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484
resultados
Última actualización
14/04/2026 [07:09:00]
Resumen de: WO2026076120A1
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing videos using neural networks. In particular, the neural network has a hybrid architecture that includes both recurrent and self-attention layers.
Resumen de: US20260099896A1
0000 A method of training a neural network configured to obfuscate a facial image and an electronic device for performing the method are provided. The method includes obtaining, based on an input facial image, an output facial image in which the input facial image is obfuscated, extracting, based on the input facial image, a feature of the input facial image for reconstructing identification information included in the input facial image from the output facial image, extracting, based on the output facial image, a feature of the output facial image corresponding to the feature of the input facial image, and training the neural network based on a difference between the feature of the input facial image and the feature of the output facial image.
Resumen de: US20260100030A1
Provided is an electronic apparatus including memory configured to store at least one instruction, and a processor configured to execute the at least one instruction to obtain a first image including an object, input the first image to a first neural network model that is configured to be trained by using a plurality of second images in relation to a plurality of predefined types, obtain first probability information including a first probability of the object corresponding to a first type among the plurality of types and a second probability of the object corresponding to a second type among the plurality of types, obtain second probability information, through a second neural network model, indicating a type of the object included in the first image, by using a plurality of third images corresponding to the first type and a plurality of fourth images corresponding to the second type based on a difference between the first probability and the second probability being less than a first threshold value and based on a first input, and identify the type of the object based on the second probability information.
Resumen de: US20260099963A1
Apparatuses, systems, and techniques are presented to generate or manipulate digital images. In at least one embodiment, a network is trained to generate modified images including user-selected features.
Resumen de: US20260100058A1
Disclosed herein are systems, methods, and devices for detecting traffic lane violations. In one embodiment, a method for detecting a potential traffic violation is disclosed comprising bounding a vehicle detected from one or more video frames of a video in a vehicle bounding box. The vehicle can be detected and bounded using a first convolutional neural network. The method can also comprise bounding, using the one or more processors of the edge device, a plurality of lanes of a roadway detected from the one or more video frames in a plurality of polygons. The plurality of lanes can be detected and bounded using multiple heads of a multi-headed second convolutional neural network. The method can further comprise detecting a potential traffic violation based in part on an overlap of at least part of the vehicle bounding box and at least part of one of the polygons.
Resumen de: US20260100186A1
0000 Disclosed is a sensor-processing system including, in some embodiments, a sensor, one or more sample pre-processing modules, one or more sample-processing modules, one or more neuromorphic integrated circuits (“ICs”), and a microcontroller. The one or more sample pre-processing modules are configured to process raw sensor data for use in the sensor-processing system. The one or more sample-processing modules are configured to process pre-processed sensor data including extracting features from the pre-processed sensor data. Each of the neuromorphic ICs includes at least one neural network configured to arrive at actionable decisions of the neural network from the features extracted from the pre-processed sensor data. The microcontroller includes a CPU along with memory including instructions for operating the sensor-processing system. In some embodiments, the sensor is a pulse-density modulation (“PDM”) microphone, and the sensor-processing system is configured for keyword spotting. Also disclosed are methods of such a keyword spotting sensor-processing system.
Resumen de: US20260100267A1
0000 Deep learning methods and systems for detecting biomarkers within volumetric biomedical imaging dataset using such deep learning methods and systems are provided. Embodiments predict the clinically useful biomarkers in optical coherent tomography images, ultrasound images, magnetic resonance imaging images, and computed tomography images using deep neural networks.
Resumen de: US20260100065A1
0000 The present disclosure provides an image style conversion method. The image style conversion method includes: acquiring a first image, wherein the first image includes text information; performing image processing on the first image by a text matting neural network model to obtain a text mask; performing style conversion on the text mask based on a preset application scenario to obtain a converted text mask; and performing image fusion on the converted text mask and a background image of the preset application scenario to obtain a converted image after image style conversion.
Resumen de: WO2026074166A1
The invention relates to a method for authenticating a product carrying a marking defined by a geometric distribution of a plurality of groups of binary modules, which method consists in capturing an initial image of the marking of the product, selecting minutiae from among its binary modules, and capturing a subsequent image on a candidate product. For each minutia, a measure of the similarity between the two images is computed. These similarity measures are organised in a matrix representing the position and the structure of the minutiae. An artificial neural network analyses this matrix to provide the authenticity class of the product.
