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SYSTEM, METHOD, AND APPARATUS FOR RECURRENT NEURAL NETWORKS

Resumen de: US20260277546A1

A method for computation with recurrent neural networks includes receiving an input drive and a recurrent drive, producing at least one modulatory response; computing at least one output response, each output response including a sum of: (1) the input drive multiplied by a function of at least one of the at least one modulatory response, each input drive including a function of at least one input, and (2) the recurrent drive multiplied by a function of at least one of the at least one modulatory response, each recurrent drive including a function of the at least one output response, each modulatory response including a function of at least one of (i) the at least one input, (ii) the at least one output response, or (iii) at least one first offset, and computing a readout of the at least one output response.

METHOD AND SYSTEM FOR HYPERGRAPH NEURAL NETWORK INFERENCE

Resumen de: US20260278332A1

A method and system for hypergraph neural network inference are provided, wherein the method includes: extracting topological information of vertices with the highest degree from a compressed hypergraph topological representation, identifying common high-degree vertices and their connected hyperedges, and transmitting them to a task queue; based on common vertices from the task queue and a common vertex table, triggering hyperedge aggregation operations on each hyperedge in parallel; temporarily storing intermediate aggregation results in an intermediate result buffer of an on-chip cache unit after the aggregation, and reusing the intermediate results of the common vertices during sequential execution based on a hyperedge storage order, thereby obtaining a final aggregation result for each hyperedge. This approach enables compact execution of computing tasks and improves inference efficiency by reducing unnecessary memory access and communication through cache reuse.

DEEP HARMONIC FINESSE: SIGNAL SEPARATION IN WEARABLE SYSTEMS WITH LIMITED DATA

Resumen de: US20260278330A1

A method for separation of non-stationary quasi-periodic signals when limited data is available is described. The method utilizes prior knowledge of time-frequency patterns in the signals to mask and in-paint spectrograms. In one implementation this is achieved through an application-inspired deep harmonic neural network coupled with an integrated pattern alignment component. The network's structure embeds the implicit harmonic priors within the time-frequency domain, while the pattern-alignment method transforms the sensed signal, ensuring a strong alignment with the network.

LASER RADAR TARGET DETECTION METHOD, SYSTEM, TERMINAL, AND STORAGE MEDIUM

Resumen de: US20260276826A1

The method comprises: acquiring target point cloud data and determining whether the total number of points in the target point cloud data exceeds a maximum point limit; when the total number of points in the target point cloud data exceeds the maximum point limit, performing weighted sampling on the target point cloud data using a point cloud sampling method based on the dynamic fusion of density and depth weights to obtain sampled point cloud data; performing voxelization on the sampled point cloud data to obtain voxelized point cloud data, processing the voxelized point cloud data using an improved feature encoding module, and outputting a target tensor; generating a pseudo-image based on the target tensor, extracting features from the pseudo-image using a two-dimensional convolutional neural network, and performing detection and regression on the extracted features using a detection head to obtain detection results.

NEURAL NETWORK MODEL TRAINING METHOD BASED ON PERIODIC FEATURE AND NEURAL NETWORK MODEL

Resumen de: WO2026189008A1

The present application provides a neural network model training method based on a periodic feature and a neural network model. The neural network model comprises an input layer and a self-attention layer. The neural network model training method based on a periodic feature comprises: calling an input layer to extract a plurality of pieces of first feature data of a training sample; performing linear transformation on the plurality of pieces of first feature data to obtain a periodic feature and an aperiodic feature; decomposing the periodic feature into a sine wave feature and a cosine wave feature; concatenating the aperiodic feature, the sine wave feature, and the cosine wave feature to obtain a concatenation result corresponding to the training sample; and using the concatenation result as an input of a self-attention layer to train a neural network model to be trained to obtain a target neural network model, the target neural network model having a function of extracting a periodic feature of data to be processed. In the embodiments of the present application, out-of-distribution periodic features of training samples can be effectively captured, thereby greatly improving model training efficiency.

