equipment (UE) may be determined. The service profiles include a first service profile of wireless communication services accessible to the first UE via a first private network site of the plurality of private network sites and a second service profile of wireless communication services accessible to the first UE via a second private network site of the plurality of private network sites. The first service profile, associated with the first UE, is transmitted to the first private network site. The second service profile, associated with the first UE, is transmitted to the second private network site.
Systems and methods described herein provide intelligent scheduling in a radio access network (RAN). A RAN device receives traffic characterization model parameters and also receives data for a communication session with a User Equipment (UE) device. The RAN device identifies traffic characteristics for the communication session based on the traffic characterization model parameters. The traffic characteristics include a predicted level of periodicity for a future time interval. The RAN device selects a scheduling discipline for the communication session based on the projected level of periodicity for the future time interval, and implements the selected scheduling discipline for the communication session.
A user device may receive a traffic category and/or slice usage policy, and may provide a request for an application. The user device may receive the application with a traffic category and/or slice usage requirement, and may install the application on the user device. The user device may subscribe to traffic categories and/or slices, and may receive approval to utilize the traffic categories and/or slices. The user device may provide, by the application and to an operating system, a connection request for a particular traffic category and/or slice, and may determine whether the application is approved for the particular traffic category and/or slice. The device may provide, to a modem, the connection request and traffic descriptors for the particular traffic category and/or slice based on the application being approved, and may establish a protocol data unit session, for the application, utilizing the particular traffic category and/or slice.
A system may select a set of containers to implement a requested service; identify parameters of the set of containers, and maintain information including parameters associated with a plurality of nodes of the virtualized environment. The parameters for one or more of the plurality of nodes may include information associating the one or more nodes with respective elements of the network. The system may compare the parameters of the one or more containers to the parameters of the one or more nodes, and select a set of nodes of the plurality of nodes on which to deploy the selected set of containers. The selecting may include selecting, for each container of the set of containers, a respective node of the set of nodes on which to deploy the each container. The system may deploy the selected set of containers to the set of nodes to implement the requested service.
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 video summary device may determine, based on video data, a plurality of events that occurred during a period of time. The video summary device may determine first measures of relevance of a plurality of portions of the period of time. A first measure of relevance of a portion of time may be determined based on one or more events of the plurality of events. The video summary device may determine second measures of relevance of a plurality of ranges of time between different portions of time of the plurality of portions of the period of time. The second measures of relevance is determined based on the first measures of relevance determined for the different portions of time. The video summary device may determine particular frames of the video data, as a video summary of the video data, based on the second measures of relevance.
A device may receive an identification request or a radio resource control request, and may process the identification request or the radio resource control request, with a machine learning model, to determine whether the identification request or the radio resource control request is secure. The device may permit the identification request or the radio resource control request based on the machine learning model determining that the identification request or the radio resource control request is secure, or may deny the identification request or the radio resource control request based on the machine learning model determining that the identification request or the radio resource control request is unsecure.
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 device may receive network data identifying reference signal data for a radio access network (RAN), control signal data for the RAN, and network key performance indicators (KPIs) associated with the RAN, and may receive energy consumption data identifying energy consumption by the RAN. The device may process the network data and the energy consumption data, with one or more machine learning models, to identify actions that reduce energy consumption at a radio unit (RU), a distributed unit (DU), or a control unit (CU) of the RAN and that control and minimize a control signal and a reference signal at the RAN. The device may cause the actions to be implemented by the RU, the DU, the CU, or the RAN to save energy at the RAN.
A device may receive network data identifying key performance indicators associated with a base station and user equipment (UEs), UE data identifying UE data records and UE locations, and geographic data identifying a geographic area and features of the geographic area. The device may correlate the network data, the UE data, and the geographic data to generate correlated data. The device may process, the correlated data, with a plurality of machine learning models, to generate a corresponding plurality of results, and may evaluate the plurality of results, with prediction models, to generate a set of results. The device may compare classification cost function weighted predictions and the set of results to generate comparisons, and may select a machine learning model, for the geographic area and from the plurality of machine learning models, based on the comparisons. The device may implement the machine learning model for the geographic area.
