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DISTANCE DETERMINATIONS USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260300718A1

0000 Apparatuses, systems, and techniques are presented to determine distance for one or more objects. In at least one embodiment, a disparity network is trained to determine distance data from input stereoscopic images using a loss function that includes at least one of a gradient loss term and an occlusion loss term.

Ocular system for deception detection

Resumen de: AU2026233962A1

A method of deception detection, assessing operational risk or optimizing learning is based upon ocular information of a subject by providing a video camera configured to record a close-up view of a subject's eye. The ocular information is processed to identify changes in ocular signals of the subject through the use of convolutional neural networks. Changes in ocular signals are evaluated from the convolutional neural networks by a machine learning algorithm. Results can then be presented in regards to the level of deception, the level of operational risk or the best way to optimize learning. The methods are facilitated by identifying at least one predictive distortion identified in the stroma capturable solely with a visible-spectrum camera correlating to a predicted response in the iris musculature. 20 ep e p

DETECTION OF UNSAFE CABIN CONDITIONS IN AUTONOMOUS VEHICLES

Resumen de: US20260296364A1

0000 Devices, systems and processes for the detection of unsafe cabin conditions that provides a safer passenger experience in autonomous vehicles are described. One example method for enhancing passenger safety includes capturing at least a set of images of one or more passengers in the vehicle, determining, based on the set of images, the occurrence of an unsafe activity in an interior of the vehicle, performing, using a neural network, a classification of the unsafe activity, and performing, based on the classification, one or more responsive actions.

NEURAL NETWORK TRAINING TECHNIQUE

Resumen de: US20260300436A1

0000 Apparatuses, systems, and techniques to train neural networks to perform image processing tasks. In at least one embodiment, one or more second neural networks are used to train one or more first neural networks based, at least in part, on a first object type in one or more images and a second object type in the one or more images, in parallel.

DETECTION AND CLASSIFICATION OF TRAFFIC SIGNS USING CAMERA-RADAR FUSION

Resumen de: US20260299088A1

0000 The disclosed systems and techniques facilitate efficient detection and classification of traffic signs in driving environments. The disclosed techniques include, obtaining, using a sensing system of a vehicle a first set of perspective camera images of an environment and a second set of radar images of the environment. The techniques further include generating, using a first neural network, one or more camera features characterizing the first set of images, generating, using a second neural network, one or more radar features characterizing the second set of images, and processing the one or more camera features and the one or more radar features to obtain an identification of one or more traffic signs in the environment.

TEMPORAL IMAGE BLENDING USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260301117A1

Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more objects in an image are caused to be generated based, at least in part, on applying one or more offsets to a motion of the one or more objects relative to one or more prior images.

SCALING ELEMENT-WISE OPERATIONS OF A NEURAL NETWORK

Resumen de: US20260300712A1

Neural networks, and in particular deep neural networks, are traditionally resource intensive as they require execution of a significant number of computations in order to generate an output. In an effort to accelerate neural networks, quantization has been employed in which the input/output of certain operations are scaled. However, the degree to which a neural network can be quantized is limited because current scaling methods, which do not ensure consistent scaling for operands, cannot be applied to the commonly used element-wise operations (e.g. addition and subtraction). The present disclosure provides scaling of element-wise operations in a neural network by using scale factors that are shared between operands, thus allowing for greater quantization of neural networks.

GRAPH NEURAL NETWORKS

Resumen de: US20260301174A1

0000 A method comprising: generating subgraphs graph based on classification scores corresponding to nodes and obtained by inputting input graph into a graph neural network, GNN, wherein each node in each input graph comprises a plurality of values corresponding respectively to a plurality of features; determining statistical measures of each feature's values in the subgraphs to generate statistical measures for each subgraph, and obtaining for each subgraph classification probabilities corresponding respectively to a plurality of classes; performing k-means clustering on the subgraphs based on the statistical measures to generate a plurality of clusters of subgraphs; generating, for each cluster of subgraphs, a set of surrogate models to approximate the classification probabilities, respectively, of the subgraphs in the cluster, in terms of the statistical measures; and determining, based on the surrogate models of each set of surrogate models, feature importance scores.

SYSTEM AND METHOD FOR HUMAN ACTIVITY RECOGNITION

Resumen de: US20260300738A1

0000 In aspects, the present approaches allow neural networks to be taught to understand patterns of human behavior without the need of expert data labeling or laboratory studies. First and second neural networks are trained to understand these patterns without labeling. Once trained, the neural networks can be deployed with a trained classifier to determine or classify human activity based upon received sensor inputs.

SELECTION-INFERENCE NEURAL NETWORK SYSTEMS

Resumen de: US20260296464A1

0000 Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a response to a query input using a selection-inference neural network.

