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135
resultados
Última actualización
25/07/2026 [07:53:00]
Resultados 50 a 75 de 135
Resumen de: WO2026146839A1
The present invention relates to a method for processing and analyzing data including a hyperspectral image on the basis of artificial general intelligence (AGI) and a foundation model (FM) and, more specifically, to a method for processing and analyzing data including a hyperspectral image on the basis of artificial general intelligence (AGI) and a foundation model (FM), the method comprising: receiving a request for a task to be performed on a hyperspectral image from a user; determining, using an LLM, whether the task needs to be processed by a first task processing module based on deep learning or a second task processing module based on a rule base; and when the hyperspectral image needs to be processed by the first task processing module based on deep learning, inputting feature information extracted by inputting the hyperspectral image to an encoder into a first sub-task module based on an artificial neural network to derive a task result for the task requested by the user to be performed on the hyperspectral image.
Resumen de: US20260195400A1
0000 A data processing method is provided. The method is applied to image processing and includes: obtaining first data collected by an image sensor; and obtaining spectral information based on the first data by using a neural network model, where the neural network model includes an attention module, and the attention module is configured to determine an attention matrix based on input data, and perform an attention operation based on the attention matrix, where the attention matrix is obtained by performing a first fusion operation on correlation information between different channels of the input data and correlation information of the channels. In this application, a degree of correlation between the different channels and a degree of correlation of the channels may be fused, so that the attention matrix can model both correlation and particularity between the different channels, thereby improving accuracy of spectral signal reconstruction.
Resumen de: US20260194611A1
Disclosed herein is a medical system (100, 300). The execution of machine executable instructions (120) causes a computational system (104) to repeatedly: generate (200) the random input vector; receive (202) a generated MRF pulse sequence (128) in response to inputting the random input vector into a GAN generator neural network (122); and append (204) the generated MRF pulse sequence to an MRF pulse sequence database (130). Execution of the machine executable instructions causes the computational system to: input (208) each generated MRF pulse sequence in the MRF pulse sequence database into a trained scoring algorithm (124) to assign the one or more score values to each generated MRF pulse sequence; and receive (210) a selected MRF pulse sequence (136) from the MRF pulse sequence database by applying a predetermined criterion (134) to the one or more score values of each generated MRF pulse sequence in the MRF pulse sequence database.
Resumen de: US20260196210A1
0000 Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating embeddings of spoken utterances. One of the methods includes obtaining audio data representing a spoken utterance; processing the audio data using an encoder neural network to generate an embedding of the spoken utterance; and processing the embedding of the spoken utterance using a prediction neural network to generate a prediction about the spoken utterance, the processing comprising: maintaining respective embeddings for a plurality of preceding spoken utterances; determining one or more embeddings of respective preceding spoken utterances that are relevant to generating the prediction about the spoken utterance; and processing (i) the embedding of the spoken utterance and (ii) the respective embeddings of the one or more determined preceding spoken utterances to generate the prediction about the spoken utterance.
Resumen de: WO2026147908A1
A computational pupillometry system comprises an imaging device configured to capture video frames of a subject's eye and processors executing instructions to perform advanced pupillary assessment. The system employs multi-frame integration techniques, including super-resolution algorithms that utilize sub-pixel shifts between frames, temporal averaging for noise reduction, and parallax-based artifact mitigation to enhance measurement accuracy. Artificial intelligence models, including temporal neural networks, analyze the enhanced pupillary data to determine pupillary parameters and calculate a light-invariant Pupil Reactivity (PuRe) score. The system processes ambient lighting conditions through computational models that analyze video frames before and after controlled stimulation, enabling consistent scoring across varying environmental conditions. Quality assurance mechanisms provide prerecording and post-recording validation with real-time feedback. The system integrates with electronic medical records through standardized healthcare protocols and supports synchronized, multi-device deployment across healthcare networks.
Resumen de: US20260195854A1
0000 An image processing method, an image processing system, an image processing apparatus, an electronic device, and a non-transitory computer-readable storage medium are provided. The image processing method includes: calculating first data based on at least one padding pixel in an input image, a coefficient in one convolution kernel and in one-to-one correspondence with the at least one padding pixel, or an output bias corresponding to the one convolution kernel. The input image is an input image of a deconvolution layer in a neural network model. The image processing method further includes calculating second data based on at least one non-padding pixel in the input image or a coefficient in the one convolution kernel and in one-to-one correspondence with the at least one non-padding pixel. The image processing method also includes calculating a pixel value of one output pixel based on the first data and the second data.
