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127
results.
Updated on
22/10/2026 [10:53:00]
Absstract of: WO2025128327A1
A method for auditing neural network performance may include receiving a first set of input data for the neural network and training the neural network. The method may further include updating an audit model of the neural network based on the training of the neural network and inputting, to the neural network the first set of input data. A first set of output data associated with the first set of input data may be received from the neural network, and an indication of one or more characteristics of the neural network that impacted the first set of output data may be generated based on the updated audit model of the neural network, the first set of input data, and the first set of output data.
Absstract of: GB2645395A
Methods, systems, and computer readable media are provided for reduced-dose angiography using machine learning (e.g., deep learning) with a concordance metric indicating a degree of similarity between an estimated segmentation of angiographic images generated by a machine learning model and segmented benchmark angiographic images provided as inputs to the machine learning model. Briefly, techniques described herein may use a neural network trained to conserve/preserve angiographic image quality while reducing the angiographic dose of potentially harmful chemical contrast and/or x-ray radiation by employing a concordance metric.
Absstract of: US20260320365A1
0000 Computer-implemented methods and systems for training a neural network to interpret neural dynamics, the method comprising obtaining functional connectivity between brain regions and artificial neurons simulated by the neural network, capturing neural activities between biological neurons, processing the captured neural activities to obtain training data for the neural network, and constructing the neural network using the training data to reproduce neural dynamics between the artificial neurons.
Absstract of: US20260321018A1
Computer systems and methods for maritime surveillance and identification of unknown vessels are provided. The system includes at least one data storage device storing: a database of known optical nadir satellite images each containing a known candidate vessel having a known unique vessel identifier; and an unknown optical nadir satellite image containing an unknown vessel. The system also includes at least one processor configured to execute a nadir-nadir image matcher configured to: compare, via a neural network, the unknown nadir image to a plurality of known nadir images in the database, the comparing including determining a similarity score indicating a similarity level between the unknown nadir image and a respective known nadir image; output, via the neural network, a ranked list of the known nadir images based on the determined similarity scores; and assign a known unique vessel identifier to the unknown nadir image based on the ranked list.
Absstract of: US20260319474A1
The present invention relates to a multi-task real-time inference scheduling system and real-time inference scheduling method of a machine tool, wherein a central control unit is connected to each of one or more individual control units through a network, receives a use context of each machine tool through each individual control unit, generates a multi-task learning model through a neural network, infers multiple tasks required to be performed by the individual control unit of each machine tool through machine learning by using real-time use contexts collected during operation of the machine tool by a use scenario, and schedules the multiple tasks of the machine tool through machine learning.
Absstract of: US20260320412A1
0000 According to some embodiments, a method includes determining test predictions by performing an inference process using a checkpointed learning model and test vectors. The checkpointed learning model includes hyperparameters and weights. The method further includes determining an attribution map by performing one or more attribution processes using the test predictions and the test vectors. The method further includes determining a score for each particular hyperparameter by analyzing the attribution map using an association classifier. The method further includes determining, based on the analysis by the association classifier, whether each particular hyperparameter should be frozen or tuned again. The method further includes updating the hyperparameters and weights of the neural network when it is determined that at least one particular hyperparameter should be tuned again.
Absstract of: US20260320806A1
0000 This application provides a device wake-up method, a system, and a chip. The method may include: processing a captured first image, determining a motion region image in the first image, and determining, by using a level-1 neural network, whether a target object exists in the motion region image; and when it is determined that the target object exists in the motion region image, performing reconfirmation, by using a level-2 neural network cascaded with the level-1 neural network, on a result output by the level-1 neural network. It can be learned that the target object is determined by using the level-1 neural network and the level-2 neural network, so that a false alarm frequency can be reduced, and system power consumption can be reduced.
Absstract of: US20260320741A1
Apparatuses, systems, and techniques to process image frames. In at least one embodiment, an application programming interface (API) is performed to indicate support to use one or more neural networks to perform frame interpolation.
Absstract of: AU2026238109A1
A method for surgical planning includes obtaining, by a computing system, a first point cloud representing a first portion of a bone or a first cloud representing at least a portion of a bone, applying, by the computing system, a point cloud neural network to generate a second point cloud based on the first point cloud, the second point cloud comprising at least one of points representing at least a second portion of the bone or points representing an axis along the bone, and generating, by the computing system, surgical planning information based on the second point cloud. ep e p
Absstract of: US20260320267A1
A micromechanics simulation framework has the steps of collecting one or more images of at least one composite material, providing, to a first neural network, an input including the one or more images, segmenting one or more microstructures in the one or more images, quantifying one or more physical descriptors of one or more segmented microstructures, passing an output including the one or more segmented microstructures and one or more quantified physical descriptors to one of: a statistical-based module using descriptor-driven stochastic construction routines with periodic boundary conditions, or a second deep generative neural network, and generating one or more stochastic representative volume element (SRVE) ensembles based on the one or more segmented microstructures and one or more quantified physical descriptors. A micromechanics simulation system is also disclosed for computing the homogenized elastic properties and the time-dependent response of composites in high-temperature environments.
