Alerta

SIDELOBE SUPPRESSION METHOD AND APPARATUS FOR RADAR PLANE IMAGING, ELECTRONIC DEVICE AND COMPUTER PROGRAM PRODUCT

Absstract of: WO2026118410A1

Provided in the present application are a sidelobe suppression method and apparatus for radar plane imaging, an electronic device and a computer program product. The method comprises: acquiring a constructed training data set, the training data set comprising radar plane image samples containing sidelobes and corresponding radar plane image samples without sidelobes or with sidelobes suppressed; on the basis of the training data set, training a constructed adaptive neural network model to obtain an adaptive sidelobe suppression model; acquiring a radar plane image to be processed; and, on the basis of the adaptive sidelobe suppression model, performing adaptive sidelobe suppression on the radar plane image to be processed. The solution of the present application essentially achieves sidelobe suppression for radars by using a signal processing means, thus enabling direct processing of radar images in a completely deployed radar system without changing hardware facilities, and allowing for dynamically adjusting the sidelobe suppression strategy according to different task requirements and scenarios; thus, the more flexible and post-deployment adjustable solution can enhance the adaptability and maintainability of radar systems.

FIXATION TOLERANT OBJECT RECOGNITION WITH A LOGARITHMICALLY-COMPRESSED VISUAL FILED

Absstract of: US20260162418A1

0000 Systems and methods are described herein that utilize neural networks to learn and implement convolutional filters that can be used over a logarithmically-compressed image. These filters are tolerant to changes in the relative visual fixation of an image, that is, a change of origin in log-polar coordinates. Deep networks, such as those that use multilayered neural networks, may be configured to implement the proposed method for filter learning and to take advantage of the exponential savings associated with a logarithmically-compressed image space with minimal sacrifice of fixation invariance.

Neural Networks based Multimodal Transformer for Multi-Task User Interface Modeling

Absstract of: US20260162416A1

0000 A method includes receiving, via a computing device, a screenshot of a display provided by a graphical user interface of the computing device. The method also includes generating, by an image-structure transformer of a neural network, a representation by fusing a first embedding based on the screenshot and a second embedding based on a layout of virtual objects in the screenshot. The method additionally includes predicting, by the neural network and based on the generated representation, a modeling task output associated with the graphical user interface. The method further includes providing, by the computing device, the predicted modeling task output.

NEURAL NETWORK POWER MANAGEMENT IN A MULTI-GPU SYSTEM

Absstract of: US20260161215A1

Systems, apparatuses, and methods for managing power consumption for a neural network implemented on multiple graphics processing units (GPUs) are disclosed. A computing system includes a plurality of GPUs implementing a neural network. In one implementation, the plurality of GPUs draw power from a common power supply. To prevent the power consumption of the system from exceeding a power limit for long durations, the GPUs coordinate the scheduling of tasks of the neural network. At least one or more first GPUs schedule their computation tasks so as not to overlap with the computation tasks of one or more second GPUs. In this way, the system spends less time consuming power in excess of a power limit, allowing the neural network to be implemented in a more power efficient manner.

COMPOSITIONS AND METHODS FOR CELLULAR PHENOTYPE ASSESSMENT OF A SAMPLE USING CONFINED VOLUME ARRAYS

Absstract of: US20260159870A1

Certain embodiments of the invention are directed to evaluating and identifying cells by recording and interpreting a time-dependent signal produced by unique cell respiration and permeability attributes of isolated viable cells. Some methods comprise dividing the sample into two or more sub-samples or sample portions, mixing each sub-sample or sample portion with one or more reagents and/or one or more reactants forming distinct sub-sample or sample portion mixtures, compartmentalizing each of the sub-sample or sample portion mixtures into a plurality of small volume compartments, monitoring characteristics of the small volume compartments over time and collecting compartment data, and transmitting the collected data to at least one neural network.

