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68
results.
Updated on
17/04/2026 [07:19:00]
Absstract of: AU2024367742A1
A method of operation of an unmanned aerial vehicle (UAV) service includes acquiring aerial images of a scene at an area of interest (AOI), wherein the aerial images are acquired with a UAV of the UAV service during a flight mission of the UAV that passes over the AOI; uploading a mission log of the flight mission to a backend data system of the UAV service, the mission log including image data that includes, or is derived from, at least a portion of the aerial images; and training a neural radiance field (NeRF) model with one or more of the aerial images, wherein the NeRF model comprises a neural network, which after the training, encodes a volumetric representation of the scene capable of generating novel views of the scene different than any of the aerial images used to train the NeRF model.
Absstract of: EP4725391A1
0001 A method and device for diagnosing renal disease are disclosed. A control method of a diagnostic device according to one embodiment comprises: obtaining a retinal image of a subject; and obtaining renal disease diagnostic information regarding the subject using a machine learning model based on the retinal image, wherein the machine learning model includes a first model and a second model, wherein the first model is a neural network model, and wherein the second model is a regression-based machine learning model.
Absstract of: EP4726608A2
0001 A computing system (10) including a processor (14) configured to receive a mesh (30) of a three-dimensional geometry (38). The processor is further configured to receive a source antenna location (40) and a destination antenna location (42) on the mesh. The processor is further configured to compute a ray path (60) as an estimated shortest path between the source antenna location and the destination antenna location. The ray path includes a geodesic path (62) over the mesh and a free space path (64) outside the mesh. The ray path is computed at least in part by computing the geodesic path at least in part by performing inferencing at a trained neural network (52). Computing the ray path further includes computing the free space path at least in part by performing raytracing from a launch point (66) located at an endpoint of the geodesic path. The processor is further configured to output the ray path to an additional computing process (70).
Absstract of: EP4726579A1
0001 A graph neural network-based node classification method and system, and a related device are provided. In the method, during training of a model, categories of a plurality of neighboring node samples in a graph data sample are first predicted to obtain category distribution of the plurality of neighboring node samples, and sampling is then performed on the plurality of neighboring node samples based on the category distribution and a sampling parameter input by a user to obtain a plurality of sampled nodes, so that category distribution of the plurality of sampled nodes is similar to or consistent with the category distribution of the plurality of neighboring node samples. In this way, features of the sampled nodes obtained through sampling can cover features of all neighboring nodes, thereby reducing calculation complexity. In addition, the category distribution of the sampled nodes is closer to true distribution of the neighboring nodes, thereby improving performance of a graph neural network.
Absstract of: WO2024254102A1
Various embodiments discussed herein are directed to improving hardware consumption and computing performance by performing neural network operations on dense tensors using sparse value information from original tensors. Such dense tensors are condensed representations of other original tensors that include zeros or other sparse values. In order to perform these operations, particular embodiments provide an indication, via a binary map, of a position of where the sparse values and non-sparse values are in the original tensors. Particular embodiments additionally or alternatively determine shape data of the original tensors so that these operations are accurate.
Absstract of: EP4726674A1
Embodiments of the present disclosure provide a method for training a fingerprint anti-counterfeiting neural network, a method for fingerprint anti-counterfeiting, an apparatus for training a fingerprint anti-counterfeiting neural network, and an apparatus for fingerprint anti-counterfeiting, comprising: obtaining a plurality of groups of training data, each group of the training data comprising: first raw domain data, second raw domain data, and third raw domain data; and training an initial classification network using the plurality of groups of training data to obtain a target classification network, wherein the initial classification network comprises a fusion subnetwork and a classification subnetwork, and for each group of the training data, the fusion subnetwork is configured to generate a first fingerprint matching pair based on a feature description matrix of the first raw domain data and the second raw domain data, and generate a second fingerprint matching pair based on a feature description matrix of the first raw domain data and the third raw domain data, and the classification subnetwork is configured to perform fingerprint classification and recognition based on the first fingerprint matching pair and the second fingerprint matching pair. The present disclosure solves the problem of low recognition accuracy rate of real and prosthetic fingerprints in related art, and achieves the effects of improving the recognition accuracy rate of real and prosthetic fingerpr
Absstract of: EP4726672A1
0001 A method for identifying objects in a set of images which are visually similar to reference objects comprises: receiving an indication of regions where reference objects are depicted; forming a reference pool with data records representing the reference objects; generating a text embedding (TE) and a visual embedding (VE) for each of the reference objects, by applying neural networks to image data from the regions where the reference objects are depicted; detecting a plurality of candidate objects in the set of images, and generating a TE and a VE for each; approving a detected candidate object for addition to the reference pool only if the reference pool contains a reference object fulfilling a first similarity criterion (C<1>), which depends both on TE similarity and VE similarity, in relation to the detected candidate object; extending the reference pool by adding all approved candidate objects; and identifying a detected candidate object as visually similar to the reference objects only if the extended reference pool contains a reference object fulfilling a second similarity criterion (C<2>), which depends on VE similarity, in relation to the detected candidate object.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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).
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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
Absstract of: 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.
Nº publicación: WO2026069497A1 02/04/2026
Applicant:
NTT INC [JP]
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Absstract of: 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.
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