Resumen de: US20260196353A1
0000 Exemplary systems, methods and Computer-accessible medium according to the exemplary embodiments of the present disclosure can provide a Multi-modal Transformer (MMT), a neural network that synergistically utilizes mammography and ultrasound to identify existing cancers and estimate future cancer risk. MMT aggregates multi-modal data through self-attention and modeling temporal tissue changes by comparing current exams to prior imaging. Thus, exemplary method, system and computer-accessible medium can be provided for detecting cancer. with which it possible to receive, with an artificial intelligence (AI) procedure, a plurality of scanning images associated with multiple modalities for at least one portion of a body, train the AI procedure on a multi-modal image dataset based on the plurality of scanning images, and predict, by the trained AI procedure, an existence of the cancer based on the multiple modalities of the plurality of scanning images.
Resumen de: US20260196241A1
Disclosed are an apparatus and a method for determining the state of pulmonary congestion by using artificial intelligence analysis. The apparatus for determining the state of pulmonary congestion by using artificial intelligence analysis, according to an embodiment, may comprise: a data reception unit that receives speech data from a user; a data preprocessing unit that extracts, from the received speech data, partial speech data corresponding to a portion of the speech data by using windowing processing, and converts the extracted partial speech data into a spectrogram; and a pulmonary congestion state determination unit that outputs, by using the spectrogram and a neural network-based pulmonary congestion state determination model, determination data including pulmonary congestion state information about the user.
Resumen de: US20260196034A1
0000 An image processing method performed by a neural processing unit is disclosed. The method includes receiving an input image including at least one object and processing the input image using a first model via the neural processing unit to detect a particular object among the at least one object, the first model being an artificial neural network-based object detector trained to detect the particular object in an image. The method further includes processing the input image using a second model via the neural processing unit to blur the particular object in the input image, the second model being an artificial neural network trained to blur a region corresponding to the particular object. An output image including the blurred particular object is generated.
Resumen de: US20260192825A1
A neural processing unit (NPU) includes a controller including a scheduler, the controller configured to receive from a compiler a machine code of an artificial neural network (ANN) including a fusion ANN, the machine code including data locality information of the fusion ANN, and receive heterogeneous sensor data from a plurality of sensors corresponding to the fusion ANN; at least one processing element configured to perform fusion operations of the fusion ANN including a convolution operation and at least one special function operation; a special function unit (SFU) configured to perform a special function operation of the fusion ANN; and an on-chip memory configured to store operation data of the fusion ANN, wherein the schedular is configured to control the at least one processing element and the on-chip memory such that all operations of the fusion ANN are processed in a predetermined sequence according to the data locality information.
Resumen de: US20260197449A1
Embodiments of the present disclosure provide a solution for visual data processing. A method for visual data processing is proposed. The method comprises: applying, for a conversion between visual data and a bitstream of the visual data with a neural network (NN)-based model, a first filter in the NN-based model on an output of a synthesis transform in the NN-based model, at least one parameter of the first filter being configured based on one or more of the following: a first format for coding the visual data, the first format indicating a relationship between a size of a first component of the coded visual data and a size of a second component of the coded visual data, or a second format of output visual data from the conversion; and performing the conversion based on the applying
Resumen de: WO2026145296A1
The present invention belongs to the technical field of collaboration between artificial intelligence and computer systems. Disclosed are a neural network structure search method and a system. The method comprises: deploying in a memory a network architecture template comprising a reconfigurable convolutional layer, wherein the convolutional layer consists of a plurality of FlexCell units, each FlexCell comprises a standard convolution operator and two depthwise separable convolution operators, and the degree of contribution of each operator is regulated by means of a trainable weight coefficient; using a graphics processing unit to perform three-dimensional hardware-aware encoding on the convolutional layer, so as to generate a structure definition matrix; and a central processing unit and the graphics processing unit collaboratively executing an improved genetic algorithm to perform parallel evaluation, crossover and mutation on candidate network structures in an encoding-based search space, so as to search out optimal network structure parameters. The present invention can automatically generate a network structure with few parameters and high precision, greatly improves search efficiency by means of heterogeneous computing collaboration, and is suitable for tasks such as image classification.
