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A CERVICAL INTRAEPITHELIAL NEOPLASIA RISK PREDICTION MODEL AND ITS CONSTRUCTION METHOD, AN ELECTRONIC DEVICE, AND A STORAGE MEDIUM

Resumen de: NL4000831A

The present invention belongs to the technical field of deep learning and discloses a cervical intraepithelial neoplasia risk prediction model and its construction method, an electronic device, and a storage medium. The method includes: a data sample acquisition step; a data analysis step, screening the original significant features; constructing a decision tree model using the original significant features as input data, obtaining the prediction result of each sample; extracting the prediction result as new significant features; merging the original significant features and the new significant features into an extended feature set, using the extended feature set to train an artificial neural network model, obtaining the cervical intraepithelial neoplasia risk prediction model. The present invention, by integrating decision tree and artificial neural network and combining clinical screening indicators, realizes cervical intraepithelial neoplasia risk assessment, improves the prediction accuracy and robustness, and greatly improves clinical diagnosis efficiency.

Method And System for Manufacturing Fiber Bragg Grating Based on Machine Vision

Resumen de: AU2025204589A1

The present disclosure relates to the field of laser micro-nano manufacturing technology and discloses a method and system for manufacturing a fiber Bragg grating based on machine vision. By combining advanced machine vision technology and neural network model technology, automatic recognition of the focal plane of the fiber core is realized, so that the laser focus can be automatically focused on the focal plane of the fiber core, thereby realizing the automatic and intelligent fiber Bragg grating manufacturing process. The method not only has the characteristics of high precision and high efficiency, but also shows broad application prospects, providing strong support for the development of optical fiber communication, sensing, and other fields. (Fig. 1) The present disclosure relates to the field of laser micro-nano manufacturing technology and discloses a method and system for manufacturing a fiber Bragg grating based on machine vision. By combining advanced machine vision technology and neural network model technology, automatic recognition of the focal plane of the fiber core is realized, SO that the laser focus can be automatically focused on the focal plane of the fiber core, thereby realizing the automatic and intelligent fiber Bragg grating manufacturing process. The method not only has the characteristics of high precision and high efficiency, but also shows broad application prospects, providing strong support for the development of optical fiber communication, se

MACHINE LEARNING APPARATUS, MACHINE LEARNING METHOD, AND COMPUTER READABLE NON-TRANSITORY RECORDING MEDIUM STORING MACHINE LEARNING PROGRAM

Resumen de: US20260154549A1

A linguistic feature amount output part receives a text describing a base class image and outputs a linguistic feature amount. An image feature amount output part receives the base class image and outputs an image feature amount. A base class image selection part receives the linguistic feature amount, the image feature amount, and the base class image and selects a base class image corresponding to the image feature amount having a distance equal to or smaller than a predetermined threshold value from the linguistic feature amount. A neural network lower layer part receives the base class image selected by the base class image selection part and a novel class image and outputs a value based the base class image and a value based on the novel class image. A base class classification output part outputs a base class classification based on the base class image and the novel class image. A novel class classification output part outputs a novel class classification based on the novel class image.

MULTI-NODE CLUSTER-BASED INFERENCE METHOD AND SYSTEM THROUGH GPU SEPARATE ALLOCATION OF PRE-TRAINED LAYER AND FINE-TUNING LAYER OF MULTIPLE DEEP LEARNING MODELS

Resumen de: US20260154775A1

0000 A multi-node cluster-based inference method through GPU separation allocation of a pre-trained layer and a fine-tuning layer of multiple deep learning models. The method includes: receiving an input value from a client; distributing the received input value, and transmitting the first input value to a first computation node including a container in which a neural network bundle of a first stage is loaded; performing, by a first container of the first computation nodes, an operation through a neural network layer of a GPU by using the received first input value as an input, and generating a first output value; and selecting a container in which the neural network bundle of the next stage is loaded, and transmitting the second output value to the computation node that includes the container in which the next stage is to be executed or the container in which to execute the next stage.

RELIABILITY PREDICTION METHOD FOR IMAGE CLASSIFICATION NEURAL NETWORK MODEL

Resumen de: US20260154949A1

0000 A reliability prediction method for an image classification neural network model includes: a reliability model is trained; and the trained reliability model predicts a reliability of the image classification neural network model. According to training and testing of the image classification neural network model, input features of the reliability model include a model training factor and a model testing factor. The model training factor characterizes data and model factors affecting the reliability of the image classification neural network model. The model testing factor characterizes a test sufficiency of the image classification neural network model. An output of the reliability model is a reliability prediction result of the image classification neural network model.

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

Resumen de: 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.

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

Resumen de: 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.