METHOD OF CONTROLLING FOR UNDESIRED FACTORS IN MACHINE LEARNING MODELS

Absstract of: US20260162184A1

A method of training and using a machine learning model that controls for consideration of undesired factors which might otherwise be considered by the trained model during its subsequent analyses of new data. For example, the model may be a neural network trained on a set of training images to evaluate an insurance applicant based upon an image or audio data of the insurance applicant as part of an underwriting process to determine an appropriate life or health insurance premium. The model is trained to probabilistically correlate an aspect of the applicant's appearance with a personal and/or health-related characteristic. Any undesired factors, such as age, sex, ethnicity, and/or race, are identified for exclusion. The trained model receives the image (e.g., a “selfie”) of the insurance applicant, analyzes the image without considering the identified undesired factors, and suggests the appropriate insurance premium based only on the remaining desired factors.

CPU USAGE RATE PREDICTION METHOD AND APPARATUS, AND COMPUTER DEVICE, READABLE STORAGE MEDIUM AND STORAGE PRODUCT

Absstract of: WO2026118833A1

The present application relates to a CPU usage rate prediction method and apparatus, and a computer device and a readable storage medium. The method comprises: during the running of a target software application, for each node in a node cluster in which the target software application is deployed, acquiring, in real time, baseline CPU usage rates of a central processing unit (CPU) of the node at a preset number of time points prior to the current time point; inputting the baseline CPU usage rates into a self-attention-mechanism-based deep learning network model, a convolutional neural network model and a recurrent neural network model, so as to obtain a first feature vector, a second feature vector and a third feature vector; and fusing the first feature vector, the second feature vector and the third feature vector, and on the basis of a fused feature vector, obtaining the current CPU usage rate of the node at the current time point.

CONTROL SYSTEM WITH NEURAL NETWORK PREDICTOR INCORPORATING FIRST PRINCIPLES

Absstract of: US20260161145A1

A predictive control system for a plant includes a predictor model trainer and a predictive controller. The predictor model trainer is configured to train a predictor model using a loss function including (i) a first error loss term based on an error between predicted values of controlled variables (CVs) generated by the predictor model and historical values of the CVs in historical state data and (ii) a second error loss term based on the predicted values of the CVs and physical relationships involving the CVs. The predictive controller is configured to control operation of the plant using the trained predictor model.

3 A METHOD AND AN APPARTUS FOR GENERATING 3D INFORMATION OF AN OBJECT USING A NEURAL NETWORK MODEL

Absstract of: KR20260086642A

본 개시는 뉴럴 네트워크 모델을 이용하여 객체의 3차원 정보를 생성하는 방법 및 장치에 관한 것이다. 일 실시예에 따르면, 뉴럴 네트워크 모델을 이용하여 객체의 3차원 정보를 생성하는 방법은 선박 환경을 촬영한 적어도 하나의 제1 이미지를 이용하여 뉴럴 네트워크 모델을 학습시키는 단계; 상기 학습된 뉴럴 네트워크 모델에 제2 이미지를 입력하는 단계; 및 상기 뉴럴 네트워크 모델로부터 상기 제2 이미지에 포함된 객체의 3차원 정보를 획득하는 단계;를 포함할 수 있다.

Processor

Absstract of: GB2702268A

A processor has a neural processing unit, or neural engine 700, which in turn has a local storage 738; a handling unit 720 generates invocation data to load a block of a tensor into the local storage from a storage of the processor; the tensor has a first number of dimensions, and the block of the tensor has a size of one in one of said dimensions such that the block consists of tensor elements arrayed in a second number of dimensions, the second number of dimensions is fewer than the first; a storage access controller receives the generated invocation data and identifies a position of the block within the tensor, then loads data corresponding to the identified block of the tensor into the local storage; one or more execution sub-unit of the neural processing unit performs an operation on the block. Preferably the tensor represents a feature map of a neural network. The storage access controller may be made up of an input reader 724, output writer 726, and DMA unit 728. The storage of the processor may be made up of an L1 cache (656a, 656b; figure 1b) or an L2 cache (660). A neural execution description (NED) may include an external storage stride. figure 2