AUTOMATIC BALL MACHINE APPARATUS UTILIZING PLAYER IDENTIFICATION AND PLAYER TRACKING

Resumen de: US20260295361A1

0000 A ball machine comprising an imaging system to capture image data and a processor configured to, for a frame of the image data, analyze the image data using a neural network to detect a plurality of persons, determine a coordinate position on a playing surface of each of the plurality of detected persons, extract features of each of the plurality of detected persons, generate a first set of feature vectors corresponding to the plurality of detected persons, associate a first feature vector to the coordinate position on the playing surface of a first detected person to generate a first unique identifier, associate a second feature vector to the coordinate position on the playing surface of a second detected person to generate a second unique identifier, and control the ball machine to launch balls based on first settings corresponding to the first unique identifier and second settings corresponding to the second unique identifier.

DISTRIBUTION SYSTEM POWER-FLOW SOLUTION BY HIERARCHICAL ARTIFICIAL NEURAL NETWORKS STRUCTURE

Resumen de: US20260300691A1

Disclosed herein is a method for solving a power flow problem in at least one system. The method includes dividing, by at least one processor, the system into a plurality of clusters, where each of the plurality of clusters has a modular architecture, constructing, by the at least one processor, a hierarchical artificial neural network (ANN) for each of the plurality of clusters, thereby generating a plurality of ANNs, and determining, by the at least one processor by the plurality of ANNs, at least one solution to a power flow problem for (i) each of the plurality of ANNs, and (2) the hierarchical ANN as a whole, where each of the plurality of ANNs is organized hierarchically such that data from at least one lower-level layer is fed into at least one upper layer in accordance with an electric correlation between at least one cluster in the plurality of clusters.

INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM

Resumen de: EP4814884A1

0001 An information processing apparatus classifies cells in an image of a tumor microenvironment obtained from a patient into a plurality of cell groups, extracts positions, in the image, of the cells included in the plurality of cell groups, creates a graph by assigning nodes to the cells included in the plurality of cell groups based on the extracted positions, connecting the nodes with edges, assigning to each of the nodes a feature quantity indicating a feature of the corresponding cell, and assigning to each of the edges a feature quantity indicating a relationship between two cells corresponding to two nodes connected by the edge, and models, using a graph neural network (GNN), spatial relationships between cells in the graph based on the assigned feature quantities of the nodes and the edges.

SYSTEM AND METHOD FOR PLAYER REIDENTIFICATION IN BROADCAST VIDEO

Resumen de: EP4815476A2

0001 A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.

SYSTEMS AND METHODS FOR GENERATING MULTIMODAL DATA USING A SINGLE-TOWER ARCHITECTURE WITH A DATA GENERATION SUBSYSTEM

Resumen de: EP4814896A2

0001 A computer-implemented method of generating multimodal data. The method comprises using a token generation neural network to generate an output sequence of multimodal tokens, and in response to a next multimodal token being a start-of-image token, generating an image by performing a reverse diffusion process conditioned on features representing the current output sequence of multimodal tokens obtained from the token generation neural network and appending a sequence of image tokens representing the image to the current output sequence of multimodal tokens as subsequent multimodal tokens in the output sequence of multimodal tokens.

GENERATION OF MOLECULAR STRUCTURES

Resumen de: EP4814972A1

Some embodiments are directed to generating molecular structures, which may include progressively denoising a spatial density function by iteratively applying a trained neural network. The neural network is trained to transform noisy spatial density functions into structured spatial density functions based on training data comprising molecular structures represented as spatial density functions. After denoising, a sum of predefined density distributions is fitted to the denoising result to obtain the molecular structure.

METHODS AND SYSTEMS FOR CROSS-DOMAIN BASED CHANGE DETECTION OF REGION DUE TO AN EVENT

Resumen de: EP4814895A1

The disclosure relates generally to methods and systems for cross-domain based change detection of region due to an event. Conventional techniques on cross-domain change detection (CDCD) rely on transformation-based approaches involving two tasks: image translation from one modal to another modal (SAR to optical) and then performing the change detection (CD) in one of the translated modals (for example, in SAR or optical). The methods and system of the present disclosure propose a deep-learning (DL) architecture called ReFUjetNet leverages generic embeddings from the extensively pre-trained domain adaptive foundation model alongside locally trained embeddings. The ReFUjetNet efficiently fuses these embeddings and employs a Kolmogorov-Arnold Network (KAN)-based convolutional neural network (CNN) classifier to generate a binary change matrix, which is then joined with another binary change matrix derived from the Gabor jet-based dissimilarity checker, resulting in the final binary change map.

Milk analyser for classifying milk

Resumen de: NZ773408A

A milk analyser (400) comprising a milk analysis unit (402) having an analysis modality wherein the milk analysis unit (402) further comprises a milk classification system (404) having an imaging device (4042, 4044) configured to image milk for generation of digital image data; a processor (3044) of a computing device (304) which is adapted to execute a program code to implement a deep learning neural network classifier trained using labelled milk images from milk within the classes into which the imaged milk may be classified and operable to generate a classification of the imaged milk; and a controller (3066) configured to output a control signal in dependence of the generated classification to control a sample intake (4022) to regulate the supply of milk to the analysis unit (402).

