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Partner-Based Item Recommendations

Resumen de: WO2026137155A1

Partner-based item recommendations are described. A computing system receives historical data associated with user engagement and transactions for a set of items. The computing system selects a set of candidate items from the set of items based on a set of categories associated with the set of items, a set of popularity metrics associated with the set of items, and a set of similarity metrics associated with the set of candidate items and the set of items. The computing system generates, for each candidate item, a respective user preference score based on the historical data and using a neural network including an attention mechanism and a weighted loss function. The computing system orders the set of candidate items according to the user preference scores and a category distribution for the set of items. The computing system outputs at least one candidate item based on the ordered set of candidate items.

GENERATING AUDIO USING AUTO-REGRESSIVE GENERATIVE NEURAL NETWORKS

Resumen de: AU2026204419A1

Abstract A method for training one or more generative neural networks for generating a prediction of an audio signal, the audio signal having a respective audio sample at each of a plurality of output time steps spanning a time window, the training comprising: obtaining, as training data, a plurality of target audio signals; training, on a set of target semantic representations generated from the target audio signals, a third generative neural network that generates a semantic representation of the audio signal, wherein the semantic representation specifies a respective semantic token at each of a plurality of first time steps spanning the time window, each semantic token representing semantic content of the audio signal at the corresponding first time step; and training, on a set of target acoustic representations generated from the target audio signals, a first generative neural network and a second generative neural network that generate an acoustic representation of the audio signal, wherein the acoustic representation specifies a set of one or more respective acoustic tokens at each of a plurality of second time steps spanning the time window, the one or more respective acoustic tokens at each second time step representing acoustic properties of the audio signal at the corresponding second time step. Abstract PREDICTION OF AUDIO WO 2024/054556 DECODER NEURAL NEURAL NETWORK REPRESENTATION ONE OR MORE REPRESENTATION REPRESENTATION NETWORKS 108 NEURAL NETWORK PCT/US2023/032

GENERATING VIDEOS USING SEQUENCES OF GENERATIVE NEURAL NETWORKS

Resumen de: US20260187995A1

0000 Methods, systems, and apparatus, including computer programs encoded on a computer storage medium. In one aspect, a method includes receiving a text prompt describing a scene; processing the text prompt using a text encoder neural network to generate a contextual embedding of the text prompt; and processing the contextual embedding using a sequence of generative neural networks to generate a final video depicting the scene.

APPARATUS AND METHOD FOR GENERATING VIDEO DESCRIPTIONS BASED ON ARTIFICIAL NEURAL NETWORKS

Resumen de: US20260188010A1

An apparatus for generating video descriptions according to an embodiment is a video description generating apparatus based on artificial neural networks, including one or more processors and a memory storing one or more programs executed by the one or more processors, and the apparatus includes a feature generating module that extracts a plurality of preset features from a video and generates one synthetic feature based on the plurality of extracted features and a description generating module that receives the synthetic feature and outputs a description text for the video.

THERMAL IMAGE DEPTH ESTIMATION METHOD AND DEVICE

Resumen de: WO2026141769A1

Disclosed are a thermal image depth estimation method and device. According to the present invention, provided is a thermal image depth estimation device comprising a processor, and a memory stored in the processor, wherein the memory stores program instructions executed by the processor so as to: develop a prototype neural network model by using a pre-collected thermal image dataset; train the neural network model on the basis of a preset loss; and optimize and distribute the trained neural network model according to an open neural network exchange (onnx) format.

METHOD AND APPARATUS FOR IMPROVING DETECTION ACCURACY OF POINT OF INTEREST UNDER VARIOUS LIGHTING CONDITIONS

Resumen de: WO2026142294A1

According to at least an embodiment, a method for implementing a neural network training computer for indoor environment mapping comprises the steps of: training a neural network in a first step by using a first dataset based on a synthetic shape; and training the neural network in a second step by using a second dataset based on a real photograph. In addition, the method comprises the steps of: collecting, for each of a plurality of objects, a plurality of object images captured under different lighting conditions; and training the neural network in a third step by using the plurality of object images.

Anomaly Detection Method And Apparatus

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.

AUTOMATED MULTI-SPEAKER AND MULTI-LINGUAL SPEECH ANALYSIS

Resumen de: WO2026142921A1

Exemplary system and methods use a combination of application modules and neural network architecture for multi-speaker and multi-language speech analysis. The exemplary system can receive a natural language input, which it decomposes into plural segments. A sub-group of the plural segments are accumulated in a buffer where each segment representing a period during which voice activity is detected. The sub-groups are analyzed for voice activity of multiple speakers and one or more text segments are generated based on the speakers. A semantic vector for each text segment is generated and stored in vector memory. Relevant data associated with each semantic vector is retrieved from the vector memory based on a similarity measure; and a response including specified information extracted from the one or more text segments is generated based on at least the relevant data.

MULTI-CHANNEL DATA PROCESSING METHOD, REALITY DISPLAY APPARATUS, DEVICE, MEDIUM, AND PRODUCT

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.