FUSION IMAGE GENERATING METHOD AND APPARATUS, AND STORAGE MEDIUM STORING INSTRUCTIONS FOR PERFORMING FUSION IMAGE GENERATING METHOD

Resumen de: US20260279031A1

A fusion image generating method using a fusion image generating apparatus, according to an embodiment, comprises the steps of: performing temporal and spatial synchronization of color images and thermal images and preprocessing into preset formats; generating multidimensional color image spectrum features from the preprocessed color images by using a color image encoder in a pre-trained neural network; generating a multidimensional thermal image spectrum feature from the preprocessed thermal images by using a thermal image encoder in the pre-trained neural network; and generating fusion images by fusing the multidimensional color image spectrum features and the multidimensional thermal image spectrum features by using a fusion image decoder in the pre-trained neural network.

METHOD AND DEVICE FOR PLAYING MUSIC USING BLOCKS

Resumen de: US20260279315A1

Disclosed are a method and a device for playing music by using blocks. The music playing method using at least one blocks, which is performed by the music playing device according to an embodiment of the present disclosure, may include capturing an image including the at least one or more blocks through a reception device mounted on the music playing device, recognizing the captured at least one or more blocks by using a trained deep learning neural network model, determining an arrangement structure of the recognized blocks, and playing the music at the music playing device, based on the determined arrangement structure.

SYSTEM AND METHOD FOR ARTIFICIAL INTELLIGENCE GOVERNANCE PLATFORM FOR RESPONSIBLE ARTIFICIAL INTELLIGENCE

Resumen de: US20260278136A1

An Artificial Intelligence Governance system, ensuring responsible AI utilization is disclosed. The system features a processing subsystem hosted on a server, orchestrating bidirectional communications across a network among numerous modules. A database module captures and manages diverse activities associated with interacting with Neural network with attention based AI models, while a user input module facilitates user engagement through API or UI. A configuration module defines organizational policies and regulatory requirements, ensuring responsible AI practices. An authentication module regulates user access based on roles, employing granular permission sets. A Neural network with attention based AI governance module uses a pre-trained AI model to classify risks in user prompts. A session management module establishes standardized API endpoints for seamless integration, and a dashboard module offers a summarized overview, governing responsible AI usage within the organization. This system provides transparency, and ethical AI practices across various organizational contexts.

METHOD FOR PROVIDING INFORMATION ABOUT IN VITRO FERTILIZED EMBRYO SCREENING, AND APPARATUS USING SAME

Resumen de: US20260279547A1

Provided are a method and an apparatus for providing information about in vitro fertilized embryo screening using embryo images. The method, executed by a processor, includes the steps of: receiving an original embryo image; extracting, from the original embryo image, both an inner cell mass (ICM) image and a trophectoderm (TE) image; converting the original embryo image, the extracted ICM image, and the extracted TE image into a binary image or gray scale images; and by using a convolutional neural network model pre-trained to screen an in vitro fertilized embryo by using converted original embryo images, converted ICM extracted images, and converted TE extracted images as inputs, determining an in vitro fertilized embryo by using the converted original embryo image, the converted ICM extracted image, and the converted TE extracted image as inputs.

GENERATIVE DRIVING MODEL

Resumen de: US20260279072A1

A computing device of a vehicle may receive a sequence of images. The computing device can execute a neural network to generate a first plurality of tokens from the images. The computing device can input the tokens into a machine learning model, such as a machine learning language processing model (e.g., a large language model or a neural network including a transformer model). The machine learning model can output a second plurality of tokens based on the execution. In doing so, the machine learning model can output one or more characters or words that represent a future state of the objects in an image. The computing device can analyze the second plurality of tokens. The computing device generate an alert or control the vehicle accordingly.

MULTI-MODAL NEURAL NETWORKS WITH DECODER-ONLY LANGUAGE MODELS

Resumen de: US20260278340A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a multi-modal neural network using contrastive and image captioning losses.

Method for Calculating Absorption Rate of Super Absorbent Polymer on Basis of Image Classification Algorithm

Resumen de: US20260279004A1

A method and system for calculating an absorbing speed of a predetermined absorbent material from video data obtained by photographing a video of the absorbent material absorbing water. Absorption video data is separated into each frame image data, an artificial neural network model is used to determine whether each frame of image data is an absorption-in-progress image or an absorption-completion image, and an absorption start time point and an absorption end time point are detected, thereby calculating the absorption speed of the absorbent material.

