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METHOD AND SYSTEM FOR IMPLEMENTING PANEL DEFECT DETECTION ON BASIS OF IMAGE GRAYSCALE EQUALIZATION

Resumen de: WO2026137839A1

The present disclosure relates to the technical field of panel defect detection, and provides a method and system for implementing panel defect detection on the basis of image grayscale equalization. The method comprises: collecting statistics about grayscale values of a panel image by means of image grayscale value statistics collection, so as to obtain a grayscale histogram of the panel image; on the basis of the grayscale histogram of the panel image, using an adaptive histogram equalization algorithm to perform grayscale value equalization processing on the panel image, so as to obtain an equalized image; performing feature labeling on the equalized image, and performing neural network training on the basis of the feature-labeled equalized image, so as to obtain a target detection model; and performing panel defect detection on the equalized image on the basis of the trained target detection model, so as to obtain a defect detection result. By performing grayscale value statistics collection and grayscale value equalization processing on the panel image, defect imaging features can be enhanced, so that the target detection model can accurately perform defect detection and locating, thereby solving the problem that existing Mura defect detection is prone to missed detection.

MULTI-DEVICE COMBINED USE-BASED IDENTIFICATION METHOD AND SYSTEM FOR RAW MATERIALS AND EXCIPIENTS IN FORMULATION

Resumen de: WO2026138708A1

A multi-device combined use-based identification method and system for raw materials and excipients in a formulation. The method comprises: performing cryogenic polishing on a cross-section of a test sample under analysis to obtain a cross-section under test; using an electron microscope to observe the cross-section under test to obtain an electron microscopy image of the cross-section under test, and determining a region under test on the cross-section under test; using an energy dispersive spectrometer to scan the region under test to obtain an energy spectrum image; on the basis of the electron microscopy image and the energy spectrum image, determining whether there are characteristic elements in the test sample, and if there are characteristic elements, identifying the characteristic elements on the basis of the energy spectrum image; and on the basis of the electron microscopy image and the energy spectrum image, determining whether there are components matching preset morphological characteristics in the test sample, and if there are components matching the preset morphological characteristics, using a pre-constructed convolutional neural network model to perform analysis processing on the electron microscopy image to identify the components matching the preset morphological characteristics. The identification method can more accurately and quickly identify components in a sample under analysis.

A COLLECTIVE, BLOCKCHAIN-BASED NEURAL NETWORK OPTIMIZATION SYSTEM AND METHOD

Resumen de: EP4769218A1

0001 The invention particularly relates to a system and method that is used artificial intelligence, distributed computing, energy efficiency, optimization, cybersecurity, privacy field and relates to a decentralized system that not only optimizes neural networks for devices with varying capacities but also provides a robust defense mechanism against cybersecurity and privacy threats.

SHOPPING BASKET MONTITORING USING COMPUTER VISION AND MACHINE LEARNING

Resumen de: EP4769361A2

0001 A system for monitoring shopping baskets (e.g., baskets on human-propelled carts, motorized carts, or hand-carried baskets) can include a computer vision unit that can image a surveillance region (e.g., an exit to a store), determine whether a basket is empty or loaded with merchandise, and assess a potential for theft of the merchandise. The computer vision unit can include a camera and an image processor programmed to execute a computer vision algorithm to identify shopping baskets and determine a load status of the basket. The computer vision algorithm can comprise a neural network. The system can identify an at least partially loaded shopping basket that is exiting the store, without indicia of having paid for the merchandise, and execute an anti-theft action, e.g., actuating an alarm, notifying store personnel, activating a store surveillance system, activating an anti-theft device associated with the basket (e.g., a locking shopping cart wheel), etc.

SOLVING MULTIPLE TASKS SIMULTANEOUSLY USING CAPSULE NEURAL NETWORKS

Resumen de: EP4769222A2

0001 The invention provides a system and method for training artificial neural networks for solving multiple tasks simultaneously, wherein the artificial neural network comprises at least one capsule layer. The invention also provides a system and a method for solving multiple tasks simultaneously, wherein the artificial neural network comprises at least one capsule layer. The invention further provides additional connected aspects.

METHOD FOR GENERATING MULTIMEDIA GAMES AND THEIR LAUNCHING SCHEDULE

Resumen de: EP4769366A1

0001 The object of the application is a computer-implemented method for generating multimedia games and their launching schedule, in which multimedia game scenarios are made of attributes stored in advance in a database, using a genetic algorithm and deep neural networks, wherein said genetic algorithm is implemented using a bidirectional neural network of the Long Short-Term Memory type, LSTM. 0002 The object of the application is also a system for generating multimedia games, said system being configured and programmed to implement the method according to the application.