UNSUPERVISED LEARNING OF OBJECT REPRESENTATIONS FROM VIDEO SEQUENCES USING ATTENTION OVER SPACE AND TIME

Resumen de: US20260187982A1

A computer-implemented video generation neural network system, configured to determine a value for each of a set of object latent variables by sampling from a respective prior object latent distribution for the object latent variable. The system comprises a trained image frame decoder neural network configured to, for each pixel of each generated image frame and for each generated image frame time step process determined values of the object latent variables to determine parameters of a pixel distribution for each of the object latent variables, combine the pixel distributions for each of the object latent variables to determine a combined pixel distribution, and sample from the combined pixel distribution to determine a value for the pixel and for the time step.

APPARATUS FOR OBTAINING MAGNETIC RESONANCE IMAGES BASE ON DEEP LEARNING MODEL AND METHOD OF CONTROLLING THE SAME

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.

MEDICAL IMAGE RECONSTRUCTION METHOD BASED ON DEEP LEARNING

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.

DECISION TREE OF MODELS: USING DECISION TREE MODEL, AND REPLACING THE LEAVES OF THE TREE WITH OTHER MACHINE LEARNING MODELS

Resumen de: US20260187484A1

Described are techniques of generating and training a neural network that include training multiple models and constructing multiple decision trees with said models. Each decision tree may include additional decision trees at various levels of that decision tree. Each decision tree has a different accuracy indicator due to the unique structuring of each decision tree, and by testing each tree through a testing dataset, the tree with the highest accuracy can be determined.

UAV PERCEPTION VALIDATION BASED UPON A SEMANTIC AGL ESTIMATE

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.

TIME-SERIES FORECASTING SYSTEM, DEVICE, AND METHOD

Resumen de: US20260187497A1

A system generates prediction data by processing an input sequence segmented along a time axis based on a predetermined period using neural networks. The neural networks comprise a first neural network configured to apply dilated attention to the input sequence segmented along the time axis based on the predetermined period, and a second neural network configured to apply random partition attention to data arranged along a feature axis.

METHOD FOR TRACKING A DENTAL MOVEMENT

Resumen de: US20260183087A1

Determine an amount of movement between an “anterior” updated instant and a “posterior” updated instant after the anterior updated instant. At the anterior updated instant, implementing an analysis method to obtain an anterior updated item of spatial information. Analyze a set of several images to obtain spatial information. Create a historical learning database relating to a dental body and the spatial attribute, with a set of historical images all depicting the dental body and an item of spatial information.Training the neural network, by providing the sets of several historical images as input and with the historical spatial information as output, with the spatial attribute defining an ordered sequence of variables in a three-dimensional reference frame. At the posterior updated instant, implement analysis to obtain a posterior updated item of spatial information. Compare the anterior and posterior updated spatial information, to obtain an amount of movement.

Graph Neural Network-Based Node Classification Method and System, and Related Device

Resumen de: US20260187410A1

A graph neural network-based node classification 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.

MODEL TRAINING METHOD, VEHICLE CONTROL METHOD, AND RELATED APPARATUS

Resumen de: US20260187456A1

0000 Provided are a model training method, a vehicle control method, and a related apparatus, which may be applied to the field of artificial intelligence. The method includes: obtaining road condition information of a target vehicle; obtaining target information based on the road condition information by using a first neural network model, where the target information is a driving intention prediction of the target vehicle, a driving route prediction, or an interaction behavior prediction between the target vehicle and an environment; and updating the first neural network model based on the target information by using an expert system or a result obtained through processing the road condition information and a label corresponding to the road condition information by the expert system.

GENERATIVE AI VIRTUAL CLOTHING TRY-ON

Resumen de: US20260187881A1

0000 An artificial intelligence (AI) network or neural network is trained, using a relatively small number of reference images of a target garment, to enable virtual clothing try-ons of the target garment. Example methods include determining a pose for a person depicted in an input image, determining an area of the input image to replace with a target garment, changing values of pixels within the area, and inputting the pose, the area, and a text prompt describing the target garment, into a neural network, to generate an output image, wherein the neural network is trained to generate the target garment. Example methods include training the neural network with images of clothing in a same class or category as the target garment to teach the neural network to shape the target garment in accordance with a pose of the person and to preserve other clothing and the background.

NEURAL NETWORK MODEL TRAINING METHOD AND COMMUNICATION APPARATUS

Resumen de: US20260187461A1

0000 This application provides a neural network model training method and a communication apparatus. The method includes: A transmit-end device performs, by using a first neural network model, forward inference on training data included in a first training process, to obtain a first forward inference result; and sends the first forward inference result to a receive-end device. Further, the transmit-end device receives a first feedback result for the first forward inference result from the receive-end device, and obtains a first intermediate result generated when forward inference is performed on training data included in the first training process. The transmit-end device calculates a gradient of the first training process based on the first feedback result, the first forward inference result, and the first intermediate result. Further, the transmit-end device updates a first model parameter to a second model parameter based on the gradient corresponding to the first training process.