Systems and methods described herein provide extended reality (XR)-aware scheduling in a radio access network (RAN). A RAN device receives downlink (DL) metadata for downlink packets in an extended reality (XR) session and uplink (UL) metadata for uplink packets in the XR session. The RAN device selects a scheduling discipline for the XR session, based on the DL metadata and the UL metadata, and implements the selected scheduling discipline for the XR session.
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 maintain information associating a set of RUs with a particular DU. The system may determine that a particular RU of the set of RUs should be disassociated from the particular DU, such as based on load metrics associated with the particular DU exceeding one or more thresholds. The system may disassociate the particular RU from the particular DU by modifying the information associating the set of RUs with the particular DU. The modified may indicate that the particular RU is no longer associated with the particular DU. The system may accordingly facilitate communications between the modified set of RUs, not including the particular RU, and the particular DU.
A source network device may receive a multicast data stream to be provided to a plurality of network devices of a network, and may generate, for the multicast data stream, a segment routing header that identifies the plurality of network devices to be provided the multicast data stream. The source network device may cause the multicast data stream to be serially provided to the plurality of network devices identified in the segment routing header without requiring the plurality of network devices to store and reconstruct the segment routing header and without requiring the plurality of network devices to maintain state.
An illustrative latency management system receives a sequence of captured frames in a video stream of an operating environment associated with a robotic device. The latency management system monitors a communication latency of a communication channel between the robotic device and a robotic device operator. The latency management system predicts one or more estimated future frames associated with the video stream based on the sequence of captured frames and the communication latency. The latency management system provides the one or more estimated future frames to the robotic device operator. Corresponding methods and systems are also disclosed.
A system described herein may maintain first information associating Uniform Resource Locator (“URLs”) with respective Internet Protocol (“IP”) addresses of one or more edge computing devices. The system may maintain second information associating User Equipment (“UE”) identifiers with one or more locations. The system may receive a request, from a UE, including an identifier of the UE and a URL, may identify a location of the particular UE based on the identifier of the particular UE, and may compare the URL to the URLs included in the first information. The system may select a particular edge computing device based on the location of the particular UE, and may output, in response to the request, a particular IP address of the selected edge computing device.
Systems and methods described herein provide life cycle management for physical network function (PNFs) via an orchestrator using PNF proxies. A network device in a service provider network receives a software package for a virtual network function (VNF) serving as PNF proxy for a type of PNF. The software package includes a proxy descriptor and external virtual links (VLs) for the type of PNF. The network device associates, an instance of the PNF proxy with an installed PNF, creates the instance of the PNF proxy as a VNF in a cloud platform, and provides a record of the created instance of the PNF proxy to an active inventory system.
Systems and methods described herein provide location-based routing for edge networks. A network device receives routing rules associating geohash tiles, for a region of interest, with one or more virtual Road Side Units (vRSUs). The network device receives a message from a vehicle client in the region of interest. The message includes a message header that identifies geospatial coordinates of a vehicle. The network device calculates a geohash based on the geospatial coordinates in the message header and associates, based on the routing rules, the geohash with one of the vRSU. The network device routes the message to the one of the vRSUs based on the associating.
H04W 40/20 - Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
H04W 4/02 - Services making use of location information
H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
59.
IDENTIFICATION OF ANOMALOUS TELECOMMUNICATION SERVICE USAGE
One or more computing devices, systems, and/or methods for identifying anomalous behavior of users are provided. In an example, users of a telecommunication service provider may be segmented into a plurality of user segments based upon telecommunication service metrics associated with the users. A machine learning model may be trained using telecommunication service information associated with users of the first user segment to generate a trained machine learning model. Using the trained machine learning model, a forecast of telecommunication service usage associated with a first user segment of the plurality of user segments. A telecommunication service usage metric, associated with a user belonging to the first user segment, may be compared with a range indicated by the forecast. The user may be flagged as having anomalous behavior based upon a determination that one or more telecommunication usage metrics, associated with the user, are outside one or more ranges indicated by the forecast.
