88 results about "Client station" patented technology

A management node local to a customer site of a private communications network stores a model. The model is a compact version of a visual language model remote from the customer site. A first screen shot of a dashboard of telemetry data measured from the private communications network is accessed. A prompt is formulated comprising the first screen shot and information to adapt the model to the private communications network via few shot learning. The prompt is submitted to the model. An output is received from the model comprising textual information about anomalies or trends depicted in the first screen shot. The output is checked against data from a statistical model of the telemetry data, the statistical model being independent of the model. In response to the check being successful, an action is triggered to manage the private communications network according to the output.

A router with network virtualization and configuration may be configured for a client site including a plurality of electronic devices. A configuration may be downloaded by the router, which then performs network configuration steps automatically including by identifying the plurality of devices at the client site and provisioning them according the LAN configuration. A user interface (UI) for configuring a network may include a router administrator dashboard for creating a detailed network configuration by assigning a plurality of ports of the router to the plurality of electronic devices in the selected network configuration, and sending the detailed network configuration to the router for use by the router in creating a network with the plurality of electronic devices at the client site.

A client driven approach for L4S support and optimization in WLANs, e.g. WiFi WLANs, is described. A client station (STA) generates and sends a SCS w / L4S Request frame including a L4S descriptor element to an access point (AP). The L4S descriptor element includes a L4S classifier mask, ECN threshold and a set of client device specified desired QoS level information to be applied with regard to the transmission of identified L4S traffic. Upon acceptance of the request, the AP selects a queue for future L4S traffic and creates an L4S filter. The created L4S filter includes classification criteria, ECN congestion threshold, and QoS information. The AP implements the created L4S filter, e.g., identifying and directing identified L4S traffic to the designated L4S queue, performing ECN marking with regard to the congestion at the L4S queue, and transmitting L4S data in accordance with the received STA specified desired QoS levels.

Apparatuses and methods concerning providing a data-communications virtual assistant are disclosed. As an example, one apparatus includes a data-communications server. The data-communications server is configured to process user-data-communications between a client station and another station participating in data-communications via the data-communications services where the client station is associated with one client entity. The server is also configured to identify a context for each respective user-data-communication between the client station and the participating station, where the context data corresponds to at least one communications-specific characteristic associated with the user-data-communications, and to retrieve structured and unstructured data relating to previous data-communications provided by the data-communications server. The server is also configured to provide the integrated contact center to particular end users based on the identified context, and including data generated from the retrieved structured and unstructured data.

A computing platform may be configured to: (i) based on a set of data points defining a feature space, construct a graph structure associated with the set of data points; (ii) generate a multi-scale representation that represents the feature space, wherein each feature of a plurality of features from the feature space is represented in a respective plurality of scales with respect to the graph structure; (iii) regularize the multi-scale representation; (iv) based on the regularized multi-scale representation, identify a plurality of clusters associated with the set of data points; and (v) transmit, to a client station, data regarding the plurality of clusters and thereby cause an indication of the plurality of clusters to be presented at a user interface of the client station.

A network directed approach for L4S support and optimization in WLANs, e.g. WiFi WLANs, is described. A client station (STA) generates and sends a MSCS w / L4S Request frame including a L4S descriptor element to an access point (AP). The L4S descriptor element includes an ECN threshold and optionally QoS information for L4S traffic. Upon acceptance of the request, the AP selects a queue for L4S traffic and creates an L4S filter. The L4S filter includes classification criteria, ECN congestion threshold, and QoS information. As part of creating the L4S filter, the AP relies on stream monitoring at the AP to obtain criteria for identifying L4S traffic. The AP implements the created L4S filter, identifying L4S traffic, directing the identified L4S traffic to the L4S queue, performing ECN congestion marking for L4S queue, and transmitting L4S data using AP monitoring based computed QoS metrics for L4S traffic.

A computing platform is configured to: (i) generate a logical flowchart for a construction project task, the logical flowchart based on at least one incongruous rule and at least one incongruous data set; (ii) generate a text-based graphical user interface (GUI) prompt based on the logical flowchart; (iii) cause the text-based GUI prompt to be presented via one or more of the first client station, the second client station, or combinations thereof; (iv) receive, from a client station, an indication of user input to the text-based GUI prompt; and (v) cause the text-based GUI prompt to present output information, via the first client station, based on the logical flowchart and the user input.