ANOMALY DETECTION SYSTEM, ANOMALY DETECTION METHOD, ELECTRONIC DEVICE AND STORAGE MEDIUM

Resumen de: US20260187231A1

0000 An anomaly detection system, an anomaly detection method, an electronic device and a storage medium are provided, which relates to the field of artificial intelligence. The anomaly detection system includes a plurality of markers. The plurality of markers are different from each other and correspond to different anomaly detection operations. A log line of a target system may be detected from different dimensions by using the plurality of markers, and marker tokens generated by different markers are obtained. In addition, the anomaly detection system further includes a model marker. The model marker may combine the marker tokens respectively generated by each marker into a token sequence and perform anomaly detection on the token sequence by using a pre-trained neural network model to obtain an overall anomaly score of the target system within a preset time period.

MULTI-DEVICE COMBINED USE-BASED IDENTIFICATION METHOD AND SYSTEM FOR RAW MATERIALS AND EXCIPIENTS IN FORMULATION

Resumen de: WO2026138708A1

A multi-device combined use-based identification method and system for raw materials and excipients in a formulation. The method comprises: performing cryogenic polishing on a cross-section of a test sample under analysis to obtain a cross-section under test; using an electron microscope to observe the cross-section under test to obtain an electron microscopy image of the cross-section under test, and determining a region under test on the cross-section under test; using an energy dispersive spectrometer to scan the region under test to obtain an energy spectrum image; on the basis of the electron microscopy image and the energy spectrum image, determining whether there are characteristic elements in the test sample, and if there are characteristic elements, identifying the characteristic elements on the basis of the energy spectrum image; and on the basis of the electron microscopy image and the energy spectrum image, determining whether there are components matching preset morphological characteristics in the test sample, and if there are components matching the preset morphological characteristics, using a pre-constructed convolutional neural network model to perform analysis processing on the electron microscopy image to identify the components matching the preset morphological characteristics. The identification method can more accurately and quickly identify components in a sample under analysis.

OBJECT TRACKING PROCESSING METHOD AND APPARATUS, DEVICE, AND MEDIUM

Resumen de: WO2026138395A1

The present invention provides an object tracking processing method and apparatus, a device, and a medium. The processing method comprises: respectively preprocessing a first-frame image and a current image to generate a corresponding first-frame initial image and a corresponding current initial image; performing feature extraction processing on the first-frame initial image and the current initial image by means of a weight-adaptive fusion encoder to generate input feature data; performing deep-level fusion processing on the first-frame initial image and the current initial image; performing feature extraction processing by means of the weight-adaptive fusion encoder to generate deep feature data; performing attention-weighted fusion processing on the input feature data and the deep feature data by means of the weight-adaptive fusion encoder to generate target feature tensor data; and processing the target feature tensor data by means of a fully convolutional neural network to generate a target image. The object tracking processing method and apparatus, the device, and the medium provided by the present invention improve object tracking performance.

FINE-GRAINED STRABISMUS DIAGNOSIS METHOD AND SYSTEM USING GRAPH NEURAL NETWORK BASED ON CAUSAL FEATURE SELECTION, AND DEVICE AND MEDIUM

Resumen de: WO2026138264A1

A fine-grained strabismus diagnosis method and system using a graph neural network based on causal feature selection, and a device and a medium. The method comprises: acquiring nine facial photographs of a patient, and performing image preprocessing on each of the nine facial photographs, in order to construct a nine-gaze-position image; using an object detection algorithm to detect an orbital region in each gaze position in the nine-gaze-position image, in order to extract feature variables of each gaze position that are related to fine-grained strabismus diagnosis; using a causal feature selection algorithm to select from among the feature variables related to each gaze position key feature variables having a direct causal relationship with fine-grained strabismus diagnosis; and inputting the key feature variables of each gaze position into a graph neural convolutional network model, and performing training and optimization with a strabismus disease dataset by means of a propagation formula, in order to output a fine-grained diagnosis result. Unrelated details are removed from entire facial images, and main eye regions are extracted to construct a nine-gaze-position image, thereby reducing the computational cost; and a causal feature algorithm is used to extract key feature variables of each gaze position, and a graph neural convolutional network model is used to learn the key feature variables of each gaze position, thereby making the graph neural convolutional network mod

DECOUPLED QUERIES FOR END-TO-END 3D TRACKING USING TRANFORMER NEURAL NETWORKS

Nº publicación: US20260187138A1 02/07/2026

Solicitante:

NVIDIA CORP [US]
NVIDIA Corporation

Resumen de: US20260187138A1

In various examples, a technique for multiple object tracking is disclosed that includes generating, using one or more processing units, one or more first encoded image features based on a first image. The technique also includes generating a plurality of first object embeddings based on the first encoded image features, wherein at least one first object embedding of the plurality of first object embeddings corresponds to a different object depicted in the first image. The technique further includes determining, using a first track query of one or more track queries and one or more machine learning operations, a first association between a first track and a first object that corresponds to the at least one first object embedding, wherein the first track corresponds to the first track query. The technique further includes computing, based on the first association, an object trajectory associating the first track with the first object.