IMAGE GENERATION USING A NEURAL NETWORK

Resumen de: US20260154963A1

0000 Apparatuses, systems, and techniques to generate an image. In at least one embodiment, one or more neural networks are to generate a second image based, at least in part, on a first image and information indicating zero or more differences between the first and second image.

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

Resumen de: 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

Resumen de: 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

Resumen de: 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

Resumen de: 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

Resumen de: 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.

PROCESSOR AND METHOD FOR SIMULTANEOUS LOCALIZATION AND MAPPING BASED ON REAL-TIME NEURAL NETWORK RENDERING USING SPARSE MIXTURE-OF-EXPERTS MODEL ACCELERATION ARCHITECTURE

Resumen de: US20260153353A1

A processor includes a sampling unit configured to hierarchically sample a 2D image collected through any image collection device and pose information of the image collection device corresponding to the 2D image, and a rendering unit configured to perform SLAM through real-time rendering for data sampled by the sampling unit, wherein the rendering unit includes a computation core configured to perform a neural network operation based on a sparse expert model including a plurality of expert neural networks and reducing a number of neural network channels by exclusively activating an expert neural network differently selected for each input batch, and a scheduler configured to schedule a processing order of input batches to improve computational efficiency of the computation core.

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

Resumen de: 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.

Neural Network Methods for Describing System Topologies

Resumen de: 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.

METHOD FOR CONTROLLING VEHICLE POWER, COMPUTER SYSTEM, AND VEHICLE

Resumen de: EP4751988A1

0001 A method for controlling vehicle power, a computer system, and a vehicle are disclosed. The vehicle includes a fuel cell, a power battery, a hydraulic system, an accessory system, and a visual sensor. The method is performed by a processing circuit of a computer system. The method includes obtaining an image of a construction earthwork by the visual sensor; identifying a type of the construction earthwork by an earthwork classification model based on the image of the construction earthwork; obtaining a power demand load spectrum of the vehicle within a first time duration; correcting the type of the construction earthwork based on the power demand load spectrum; inputting the power demand load spectrum and the corrected type of the construction earthwork into a pre-trained neural network prediction model, and predicting a power load spectrum of the vehicle within a second time duration by the neural network prediction model; and determining optimal power allocation between the fuel cell and the power battery based on the predicted power load spectrum, so as to minimize an operating cost objective function within the second time duration.

ADAPTIVE REAL-TIME ADJUSTMENTS IN DEEP NEURAL NETWORKS

Resumen de: EP4752786A1

In some embodiments, there may be provided a method that includes In some embodiments, there may be provided a method that includes receiving an indication to perform a single class inference task using a machine learning model that is trained to perform a multi-class inference task; in response to the indication, applying, during a timeframe of execution of the machine learning model hosted by the user equipment, at least one mask mapped to the single class inference task; and reconfiguring the machine learning model to a state where the first set of nodes and the second set to perform the multi-class inference task. Related systems, methods, and articles of manufacture are also disclosed.

VIDEO SYNTHESIS WITHIN A MESSAGING SYSTEM

Resumen de: EP4752837A2

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for video synthesis. The program and method provide for accessing a primary generative adversarial network (GAN) comprising a pre-trained image generator, a motion generator comprising a plurality of neural networks, and a video discriminator; generating an updated GAN based on the primary GAN, by performing operations comprising identifying input data of the updated GAN, the input data comprising an initial latent code and a motion domain dataset, training the motion generator based on the input data, and adjusting weights of the plurality of neural networks of the primary GAN based on an output of the video discriminator; and generating a synthesized video based on the primary GAN and the input data.

SYSTEM AND METHOD FOR DYNAMICALLY ADJUSTING LEVEL OF DETAILS OF POINT CLOUDS

Resumen de: EP4752843A2

Some embodiments of an example method disclosed herein may include receiving point cloud data representing one or more three-dimensional objects; receiving a viewpoint of the point cloud data; selecting a selected object from the one or more three-dimensional objects using the viewpoint; retrieving a neural network model for the selected object; generating a level of detail data for the selected object using the neural network model; and replacing, within the point cloud data, points corresponding to the selected object with the level of detail data.

AI-driven system and method for generating style fingerprints and compositions

Nº publicación: GB2644802A 03/06/2026

Solicitante:

MONARRCH INC [US]
Monarrch Inc

Resumen de: GB2644802A

The present invention relates to an Al-driven system for generating style transfer fingerprints and compositions. It includes modules for integrating with sonic libraries, extracting metadata and audio features, and employing deep neural networks for style transfer. A style fingerprint generation module captures the artist's sonic characteristics, stored securely in a database linked to artist profiles. A composition generation module utilizes these fingerprints to create new audio compositions that authentically reflect the artist's unique style. The method involves connecting the artist's library, preprocessing audio, extracting features, training a style transfer model, generating a style fingerprint, and producing compositions.