PROCESSING METHOD FOR NEURAL NETWORK MODEL, AND SECURE ELEMENT AND COMPUTING APPARATUS

Resumen de: WO2026129814A1

Provided in the embodiments of the present application are a processing method for a neural network model, and a secure element and a computing apparatus. The computing apparatus comprises: a storage element, which is used for storing a first neural network code for the first model inference of a neural network model and at least some first network parameters for the first model inference; a secure element, which is used for storing the remaining first network parameters for the first model inference, and/or storing a second neural network code and second network parameters for the second model inference of the neural network model, and executing the second model inference on the basis of the second network parameters and the second neural network code; and a general-purpose computing element, which is used for executing the first model inference on the basis of the first neural network code and the first network parameters. The embodiments of the present application can prevent a neural network model from being physically attacked, thereby improving the security of the neural network model.

OBJECT CLASSIFICATION

Resumen de: US20260179374A1

0000 In various examples, multilabel hierarchical classification of objects for autonomous systems and applications is described herein. Systems and methods are disclosed that use one or more neural networks to classify objects, such as traffic signs, using multilabel classification and/or hierarchical classification. For instance, a multilabel subnetwork of the neural network(s) may classify an object based at least on one or more attributes associated with the object. As such, the output from the multilabel subnetwork may include at least a classification associated with the object and an attribute classification(s) associated with the object. A hierarchical subnetwork of the neural network(s) may also classify the object using one or more class labels, where a class label indicates another classification and/or a class group associated with the object. The systems and methods may then use the classification, the attribute classification(s), and/or the class label(s) to determine a final classification associated with the object.

SYSTEMS AND METHODS FOR DIAGNOSING AND/OR TREATING PATIENTS

Resumen de: US20260179222A1

Devices, systems, and methods are provided for recognizing, diagnosing, mapping, sensing, monitoring and/or treating selected areas within a patient's body. The systems, devices and methods may be used to map, detect and/or quantify images and/or physiological parameters collected from the patient. One such system comprises an optical imaging device, such as an endoscope, and a processor coupled to the imaging device. The processor includes a software application configured to recognize the images captured by the optical imaging device and determine if the tissue contains a medical condition and may include an artificial neural network configured to develop at least one set of computer-executable rules useable to recognize the medical condition in the captured tissue images. The systems, devices and methods provided herein allow for a more objective and comprehensive inspection of the targeted areas within a patient so as to improve the diagnosis and ultimate treatment of patients.

SCALABLE FORM MATCHING

Resumen de: US20260178825A1

0000 Disclosed are a method and apparatus for determining a given template of a form used by a filled in instance of that type of form from amongst a great number of form templates (a hundred or more). The given instance is evaluated by a neural network that has been trained by a single example of each template in order to reduce the total number of templates down to a manageable amount. Given a list of closest matching templates, the instance is aligned to each of the closest matching templates. The comparison generates a match score. The form template having the greatest match score is the correct form template. Filtering the instance through a one-shot learning neural network before performing a precise comparison enables the process to scale to any number of template forms.

GENERATING DOCUMENT-GROUNDED TRAINING DATA FOR GENERATIVE MODELS

Resumen de: US20260178924A1

0000 The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a training dataset for AI agents by using large language models to simulate a conversation between a user and an AI agent. In some embodiments, the disclosed systems determine a synthetic persona by selecting a plurality of characteristics defining the synthetic persona. In some embodiments, the disclosed systems generate a synthetic prompt emulating text input by the synthetic persona utilizing a large language model to process a digital document associated with the synthetic persona. In some embodiments, the disclosed systems generate a synthetic response emulating text generated by an artificial intelligence agent responsive to the text input by the synthetic persona utilizing a second large language model to process the synthetic prompt. In some embodiments, the disclosed systems modify parameters of a neural network using the synthetic prompt and the synthetic response as training data.

IMAGE ENHANCEMENT USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260179180A1

Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.

