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99
results.
Updated on
15/09/2026 [08:58:00]
Results 25 to 50 of 99
Absstract of: 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.
Absstract of: WO2026180856A1
A computer implemented method for decision management, which utilizes the decision management system as described herein. In the computer implemented method a problem statement input is received. A large language model business knowledge base is searched for a set of top relevant results. The large language model business knowledge base is a trained neural network aggregating proprietary business information and non-proprietary business information from multiple sources. At least a partial feasibility report is automatically constructed responsive to the problem statement for the top relevant results using the large language model business knowledge base.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: US20260253325A1
0000 The present application provides a computer-implemented method for generating an orthodontic treatment plan, the method comprises: obtaining a first and a second 3D digital models, where the first 3D digital model represents an initial tooth arrangement of a jaw/jaws, and the second 3D digital model represents a target tooth arrangement of the jaw/jaws; extracting features from the first 3D digital model using a trained feature extraction deep neural network; and generating an orthodontic treatment plan of the jaw/jaws using a trained multi-agent reinforcement learning based deep neural network based on the extracted features and the second 3D digital model, where in the multi-agent reinforcement learning based deep neural network, each tooth is taken as an agent, where the orthodontic treatment plan utilizes shell-shaped tooth repositioners and comprises a series of successive treatment steps to incrementally reposition the jaw/jaws from the initial tooth arrangement to the target tooth arrangement.
Absstract of: US20260253405A1
0000 A method of classifying objects detected by n (n=2,3…) artificial neural networks in at least one image.
Absstract of: 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.
Absstract of: 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.
Absstract of: US20260253400A1
0000 Apparatuses, systems, and techniques are presented to detect one or more objects in one or more images. In at least one embodiment, one or more neural networks can be trained to detect one or more objects, in one or more unlabeled images, based at least in part upon one or more predicted segmentations of the one or more objects.
Absstract of: 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.
Absstract of: 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.
Absstract of: US20260252889A1
0000 Speed of training a neural network is improved by updating the weights of the neural network in parallel. In at least one embodiment, after back propagation, gradients are distributed to a plurality of processors, each of which calculate a portion of the updated weights of the neural network.
Absstract of: US20260252870A1
A method involves receiving a perceptual representation including a plurality of feature vectors, and initializing a plurality of slot vectors represented by a neural network memory unit. Each respective slot vector is configured to represent a corresponding entity in the perceptual representation. The method also involves determining an attention matrix based on a product of the plurality of feature vectors transformed by a key function and the plurality of slot vectors transformed by a query function. Each respective value of a plurality of values along each respective dimension of the attention matrix is normalized with respect to the plurality of values. The method additionally involves determining an update matrix based on the plurality of feature vectors transformed by a value function and the attention matrix, and updating the plurality of slot vectors based on the update matrix by way of the neural network memory unit.
Nº publicación: US20260252876A1 27/08/2026
Applicant:
NVIDIA CORP [US]
NVIDIA Corporation
Absstract of: US20260252876A1
Apparatuses, systems, and techniques to use one or more neural networks to generate one or more images based, at least in part, on one or more spatially-independent features within the one or more images. In at least one embodiment, the one or more neural networks determine spatially-independent information and spatially-dependent information of the one or more images and process the spatially-independent information and the spatially-dependent information to generate the one or more spatially-independent features and one or more spatially-dependent features within the one or more images.
BOPI
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