Absstract of: US20260321031A1
Panoptic scene completion may be achieved using a neural network comprising a sparse generative encoder-decoder network that performs scene-level occupancy prediction in a fixed-size voxel grid. To segment individual object instances, an instance decoder may segment individual object instances from the scene-level occupancy predictions to predict instance masks in the (occupied) voxel space in a class-agnostic fashion. For each predicted instance (e.g., transformer query), the instance decoder may regress a semantic embedding or token representing the semantic features (e.g., a CLIP token) for a corresponding object instance, enabling zero-shot prompting at test and/or inference time. As such, the neural network may produce semantic features that may be used for open-vocabulary querying directly from 3D measurements, and a representation of the resulting labeled masks may be provided to one or more downstream components such as a control policy of an ego-machine to make decisions on navigation, grasping, and/or manipulation actions.
Absstract of: US20260320155A1
A time-series data processing method includes: constructing a time-series curve diagram based on first time-series data and second time-series data; performing feature extraction on the time-series curve diagram to obtain a spatial relationship feature, performing association relationship recognition on the time-series curve diagram by using a neural network model to obtain a text, and performing feature extraction on the text to obtain an association relationship feature; merging the spatial relationship feature with the association relationship feature to obtain a merged feature, and performing feature retrieval in a time-series knowledge base based on the merged feature to obtain a candidate feature; and determining a similarity between the merged feature and the candidate feature, and determining a predicted label for the first time-series data based on the similarity and a time-series change label for the candidate feature.
Absstract of: US20260317942A1
To provide a plating apparatus that makes it possible to estimate a current density at a plating-target surface with high precision even if a change in plating conditions occurs. The plating apparatus includes: a sensor configured to measure a potential of a plating solution in a vicinity of a peripheral portion of a substrate held by a substrate holder; and a state estimator configured to calculate, using the measured potential as input, a state estimate representing a current density at the peripheral portion of the substrate, based on an observation model and a state transition model. The state estimator is configured to execute computation based on the observation model by using a first trained neural network model. When a state variable representing the current density at the peripheral portion of the substrate is input, the first trained neural network model is configured to output an estimate representing the potential of the plating solution in the vicinity of the peripheral portion.
Absstract of: WO2026214199A1
A braking assistance method and system based on road surface recognition. The braking assistance method comprises: S1, acquiring a road surface image, and processing the road surface image by means of a trained lightweight convolutional neural network, to obtain a road surface type, wherein the lightweight convolutional neural network introduces an inverted residual structure combined with a hybrid attention mechanism and introduces depthwise separable convolution; S2, determining road condition information by combining the road surface type and a preset database; S3, acquiring a rate of change in displacement of a brake pedal, and generating a braking intention of a driver on the basis of the rate of change in displacement of the brake pedal; and S4, dynamically adjusting the distribution of braking force on the basis of the road condition information and the braking intention of the driver, generating a braking control instruction, and sending the braking control instruction to an actuator. By means of the method, when a vehicle travels under adverse road conditions, the vehicle speed can be controlled to maintain vehicle stability, thereby solving the problem of poor dynamic stability in existing vehicle braking assistance.
Absstract of: WO2026214606A1
Disclosed in the present application are a lightweight image recognition method and system for a character-wheel camera‑based water meter. The embodiments of the present application implement the collection of a water-meter image, digit recognition and data transmission on the basis of an ESP32 development board, which is connected to a server. In the embodiments of the present application, a neural network is used to perform water‑meter digit recognition, and model parameters are optimized by means of forward propagation and backward propagation algorithms. In the present invention, a development board directly uses a character‑wheel recognition network to perform digit recognition on the current water‑meter image, so as to obtain a water‑meter reading result. It is unnecessary to upload the water‑meter image to a server for reading recognition, thereby helping improve the recognition efficiency of water‑meter readings. Moreover, simply by means of installing the development board on a character-wheel water meter, automatic reading can be implemented, thereby significantly improving the intelligence level of the conventional character-wheel water meter. The system in the embodiments of the present application has the characteristics of a lightweight design and a high efficiency, and is suitable for application scenarios of the automatic reading of smart water meters.
Absstract of: US20260322523A1
An artificial intelligence-based identification method for a wireless composite jamming signal forms a novel neural network structure ACSNet through the integration of an asymmetric convolutional block (ACB), aiming to address composite jamming signal identification in wireless communication systems. The model of the artificial intelligence-based identification method enhances the detection capability of neural networks for subtle signal feature variations based on artificial intelligence, thereby improving the identification accuracy for intricate jamming patterns. The artificial intelligence-based identification method can effectively address the challenge of jamming signal identification under a low jamming-to-noise ratio (JNR). Additionally, ACSNet demonstrates significant resilience to variations in power ratios (PRs) of composite jamming signals, highlighting its adaptability to real-world environments with imbalanced jamming signal power distributions.
