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Driving scene search and driving policy training

Resumen de: GB2705187A

A computer-implemented method for storing and retrieving driving scenes, the method includes: determining one or more natural language descriptions of one or more driving scenes based on inputting, to a large language model, driving scene information of the one or more driving scenes and one or more prompts requesting the one or more natural language descriptions of the one or more driving scenes; creating one or more embeddings of the one or more natural language descriptions using a trained neural network and storing the one or more embeddings; receiving a natural language query associated with a query driving scene; creating an embedding of the natural language query using the trained neural network; selecting, from the one or more driving scenes, one or more similar driving scenes based on the one or more embeddings and the embedding of the natural language query; and outputting the selected one or more similar driving scenes. FIG. 3

A System for Mineral Prediction Based on Neural Networks

Resumen de: KR20260144543A

본 발명은 뉴럴 네트워크에 기반한 광물 예측 시스템에 관한 것이다. 구체적으로, 지구과학 자료 해석을 위하여, 복수의 측정 포인트들을 포함하는 공간 데이터를 수집하고, 공간 데이터를 전처리하여, 입력층 데이터를 생성하는 입력층 데이터 처리 모듈, 입력층 데이터에 대하여, 복수의 측정 포인트상의 로그 변환된 리튬 농도기반의 지구화학적 문턱값에 기반한 리튬 탐색 프로세스를 수행하는, 다수 레이어로 구성된 은닉층 데이터 처리 모듈 및 리튬 농도 탐색 프로세스 수행 결과에 기반하여, 리튬 이상대 영역을 생성하는 출력층 데이터 처리 모듈을 포함한다.

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.

APP SEARCH BASED ON VERBAL DESCRIPTION OF ICON

Resumen de: US20260300374A1

A computer implemented method includes receiving a search request from a user device, the search request including a verbal description of attributes of an icon corresponding to an app. The search request is processed via a natural language processing model to derive an intent of the search request to search for icon attributes. An icon knowledge base is searched in response to the derived intent. The icon knowledge base includes records with icon attributes generated by a convolutional neural network image processor based on icons collected from an app storage, to identify a ranked list of apps. A search results is provided to the user device and identifies at least a top ranked app having an icon matching the verbal description of attributes.

DATA PROCESSING METHOD, NEURAL NETWORK MODEL TRAINING METHOD AND RELATED APPARATUS

Resumen de: WO2026200491A1

Disclosed in the present application are a data processing method, a neural network model training method and a related apparatus. The data processing method comprises: acquiring first historical data to be inferred and first data to be inferred, and on the basis of the first historical data to be inferred and the first data to be inferred, obtaining a first data sequence; on the basis of a label of the first historical data to be inferred and an initial label of the first data to be inferred, obtaining a first label sequence, wherein labels in the first label sequence correspond to data in the first data sequence on a one-to-one basis in terms of sequence positions; and on the basis of the first data sequence, obtaining first association weights between data in the first historical data to be inferred and data in the first data to be inferred, and obtaining an inference result of the first data to be inferred in combination with the first association weights and the first label sequence. The embodiments of the present application facilitate a reduction in the training or fine-tuning overheads of an AI model, and reduce the training latency of the AI model.

METHOD AND APPARATUS FOR AI-BASED CHANNEL ESTIMATION VIA MIXTURE OF EXPERTS IN A WIRELESS COMMUNICATION SYSTEM

Resumen de: WO2026205933A1

The present disclosure relates to a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE). A method includes receiving, by a first electronic device, a signal from a second electronic device over a channel. The method further includes preprocessing, by the first electronic device, the signal to generate a noisy channel estimate. The method additionally includes estimating, by the first electronic device, the channel based on the noisy channel estimate using a neural network having a mixture of experts (MoE) architecture and a router.

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.

INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM

Resumen de: US20260301441A1

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.

IMAGE RECOGNITION AND DETECTION METHOD, IMAGE INFORMATION DETECTION METHOD AND IMAGE PROCESSING SYSTEM

Resumen de: WO2026199807A1

The present invention relates to the technical field of medical instruments. Provided are an image recognition and detection method, an image information detection method and an image processing system. The image recognition and detection method comprises: inputting an image to be tested into a feature extraction unit; acquiring a feature map extracted by the feature extraction unit on the basis of said image; on the basis of the feature map, respectively enabling a first neural network to execute a first prediction process and enabling a second neural network to execute a second prediction process; and acquiring a category classification result about a target object in said image on the basis of a prediction result of the first prediction process, and acquiring quantization information about the target object on the basis of a prediction result of the second prediction process. The present invention can predict karyotypes on the basis of the microscopic morphology of cells in images, and takes into account both the overall presentation of the field of view and microscopic features of individual cells so as to identify titer information, thereby improving the accuracy of karyotype and titer identification and reducing occurrences of incorrect or failed predictions.

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.

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.

METHOD FOR RECOGNIZING SPACES TO BE OPTIMIZED IN BUILT ENVIRONMENT ON BASIS OF MORPHOLOGICAL HIERARCHY MODEL

Resumen de: WO2026199950A1

A method for recognizing spaces to be optimized in a built environment on the basis of a morphological hierarchy model, comprising collecting and preprocessing data of a built environment, preliminarily performing hierarchical classification of the morphologies of the built environment, correcting the hierarchical classification of the morphologies of the built environment, preliminarily recognizing spaces to be optimized, performing hierarchical recognition of said spaces, and performing recognition result interaction and feedback. According to the method, data collection is performed by means of a laser radar system, an aerial photography unmanned aerial vehicle, a street view collection vehicle, and a low earth orbit satellite, and recognition and display of spaces to be optimized in a built environment are performed on the basis of multi-task Bayesian federated learning, an XGBoost model, a vision transformer, and a convolutional neural network. The described method can facilitate rapid recognition of spaces to be optimized in a built environment in the field of urban planning, and improve the rationality and accuracy of recognition results of said spaces in the built environment by means of morphological hierarchical classification.