Resumen de: US20260195593A1
0000 A method of performing a reshape operation specified in a reshape layer of a neural network model is described. The reshape operation reshapes an input tensor with an input tensor shape to an output tensor with an output tensor shape. The tensor data that has to be reshaped is directly routed between tile memories of the hardware accelerator in an efficient manner. This advantageously optimizes usage of memory space and allows any number and type of neural network models to be run on the hardware accelerator.
Resumen de: US20260192000A1
Two major treatment strategies employed in fighting non-small cell lung cancer (NSCLC) are tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). The choice of strategy is based on heterogeneous biomarkers expressed by the lung tumor tissue. A major challenge for molecular testing of these biomarkers is the insufficiency of biopsy specimens from patients with advanced NSCLC. Disclosed herein is a method for predicting a response to immune-checkpoint blockade immunotherapy. The method generally involves imaging the subject with positron emission tomography with 2-deoxy-2-fluorine-18 fluoro-D-glucose integrated with computed tomography to produce 18F-FDG PET/CT images of the tumor, analyzing the images using PET, CT, and Kulbek Leibler Divergence statistical (KLD) features or, alternatively using deep leaning such as Neural Networks; generating a radiomic signature from the identified features or Network characteristics; and computing a radiomic score based on the radiomic signature that is predictive of responsiveness to ICIs or TKIs.
Resumen de: US20260195566A1
The present application relates to a noise-resistant communication method and apparatus based on a fully connected neural network. The method includes: encoding, before signal transmission, target information based on encoding sequences to obtain and transmit a target signal, and receiving, upon signal transmission, the target signal, and decoding the target signal via a fully connected neural network to obtain the target information, where the fully connected network takes the target signal as input and the target information as output, and a hidden layer of the fully connected neural network includes a plurality of fully connected layers for decoding. The method decodes a transmitted signal affected by noise interference via the neural network, so that a problem of an increased bit error rate caused by strong noise interference in the transmitted signal is effectively avoided.
Resumen de: EP4773045A2
0001 A system and method of predicting a team's formation on a playing surface are disclosed herein. A computing system retrieves one or more sets of event data for a plurality of events. Each set of event data corresponds to a segment of the event. A deep neural network, such as a mixture density network, learns to predict an optimal permutation of players in each segment of the event based on the one or more sets of event data. The deep neural network learns a distribution of players for each segment based on the corresponding event data and optimal permutation of players. The computing system generates a fully trained prediction model based on the learning. The computing system receives target event data corresponding to a target event. The computing system generates, via the trained prediction model, an expected position of each player based on the target event data.
Resumen de: WO2025045319A1
The invention relates to a method for explaining and/or verifying a behaviour of a neural network trained with a training database, said method having the following steps: - determining sensor information by means of a sensor, so that sensor information is available, - applying the neural network to the sensor information, so that target information is available, - determining similarity information, wherein a comparison is made of the sensor information or part of the sensor information with an information database, so that the similarity information is available, linking the similarity information with the target information, so that a target information tuple is available, on the basis of which the explanation and/or verification of the behaviour of the neural network can be realised.
Resumen de: WO2026137618A1
Disclosed in the present invention are a formation rock and soil parameter testing method based on a controllable neutron source, a ground acquisition computer, a system, and a computer-readable storage medium. The method comprises: acquiring geomechanical and physical property synthetic data and geomechanical and physical property in-situ measured data, and preprocessing same to obtain target rock and soil synthetic data and target rock and soil in-situ measured data; determining a preset dual neural network model, and performing model training on the preset dual neural network model on the basis of the target rock and soil synthetic data and the target rock and soil in-situ measured data to obtain a formation rock and soil parameter prediction model; and acquiring current geomechanical and physical property in-situ measured data, inputting same into the formation rock and soil parameter prediction model, and outputting a formation rock and soil parameter prediction result. In the present invention, by combining a controllable neutron source, cone penetration test, and a data processing method, and introducing a neural network model, effective measurement and accurate analysis of key formation parameters can be implemented, thereby obtaining more accurate formation parameter data.