A method for fusing at least two output signals of at least two sensor devices of a motor vehicle, a corresponding computer program product, a corresponding non-transitory computer-storage medium, as well as corresponding electronic computing device

Absstract of: GB2702296A

A method for fusing at least two output signals (22, 24) of at least two sensor devices (14, 16) of a motor vehicle (10) by an electronic computing device (12) of the motor vehicle, comprising the steps of: receiving at least a first output signal (22) of at least a first sensor device (14) of the at least two sensor devices by the electronic computing device; receiving at least a second output signal (24) of at least a second sensor device (16) of the at least two sensor devices by the electronic computing device; providing a deep neural network (30) for determining a time offset between the at least two received output signals by the electronic computing device; determining the time offset by the deep neural network; fusing the at least two output signals depending on the determined time offset by the electronic computing device. Furthermore, the present invention relates to a computer program product, a non-transitory readable computer-storage medium, as well as an electronic computing device. Fig. 1

APPARATUS AND METHOD FOR ASSISTING CYBER THREAT DETECTION AND RESPONSE BASED ON NEURAL NETWORK PROCESSING UNIT

Absstract of: KR20260082451A

본 개시는 신경망 처리 장치 기반의 사이버 위협 탐지 및 대응을 위한 어시스턴트 장치 및 방법에 관한 것으로, 특히, 장치는, 외부 장치와 통신을 수행하는 통신모듈; 신경망 처리 장치 기반의 사이버 위협 탐지 및 대응을 위한 어시스턴트 동작을 수행하기 위한 적어도 하나의 프로세스가 저장된 메모리; 및 상기 프로세스에 따라 상기 어시스턴트 동작을 수행하는 프로세서를 포함하며, 상기 프로세서는, 페이로드를 데이터로서 수집하여 전처리하고, 전처리된 데이터를 사전 학습된 인공지능 기반 탐지 모델로 입력하고, 상기 탐지 모델로부터 사전 설정된 판단기준에 기초한 공격 유무에 대한 판단 결과가 출력되면, 상기 판단 결과를 언어 모델에 입력하고, 상기 언어 모델로부터 상기 판단 결과에 기초한 답변이 출력되면, 상기 답변을 사용자에게 제공하도록 구성될 수 있다. '과학기술정보통신부KISA의 "2024년 AI 보안 제품 및 서비스 사업화 지원사업" 산출물 또는 지원을 받아서 상용화된 제품·서비스'

METHODS, SYSTEMS, ARTICLES OF MANUFACTURE, AND APPARATUS TO TAG SEGMENTS IN A DOCUMENT

Absstract of: US20260154982A1

0000 Methods, apparatus, systems, and articles of manufacture are disclosed to tag segments in a document. An example apparatus includes processor circuitry to execute machine readable instructions to generate node embeddings for nodes of a graph, the node embeddings based on features extracted from text segments detected in a document, the text segments to be represented by the nodes of the graph; sample edges corresponding to the nodes to generate the graph; generate first updated node embeddings by passing the node embeddings and the graph through layers of a graph neural network, the first updated embeddings corresponding to the node embeddings augmented with neighbor information; generate second updated node embeddings by passing the first updated embeddings through layers of a recurrent neural network, the second updated embeddings corresponding to the first updated node embeddings augmented with sequential information; and classify the text segments based on the second updated node embeddings.

