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FINGERPRINT INDEXING USING CONVOLUTIONAL NEURAL NETWORK

Resumen de: US20260236527A1

A method of filtering fingerprint candidates, the method being carried out by an indexing module arranged to access a Convolutional Neuronal Network, CNN configured to output at least one feature of an input image. The method comprises processing an image representative of local information of a searched fingerprint, by the CNN to obtain at least one feature for the searched fingerprint; retrieving a candidate fingerprint in a database; determining whether at least one feature of the retrieved candidate fingerprint matches with the at least one feature of the searched fingerprint; if the at least one feature of the retrieved candidate fingerprint matches with the at least one feature of the searched fingerprint, passing the at least one candidate fingerprint to a matching module for further comparison between the candidate fingerprint and the searched fingerprint. Other aspects are also considered.

CONTROLLING AGENTS USING Q-TRANSFORMER NEURAL NETWORKS

Resumen de: US20260236740A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent interacting with an environment using a Transformer neural network.

CLASS AGNOSTIC OBJECT MASK GENERATION

Resumen de: US20260237074A1

Class agnostic object mask generation uses a vision transformer-based auto-labeling framework requiring only images and object bounding boxes to generate object (segmentation) masks. The generated object masks, images, and object labels may then be used to train instance segmentation models or other neural networks to localize and segment objects with pixel-level accuracy.

NEURAL NETWORK-BASED IMAGE LIGHTING

Resumen de: US20260237124A1

Apparatuses, systems, and techniques are presented to generate image data. In at least one embodiment, one or more neural networks are used to cause a lighting effect to be applied to one or more objects within one or more images based, at least in part, on synthetically generated images of the one or more objects.

QUANTUM, BIOLOGICAL, COMPUTER VISION, AND NEURAL NETWORK SYSTEMS FOR INDUSTRIAL INTERNET OF THINGS

Resumen de: AU2026208040A1

Abstract Computer-implemented method transmits a predictive model from a first device to a second device. The first device receives data values of a data stream comprising sensor data collected from one or more sensor devices in an industrial environment. Using the received data values, the first device generates and refines a first predictive model for predicting future data values, including determining and adjusting predictive model parameters based on newly received data values. The first device transmits the predictive model parameters to the second device. The second device receives the predictive model parameters and parameterizes a second predictive model using the received parameters. The second predictive model predicts future values of the first device. Based at least in part on the predicted future values, the second device causes a physical control action to be performed with respect to a physical component in the industrial environment. The predictive model parameters may be updated and retransmitted during operation. Abstract to FIG. 3 Self Organizing Network Coding to FIG. 2 Data Pool Predictive Trained Diagnostics Maintenance Models Data Rights Config. Semantic Semantic System Pool Management Automation Processing Pricing Policy Decision User Order Analysis Automation Engine Analytics Private Cloud Ad and Anticipation Location Based Customer Storage Content Targeting of State Services Applications Analytics Expert Pattern Speech Other Services Dynamic Systems

NEURAL NETWORKS WITH SUBDOMAIN TRAINING

Resumen de: US20260236778A1

0000 Heterogenous neural networks are disclosed that have activation functions that hold multi-variable equations. These variables can be passed from one neuron to another. The neurons may be laid out in a topologically similar fashion to a physical system that the heterogenous neural network is modeling. A neural network may have inputs of more than one type. Only a portion of the inputs (a subdomain) may be optimized In such an instance, the neural network may run forward, backpropagate to all inputs, and then perform optimization only on those inputs which will be optimized.

TECHNIQUE TO PERFORM NEURAL NETWORK ARCHITECTURE SEARCH WITH FEDERATED LEARNING

Resumen de: US20260236742A1

Apparatuses, systems, and techniques to select a neural network architecture from a plurality of neural networks in a federated learning (FL) setting. In at least one embodiment, a neural network is trained by combining training results from different FL computing systems, where each of the different FL computing systems, for example, trains different portions of the neural network.

DETERMINING OBJECT ORIENTATION FROM AN IMAGE WITH MACHINE LEARNING

Resumen de: US20260237177A1

Apparatuses, systems, and techniques to determine orientation of an objects in an image. In at least one embodiment, images are processed using a neural network trained to determine orientation of an object.

EXPLAINABLE GRAPH NEURAL NETWORK

Resumen de: US20260236771A1

An explainable graph neural network is disclosed. A recommendation system generates a recommendation by using a trained heterogenous graph neural network. A recommendation explainer may generate an explanation for the recommendation. To do so, the recommendation explainer may perturb features of a node of a heterogenous interaction graph. Further, the recommendation explainer may perturb a structure of the heterogenous interaction graph.

RISK-BASED TRUST SCORING AND OPERATIONALIZATION FRAMEWORK FOR GENERATIVE ARTIFICIAL INTELLIGENCE MODELS

Resumen de: US20260236736A1

A method for providing a Generative Artificial Intelligence (GenAI) system with trustworthiness evaluation, wherein the method comprises processing a training dataset that the GenAI was trained upon; constructing a plurality of latent knowledge anchors (LKAs) by applying topic modeling on the processed training dataset, wherein the plurality of LKAs comprises domain-specific knowledge representations within the processed training dataset; training a neural network classifier using the LKAs and the processed training data; evaluating, using the neural network classifier, a response generated by the GenAI to generate a trust score, wherein the trust score indicates the alignment level of the response to the training dataset and/or the comprehensiveness of the response compared to the training dataset; in response to the trust score exceeding a threshold, presenting the response to a user; and in response to the trust score not exceeding the threshold, issuing a command for the GenAI to conduct additional actions.