Resumen de: WO2026138838A1
The present application provides a multi-channel data processing method, a reality display apparatus, a device, a medium, and a product. The method comprises: acquiring predefined line data output by at least two image signal processor channels; performing, in parallel, network inference on the predefined line data output by the at least two image signal processor channels, thereby eliminating the need to perform tiling processing on a full image to improve image processing accuracy, and reducing overall system computational power and data cache space requirements; and partitioning the predefined line data output by at least a portion of the image signal processor channels into a plurality of tiles, performing network inference on each tile of data according to a predefined policy, wherein the predefined policy involves a mobile terminal moving from a first end toward an opposite second end, and returning to the first end upon reaching the second end, until tile data inference is completed, thereby reducing neural processing unit memory overhead, improving the ability of a single neural network processing unit to process a plurality of image data channels in parallel, allowing for high frame rates and high resolutions, and reducing network inference latency.
Resumen de: US20260187451A1
0000 A method for making predictions includes identifying, for each node in the heterogeneous graph structure, a set of node-target paths that connect the node to a target node; assigning, to each of the node-target paths, a path type identifier indicative of a number of edges and corresponding edge types in the associated node-target path; and extracting a semantic tree from the heterogeneous graph structure. The semantic tree includes the target node as a root node and defines a hierarchy of metapaths that each correspond to a subset of the node-target paths in the heterogeneous graph structure assigned to a same path type identifier. The semantic tree is encoded by generating a metapath embedding corresponding to each metapath in the semantic tree. A label is predicted for the target node in the heterogeneous graph structure based on the set of metapath embeddings.
Resumen de: US20260186783A1
0000 Disclosed are a method for generating an instruction sequence, an electronic device, and a storage medium. The method for generating an instruction sequence includes: determining a computation graph corresponding to a neural network model to be compiled and resource status information on hardware executing the instruction sequence; partitioning the computation graph, to determine multiple computation sub-graphs; generating, based on the computation sub-graphs and the resource status information, instruction sub-sequences corresponding respectively to the computation sub-graphs; and determining, based on the instruction sub-sequences, a target instruction sequence corresponding to the neural network model to be compiled.
Resumen de: US20260187991A1
0000 Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing a multi-modal machine learning task on a network input that includes text and an image to generate a network output. One of the systems includes a vision-language model (VLM) neural network. The VLM neural network includes a VLM backbone neural network and an attention-based feature adapter. The VLM neural network has access to an external dataset that stores multiple text items.
Resumen de: US20260186092A1
0000 The present disclosure provides an apparatus for restoring the quality of magnetic resonance images based on a deep learning model and a method of controlling the same. The method includes: obtaining a training image corresponding to each magnetic resonance image by applying at least one of a plurality of elements set in connection with the quality of the magnetic resonance image to a magnetic resonance signal corresponding to the magnetic resonance image; obtaining a training dataset including the magnetic resonance image as label data and the obtained training image as input data matching the label data; and training a neural network model based on the training dataset and context data corresponding to the training image. Obtaining the training image includes distorting the magnetic resonance signal by applying the at least one of the plurality of elements and obtaining the training image based on the distorted magnetic resonance signal.
Resumen de: WO2026139108A1
The present invention belongs to the interdisciplinary technical field of medical image processing and artificial intelligence. Disclosed is a medical image reconstruction method based on deep learning. The method comprises: acquiring a medical image data set, and labeling corresponding key anatomical structure information; on the basis of a feature encoder and a graph structure processor, constructing a medical image reconstruction model; the feature encoder performing feature extraction on the medical image data set by means of a convolutional neural network, so as to obtain a multi-scale feature map; the graph structure processor constructing an initial landmark point set of the multi-scale feature map by means of a graph attention network, and analyzing topological relationships between landmark points, so as to obtain an optimized landmark point arrangement; on the basis of the optimized landmark point arrangement, constructing an anatomical structure graph; on the basis of the anatomical structure graph, designing an adaptive loss function to train the medical image reconstruction model; and on the basis of the trained medical image reconstruction model, obtaining a corresponding reconstructed image. The present invention achieves the objective of improving the detail representation of a reconstructed image while maintaining the accuracy of an anatomical structure.
Nº publicación: US20260188129A1 02/07/2026
Solicitante:
WING AVIATION LLC [US]
Wing Aviation LLC
Resumen de: US20260188129A1
0000 A method for perception validation of an aerial vehicle includes: acquiring an image of a ground area with an onboard camera system of the aerial vehicle, generating an above ground altitude (AGL) estimate with a neural network trained to output the AGL estimate in response to the image fed as an input to the neural network, generating a motion estimate or a position estimate based upon sensor data output from a sensor disposed onboard the aerial vehicle, and cross-validating the motion or position estimate against the AGL estimate.
BOPI
Sede Electrónica