A computer-implemented system for fully automating the training, auto-tuning, and deployment of machine learning models at customer sites, especially models for robotic grasping tasks. The system automatically: (1) detects / predicts performance degradation of one or more machine learning models; (2) triggers a new model training / fine-tuning job in response to such detection / prediction; and (3) deploys the new model upon training completion, without stopping or pausing the production line.

Embodiments of a device and method are disclosed. In an embodiment, a method of communications involves at a head end (HE), receiving an authentication message from a wireless access point (AP) deployed at a customer site, at the HE, performing a load balance operation in response to the authentication message to select a first authenticator from authenticators of the HE, at the HE, performing an authentication operation using the first authenticator based on the authentication message to generate an authentication request, and from the HE, transmitting the authentication request to an authentication server.

A network system is provided between at least a first client site and a second client site. A client site network component is implemented at least at the first client site, the client site network component aggregating one or more diverse network connections so as to configure an aggregated connection that has increased throughput. At least one network server component may be configured to connect to the client site network component using the aggregated connection. A cloud network controller may be configured to manage the data traffic and a virtual edge providing transparent lower-link encryption for the aggregated connection between the client site network component and the network server component. The network server component includes a virtual control plane interface configured to establish a unicast path between the network server component and each of a plurality of remote network server components.

An example computing platform is configured to (i) receive a two-dimensional (2D) image file comprising a construction drawing, (ii) generate, via semantic segmentation, a first set of polygons corresponding to respective areas of the 2D image file, (iii) generate, via instance segmentation, a second set of polygons corresponding to respective areas of the 2D image file, (iv) generate, via unsupervised image processing, a third set of polygons corresponding to respective areas of the 2D image file, (v) based on (a) overlap between polygons in the first, second, and third sets of polygons and (b) respective confidence scores for each of the overlapping polygons, determine a set of merged polygons corresponding to respective areas of the 2D image file, and (vi) cause a client station to display a visual representation of the 2D image file where each merged polygon is overlaid as a respective selectable region of the 2D image file.

A wireless network interface of a first client station negotiates with an access point a first component channel of an operating channel via which the first client station is to receive wakeup frames from the access point. A wakeup radio of the first client station receives a wakeup packet from the access point. The wakeup packet spans the operating channel, which comprises at least four component channels, and one or more of the component channels within the operating channel are punctured so that the access point does not transmit the wakeup packet in the one or more component channels that are punctured. The wakeup packet includes a first wakeup frame for the first client station in the first component channel and one or more respective second wakeup frames for one or more second client stations in one or more respective second component channels.

A database (POH) provides semi-structured data about a first deployed system and other deployed systems, wherein each deployed system is an industrial automation system at a different location, and wherein each deployed system consists of modules. A data ingestion component (DIC) uses an ontology (ONT) to integrate the semi-structured data into a single knowledge graph (KG), by representing a structural development of each deployed system over time, by creating an event node (EN) for each structure-changing event in the deployed system and attaching a subgraph to the event node representing a context of the structure-changing event. A generative deep-learning component (DL) has been trained with the knowledge graph to perform knowledge graph completion. It receives as input a new subgraph with a new event node and generates a recommended action for a structural change of one of the deployed systems, by completing the new subgraph with at least one action node (AN) identifying the recommended action and at least one module node (MN) identifying a first module that is added, replaced, or removed by the recommended action in the first deployed system. This modelling strategy for the knowledge-graph breaks down the continuous flow of changes at a customer site into a discrete set of subgraphs for which well-established similarity metrics exist, allowing for an automated recommending of changes to the deployed system. This automation of a previously time-consuming, costintensive, and largely manual process yields a number of benefits.

Disclosed methods and systems for managing on-premises information handling resources establish, by an in-cloud web portal, an agentless connection to an on-premises information handling resource and perform, via the agentless connection, a configuration management operation to modify a configuration of the on-premises handling resource. Establishing the agentless connection and performing the configuration management operation may include one or more appropriate API calls. Disclosed teachings enable a web-accessible, public cloud portal to perform active configuration management of on-premises resources scattered across any number of distinct and potentially isolated customer sites without deploying and maintaining management agent software within the customer's private cloud. The on-premises resources may include a multi-node cluster provided by a node or nodes of one or more HCI appliances.