APPARATUS AND METHOD FOR PROCESSING SENSOR DATA, SENSOR SYSTEM

Resumen de: US20260179315A1

0000 Processing of sensor data from sensor(s). The sensor data are provided as an unordered point cloud. The points of the unordered sequence are then converted into a regular structure using a point-processing neural network and made available for further processing. A transfer device is configured to receive a group of input data elements from the sensors. Each input data element of this group of input data elements includes a point that specifies at least one position. The transfer device also includes a point-processing neural network. This point-processing neural network is configured to map the points of the group of input data elements to a regular output data structure. A processing device is configured to detect an object and/or ascertain properties of an object using the regular output data structure. For the conversion of points of an unordered point cloud to a regular structure, a point-processing neural network is provided.

SYSTEMS AND METHODS FOR AUTOFOCUS AND AUTOMATED CELL COUNT USING ARTIFICIAL INTELLIGENCE

Resumen de: US20260177470A1

Systems and methods for autofocus using artificial intelligence include (i) capturing a plurality of monochrome images over a nominal focus range, (ii) identifying one or more connected components within each monochrome image, (iii) sorting the identified connected components based on a number of pixels associated with each connected component, (iv) generating a focus quality estimate of at least a portion of the sorted connected components using a machine learning module, and (iv) calculating a target focus position based on the focus quality estimate of the evaluated connected components. The calculated target focus position can be used to perform cell counting using artificial intelligence, such as by (i) generating a seed likelihood image and a whole cell likelihood image based on output—a convolutional neural network and (ii) generating a mask indicative quantity and/or pixel locations of objects based on the seed likelihood image.

METHODS OF AUTOMATICALLY ANALYZING BINARY CODE AND RELATED COMPUTING SYSTEMS AND COMPUTER-READABLE MEDIA

Resumen de: US20260178294A1

0000 Methods of automatically analyzing binary code and related computing systems and computer-readable media are disclosed. A method includes processing the binary code to generate an abstract syntax tree (AST), a control flow graph (CFG), and a data flow graph (DFG) representing an algorithmic structure of the binary code. The method also includes encoding the AST, the CFG, and the DFG using graph convolutional neural networks to obtain a AST encodings, CFG encodings, and DFG encodings. The method further includes performing self-supervised contrastive learning on the AST encodings, the CFG encodings, and the DFG encodings to generate a set of aligned embeddings representing the algorithmic structure of the binary code across the AST, the CFG, and the DFG. A computing system includes a processor and a data storage device having computer-readable instructions stored thereon. The computer-readable instructions are configured to instruct the processors to perform the method.

MODEL STRUCTURE, METHOD FOR TRAINING MODEL, IMAGE ENHANCEMENT METHOD, AND DEVICE

Resumen de: US20260179360A1

Embodiments of this application disclose a model structure, a method for training a model, an image enhancement method, and a device, and may be applied to the computer vision field in the artificial intelligence field. The model structure includes: a selection module, a plurality of first neural network layers, a segmentation module, a transformer module, a recombination module, and a plurality of second neural network layers. The model overcomes a limitation that the transformer module can only be used to process a natural language task, and may be applied to a low-level vision task. The model includes the plurality of first/second neural network layers, and different first/second neural network layers correspond to different image enhancement tasks. Therefore, after being trained, the model can be used to process different image enhancement tasks.

CREDIT ELIGIBILITY PREDICTOR

Resumen de: US20260179153A1

Aspects extract, from payroll data of employees of an organization, data historically associated to previous instances of certified tax credit eligibility; normalize the extracted data with respect to data type and data value; generate from the normalized extracted data via a neural network classifier multi-class outputs for each employee that indicate strengths of likelihood that each employee is currently eligible for each of a plurality of different tax credits; filter the normalized extracted data by removing portions associated to employees indicated within the multi-class outputs as having no currently eligible likelihood for the different tax credits, thereby generating a remainder set of normalized extracted data associated to remainder eligible ones of the employees; and prioritize application for the tax credits for the remainder eligible employees as a function of respective values and likelihoods of eligibility within the remainder set of normalized extracted data.

METHOD AND APPARATUS FOR IMPROVING ACCURACY OF INTEREST POINT DETECTION UNDER DIFFERENT LIGHT CONDITIONS

Resumen de: US20260178905A1

According to at least one embodiment, a computer-implemented method of training a neural network for mapping an indoor environment includes: training the neural network in a first stage using a first dataset based on synthetic shapes; and training the neural network in a second stage using a second dataset based on real photographs. The method further includes, for each object of a plurality of objects, collecting a plurality of images of the object, wherein the plurality of images of the object are respectively produced under different lighting conditions; and training the neural network in a third stage using the plurality of images of the object.