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

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.

NEURAL NETWORK MODEL INFERENCE METHOD AND COMPUTER DEVICE

Resumen de: WO2026199919A1

The present description provides a neural network model inference method and a computer device. A data owner receives N ciphertext neural network models sent by a model owner. The N ciphertext neural network models are obtained by performing obfuscation processing on a first neural network model. A true neural network model comprises a first linear layer, and N-1 fake neural network models obtained by means of obfuscation comprise second linear layers, wherein parameters of the second linear layers are 0. The first linear layer and the N-1 second linear layers are respectively encrypted on the basis of N key shares, wherein the N key shares are generated on the basis of a distributed encryption protocol supporting homomorphic operations. The data owner uses the N ciphertext neural network models to perform inference on target data to obtain the sum of N ciphertext outputs, thereby obtaining an inference result for the target data on the basis of the sum of the N ciphertext outputs. By using the N key shares for encryption and generating the fake neural network models for model obfuscation, the security of the models is improved.

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 MODEL INFERENCE METHOD AND COMPUTER DEVICE

Resumen de: WO2026199920A1

A neural network model inference method and a computer device. A data party first receives an encrypted neural network model sent by a model party. A first linear layer of the encrypted neural network model corresponds to a first ciphertext linear layer and N-1 second ciphertext linear layers, the first ciphertext linear layer being obtained by directly encrypting the first linear layer, and the second ciphertext linear layer being obtained by encrypting a linear layer with all zero parameters. The first ciphertext linear layer and the second ciphertext linear layer are respectively encrypted using N key shares, the N key shares being generated on the basis of a distributed encryption protocol supporting homomorphic operations. The data party uses the encrypted neural network model to perform target data inference to obtain an intermediate result output by another layer preceding the first linear layer, and inputs the intermediate result into the first linear layer and the N-1 second linear layers, respectively, to obtain N outputs respectively corresponding to N ciphertext linear layers. An inference result of the target data is obtained according to a sum of the N outputs.

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.

PERSISTENT-PHOTOCONDUCTIVITY-BASED BIO-INSPIRED SENSING-MEMORY-COMPUTING INTEGRATED PHOTOELECTRIC DETECTION SYSTEM FOR MOVING TARGET

Resumen de: WO2026199857A1

The present application relates to the fields of bio-inspired intelligent vision and brain-inspired computing. Disclosed is a persistent-photoconductivity-based bio-inspired sensing-memory-computing integrated photoelectric detection system for a moving target. The system comprises a synapse-like bio-inspired photodetector array and a brain-inspired memristor array, which are interconnected, wherein the synapse-like bio-inspired photodetector array applies a fixed bias voltage to a device in each pixel when detecting visual information of the moving target, so as to convert spatial illumination information corresponding to each pixel into a persistently decaying photocurrent, and the synapse-like bio-inspired photodetector array continuously receives, during the persistent decay of the photocurrent, a plurality of frames of illumination information of the moving target, and reads the photocurrent of each pixel after the last frame of illumination information is inputted, so as to determine a feature map that stores all historical moment information; and the brain-inspired memristor array executes deep-neural-network operations on the feature map on the basis of Ohm's law and Kirchhoff's laws, and outputs a current corresponding to a task result label, so as to identify the moving target. The present application can meet the requirements of deep neural networks and complex dynamic vision tasks.

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.

METHOD AND SYSTEM FOR DETECTING AN OBJECT IN AN IMAGE

Resumen de: WO2026201760A1

The invention relates to a computer-implemented method for detecting an object in a digital image by means of a client-server system, wherein the following steps are carried out: a) providing a particular set of local training images to one of the at least two clients, wherein each local training image from the set comprises an image of the object, and the image includes a permissible or an impermissible representation of the object, which is characterised as such, b) generating and training a particular local embedding model on the basis of artificial intelligence for a feature of the object, by means of the particular client, from the particular set of local training images using an encoder, provided in each case to the particular client, in the form of a neural network having input nodes, intermediate layers and output nodes, wherein the number of output nodes is smaller than the number of input nodes, and the embedding model is formed by the output nodes of the encoder, c) transmitting the particular local embedding model from the particular client to the server, and aggregating the respective local embedding models into a global embedding model, and distributing the global embedding model to at least one of the at least two clients, d) capturing a current image of a produced product, which comprises a representation of the object, e) carrying out a feature analysis by applying the particular local embedding model, and detecting the object in the current image when a corr

GENERATION OF MOLECULAR STRUCTURES

Resumen de: WO2026201767A1

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: US20260301397A1

0000 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.

RECURRENT SELF-REPRESENTATION NEURAL NETWORK

Nº publicación: US20260300686A1 01/10/2026

Solicitante:

INNERVOICE PBC [US]
InnerVoice PBC

Resumen de: US20260300686A1

0000 Disclosed are systems and methods that implement a form of subjective awareness of experience for a neural network and provide the ability to quantitively determine if a neural network is self-aware. The disclosed implementations include a recurrent self-representation system that includes a neural network and an introspective system that is communicatively coupled with the neural network. The introspective system generates and provides recurrent self-representations to the neural network as inputs of an input set. The neural network utilizes those recurrent self-representations to understand its own experience and utilize that understanding to influence the neural networks processing of inputs and generation of outputs.