Absstract of: WO2026213897A1
Disclosed in the present invention are a face forgery detection method and system based on a graph convolutional neural network. The method comprises: acquiring a facial video to be subjected to forgery detection; and inputting said facial video into a trained detection model, so as to obtain a determination result indicating whether face forgery is present in said video. The detection model comprises: an encoder, which is used for generating features of each image frame on the basis of said facial video; a spatiotemporal-graph construction module, which is used for constructing, on the basis of the features of each image frame, a spatiotemporal graph in which spatial graphs and temporal graphs are integrated; a spatiotemporal-graph convolution module, which is used for performing feature learning on the basis of the spatiotemporal graph, so as to obtain class features; and a classifier, which is used for predicting, on the basis of the class features, a forgery detection result for the facial video. In the present invention, a spatiotemporal graph in which spatial graphs and temporal graphs are integrated is constructed while satisfying the spatiotemporal consistency requirements of a facial video, thereby implementing efficient, accurate and highly-generalizable face forgery detection.
Absstract of: US20260322951A1
0000 Systems, methods and apparatus of vehicle security operations during parking. For example, a vehicle includes: a proximity sensor configured to detect presence of an object approaching the vehicle when the vehicle is in a parking state; at least one camera configured to monitor surroundings of the vehicle when the vehicle is in the parking state; and an artificial neural network configured to extract identification information of the object from images generated by the camera and determine a security classification of the presence of the object. The identification information is stored in the vehicle and/or transmitted to a server or a mobile device, in response to the security classification being in a predefined category.
Absstract of: US20260320983A1
Apparatuses, systems, and techniques are presented to introduce noise or variation into images. In at least one embodiment, one or more neural networks are used to determine noise to be added to one or more images of one or more objects based, at least in part, upon one or more features of the one or more objects.
Absstract of: US20260320385A1
0000 A model training apparatus is configured to obtain a plurality of intermediate models determined by performing iteration training on a neural network model, each intermediate model including values of model parameters such as model weights and statistics of the neural network model. The model training apparatus may perform weighted summation on the values of each model parameter in different intermediate models to obtain the value of the corresponding model parameter in an aggregated model. The model training apparatus may adopt the aggregated model as the training results of the neural network model while the iteration is completed, which is beneficial to improving the generalization performance of the trained neural network model.
Absstract of: US20260319769A1
A compiler of a computing device is described that identifies a sequence of neural network models frequently invoked by an application of the computing device, compiles the models in that sequence, and loads a static random access memory (SRAM) of a hardware accelerator with the compiled models only when the same compiled models-from another, but same, sequence that was previously invoked-are not already present in the SRAM. This prevents unnecessary reloading of compiled models into the SRAM, thereby increasing runtime speed and conserving computational energy.
Absstract of: US20260320995A1
Methods and apparatuses are provided for processing of picture data or picture feature data using a neural network with two or more layers. The present disclosure may be applied in the field of artificial intelligence (AI)-based video or picture compression technologies, and in particular, to the field of neural network-based video compression technologies. According to one or more embodiments, two kinds of data are combined during the processing by the neural network. The two kinds of data are obtained from different stages of processing by the network. Some of the advantages may include greater scalability and a more flexible design of the neural network architecture which may further lead to better encoding/decoding performance.
Absstract of: US20260315867A1
0000 An electronic apparatus is disclosed. The electronic apparatus includes: a memory including at least one instruction, a processor coupled with the memory and configured to control the electronic apparatus, and the processor is configured, by executing the at least one instruction, to: obtain a moving image, identify a person and pose data of the person from a plurality of frames in the moving image, obtain exercise pattern information corresponding to the plurality of frames using pose data of the identified person, recognize an exercise motion of the identified person by inputting at least one frame of the moving image into at least one neural network model, and obtain exercise feature information corresponding to the recognized exercise motion, identify, based on the exercise feature information and the exercise pattern information, a first frame interval and a second frame interval different from the first frame interval from among the plurality of frames in the moving image, and provide information on an exercise motion corresponding to the second frame interval by comparing the first frame interval with the second frame interval.
Absstract of: US20260320156A1
0000 Apparatuses, systems, and techniques to train neural networks to perform classification. In at least one embodiment, one or more neural networks are trained to perform classification based, at least in part, on grouping one or more sets of neural network training data according to behaviors of one or more objects within one or more images represented by the training data.
Nº publicación: GB2705387A 14/10/2026
Applicant:
NVIDIA CORP [US]
Nvidia Corporation
Absstract of: GB2705387A
Apparatuses, systems, and techniques are described to predict priority of a connection request (e.g., a next connection request). For example, processing circuitry uses one or more neural networks to indicate priority of one or more next connection requests within one or more radio access network (RAN) networks. A base station of a UE device can use, perform, or otherwise execute the one or more neural networks to generate a prediction of what a next connection request will be. Fig. 1
BOPI
Electronic site
