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95
resultados
Última actualización
14/09/2026 [07:57:00]
Resultados 25 a 50 de 95
Resumen de: WO2026182308A1
The present invention relates to an outlier correction server and method using a data correction technique utilizing a long short-term memory (LSTM)-based variational autoencoder-generative adversarial network (VAE-GAN) model to effectively improve the quality of multivariate time-series data, and to a system including same. The outlier correction server comprises a memory storing at least one instruction and at least one processor that executes the at least one instruction, wherein the processor collects input data, preprocesses the input data to generate a preprocessing result, and detects an outlier from the preprocessing result by using a pre-trained neural network model.
Resumen de: US20260260401A1
Various embodiments of the teachings herein include systems for automatically transforming economic, organizational, and/or industrial content and/or processes capturable by natural language into a digital representation. An example includes: modules for capturing and/or recording user-specific data; processors to process captured data for forwarding to an AI and create digital representations in a recording language; an interface to a second processor associated with a neural network having an AI trained to carry out pattern analysis, pattern recognition, and/or pattern prediction on the basis of the processed user-specific recording data; and a second interface to transmit results from the data editing of the AI to the first processor to generate a digital representation made available to the user via a display module.
Resumen de: US20260260106A1
A learning apparatus and method for implementing an edge device using a resistive element and an analysis apparatus and method using the same are disclosed. The learning apparatus for implementing an edge device using a resistive element according to an embodiment of the present application may include a first learning unit determining a weight of an artificial neural network through learning based on first training data and reflecting the determined weight in a first resistive element, and a second learning unit updating the weight of the artificial neural network through learning based on second training data collected through the device and reflecting the updated weight in the second resistive element.
Resumen de: US20260261668A1
0000 A method and an apparatus for image filtering in video coding using a neural network are provided. The method includes generating a deblocking strength map indicating boundaries of prediction blocks or partition blocks. The deblocking strength map is input into a neural network, and the neural network filters an input frame based on the deblocking strength map.
Resumen de: US20260260475A1
0000 Apparatuses, systems, and techniques are presented to generate one or more interfaces. In at least one embodiment, one or more neural networks are used to generate one or more second graphical user interfaces based, at least in part, on one or more functional features of one or more first graphical user interfaces.
Resumen de: US20260260115A1
Embodiments are generally directed to dynamically dividing activations and kernels for improving memory efficiency. An embodiment of a method in a compute engine performing machine learning comprises: receiving, by a convolutional layer of a convolutional neural network (CNN) implemented on the compute engine, a plurality of activation groups contained in an input data, wherein the convolutional layer includes one or more kernel groups and the one or more kernel groups each include a plurality of kernels; determining a plurality of memory efficiency metrics based on the number of activation groups of the plurality of activation groups and the number of kernels of the plurality of kernels; selecting a first optimal number of activation groups and a second optimal number of kernels that are associated with an optimal memory efficiency metric in the plurality of memory efficiency metrics; and performing a convolutional operation on the input data based on the first optimal number and the second optimal number.
Resumen de: US20260260113A1
0000 A machine learning system is provided to enhance various aspects of machine learning models. In some aspects, a substantially photorealistic three-dimensional (3D) graphical model of an object is accessed and a set of training images of the 3D graphical mode are generated, the set of training images generated to add imperfections and degrade photorealistic quality of the training images. The set of training images are provided as training data to train an artificial neural network.
Resumen de: US20260260447A1
The present application relates to a target detection method and apparatus, a device, and a storage medium. A main technical solution includes: acquiring video set data, inputting the video set data to a backbone network to obtain video frame feature data, inputting the video frame feature data to a convolutional neural network to obtain candidate box data of a target object, optimizing the candidate box data of the target object according to a preset uncertainty estimation loss model to obtain candidate box feature data, and inputting the candidate box feature data to a preset cross-frame and cross-view model for updating and then outputting to obtain a target box and corresponding target detection data.
Resumen de: US20260261689A1
0000 There is provides a neural network-based video decoding method including receiving a latent feature corresponding to a compressed version of a first video frame of a first plurality of a plurality of video frames included in a video content, the latent feature being a combination of a weighted frame-specific embedding of the first video frame with weighted one or more group-of-pictures (GOP) features of the first plurality of the plurality of video frames, wherein the weighted frame-specific embedding is a product of applying a first weight to a frame-specific embedding and the weighted one or more GOP features are products of applying a second weight to the one or more GOP features, wherein the first weight and the second weight are selected as levers for content-specific fine-tuning. The method also including decoding the latent feature to provide an uncompressed video frame corresponding to the first video frame
Resumen de: US20260260316A1
An image processing method using a neural network model, and an electronic device are provided. The method may comprise: acquiring a low-resolution image; extracting, from the low-resolution image, luminance information through a luminance channel; acquiring a first feature vector on the basis of the luminance information by using the neural network model to which a first weight is applied; acquiring an output image from the first feature vector by using the neural network model to which a second weight is applied; and generating, on the basis of the output image, a high-resolution image with respect to the low-resolution image. The first weight and the second weight can be different.