One or more computing devices, systems, and/or methods for constructing and utilizing a search structure for identifying tasks to assign to entities are provided. The search structure is constructed to partition tasks and represent time constraints for performing the tasks and distance constraints corresponding to locations of the tasks. A search of the search structure is performed to identify a set of nearest tasks with respect to a target task. Tasks within the set of nearest tasks may be assigned to an entity to perform.
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 associated with a cloud computing environment may receive a selection of a modified template with a cloning agent, and may generate a modified template clone, with the cloning agent, based on the selection of the modified template. The device may analyze, via the cloning agent, virtual hardware of the cloud computing environment to generate fingerprints, and may identify, via the cloning agent, the cloud computing environment based on the fingerprints. The device may determine, via the cloning agent, one or more application programming interfaces based on identification of the cloud computing environment, and may utilize, via the cloning agent, the one or more application programming interfaces to obtain configuration data. The device may configure, via the cloning agent, a virtual machine of the modified template clone, based on the configuration data and to generate a configured virtual machine, and may enable the configured virtual machine to be utilized.
A protective structure, to protect an antenna from damage, is provided. The protective structure includes a body. The body defines one or more prong-receiving apertures in a first surface of the body, wherein through each aperture of the one or more prong-receiving apertures, the body is configured to receive a prong of one or more prongs of the antenna. The body defines a radio frequency (RF) connection aperture extending from the first surface of the body to a second surface of the body, wherein the body is configured to receive a cable through the RF connection aperture to couple a cable connector of the cable to an RF connector of the antenna.
A method may include receiving, by at least one network device and from a user device, a registration message including a service identifier and at least one of a network slice identifier or a network slice token. The method may also include determining, based on information included in the registration message, whether the user device is authorized to use a network slice associated with the service identifier. The method may further include setting up a data session to be serviced by the network slice, in response to determining that the user device is authorized to use the network slice.
A method, a device, and a non-transitory storage medium are described in which a security service of end device profiles is provided. The service may include obtaining a profile for a card of an end device from a third party device in which the profile includes first and second executables. For example, the first and second executables may each include a subscriber identification module. The first executable may initialize and subsequently perform a switching procedure that enables the second executable to replace the use of the first executable. The first executable may also generate a key that can be used to provision the second executable on the end device.
The present teaching relates to method, system, medium, and implementations for job pricing. When receiving information about a new task of a job type, a mixture model representing historic job completion data and including multiple cluster-based models is used to predict job pricing. The multiple cluster-based models characterize corresponding multiple clusters identified from historic job completion data and are used to generate multiple predictions of duration to complete the new task. The predictions generated based on the cluster-based models are integrated based on the mixture model to generate an overall job pricing estimate for the new task.
A routing optimization system may determine a plurality of routes based on one or more changes to an initial route. Each change generates a respective route of the plurality of routes. The routing optimization system may store route information for each route. The route information, for each route, includes cost information identifying a cost associated with the route. The route information is stored in a respective entry of a data structure. The routing optimization system may determine that a particular route, associated with a lowest cost out of costs associated with the plurality of routes, is to be selected from the plurality of routes. The routing optimization system may determine whether an entry, of a plurality of entries associated with the plurality of routes, is empty. The routing optimization system may select the particular route based on the plurality of entries after determining that the entry is not empty.
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.
In some implementations, a first device may obtain first channel state information (CSI) of a second device. The first device may determine that a variance of the first CSI exceeds a variance threshold. The first device may determine that a motion event has occurred based on determining that the variance, of the first CSI, exceeds the variance threshold. The first device may determine a location of the motion event based on the first CSI and second CSI of a third device. The first device may adjust an operation of one or more devices associated with the location based on determining the motion event.
H04W 64/00 - Locating users or terminals for network management purposes, e.g. mobility management
H04B 7/06 - Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
A cable identification system is provided. The cable identification system may include a laser pulse generator configured to emit laser pulses into the first optical-fiber cable segment. The cable identification system may include a polarization disturbance device configured to induce a change in polarization of a second optical-fiber cable segment via changing a position of the second optical-fiber cable segment. The cable identification system may include a polarization detection device configured to determine measures of polarization based upon backscattered light received from the first optical-fiber cable segment when the second optical-fiber cable segment has different positions. The polarization detection device may be configured to determine whether the first optical-fiber cable segment is connected to the second optical-fiber cable segment based upon the measures of polarization.