Devices and methods for augmenting wireless environment reporting are provided. The information provided by 802.11k reports may have limited functionality, as they do not offer extended details that could be beneficial to the network infrastructure. Augmented wireless environment reporting allows network devices and client devices to request for Wireless Environment Reports (WERs) based on several augmented parameters. A device, such as a network device or a client device, generates a WER request including at least one of a condition, a Peer-to-Peer (P2P) technology specification, a non-IEEE 802.11 technology specification, or an element restriction and transmits the WER request to another device such as a client station. The other devices generates the WER with augmented information as requested and transmits to the device. The information included in the WER report may be processed and utilized to perform network analytics, network management tasks, and other functions useful to the network infrastructure.

A computing platform is configured to: (i) receive, from a first client station, a data file; (ii) obtain metadata associated with the data file; (iii) determine, based on at least a first set of metadata from the obtained metadata associated with the data file, a naming structure to use for the data file; (iv) generate, based on the determined naming structure and at least a second set of metadata from the obtained metadata, a proposed name for the data file; and (v) transmit, to a second client station, a communication identifying the proposed name and thereby cause an indication of the proposed name for the data file to be presented at a user interface of the second client station.

Techniques and apparatus for managing at least one client stations (STAs) within a wireless network are described. An example technique includes receiving a request from, or anticipating a request by, the at least on STA for an expansion of bandwidth resources. A determination is made regarding constraints and the feasibility of the expansion of bandwidth resources based on target parameters. In response to the determination, expanding bandwidth resources of at least one access point of the one or more access points within the wireless network.

The embodiment of the invention relates to a multi-system AI repository controller. Systems and methods are provided for creating a hierarchical repository of machine learning (ML) models in which ML models are stored as open container images (OCIs). The OCI includes a layer or portion of a complete ML model so that the model can be stored separately and as a smaller file. The ML model may be pre-packed for automated downloading and integration at the customer site. In some examples, the OCI may identify / store the model in a directory structure defining the model and its configuration files. The OCI may include a combination of layers and configuration files that allow the AI platform to optimize storage and simplify the process of downloading or updating a given user namespace rather than downloading a single large file for the model.

Certain aspects of the disclosure are directed to context aggregation in a data communications network. According to a specific example, user-data communications between a client-specific endpoint device and the other participating endpoint device during a first time period can be retrieved from a plurality of interconnected data communications systems. The client station can be configured and arranged to interface with a data communications server providing data communications services on a subscription basis. Context information for each respective user-data communication between the client station and the participating station during the first time period can be aggregated, such that subsequent user-data communications received from the participating station and intended for the client entity, can be routed based on the aggregated context information.

Techniques and apparatus for managing at least one client stations (STAs) within a wireless network are described. An example technique includes receiving a request from, or anticipating a request by, the at least on STA for an expansion of bandwidth resources. A determination is made regarding constraints and the feasibility of the expansion of bandwidth resources based on target parameters. In response to the determination, expanding bandwidth resources of at least one access point of the one or more access points within the wireless network.

A method and system for dynamic network keep-alive is described. In one embodiment, the method includes establishing a link with an access point, determining a client station time interval, and iteratively adjusting a duration of the client station time interval to approximate the access point time interval until the client station time interval is close to the access point time interval. Thus, the keep-alive process is performed in an effective manner.

A management node local to a customer site of a private communications network stores a model. The model is a compact version of a visual language model remote from the customer site. A first screen shot of a dashboard of telemetry data measured from the private communications network is accessed. A prompt is formulated comprising the first screen shot and information to adapt the model to the private communications network via few shot learning. The prompt is submitted to the model. An output is received from the model comprising textual information about anomalies or trends depicted in the first screen shot. The output is checked against data from a statistical model of the telemetry data, the statistical model being independent of the model. In response to the check being successful, an action is triggered to manage the private communications network according to the output.

The present invention provides a vehicle path planning method and device, wherein the method includes: a data acquisition step, an initial calculation step, a repartitioning step, an iterative calculation step, an update processing step, and an iterative stop judgment step, wherein in the initial calculation step, the customer sites are clustered and partitioned according to the geographical locations of the multiple customer sites in the input data to obtain multiple partitions, and the local optimized route and local route cost of each partition are calculated; in the repartitioning step, the partition adjustment network obtained by pre-training is used to merge and repartition two partitions in the multiple partitions to obtain two new partitions. The vehicle path planning method and device provided by the present invention can improve the performance and efficiency of large-scale VRP solutions by dividing multiple customer sites into multiple partitions and performing VRP solutions in each partition respectively, thereby avoiding direct solutions to a large number of customer sites.