Resumen de: US20260260488A1
0000 A system and method of identifying an adverse event using a video of a surgery and a two-stage surgical phase recognition module. The method includes receiving, by the module, a video of the surgery, where the video comprises a sequence of video frames. The module comprises a first stage that includes a neural network and a second stage that includes a multi-stage temporal convolution network. The method includes extracting, using the first stage, visual information content of a single frame based on the single frame; identifying, using the second stage, surgical phases captured in the frames of the video based on the visual information content from the first stage; and identifying, using the identified surgical phases, an adverse event during the surgery. An adverse event includes the omission of a surgical phase and an injury to the patient. The identification can occur in real-time or near-real-time.
Resumen de: US20260260498A1
A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.
Resumen de: US20260260129A1
Embodiments of this application disclose a neural network training method. A first module in a federated neural network is deployed in each of a plurality of first devices. The first module includes a feature extraction module. A plurality of second modules in the federated neural network and early exit modules connected to the respective second modules are deployed in a second device. It can be learned that, in a federated learning process, the second device may include a plurality of early exit nodes, and each early exit node corresponds to one second module and a corresponding early exit module connected to the second module. In this way, target networks of different structures may be flexibly deployed in different first devices. For example, flexible scheduling may be performed based on resource statuses of different first devices, such that each first device can implement efficient data processing.
Resumen de: US20260257689A1
0000 Provided are methods for testing of a control system of a vehicle using generated rulebook based scenarios, which can include determining a simulated environment, receiving a hierarchical plurality of autonomous vehicle rules, determining a trajectory of a simulated vehicle within the simulated environment, generating a plurality of simulated scenarios for the simulated vehicle, identifying at least one violation of at least one autonomous vehicle rule by the simulated vehicle in a set of the simulated scenarios, determining a scenario score for each simulated scenario based on the violations, and identifying at least one simulated scenario for a trained neural network of a vehicle based on the scenario scores.
Resumen de: WO2025090089A1
Systems, methods, and computer program products for machine unlearning on identity graph neural networks may obtain an identity graph including a plurality of graphs, each graph including a plurality of edges and a plurality of nodes for the plurality of edges, and, in each graph, each edge and each node is associated with a same identity; apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; split the biconnected identity graph into a plurality of biconnected components, such that there are no articulation points in each biconnected component; for each biconnected component, train a graph neural network that corresponds to that biconnected component to generate a graph embedding and a local minima; train an ensemble neural network on the graph embedding and the local minima of each biconnected component; and provide the trained ensemble neural network.
Resumen de: US20260252966A1
0000 Method for estimating total organic carbon (TOC) of rock samples in an automated manner and without the use of destructive techniques. The technique obtains hyperspectral data from rock samples and trains and uses machine learning algorithms, among them artificial neural networks (ANN), to estimate TOC based on the obtained hyperspectral data. The method described herein may be applied to rock samples from different sedimentary basins, provided that the algorithms are trained with samples from all basins. The method has been shown to be generalizable to other rock samples belonging to the basin or sedimentary basins used in the training of the algorithms. The method eliminates the subjectivity of the human analyst and optimizes the time and resources expended in conventional TOC estimation.
Resumen de: US20260252951A1
A Self-Explaining Decision Architecture (SEDA) for machine learning-based decision-making systems capable of generating intuitive explanations for its decisions in real time. SEDA makes use of a feature extraction subsystem and a sequence interpretation subsystem to identify patterns in data followed by a decision generation subsystem that determines appropriate actions based on those patterns. Internal state information from each of these subsystems is used to generate explanations of the system's decisions. Using this information to create explanations provides insight as to the data elements the system focused on when making decisions as well as the reasoning that was used. In at least one embodiment the system uses deep learning components including a combined convolutional neural network and long short-term memory network with attention mechanisms.
Resumen de: US20260252589A1
0000 Certain aspects of the disclosure provide for a difference analysis method. In certain aspects, a difference analysis method may include embedding a set of source documents into a knowledge graph, wherein each source document is embedded in the knowledge graph as a set of segments and a set of associations connecting two or more segments. A difference may be determined between a first segment in the set of segments of a first source document and a second segment in the set of segments of a second source document. In response to determining the difference between the first segment in the set of segments of the first source document and the second segment in the set of segments of the second source document, determining a significance of the difference on the second source document based on one or more associations of the set of associations connected to the second segment.