A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an integrated network slice encryption service. The integrated network slice encryption service may manage and provision encryption and/or decryption services associated with a third party and relative to a network slice and end device application associated with a service provider and application provider. The integrated network slice encryption service may provision end devices, core network devices, and application layer devices of an external network.
Systems and methods described herein provide dynamic orchestration of deep packet inspection (DPI) probes in a transport network. A network device receives an event report for a critical severity event in a network and stores the event report with other event reports to form an event data set. The network device correlates the event data set with a monitoring condition and selects a workflow for a DPI probe deployment that corresponds to the monitoring condition. The network device sends, to a service orchestrator device, a call to deploy a DPI probe in the network based on the workflow.
H04L 43/028 - Capturing of monitoring data by filtering
H04L 41/0604 - Management of faults, events, alarms or notifications using filtering, e.g. reduction of information by using priority, element types, position or time
73.
SYSTEMS AND METHODS FOR NETWORK DESIGN AND CONFIGURATION BASED ON USER-LEVEL USAGE MODELING
A system described herein may identify sets of seed parameters that are each associated with a respective User Equipment (“UE”) of a group of UEs. The system may generate, based on the seed parameters for each UE, a respective set of UE usage metrics for each UE, and may generate a set of aggregate UE usage metrics based on the generated sets of UE usage metrics. The system may compare the aggregate UE usage metrics to a measure of network capacity; determine an amount of time that a measure of UE usage exceeds the measure of network capacity; determine that the amount of time, that the measure of UE usage exceeds the measure of network capacity, exceeds a threshold amount of time; and modify network configuration parameters based on determining that the amount of time, that UE usage exceeds the network capacity, exceeds the threshold amount of time.
The present teaching relates to method, system, medium, and implementations for increasing content insertion opportunities. A prediction input is received that characterizes utilization of content stream utilization channels (CSUCs). Schedule parameters are automatically predicted for the prediction input using prediction models obtained via machine learning based on grouped historic data related to CSUCs, where grouping is based on an operational mode in which the prediction models operate. Using the predicted schedule parameters, insertion opportunity may be identified with respect to CSUCs and insertion schedules are generated specifying insertions of content streams into identified CSUCs at respective insertion times.
A device establishes a first encrypted tunnel with a first active security gateway at a first data center to enable the device to communicate, via the first encrypted tunnel, with a first access and mobility management function (AMF) at the first data center. The device forwards, via the first encrypted tunnel and the first active security gateway, a first User Equipment device (UE) message to the first AMF. The device determines an occurrence of a failure or overload condition at the first active security gateway, and establishes, based on the determined occurrence of the failure or overload condition, a second encrypted tunnel with a standby security gateway at a second data center to enable the device to communicate, via the second encrypted tunnel, with the first AMF. The device forwards, via the second encrypted tunnel and the standby security gateway, at least one second UE message to the first AMF.
Techniques for identifying and tracking objects in digital content are disclosed. In one embodiment, a method is disclosed comprising obtaining a frame of digital content, the frame comprising pixel data, detecting an object using the pixel data, determining a set of attributes for the detected object, the set of attributes comprising position, object segment and affine attributes, determining a similarity measurement for the detected object and a second object using the set of attributes corresponding to the detected object and the second object's set of attributes, and using the similarity measurement to make a similarity determination whether or not the detected object and the second object are a same object.
A system described herein may maintain one or more smart contracts on a distributed ledger. The system may receive a request, associated with a User Equipment (“UE”), for access to a particular network (e.g., a private network), identify one or more attributes of the UE, and select a particular smart contract based on the attributes of the UE. The system may execute the selected particular smart contract, which may include performing operations, indicated by the particular smart contract, using the one or more attributes of the UE as inputs. Executing the particular smart contract may further include identifying outputs that result from performing the particular set of operations using the attributes of the UE as inputs, such as a network access policy for the UE. The system may output a response to the request, indicating the network access policy determined based on executing the particular smart contract.