CAMERA-SPECIFIC EMBEDDINGS IN BIRDS-EYE-VIEW NEURAL NETWORK

Resumen de: US20260237189A1

0000 An apparatus for processing image data includes a memory for storing the image data and processing circuitry in communication with the memory. The processing circuitry is configured to obtain image data including a current set of multiple camera images from multiple cameras. According to such an example, the apparatus may also generate respective feature vectors from each of the multiple camera images with a shared image feature encoder using camera-specific positional embeddings associated with different respective cameras used to capture the multiple camera images. The apparatus may also perform a perception task using the respective feature vectors.

METHOD AND SYSTEM FOR AI-BASED REAL-TIME ANALYSIS OF ENTERPRISE DATA

Resumen de: US20260236871A1

0000 A system for an automated real-time analysis and generation of predictive insights based on enterprise data including a processor of a predictive insights server (PIS) node configured to host a machine learning (ML) module coupled to at least one user-entity node and to a plurality of associated nodes over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire action-related raw data from the at least one user-entity node, the action-related raw data including digital metrics associated with a target enterprise and the plurality of associated nodes; perform normalization of the action-related raw data based on the digital metrics; parse the normalized data to derive a plurality of classifying features; generate a feature vector based on the plurality of classifying features; ingest the feature vector into the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of predictive insight parameters from an insight predictive model generated by the ML module using outputs of the ANN based on the feature vector; and generate at least one actionable insight for the at least one user-entity node based on the plurality of predictive insight parameters.

SYSTEMS, APPARATUSES, AND METHODS FOR MONITORING ORGAN HEALTH

Resumen de: WO2026169639A1

A system for training at least one graph neural network (GNN) for monitoring organ health includes at least one memory configured to store instructions and at least one processor configured to execute the instructions to cause the system to obtain a plurality of parameters from healthy individuals and diseased individuals, perform causal discovery to determine one or more causal relationships between the plurality of organ parameters, train the at least one GNN with the one or more causal relationships to create a trained GNN configured to output a multi-organ health status based on patient parameters. The system is also configured to train at least one acute event prediction model to predict at least one acute or adverse event for at least one organ

DEVICE SETTINGS OPTIMIZATION

Resumen de: WO2026169963A1

A method for optimizing device settings of a plurality of devices includes receiving a performance characteristics target for devices manufactured according to a common design, wherein each device is configurable via device settings and exhibits performance variations over process variations and device operating conditions. The method includes defining a plurality of test cases, each including a combination of a respective device, a process variation, and a device operating condition. The method includes applying a first genetic adaptation algorithm to produce test results, training a neural network to generate predicted performance characteristics, and determining, by using the trained neural network as a surrogate model and applying a second genetic adaptation algorithm, a global set of device settings that collectively achieves the performance characteristics target across the plurality of devices.

DIGITAL IMAGE PROCESSING FOR GENERATING MULTI-CHANNEL IMAGES

Resumen de: WO2026169910A1

Systems, apparatuses, and methods disclosed herein relate to a digital image processing system. In one aspect, the digital image processing system includes one or more processors and one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the digital image processing system to access a digital image depicting a portion of a biological sample obtained from a subject. A projecting space and a staining category for an image conversion is defined by the digital image processing system, causing the digital image to be projected to the projecting space using a neural network model. The projected image is output by the digital image processing system.

NEURAL NETWORK TRAINING METHOD AND RELATED DEVICE

Resumen de: EP4790604A1

Embodiments of this application disclose a neural network training method. A first module in a federated neural network is deployed in each of a plurality of first devices. The first module includes a feature extraction module. A plurality of second modules in the federated neural network and early exit modules connected to the respective second modules are deployed in a second device. It can be learned that, in a federated learning process, the second device may include a plurality of early exit nodes, and each early exit node corresponds to one second module and a corresponding early exit module connected to the second module. In this way, after federated learning, when a target network is deployed in the first device, a structure of the target network may be in a plurality of forms based on the plurality of early exit nodes. In other words, target networks of different structures may be flexibly deployed in different first devices. For example, flexible scheduling may be performed based on resource statuses of different first devices, so that each first device can implement efficient data processing through a target network of an appropriate scale.

DEVICE FOR ACQUIRING MAGNETIC RESONANCE IMAGE ON BASIS OF DEEP LEARNING MODEL AND CONTROL METHOD THEREOF

Nº publicación: EP4790443A1 12/08/2026

Solicitante:

AIRS MEDICAL INC [KR]
Airs Medical Inc.

Resumen de: EP4790443A1

The present disclosure provides an apparatus for restoring the quality of magnetic resonance images based on a deep learning model and a method of controlling the same. The method includes: obtaining a training image corresponding to each magnetic resonance image by applying at least one of a plurality of elements set in connection with the quality of the magnetic resonance image to a magnetic resonance signal corresponding to the magnetic resonance image; obtaining a training dataset including the magnetic resonance image as label data and the obtained training image as input data matching the label data; and training a neural network model based on the training dataset and context data corresponding to the training image. Obtaining the training image includes distorting the magnetic resonance signal by applying the at least one of the plurality of elements and obtaining the training image based on the distorted magnetic resonance signal.