A router with network virtualization and configuration may be configured for a client site including a plurality of electronic devices. A configuration may be downloaded by the router, which then performs network configuration steps automatically including by identifying the plurality of devices at the client site and provisioning them according the LAN configuration. A user interface (UI) for configuring a network may include a router administrator dashboard for creating a detailed network configuration by assigning a plurality of ports of the router to the plurality of electronic devices in the selected network configuration, and sending the detailed network configuration to the router for use by the router in creating a network with the plurality of electronic devices at the client site.

A first client station receives a multi-user physical layer (PHY) data unit from an access point. The multi-user PHY data unit includes i) a PHY preamble, and ii) an MU-MIMO transmission. The PHY preamble includes respective subfields that indicate respective numbers of spatial streams allocated to respective client stations. The respective subfields have been encoded according to an encoding that supports allocating up to sixteen spatial streams to up to eight intended receivers. The respective subfields are arranged in the PHY preamble according to an order. The first client station determines a position of a particular subfield corresponding to the first client station within the order, and uses the position of the particular subfield to decodes the particular subfield to determine a number of spatial streams allocated to the first client station. The first client station processes the determined number of spatial streams in the MU-MIMO transmission.

A system can receive a certificate, wherein the certificate comprises a vendor public key for a customer site, wherein the vendor public key is based on a vendor secret key and a first number that comprises a primitive root modulo of a second prime number. The system can validate the certificate, wherein the validation comprises validating that the certificate was generated by vendor equipment associated with a vendor entity that corresponds to the vendor public key, and wherein the validating comprises validating that the identifier of an application of the customer site in the certificate matches the local copy of the identifier of the application of the customer equipment associated with the customer site. The system can, based on the validating, enable access to the application of the customer equipment associated with the customer site by the vendor equipment associated with the vendor entity.

A system and method are provided for improving success rate of beacon frame reception from an access point (AP) in wireless networks by determining an optimal wake-up advance amount to compensate for the AP's clock inaccuracy, optimizing client device's wake-up schedules based on the optimal wake-up advance amount for reliable data transmission and minimizing power usage. The client device determines the optimal wake-up advance amount by analyzing beacon frames, identifying differences between the AP's clock and the client station's own clock. Optimizing the wake-up schedules of the client device from a power-saving mode allows the client station to prepare in advance of beacon frame broadcasts, ensuring their consistent reception.

An example egress network device includes at least one computer processor and a memory. The memory includes instructions that cause the at least one computer processor to receive messages from each of a plurality of ingress network devices. Each message specifies a multicast source as an anycast address that belongs to two or more sources, a multicast group, and a customer site identifier that uniquely identifies a customer network device via which the anycast address is reachable. The instructions cause the at least one computer processor to select, based on the customer site identifiers, one of the plurality of ingress network devices to which to send a multicast join message of a plurality of multicast join messages for the multicast source and multicast group. The instructions cause the at least one computer processor to send the multicast join message to the selected one of the plurality of ingress network devices.

Methods and systems for dynamic network keep-alive are described. In one embodiment, the method comprises establishing a link with an access point, determining a client station time interval and iteratively adjusting the duration of the client station time interval to approximate the access point time interval until the client station time interval is a close approximation of the access point time interval, thereby performing the keep-alive process at an efficient manner.

An access point (AP) device transmits a first downlink transmission via a first WLAN communication channel having a first radio frequency (RF) bandwidth, and transmits a second downlink transmission via a second WLAN communication channel having a second RF bandwidth. The second downlink transmission including a trigger frame configured to prompt one or more client stations to transmit one or more respective acknowledgments of one or more packets transmitted by the AP device via the first WLAN communication channel. The AP device receives one or more uplink transmissions from the one or more respective client stations. The one or more uplink transmissions from the one or more respective client stations are received via the second WLAN communication channel. The one or more uplink transmissions via the second WLAN communication channel overlap in time with the first downlink transmission via the first WLAN communication channel.