APPARATUS AND METHOD FOR PROCESSING SCALABLE FEATURE MAPS USING ARTIFICIAL NEURAL NETWORKS

Resumen de: US20260279040A1

A neural processing unit (NPU) for decoding video or feature map is provided. The NPU may comprise at least one processing element (PE) to perform an inference using an artificial neural network. The at least one PE may be configured to receive and decode data included in a bitstream. The data included in the bitstream may comprise data of a base layer. Alternatively, the data included in the bitstream may comprise data of the base layer and data of at least one enhancement layer. The data of the base layer included in the bitstream may include a first feature map. The data of the at least one enhancement layer included in the bitstream may include a second feature map.

BLOCKCHAIN-BASED DEEP NEURAL NETWORK MODEL FEDERATED RECOMBINATION METHOD AND SYSTEM

Resumen de: US20260278394A1

0000 A blockchain-based deep neural network model federated recombination method includes constructing a consortium blockchain for model federated recombination; configuring a smart contract for the model federated recombination; storing data in a model federated recombination process; training a source model of a model training party, and providing the source model to a model recombination verification party for federated combination; performing model federated recombination, wherein the model recombination verification party performs federated recombination on the source model to obtain a target model; and calculating a contribution value of the model training party. For the reusability of a deep neural network model and the finiteness of computing power and data during single-node user model recombination, multi-node model federated recombination is performed. For the data security and privacy in a federated process and the mutual distrust between multiple nodes in federation, a Hyperledger Fabric-based consortium blockchain and a smart contract are constructed.

GENERATING SEGMENTATION MASKS FOR OBJECTS IN DIGITAL VIDEOS USING POSE TRACKING DATA

Resumen de: US20260279093A1

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate joint-based segmentation masks for digital objects portrayed in digital videos. In particular, in one or more embodiments, the disclosed systems utilize a video masking model having a pose tracking neural network and a segmentation neural network to generate the joint-based segmentation masks. To illustrate, in some embodiments, the disclosed systems utilize the pose tracking neural network to identify a set of joints of the digital object across the frames of the digital video. The disclosed systems further utilize the segmentation neural network to generate joint-based segmentation masks for the video frames that portray the object using the identified joints. In some cases, the segmentation neural network includes a multi-layer perceptron mixer layer for mixing visual features propagated via convolutional layers.

NEURAL NETWORK APPLICATION-SPECIFIC INTEGRATED CIRCUIT

Resumen de: WO2026193497A1

This disclosure describes application-specific integrated circuits (ASICs) that implement neural-network inference using non-reconfigurable physical logic circuitry. In various embodiments, machine-learned functional information is physically embodied in selections of logic functions, connections, wires, omitted connections, constants, arrangements of logic circuitry, or combinations thereof, rather than in memory-stored weights or parameters. The logic circuitry may include deep layers of sub-circuitry arranged between inputs and outputs, and may implement most of the neural-network operations directly in fixed logic. During inference, the use of machine-learned functional information may be input-dependent, such that different portions of the embodied logic contribute based on the input. The disclosed circuits may be synthesized from trained node networks and deployed as fixed-silicon implementations that provide efficient inference with low latency, reduced memory dependence, and hardware-native execution of learned functionality.

LIVE GRAPH NEURAL NETWORK FRAMEWORK FOR FRAUD DETECTION

Resumen de: WO2026188537A1

System and method for providing a live graph neural network framework, the method comprises: providing, in response to a trigger event, a subgraph including a set of nodes connected together by a set of edges based on a first dataset, the set of nodes including a seed node associated with the trigger event, each node including one or more attributes representative of characteristics of the data of the first dataset; providing an input dataset based on transforming the set of nodes and the set of edges of the subgraph into the input dataset; providing a set of embeddings including node embeddings and edge embeddings representative of the subgraph inferenced from the input dataset; and applying a neural network model to the set of embeddings wherein the neural network model is configured to determine a likelihood of node connections in the subgraph.

UNIFIED VISUAL UNDERSTANDING AND IMAGE GENERATION NEURAL NETWORKS

Resumen de: WO2026193311A1

Systems and methods for performing both visual understanding tasks and image generation tasks using the same multi-modal neural network.

