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114
resultados
Última actualización
14/07/2026 [08:18:00]
Resumen de: US20260195883A1
0000 A method for analyzing a cross-section of a joint includes: capturing, using a camera, an image of the cross-section of the joint; analyzing, using one or more neural networks, the image of the cross-section of the joint to classify two or more body segments that comprise the joint; isolating, using the one or more neural networks, the body segments of the joint; reassembling the body segments to form an assembled image of the joint; identifying key points in the assembled image of the joint; and measuring, using the key points, a value for a characteristic of the joint. A system for automated sectional analysis of a joint is also provided.
Resumen de: US20260195584A1
0000 A method is for training a neural network for determining features of objects for object tracking. The method includes generating a training dataset with a plurality of training data elements. Each training data element has a first set of sensor data relating to a first state of an environment with a set of a plurality of objects and a second set of sensor data relating to a second state of the environment. In the second state, the positions of the objects have at least partially changed compared to the first state. The method also includes determining features of the objects by feeding the first set of sensor data to the neural network and determining features of the objects by feeding the second set of sensor data to the neural network. A loss is determined depending on the generated features, and the neural network is trained to reduce the loss.
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: 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: 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: 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: 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: 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: 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: 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: US20260192826A1
A navigation path can be determined for an object using one or more neural networks. In various embodiments, image data is obtained that is representative of an environment in which the object is to be navigated. Relevant features are identified from the image, and a curve fit to those features. Loss values for the potential paths are scaled based at least in part upon the distance of those features in the real world. This can include, in at least some embodiments, performing the scaling as a function of the curvature of the curve fit to the features. Temporal smoothing can be performed with respect to prior path predictions in order to prevent sudden changes in the predicted path. The paths are analyzed to select a path with a highest confidence value that also at least satisfies a minimum confidence criterion. The path can be converted into three-dimensional navigation information.
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: 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: US20260195981A1
A computing system and method generate bounding boxes for objects in a three-dimensional scene. A grid generator creates a 3D grid, and a histogram generator builds a histogram of primitive intersections with grid cells. A neural network receives the histogram as input and generates bounding boxes around objects. A box optimizer minimizes the sum of surface areas of the bounding boxes. The histogram can be compressed, using a single bit per bin to indicate primitive presence. Primitives are assigned to child nodes based on proximity or fit. The system provides efficient and optimized bounding box generation for computer graphics applications.
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: US20260196015A1
0000 Apparatuses, systems, and techniques to update lower resolution images. In at least one embodiment, color information from one or more upsampled images may be obtained so that the color information from the one or more upsampled images may be caused to be applied to one or more subsequent lower resolution images.
Resumen de: US20260196020A1
Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, a set of exemplars corresponding to a class is accessed, and the set of exemplars is blended to generate a blended exemplar. The blended exemplar is aggregated with a noise sample to generate a noisy exemplar. An output corresponding to the class is generated based on processing the noisy exemplar using a generator neural network. The output is output.
Resumen de: WO2026146838A1
The present invention relates to a method for generating a foundation model for a hyperspectral image by using artificial intelligence. The method for generating a foundation model for a hyperspectral image by using artificial intelligence: masks a masking patch area randomly determined for a hyperspectral image to be learned; randomly rearranges the positions of some of unmasked patches or adds noise; extracts feature information by inputting the unmasked patches to an encoder; inserts masking feature information into the extracted feature information and inputs same to a decoder to generate a reconstructed hyperspectral image in which the hyperspectral image is reconstructed; and trains the encoder and the decoder so as to minimize the difference between the hyperspectral image and the reconstructed hyperspectral image, thereby making it possible to train and generate a foundation model including an artificial neural network-based encoder and decoder.
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: US20260197476A1
0000 Information processing with improved inferencing for machine learning is disclosed. In one example, a neural network is analyzed before inferencing is performed and generates control information for controlling compression and decoding of a feature amount processed by the neural network. Inferencing is performed using input data and the neural network and a processing result obtained by processing the feature amount is output as a computing result. The feature amount is compressed on the basis of the control information and recorded as a compressed feature amount. A decoder decodes the compressed feature amount temporarily recorded in the memory on the basis of the control information and outputs the decoded feature amount to the computing unit.
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: 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.
Nº publicación: US20260187485A1 02/07/2026
Solicitante:
SIEMENS AG [DE]
Siemens Aktiengesellschaft
Resumen de: US20260187485A1
Various embodiments of the teachings herein include an anomaly detection method. Some examples include: creating a knowledge graph according to data required to produce a product; acquiring measured values of multiple nodes in the knowledge graph in an upstream inspection process; calculating a characteristic deviation value of each of the multiple nodes based on the measured values of the multiple nodes in the upstream inspection process; calculating a similarity degree between any two nodes in the knowledge graph; and generating anomaly probabilities of respective nodes using a graph neural network according to the characteristic deviation value of each of the multiple nodes and the similarity degree between said any two nodes.
BOPI
Sede Electrónica