Resumen de: WO2026075588A1
A method (200) is disclosed for generating an explainability output for node level predictions generated by a GNN on an input graph. The method comprises, for individual nodes in the input graph, for each incoming neighbour node of the node, creating an ordered group comprising the node and the incoming neighbour node (210), and then combining the ordered groups into a plurality of batches, each batch comprising an ordered group (220). The method further comprises, for individual batches, and for individual nodes in the batch, identifying edges in the GNN computation graph of the node that connect to nodes outside of the batch, and detaching the identified edges in the backward pass direction (230), and using the GNN to generate, in parallel, node level predictions for the nodes in the batch by performing a forward pass through the GNN computation graphs of the nodes in the batch (240). The method further composites using a gradient based explainability method to generate, in parallel, importance scores of incoming neighbour nodes for the nodes in the batch by performing a backward pass through the GNN computation graphs of the nodes in the batch (250). The method further comprises, for individual nodes in the input graph, assembling the generated importance scores of incoming neighbour nodes into an explainability output for the node prediction generated by the GNN (260).
Resumen de: WO2026073521A1
A shale fracture seismic identification method based on a 3D U-Net convolutional neural network combined with ant tracking is disclosed. The method establishes a geological model of shale fractures using single-well data and performs seismic forward modeling to determine the advantageous frequency band for fracture identification. Spectral-peak decomposition is applied to obtain the advantageous frequency-band data volume, which is processed using a 3D U-Net convolutional neural network and ant-tracking computation to generate a 3D U-Net Ant Tracking volume. The results are verified using microseismic data, and along-layer attributes of the 3D U-Net Ant Tracking volume are extracted to determine the regional planar distribution characteristics of shale fractures. The method effectively reduces exploration costs by combining single-well and seismic data, significantly improves seismic resolution through integrated application of seismic forward modeling, spectral-peak decomposition, and advantageous frequency-band data computation, and expands the range of fracture identification by extracting along-layer slices from the data volume.
Resumen de: WO2026075993A1
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a generative neural network using an inference-aware fine-tuning framework to mitigate the difference between how the generative neural network has been trained and how the generative neural network will be used at inference time.
Resumen de: EP4722968A2
Methods and systems that provide data privacy for implementing a neural network-based inference are described. A method includes injecting stochasticity into the data to produce perturbed data, wherein the injected stochasticity satisfies an ε-differential privacy criterion and transmitting the perturbed data to a neural network or to a partition of the neural network for inference.
Resumen de: WO2026070140A1
The present disclosure relates to vehicle control based on a neural network. In particular, the present disclosure relates to determining confidence in the output of a neural network by comparing a firing pattern observed during operation of a vehicle with a reference firing pattern obtained by observing firing of neurons during a training phase.
Resumen de: WO2026065613A1
An electronic nose instrument operable in both scheduled and on-demand modes and a method for online real-time detection and analysis of multi-component odors. A hardware unit of the electronic nose instrument mainly comprises: a gas-sensitive sensor array module (I), a headspace sampling module (II), a pressurization cylinder (III), a computer control and analysis module (IV), a backup power supply (V), and a clean air cylinder (VI). A main housing integrates the first four components. Within a cycle time T=180-600 s, the pressurization cylinder (III) significantly increases a gas-sensitive response by means of short-term pressure multiplication. The gas-sensitive sensor array obtains a 50-dimensional sensing sample for single detection. A large odor dataset X comprises online detection data from the electronic nose instrument, and offline detection data from olfactometry and chromatography etc. The detection data is decomposed into multiple single-concentration sub-tasks. A machine learning cascade model is formed by multiple learning groups consisting of single neurons, and shallow and deep neural networks. The electronic nose instrument can flexibly achieve online real-time identification of odor pollutants and multi-component concentration estimation and prediction.
Resumen de: WO2026064957A1
Disclosed is a feature fusion classification method for multiple types of packaging bag, relating to the technical field of classification of multiple types of packaging bag. On the basis of a random forest concept, the present invention provides a feature fusion classification algorithm for multiple types of packaging bag. By means of three different classification methods: a support vector machine, template matching, and a neural network, denoising processing is performed on images of various packaging bags transmitted from a camera using a median filter, and on the basis of a homomorphic filtering algorithm, enhancement processing is performed on the denoised images. The images are classified by separately using a support vector machine model, a template matching algorithm, and a neural network model, and a majority rule-based voting mechanism is implemented for prediction results of the three methods, to obtain a final result. The voting mechanism-based feature fusion classification algorithm for multiple types of packaging bag of the present invention provides high accuracy, reduces error generation, and obtains more accurate results.