Resumen de: US20260253266A1
0000 A computer-implemented method of generating multimodal data. The method comprises using a token generation neural network to generate, autoregressively, an output sequence of multimodal tokens, and in response to a next multimodal token being a start-of-image token, generating an image using an image generation subsystem conditioned on features representing the current sequence of multimodal tokens obtained from the token generation neural network. The method further comprises processing the image to convert pixels of the image into a sequence of image tokens, each image token comprising a block encoding of values of the pixels in a different region of the image that maps a set of values of the pixels to a respective image token, and appending the sequence of image tokens to the current output sequence of multimodal tokens as the next multimodal tokens in the output sequence of multimodal tokens.
Resumen de: US20260249882A1
0000 A method and system are provided for safe motion planning of an autonomous system using a neural-network-based approximation of a Hamilton-Jacobi (HJ) value function. The method includes computing signed distance fields (SDFs) from occupancy grid maps, deriving temporal differences between SDFs, and using these differences as input to a hypernetwork that generates parameters for a main network. The main network computes a residual of the HJ value function, which is modified using a leaky rectified linear unit and combined with a selected SDF to form an intermediate value function. A state-dependent slack function is added to produce a final HJ value function, which is used as a safety constraint in motion planning. The system enables real-time, adaptive, and robust planning in dynamic and partially observable environments.
Resumen de: US20260253376A1
A system and method for analyzing images of built-environment structures at multiple geographic scales using patchwise segmentation and domain-specific embedding. The system extracts image patches at a plurality of spatial scales from imagery, generates patch embeddings via a neural network trained using semi-supervised or unsupervised learning on built-environment structure images, receives a query comprising an example image or textual descriptor, computes similarity scores between query and patch embeddings using a configurable metric, and aggregates scores across scales to produce a composite similarity result. The system supports configurable weighting of scale contributions, overlapping patches for boundary fidelity, hierarchical resolution processing for bandwidth efficiency, feedback-driven model refinement, and deployment across cloud, edge, and hybrid configurations. Applications include material identification, condition assessment, damage detection, and regional trend analysis for roofing and other built-environment structures.
Resumen de: US20260252841A1
0000 A method includes providing a semantic vector data as input to a first graph neural network to produce first prediction data for a first time, the first graph neural network including a graph data structure that has (1) a directed edge having a correlation weight and (2) an undirected edge having a causal weight, and the first graph neural network being configured to generate a first aggregation value based on a plurality of weight values associated with a plurality of nodes of the graph data structure. The semantic vector data is provided as input to a second graph neural network to produce second prediction data for a second time, the second graph neural network being produced based on the graph data structure and configured to generate a second aggregation value based on (1) the plurality of weight values and (2) a temporal dependency.
Resumen de: US20260252884A1
A method for training a ligand information generation model performed by an electronic device includes obtaining sample receptor information and sample ligand information, a binding affinity between a ligand described by the sample ligand information and a receptor described by the sample receptor information being not less than a set affinity; denoising reference noise data based on the sample receptor information using a neural network model undergoing training to obtain predicted ligand information; determining a first loss for characterizing a difference between the sample ligand information and the predicted ligand information; and training the neural network model based on the first loss to obtain a ligand information generation model, the ligand information generation model being configured to generate reference ligand information based on reference receptor information.
Resumen de: US20260252891A1
A forward propagation apparatus is a forward propagation apparatus for a neural network, including: a mask generation unit that generates a binary mask; and a layer execution unit that performs an operation for a sparse convolutional layer according to a value at each coordinate of the binary mask, in which the mask generation unit: generates heat maps by performing an operation for a convolutional layer on an input feature map; generates a composite heat map obtained by combining the heat maps, into one heat map by summing up values of heat maps on a coordinate-by-coordinate basis; and generates the binary mask by binarizing a value at each coordinate of the composite heat map by using a predetermined threshold.
Nº publicación: US20260252886A1 27/08/2026
Solicitante:
CAPITAL ONE SERVICES LLC [US]
Capital One Services, LLC
Resumen de: US20260252886A1
In some embodiments, a method includes segmenting updates associated with a record into a set of update subsets and generating first and second vectors based on first and second update subsets using a first neural network. The first update subset is associated with a first session and a timestamp, and the second update subset is associated with a second session. The method includes determining a first output using a second neural network based on the first and second vectors and a time difference between the first and second sessions. The method includes selecting a segment of a periodic time interval based on the timestamp, determining a second output using a third neural network based on a ratio based on the segment and the periodic time interval, and generating a characterizing vector using a fourth neural network based on the first and second outputs.
BOPI
Sede Electrónica