A METHOD AND SYSTEM FOR ANOMALY DETECTION IN AN OPERATIONAL ASSET, AND A METHOD FOR REPAIRING AN OPERATIONAL ASSET

Absstract of: US20260153862A1

A method for anomaly detection in an operational asset includes collecting a source domain dataset corresponding to a first operating condition of the operational asset, wherein samples from the source domain dataset belong to a healthy class, and a faulty class; collecting a target domain dataset corresponding to a second operation condition of the operational asset, wherein samples from the target domain dataset belong to the healthy class; inputting the source domain dataset and the target domain dataset as input data into a neural network; extracting, by the neural network, features from the input data, wherein a first subset of features is discriminative of the healthy class and a second subset of features is domain invariant; reducing a dimensionality of the features into reduced features; and classifying the reduced features into a normal class and an anomaly class using a one-class classifier.

Artificial Intelligence-Assisted Radiographic Detection and Classification of Leadless Implanted Electronic Devices

Absstract of: US20260154840A1

0000 Systems and methods for artificial intelligence (“AI”)-assisted radiographic detection of leadless implanted electronic devices (“LLIEDs”) are provided. In general, AI systems and methods are constructed and implemented to provide a highly accurate C model that can assist physicians and support personnel with radiographic (e.g., chest x-ray) detection of the presence (or absence) of any LLIED, and the localization of any detected LLIED(s), prior to performing a scheduled or emergency MRI examination. A two-tier cascading neural network methodology is used to detect the locations of LLIEDs in the first tier and to classify or otherwise identify the type of detected LLIEDs in the second tier.

IMAGE PROCESSING METHOD AND NEURAL PROCESSING UNIT USING OBJECT-SPECIFIC NEURAL NETWORK MODELS

Absstract of: US20260154956A1

An image processing method is disclosed. The method includes receiving an input image including at least one object, and classifying the at least one object in the input image using a first model based on an artificial neural network trained to classify objects into one of a plurality of predetermined categories. At least one second model corresponding to the classified category of the at least one object is determined from among a plurality of second models, each of which is based on an artificial neural network trained to output a specialized processing applied image specific to a respective category. An output image is obtained by inputting the input image, or a region thereof corresponding to the at least one object, into the determined at least one second model.

ENHANCED OBJECT IDENTIFICATION USING ONE OR MORE NEURAL NETWORKS

Absstract of: US20260154959A1

Apparatuses, systems, and techniques to identify one or more objects in one or more images. In at least one embodiment, one or more objects are identified in one or more images based, at least in part, on a likelihood that one or more objects is different from other objects in one or more images.

VIRTUAL NEURON SYSTEM FOR ENHANCED REASONING IN NEURAL NETWORK-BASED GENERATIVE MODELS

Absstract of: WO2026115424A1

A system and method for enhancing reasoning in neural network-based generative models through software-defined Virtual Neurons—active processing units with configurable activation functions and weighted connections—instantiated during inference without modifying pretrained parameters. Virtual Neurons maintain state vectors in dedicated in- memory data structures managed by a cognitive overlay layer, distinct from external storage and context-token buffers used by prior art. Organization follows hierarchical tiers scaled by α* = (γCs/βCf)^(1/(β-γ)); for γ ≈ 0.8, β ≈ 1.5, α* ≈ √2, providing optimal path length and cache locality. Cognitively-isolated sandboxes with data-structure-level barriers enable Virtual Double hypothesis exploration by cloning internal cognitive state graphs (not token sequences), with O(1) rollback. A multilayer introspective feedback system evaluates internal cognitive structure (not output scores) and triggers structural modifications. Combined interaction produces runtime adaptation, speculative branching, and self-correction not achievable by components alone.

Apparatus for Plausible Neural Network Artificial Intelligence (PNNAI) Engine

Absstract of: US20260154528A1

A method of training a hierarchical neural network in hardware comprising a plurality of neurons having tree structure connections between respective ones of the plurality of neurons to compute plausible inferences based on input data. The method includes initiating a WTA neuron ensemble to connect to data input neurons and produce a winner-take-all, attaching data records match with top neurons and branching out at WTA junctions to form a hierarchy network tree, converting a stream of the input data into neuron bipolar signals, updating neuron weights with Hebbian and anti-Hebbian rules, and repeating the attaching, converting, and updating until the input data is exhausted.