A system described herein may provide a technique for the augmentation of one or more models based on target distributions of values associated with the models. Such models may be associated with a wireless network, and may associate a set of states to a set of values and/or to a set of actions. Such states may include particular sets of configuration parameters associated with the wireless network, the values may include Key Performance Indicators (“KPIs”) associated with a randomness state model or other metrics associated with the wireless network, and the actions may include one or more actions to perform with respect to the wireless network when given a particular state and/or set of KPIs.
Techniques for packet flow description (PDF) management are disclosed. In one embodiment, a method is disclosed comprising receiving, by a network exposure function (NEF), a packet flow description (PFD) management request via an application function (AF), the PFD management request, the PFD management request comprising application identification information identifying an application, using, by the NEF, the application identification information to identify a set of unified data repositories (UDRs) affected by the PFD management request, and instructing, by the NEF, at least one UDR of the set of UDRs to make a PFD change to the identified application's application data in accordance with the PFD management request.
A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an handover management service. The handover management service may include receiving a measurement report from an end device. The measurement report may include an SINR value in combination with RSRP and/or RSRQ values. The measurement report may include values from a source cell, a candidate target cell, or both source and candidate target cells. The handover management service may determine whether to perform a handover, based on the measurement report in which a handover criteria does not include an RSRQ value. The handover management service may also determine whether to perform a handover based on handover criteria that includes a variable value for a threshold value. The variable value, among values, may be selected and correlated to a value included in the measurement report.
One or more computing devices, systems, and/or methods for dynamic remote configuration of a reconfigurable intelligent surfaces component are provided. A controller is hosted remote to a reconfigurable intelligent surfaces network, such as within a cloud computing environment. The controller receives characteristics (e.g., signal degradation information from a base station and/or user equipment) of signals transmitted through the reconfigurable intelligent surfaces network between devices. The controller evaluates the characteristics to generate tuning parameter values to apply to cells of the reconfigurable intelligent surfaces component of the reconfigurable intelligent surfaces network. The controller transmits a control signal over a communication channel to the reconfigurable intelligent surfaces component for modifying operation of the cells using the tuning parameter values.
H04B 17/336 - Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
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
83.
SYSTEMS AND METHODS FOR DYNAMICALLY DETERMINING ABNORMAL PERIODIC SIGNALS IN A NETWORK
A device may calculate a PRB seasonal strength based on PRB data from base stations, and may scale and normalize the PRB data based on the PRB seasonal strength. The device may calculate and combine FFTs for the normalized and scaled PRB data, may calculate a z-score for the combined FFT, and may calculate FFT IQRs for frequencies of the combined FFT. The device may filter the FFT IQRs based on the z-score, may process the FFT IQRs, with a clustering model, to identify clusters of periodic interference patterns, and may aggregate the clusters. The device may identify peak data in the PRB data, and may process the peak data, with a model, to determine a parameter for clustering. The device may process the PRB data, with the clustering model, to identify final clusters of periodic interference patterns, and may perform actions based on the final clusters.
An illustrative virtual meeting management system associates a user device of a participant of a virtual meeting with a physical location from which the participant and one or more additional participants participate in the virtual meeting. The virtual meeting management system determines that the participant provides a user input via the user device during the virtual meeting. The user input indicates that the participant will speak. Based on the user input and the associating, the virtual meeting management system provides an active speaker indicator for presentation in a virtual meeting interface provided to one or more participants of the virtual meeting. The active speaker indicator specifies a participant identifier representative of the participant and a place identifier representative of the physical location from which the participant participates in the virtual meeting. Corresponding methods and systems are also disclosed.
G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G10L 25/78 - Detection of presence or absence of voice signals
G06K 7/14 - Methods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
G06Q 10/1093 - Calendar-based scheduling for persons or groups
G10L 25/57 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for processing of video signals
85.