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR OPTIMIZING MACHINE LEARNING MODELS USING GRAPH-BASED EMBEDDINGS

Resumen de: WO2026192923A1

Systems, methods, and computer program products are provided for optimizing machine learning models using graph-based embeddings. An example system includes a processor to generate a graph structure, generate a plurality of graph embeddings based on neighboring nodes of a node in the graph structure, generate transformed embeddings by inputting a graph embedding to a neural network to generate an output, the output including one or more transformed embeddings that correspond to the graph embedding, combine the transformed embeddings with trainable intermediate embeddings of a machine learning model to provide an intermediate input for the machine learning model, input the intermediate input to the machine learning model, and perform a training procedure on the plurality of graph embeddings, the neural network, the trainable intermediate embeddings, and the large foundational machine learning model based on an output of the machine learning model that was generated based on the intermediate input.

PARTIAL DISCHARGE CLASSIFICATION METHOD, DEVICE, AND COMPUTER PROGRAM USING ARTIFICIAL NEURAL NETWORK MODEL

Resumen de: WO2026192201A1

This partial discharge classification method using an artificial neural network model comprises the steps of: generating a prescribed pattern by analyzing time-series data for an acoustic signal; and classifying the type of partial discharge corresponding to the prescribed pattern when the prescribed pattern is input to a trained artificial neural network model.

MEDICAL IMAGE OBJECT SEGMENTATION SYSTEM AND OPERATING METHOD BASED ON SEMI-SUPERVISED LEARNING USING PARALLEL NEURAL NETWORKS

Resumen de: KR20260137169A

0001a 본 발명은 병렬 인공 신경망을 활용한 준지도 학습 기반 의료 영상 내 객체 분할 시스템 및 동작 방법에 관한 것으로서, 레이블링된 의료 영상 데이터뿐만 아니라 레이블링되지 않은 의료 영상 데이터를 학습에 활용하여 의료 영상 내 특정 객체를 보다 정밀하게 분할하는 기술에 관한 것이다. 본 발명은 3차원 의료 영상 데이터를 복수 개의 2차원 의료 영상으로 변환한 후, 변환된 의료 영상을 타겟 네트워크와 온라인 네트워크에 병렬적으로 입력하여 객체 분할을 수행하는 기술을 포함한다. 본 발명은 타겟 네트워크에서 의사 레이블을 생성하고, 온라인 네트워크가 이를 학습하도록 구성하여 레이블링되지 않은 의료 영상 데이터도 효과적으로 학습에 반영하는 기술을 포함한다.

FINGER PRESSING STATE DETECTION METHOD, AND TRAINING METHOD, ELECTRONIC DEVICE AND MEDIUM

Resumen de: EP4807589A1

The present disclosure relates to the technical field of signal detection, and discloses a finger pressing state detection method, training method, electronic device, and medium, for improving generalization capability of finger pressing state detection. The method partially includes: pre-processing an ultrasonic echo signal to obtain model input data, where the ultrasonic echo signal is a corresponding ultrasonic echo signal after an ultrasonic array emits an ultrasonic signal to a pressing area; and inputting the model input data into a trained neural network model, so that the neural network model outputs a finger pressing state.

CONTRASTIVE IMAGE CAPTIONING NEURAL NETWORKS WITH VECTOR QUANTIZATION

Resumen de: WO2025128886A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an image processing neural network to generate discrete representations of input images. The discrete representations can then be used for any of a variety of downstream tasks.

METHOD FOR DETECTING INFORMATION ABOUT OBJECTS AND OBJECT PARTS IN A SPACE

Nº publicación: EP4807699A1 16/09/2026

Solicitante:

AIT AUSTRIAN INST TECHNOLOGY [AT]
AIT Austrian Institute of Technology GmbH

Resumen de: EP4807699A1

Method for detecting information about objects and object parts in a space using a LiDAR sensor, which has a field of view and in which measurement data is converted into a 3D model of information about objects in the field of view, comprising the steps:a.) acquiring a 3D point cloud by the LiDAR sensor in the field of view,b.) converting the 3D point cloud to a 2D digital image in a calculating component,c.) providing the 2D digital image to a detecting component which comprises a trained neural network module,c.1) whereas the module estimates basic geometrical 3D lines of objects, preferably including endpoints of lines andc.2) whereas the module establishes a model of information about objects in the field of view based on the model of 3D lines.