INFORMATION PROCESSING SYSTEM, OPERATING METHOD, AND COMPUTER PROGRAM FOR AUTOMATING BASIC EVALUATION OF ELDERLY WELFARE FACILITY

Absstract of: WO2026116831A1

A method for operating an information processing system for automating a basic evaluation of a beneficiary of a welfare facility may comprise the steps of: acquiring state data of the beneficiary of the welfare facility; acquiring service provision history for the beneficiary; inputting the state data of the beneficiary and the service provision history to an artificial neural network model; acquiring state change pattern data of the beneficiary output by the artificial neural network model; and generating basic evaluation data on the basis of the state change pattern data.

METHOD AND SYSTEM FOR INTEGRATING CONVOLUTIONAL NEURAL NETWORK AND LIGHTWEIGHT ARTIFICIAL INTELLIGENCE FOR ON-DEVICE IMAGE CLASSIFICATION

Absstract of: WO2026116516A1

Provided are a method and system for integrating a CNN with lightweight artificial intelligence for on-device image classification. An on-device integrated artificial intelligence system, according to an embodiment of the present invention, comprises: a first network which is a non-lightweight network that extracts features from an input image; and a second network which is a lightweight network that performs inference on the input image on the basis of the extracted features. Accordingly, the performance of the lightweight artificial intelligence can be complemented by integrating the feature point extraction function of the CNN into a lightweight network in an on-device environment.

Neural Network Methods for Describing System Topologies

Absstract of: US20260154550A1

A neural network in one embodiment is built by decomposing a structure into different building materials creating neurons that represent building materials and open spaces in a structure. Subsystems in the building have their neurons concatenated together to create same length neuron strings. In some embodiments, neurons in a short neuron string are split to make longer neuron strings. In some embodiments, neurons are added to some neuron strings to represent inside features, air features, and outside features.

INSTRUCTION GENERATION USING ONE OR MORE NEURAL NETWORKS

Absstract of: US20260154955A1

0000 Apparatuses, systems, and techniques are presented for generating instructional text. In at least one embodiment, an instructional video is analyzed to determine logical steps of a process or task demonstrated in that video, and instructive text is generated for those logical steps.

ELECTRONIC DEVICE AND METHOD FOR IDENTIFYING SPEECH SIGNALS FOR INVOCATION IN VARIOUS LANGUAGES, AND NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM

Absstract of: WO2026116542A1

A processor of an electronic device, according to one embodiment, may acquire: from a first neural network into which a speech signal received through a microphone has been input, a first sequence of portions of the speech signal corresponding to designated frame units; from a second neural network into which designated text has been input, a second sequence of one or more phonetic symbols for the designated text; from a third neural network into which the first sequence and the second sequence have been input, a first dataset indicating the degree to which each of the one or more phonetic symbols corresponds to each of the portions of the speech signal; and, from a pattern discriminator into which the first dataset has been input, predicted phonemes of the portions of the speech signal.

NEURAL NETWORK MODEL FOR SEQUENCE PREDICTION WITH ATTENTION TO ENTITY RELATIONSHIPS

Nº publicación: WO2026117281A1 04/06/2026

Applicant:

MICROSOFT TECHNOLOGY LICENSING LLC [US]
MICROSOFT TECHNOLOGY LICENSING, LLC

Absstract of: WO2026117281A1

An example formulates a training input for a neural network model with attention to include action data and descriptive content. The action data includes a first entity identifier (ID) and a first sequence of actions associated with the first entity ID. The descriptive content describes a first entity associated with the first entity ID. An action in the first sequence of actions includes an electronic transmission involving the first entity and a second entity. An example uses the training input, including the first entity ID, and a non-standardized tokenizer, to train the neural network model with attention to generate and output a second sequence of actions.