SYSTEMS AND METHODS FOR NEW-RADIO-AWARE LTE SCHEDULING
New Radio (NR)-aware LTE scheduling is provided. An access station for a radio access network includes a first scheduling function. The first scheduling function identifies a User Equipment (UE) device that has a first active wireless connection and a second active wireless connection to the radio access network. The first scheduling function determines that expanded coverage is needed for an uplink transmission for the second active wireless connection and obtains uplink scheduling information for the second active wireless connection. The first scheduling function adjusts uplink scheduling for the first active wireless connection such that power sharing is prioritized for uplink time intervals of the second active wireless connection over overlapping uplink time intervals of the first active wireless connection.
A first device may include a pod and a processor. The processor may be configured to: receive a request, from a second device, to transfer a content item to the second device; and determine whether the content item can be transferred from the pod to the second device using a content caching container (CCC). When the processor determines that the content item cannot be transferred from the pod, the processor may be further configured to: send a reply, to the second device, indicating that the content item cannot be transferred from the pod to the second device; and enable caching the content item at the pod. When the processor determines that the content item can be transferred from the pod, the processor may be further configured to transfer the content item via a first CCC pm the pod to the second device.
A device described herein may maintain information associating sets of traffic attributes with respective sets of Quality of Service (“QoS”) parameters, and may receive a request to establish a traffic flow. The request may indicate one or more traffic attributes associated with the requested traffic flow. The device may compare the one or more traffic attributes, included in the request, to the information associating the sets of traffic attributes with the respective sets of QoS parameters, identify a particular set of QoS parameters with which the traffic flow is associated, establish a communication session with a network based on the particular set of QoS parameters, communicate traffic, associated with the traffic flow, with the network via the established communication session. The device may include an application programming interface (“API”) via which an application associated with the traffic flow may specify the traffic attributes in the request.
A system described herein may provide a technique for a policy and/or rules element of a core network, such as a Policy Control Function (“PCF”), to identify a first network slice with which a communication session between a User Equipment (“UE”) and a core network, such as a protocol data unit (“PDU”) session, is associated. The policy and/or rules element may identify a second network slice with which the communication session should be associated, such as based on traffic monitoring, network congestion monitoring, or other criteria, and may output an indication, indicating a second network slice, to a session control element, such as an Session Management Function (“SMF”). The session control element may cause the communication session to be moved from the first network slice to the second network slice based on receiving the indication.
Techniques for generating emotionally-aware digital content are disclosed. In one embodiment, a method is disclosed comprising obtaining audio input, obtaining a textual representation of the audio input; using the textual representation of the audio input to identify an emotion corresponding to the audio input; generating an emotionally-aware facial representation in accordance with the textual representation and the identified emotion; using the emotionally-aware facial representation to generate one or more images comprising at least one facial expression corresponding to the identified emotion; and providing digital content comprising the one or more images.
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
90.
SYSTEMS AND METHODS FOR SERVICE BASED NETWORK RESOURCE ALLOCATION
A novel method for service based network resource allocation is disclosed. In one embodiment, a method is disclosed comprising receiving from a UE at a base station over a Primary Cell (PCell), a request message to perform an action with respect to a Secondary Cell (SCell), the request message including service information. The method then determines a specific action to be performed with respect to the SCell based on the service information. Finally, the method performs the specific action with respect to the SCell at the base station and transmits a connection reconfiguration message to the UE to direct the UE to perform the specific action based on a service to be supported.
A method, a device, and a non-transitory storage medium are described in which a frequency and network slice selection service is provided. The service may configure network slices at a cell level which may include carrier frequencies or bands that may be ranked in terms of preference for a network slice. The service may provide frequency/band-network slice information to end devices via system information block and radio resource control messages. End devices may perform a cell search procedure and use other criteria to select and establish a network slice connection via a frequency/band of a network slice and in accordance with the service.
A system described herein may receive a request to bridge an asset from a first distributed ledger to a second distributed ledger. The system may record, to the first distributed ledger, information indicating that the asset is frozen. The first distributed ledger may maintain information indicating that the frozen asset remains associated with an owner of the asset. The system may record, to the second distributed ledger, a bridged version of the asset, and may link the asset to the bridged asset. The system may identify a transfer of the bridged version of the asset to a second owner, and record, to the first distributed ledger and based on the linking of the asset to the bridged asset, information disassociating the first owner from the asset.