THREE-DIMENSIONAL INTERSECTION STRUCTURE PREDICTION FOR AUTONOMOUS DRIVING APPLICATIONS

Resumen de: US20260285315A1

0000 In various examples, a three-dimensional (3D) intersection structure may be predicted using a deep neural network (DNN) based on processing two-dimensional (2D) input data. To train the DNN to accurately predict 3D intersection structures from 2D inputs, the DNN may be trained using a first loss function that compares 3D outputs of the DNN—after conversion to 2D space—to 2D ground truth data and a second loss function that analyzes the 3D predictions of the DNN in view of one or more geometric constraints—e.g., geometric knowledge of intersections may be used to penalize predictions of the DNN that do not align with known intersection and/or road structure geometries. As such, live perception of an autonomous or semi-autonomous vehicle may be used by the DNN to detect 3D locations of intersection structures from 2D inputs.

DEFECT DETECTION SYSTEM FOR MOBILE DEVICE COSMETIC INSPECTION

Resumen de: WO2026198555A1

A system may detect cosmetic defects on mobile device surfaces during automated processing. An imaging device may capture images of a mobile device surface under one or more lighting conditions while the mobile device is positioned within an inspection cell. A lighting component may provide the lighting conditions. A defect detection model comprising a convolutional neural network may process the captured images to generate a segmentation output identifying defect regions. The convolutional neural network may be trained on a dataset comprising real images with segmentation masks and synthetic images generated using three-dimensional rendering. The captured images may be received and processed by the defect detection model to generate the segmentation output, and a composite image of the mobile device surface indicating detected defects may be generated. The composite image may include visual indications of defect locations and associated metadata such as defect type, location, and severity characteristics.

TECHNIQUES TO USE A NEURAL NETWORK TO EXPAND AN IMAGE

Resumen de: US20260289724A1

0000 Apparatuses, systems, and techniques for texture synthesis from small input textures in images using convolutional neural networks. In at least one embodiment, one or more convolutional layers are used in conjunction with one or more transposed convolution operations to generate a large textured output image from a small input textured image while preserving global features and texture, according to various novel techniques described herein.

APPARATUS AND METHOD FOR ADAPTIVE IMAGE SCALING AND OBJECT TRACKING USING NPU

Resumen de: US20260289809A1

0000 An electronic device mounted on a fixed or a movable apparatus is provided. The electronic device may comprise a neural processing unit (NPU), including a plurality of processing elements (PEs), configured to process an operation of an artificial neural network model trained to detect or track at least one object and output an inference result based on at least one image acquired from at least one camera; and a signal generator generating a signal applicable to the at least one camera.

DATA FOR MACHINE LEARNING MODELS

Resumen de: US20260290075A1

A machine learning model (MLM) may be trained and evaluated. Attribute-based performance metrics may be analyzed to identify attributes for which the MLM is performing below a threshold when each are present in a sample. A generative neural network (GNN) may be used to generate samples including compositions of the attributes, and the samples may be used to augment the data used to train the MLM. This may be repeated until one or more criteria are satisfied. In various examples, a temporal sequence of data items, such as frames of a video, may be generated which may form samples of the data set. Sets of attribute values may be determined based on one or more temporal scenarios to be represented in the data set, and one or more GNNs may be used to generate the sequence to depict information corresponding to the attribute values.

IMAGE GENERATION USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260289296A1

Apparatuses, systems, and techniques are presented to generate image or video content. In at least one embodiment, one or more neural networks are used to generate one or more time-lapsed images of a second object based, at least in part, on one or more images of a first object.

Predictive Access Control for Application Programming Interfaces Using Non-Fungible Token Bindings and Recurrent Neural Network Forecasting of Performance Metrics

Nº publicación: US20260289563A1 24/09/2026

Solicitante:

BANK OF AMERICA CORP [US]
Bank of America Corporation

Resumen de: US20260289563A1

Cryptographic control regulates access by DevOps computing to an API service through an API gateway. A processor applies a long short term memory neural network to a plurality of performance metrics of the application programming interface service in real-time, the plurality of performance metrics including a time to first hello world value, a request per minute count, an average latency value, a maximum latency value, an errors per minute count, an application programming interface uptime value, a memory usage value, and a central processing unit usage value. The long short term memory neural network produces a forecast output predicting a future value of at least one of the plurality of performance metrics. When the forecast output indicates the future value is outside a predetermined performance range, the processor disables access in accordance with a smart contract bound to a non-fungible token. A non-fungible token repository data structure is dynamically updated.