Resumen de: WO2026069497A1
This information processing device performs predetermined processing using a neural network model and comprises an inference unit that performs the predetermined processing using the model. The model includes a positional encoding unit that calculates relative positional information of each token in a token string using a wavelet function, and an attention mechanism that calculates a latent representation of the token string using the positional information.
Resumen de: WO2026070418A1
In this information processing method using a neural network for a structure represented as a set of nodes arranged in space, a computer executes processing including: receiving input of a state of each node; calculating, on the basis of states between the nodes, a frame representing a coordinate axis for each node; and extracting, using the frame, information having predetermined symmetry of the structure from each node.
Resumen de: US20260094399A1
0000 Apparatuses, systems, and techniques to generate bounding box information. In at least one embodiment, for example, bounding box information is generated based, at least in part, on a plurality of candidate bounding box information.
Resumen de: WO2026071683A1
According to an embodiment of the present disclosure, disclosed is a method for predicting the price of cryptocurrency on the basis of an artificial neural network. The method may comprise the steps of: acquiring monitoring reference information from a user terminal; generating a chart image according to the monitoring reference information; generating a pattern prediction result corresponding to the chart image on the basis of an artificial neural network-based pattern prediction model; and transmitting, to the user terminal, notification information generated on the basis of the pattern prediction result.
Resumen de: WO2026069149A1
It is described a method for processing an image using a vision graph neural network, said vision graph neural network comprising a window-based grapher module (7) including a first fully connected layer with batch normalization (9), a windows partitioning module (10), a dynamic graph convolution module (11), a windows reverse module (12), a second fully connected layer with batch normalization (13) and a skip connection (15), wherein said window-based grapher module (7) is configured to: - process a feature vector (X) of said image (2) through said first fully connected layer with batch normalization (9) to obtain a normalized feature vector; - partition said normalized feature vector into a plurality of non-overlapping windows using said windows partitioning module (10); - for each window, construct a graph where nodes represent patches of said image (2) within the respective window and edges represent relationships between said nodes, and apply a graph convolutional operation to each graph to update node features within each window using said dynamic graph convolution module (11); - reshape the updated node features from each window back into the format of said normalized feature vector using said windows reverse module (12); - process the reshaped feature vector through said second fully connected layer with batch normalization (13); combine said feature vector (X) directly with an output of said second fully connected layer with batch normalization (13) using said skip c
Resumen de: WO2026066156A1
Disclosed in the present invention is a method for constructing a GAN-based defect detection model for pole pieces of a blade battery. The method comprises the following steps: collecting several images of defective target pole pieces; pre-processing the images to obtain pre-processed images, and extracting valid defective regions from the images; acquiring contour information for the valid regions, and accurately classifying the contour information on the basis of characteristic parameters; performing data augmentation on classified data, so as to obtain an augmented dataset; and in the dataset, using defect types and position information as labels to train a neural network, and using a neural network model as a defect detection model for pole pieces of a blade battery. In the present invention, a dataset that has undergone data augmentation is inputted into a network as a training set, such that the problem of a severe shortage of training samples caused by numerous types of battery pole piece defects and low occurrence probabilities of individual samples is solved, and the detection accuracy of a defect detection model for pole pieces of a battery is greatly improved, thereby enabling the rapid and accurate detection of various defects and position information of pole pieces of a blade battery.
Resumen de: US20260094429A1
Techniques related to poly-scale kernel-wise convolutional neural network layers are discussed. A poly-scale kernel-wise convolutional neural network layer is applied to an input volume to generate an output volume and include filters each having a number of filter kernels with the same sample rate and differing dilation rates optionally in a repeating pattern of dilation rate groups within each of filters with the pattern of dilation rate groups offset between the filters the poly-scale kernel-wise convolutional neural network layer.
Resumen de: EP4718327A1
0001 An electronic device for executing a neural network model including a non-linear operation and an operation method thereof are provided. The operation method of the electronic device includes obtaining data to be inferred and obtaining an inference result of the data output from the neural network model as the data is input to the neural network model including a plurality of nodes, wherein, in an inference process, a first weight applied when a value of a first node among the plurality of nodes is transmitted to a second node may be updated based on a value of a first reference node, which is any one of the plurality of nodes.
Nº publicación: EP4718326A2 01/04/2026
Solicitante:
SHANGHAI CAMBRICON INF TECH CO LTD [CN]
Resumen de: EP4718326A2
0001 The technical solution involves a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus, respectively; the storage component is used to store data; the interface apparatus is used to implement data transfer between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card is used to perform an artificial intelligence operation.
BOPI
Sede Electrónica