A method, a network device, and a non-transitory computer-readable storage medium are described in relation to a hybrid network slicing service. The hybrid network slicing service may enable the initial configuration of a network slice according to network slice requests that may include customized and user-specified network performance criteria. The hybrid network slicing service may enable network slice requests to specify selection of network resources and use/availability based on entity-based criteria including end device and/or application specific associations. The hybrid network slicing service may optimize network slice configurations and generate network slice templates.
H04L 41/122 - Discovery or management of network topologies of virtualised topologies e.g. software-defined networks [SDN] or network function virtualisation [NFV]
H04L 43/55 - Testing of service level quality, e.g. simulating service usage
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
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
In some implementations, a device may provide, as input to a model, input data indicating current value(s) corresponding to key performance indicator(s) (KPI) associated with current calls to a contact center, enterprise event(s), and stability event(s) associated with the contact center. The device may receive, as an output from the machine learning model, predicted value(s) corresponding to the KPI(s). The device may determine predicted rate(s) of change of the KPI(s) based on a comparison of the current and predicted value(s). The device may assign severity level(s) corresponding to the KPI(s) based on the predicted rate(s) of change. The device may determine a service level impact based on the predicted value(s). The device may determine a performance degradation score based on the severity level(s) and the service level impact. The device may determine and automatically apply a strategy based on the performance degradation score.
One or more computing devices, systems, and/or methods for system parameter identification and network component configuration are provided. A state comprising a system parameter combination, a traffic model, and a channel assignment may be generated. A network traffic scenario is executed through a virtualized testbed using the state. A reward for the system parameter combination may be generated based upon key performance indicators output by the network traffic scenario. A reward policy and rewards generated for system parameter combinations are used to select a system parameter combination that is used to configure a network component of a communication network.
Systems and methods described herein provide a network-based solution to efficiently transition private network subscribers from a macro network to a private network. A network device in the macro network stores a neighbor list identifying a private network that has overlapping coverage with a cell for the macro network. The network device detects that a user equipment (UE) device connected to the network device has a subscription to the private network and, in response to the detecting, sends, a release message to the UE device. The release message includes a redirection to the private network.
A device comprises a processor that may be configured to obtain a Public Land Mobile Network (PLMN) identifier (ID) from a signal broadcast by a network and determine whether the obtained PLMN ID is an original preferred PLMN ID of the device. When the processor determines that the obtained PLMN ID is the original preferred PLMN ID, the processor may attempt to attach to the network over a wireless link. When the attempt to attach to the network fails, the processor may rewrite, in a list of PLMN IDs, a non-preferred PLMN ID as a new preferred PLMN ID.
Systems and methods described herein provide a hierarchical Account Balance Management Function (ABMF) architecture for a Converged Charging System. Multiple distributed account management devices and a centralized account management device are provided. The centralized account management device includes one or more processors to allocate, to the multiple distributed account management devices, a pre-authorized consumption limit for a shared consumption plan; receive, from one of the multiple distributed account management devices, a request for an additional consumption limit from the shared consumption plan; allocate, to the one of the multiple distributed account management devices, the additional consumption limit; and update an account balance for the shared consumption plan based on allocating the additional consumption limit.
A system described herein may identify that a first container and a second container of a virtualized environment are instantiated at a particular node that is associated with a particular address (e.g., an Internet Protocol (“IP”) address). The system may identify a logical association between the first and second containers, such as a logical and/or hierarchical association specified by a custom resource definition. The system may generate a first Domain Name System (“DNS”) record (e.g., an “A” record) associating the first container with the address of the particular node, and may a second DNS record (e.g., a “CNAME” record) associating the second container with the first container. The system may provide the first and second DNS records to a DNS server, which may provide the address of the particular node when receiving a DNS request specifying the second container.
A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an radio access management service. The radio access management service may include calculating a predicted total resource allocation value for a prospective access and transmission grant associated with a prospective time window, and a value indicating a number of end devices that can be supported. The service may generate and transmit an access and transmission grant message directed to end devices, based on the value, in which the message may include both random access response information and contention resolution information.
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