Venner Shipley LLP

A computer-implemented method and system for access control are provided. A request to access a secure resource is received, and an access control data representation is created encoding attributes of the request. A plurality of policy classes is assembled and selected based on the access control data representation, and the plurality of policy classes may include a principal-related, a resource-related, an access-method-related, and a system-related policy class. Each policy class is evaluated by applying a logical OR across its member policies to generate a plurality of evaluations, and a logical AND is applied across the plurality of evaluations to determine whether all policy classes are satisfied. Access to the secure resource is allowed when at least one policy from each implicated policy class is satisfied and denied when any one policy class is not satisfied. Short-circuit evaluation terminates processing upon determining any policy class is unsatisfied.

Systems and methods for performing inter-proximal reduction (IPR) comprise removing a portion of a first tooth and scanning the modified portion with an intra-oral scanning system to obtain a partial scan. The system compares the modified portion to the original tooth to determine removed matter and provides real-time guidance about additional removal needed. The procedure can conclude by finalizing modifications according to the IPR procedure specifications. The system includes an intra-oral scanner for tooth imaging, a display screen, and a controller with stored instructions. The controller receives treatment plans with IPR specifications, processes scan imaging of modified teeth, compares scans to specifications for conformance, and generates guidance data about needed additional tooth matter removal. Guidance can be displayed through heat maps, color coding, or indicators projected directly onto teeth or via heads-up displays to guide precise tooth matter removal while preventing over-reduction.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of capturing data from an event; after conclusion of the event, isolating the captured data; analyzing the isolated data to determine one or more categories for the isolated data; extracting one or more facts from the data based on relevance to the event and accuracy; concatenating the one or more facts into knowledge units; grouping related knowledge units based on the one or more determined categories; and creating and training the targeted generative model using the knowledge units, wherein the targeted generative model is trained and operates within a secure, isolated environment, restricted to event-specific data, and while leveraging generative model infrastructure. Other embodiments are disclosed herein.

A hybrid circuit interrupter includes a primary inductor, a mechanical switching element coupled to the primary inductor, and a solid-state switching circuit coupled to the primary inductor and connected in parallel with the mechanical switching element, wherein the solid-state switching circuit is structured and configured to commutate current away from the mechanical switching element during a fault condition, and wherein the solid-state switching circuit includes a secondary inductor, a first number of power electronics elements in series with the secondary inductor, and a second number of power electronics elements structured and configured to regulate current through the mechanical switching element.

A sensor including: a substrate having a cavity formed in a first main surface of the substrate; a first mesa that is formed in the cavity; a second mesa that is formed in the cavity a first magnet that is formed in the cavity, the first magnet being arranged to at least partially fill the cavity, the first magnet being arranged to surround the first mesa and the second mesa.

A method for implementing an information security checking operation on a project stored in a cloud server, the method includes: running a checking operation and a scanning operation on a plurality of software programs of the project; in the case where one software program has a weakness that cannot be immediately fixed, transmitting an exception application request to a user end device (2) instructing the user end device (2) to submit an application for an exception; in response to receipt of the application of exception, transmitting the application of exception to a verification end device (3); and, in response to receipt of an allowance of the application from the verification end device (3), putting the project online and starts tracking the software program that has a weakness.

A method for network access control, communication system, and related device are disclosed. The method for network access control includes: generating a corresponding data request primitive data structure and sending the data request primitive data structure to a MAC layer in response to an association request command from a terminal device using a network layer, wherein the data request primitive data structure comprises information about a short address and an extended address of the terminal device; generating a corresponding pending data structure and storing the pending data structure in a preset pending queue in response to the data request primitive data structure using the MAC layer, wherein the pending data structure comprises a corresponding association response command frame and the information about the short address and the extended address of the terminal device; and acquiring a corresponding association response command from the pending data structure found in the pending queue and sending the association response command to the terminal device in response to a polling command from the terminal device, based on the information about the short address and the extended address carried in the polling command, using the MAC layer. The technical solution of the present disclosure can improve the network access success rate and speed of a terminal device.

The systems, methods and apparatus described herein are directed towards at least the use of a two-stage augmentation process for vocal audio samples to generate a training dataset for a machine-learning embedding model, the training of machine-learning embedding models on such training data, and the use of such a trained machine-learning embedding models to generate embedding of audio snippets for downstream tasks. The two-stage augmentation process has a first augmentation stage, in which strong augmentations are applied at an audio sample level, and a second augmentation stage, in which weak augmentations are applied at an audio snippet level, where an audio snippet is a section of audio extracted from an audio sample after the first augmentations are applied.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for updating a local operating state of a first user device. In one aspect, a method includes identifying a context of the first user device; identifying a second user device within a proximity of the first user device; determining relevant aspects of a local operating state of the first user device that are relevant to the context; performing an automated data exchange session between a first agent executing on the first user device and a second agent executing on the second user device to determine one or more actions that each define an update to the local operating state; and performing the one or more actions by the first user device to update the local operating state of the first user device.

This specification relates to processing data using machine learning models. A computer implemented method is provided comprising: receiving an input query; and generating an output. The generating comprises, for a plurality of iterations: generating, using a machine-learning model, a data element for the iteration based on the input query, wherein the data element comprises: (i) an output data element indicative of an output of the machine-learning model; and/or (ii) an interrupt data element indicative of one or more interrupt options. For one or more of the plurality of iterations, the method comprises: in response to determining that the data element for the iteration and/or a previous iteration is an output data element, causing the output indicated by the output data element to be output; and in response to determining that the generative data element for the iteration and/or a previous iteration is an interrupt data element, causing the one or more interrupt options indicated by the interrupt data to be exposed via an interface. The method further comprises, during generation of the output: receiving, via the interface, data indicative of a selection of at least one interrupt option of the one or more interrupt options; and adapting the generation of the output based on the at least one selected interrupt option.