NEURAL NETWORK OPTIMIZATION FOR RESOURCE CONSTRAINED DEVICE DEPLOYMENT

Resumen de: US20260178891A1

0000 Described herein are systems and methods for optimizing neural network models for deployment on resource-constrained computing devices through layer-specific quantization. An original neural network model and deployment constraints are received as inputs. The optimization process alternates between a learning phase that updates model weights using task-specific loss functions and a compression phase that determines optimal bitwidth allocations for each layer through multiple-choice knapsack optimization. The compression phase computes quantization errors for different bitwidth options per layer and selects optimal bitwidth combinations while satisfying deployment constraints. The process iteratively updates a penalty parameter and continues until convergence, producing an optimized neural network model with quantized weights and layer-specific bitwidth allocations that maintains performance while meeting size, computational, and latency constraints for the target device.

GENERATING AUTOMATED ASSISTANT RESPONSES AND/OR ACTIONS DIRECTLY FROM DIALOG HISTORY AND RESOURCES

Resumen de: US20260179618A1

Training and/or utilizing a single neural network model to generate, at each of a plurality of assistant turns of a dialog session between a user and an automated assistant, a corresponding automated assistant natural language response and/or a corresponding automated assistant action. For example, at a given assistant turn of a dialog session, both a corresponding natural language response and a corresponding action can be generated jointly and based directly on output generated using the single neural network model. The corresponding response and/or corresponding action can be generated based on processing, using the neural network model, dialog history and a plurality of discrete resources. For example, the neural network model can be used to generate a response and/or action on a token-by-token basis.

STAGED NEURAL NETWORK CLASSIFICATION

Resumen de: US20260178882A1

Events may be classified in a staged manner using one or more neural networks and explicit classifiers. For example, a method may be performed which comprises classifying an event using a first explicit classifier, a neural network classifier, and a second explicit classifier. In such a method, classifying the event using the first explicit classifier may provide an initial classification for the event, and that initial classification may be a basis for classifying the event using the neural network classifier. Similarly, classifying the event using the neural network classifier may provide a neural network classification for the event, and the event may be classified using the second explicit classifier on the basis of the neural network classification. When the event is processed by the second explicit classifier, this may provide an output classification, and that output classification may be used as a basis for processing the event.

IMAGE GENERATION USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260179197A1

Apparatuses, systems, and techniques are presented to generate one or more images. In at least one embodiment, one or more neural networks are used to generate one or more images based, at least in part, on one or noise values.

NEURAL NETWORK CIRCUIT AND NEURAL NETWORK OPERATION METHOD

Resumen de: US20260178895A1

A neural network circuit having multiple neural network operation cores having convolution operation circuits that perform convolution operations and quantization operation circuits that perform quantization operations, wherein the multiple neural network operation cores are connected so as to be able to input and output data.

ANTI-OVERFITTING LIGHTWEIGHT ANOMALY DETECTION NEURAL NETWORK MODEL RETRAINING METHOD

Resumen de: US20260178961A1

Disclosed in the present invention is a lightweight anomaly detection neural network model retraining method with anti-overfitting, which retrains an anomaly detection model based on depth variational autoencoders. When a data distribution changes, a conditional distribution of a hidden state and reconstructed data samples obtained by an encoder and a decoder of the depth variational autoencoders will also change. The present invention uses a mapping function to adjust the conditional distribution of the hidden state and the reconstructed data obtained by the calculation of an old model to adapt to a new data distribution. The mapping function has simple and convex characteristics, and can ensure a fast convergence rate and light overhead in a retraining process on a premise of using a loss function form defined by the present invention. In addition, the present invention provides a rumination module for data enhancement of new observation data to solve a problem of insufficient new observation sample data in an initial period when cloud service characteristics change.

METHOD AND DEVICE FOR PERFORMING FEDERATED LEARNING IN SATELLITE COMMUNICATION SYSTEM

Nº publicación: US20260178932A1 25/06/2026

Solicitante:

UNIV INDUSTRY COOPERATION GROUP KYUNG HEE UNIV [KR]
University-Industry Cooperation Group of Kyung Hee University

Resumen de: US20260178932A1

0000 A method for performing federated learning in a satellite communication system includes receiving, at a base station device, association information from each of all satellites within a coverage, setting, one or more satellites available for training among the connected satellites as training satellites based on the received association information, generating, training information for each of the training satellites based on the received association information, transmitting, the generated training information and a global neural network model to each of the training satellites, receiving, each of local neural network models whose training has been completed from the training satellites, and transmitting, the received local neural network models to a server.