Alerta

NEURAL NETWORK FOR BULK SORTING

Resumen de: AU2026220313A1

A bulk sorting system for sorting objects (1) in bulk is provided. The bulk sorting system comprises: at least one radiation source (10) arranged to radiate the objects, at least one optical sensor (12) arranged to capture reflected radiation (22) of 5 the objects and acquire the reflected radiation as multi- or hyperspectral data (24); a processing circuit (16) configured to analyze the reflected radiation of the objects by inputting the multi- or hyperspectral data into a convolutional neural network (CNN) (18) with at least two convolutional layers in order to either detect and classify the objects in the multi- or hyperspectral data and/or semantically segment the multi- or 10 hyperspectral data; and a mechanical sorter (20) configured to sort the objects according to their classification and/or segmentation using the analysis of the processing circuit such that different overlapping and/or stacked objects are separated or treated as a single group of objects. To be published with Fig. 1. ug u g Fig. 1 ug u g

A METHOD OF TRAINING AN ARTIFICIAL DEEP NEURAL NETWORK FOR ESTIMATION OF HEMODYNAMIC PARAMETER, A METHOD OF ESTIMATION OF HEMODYNAMIC PARAMETER, COMPUTER PROGRAM PRODUCTS AND COMPUTER SYSTEMS

Resumen de: US20260268486A1

A method of training of an artificial deep neural network (ADNN) for estimation of a hemodynamic parameter from a geometry of a blood vessel tree comprising a step of obtaining of a set of geometries of vessel trees, according to the invention is realized with the ADNN having an architecture adapted for point cloud processing with distance based point grouping. The distance is defined as geodesic distance along the blood vessel tree. A method of estimation of hemodynamic parameters from a geometry of a blood vessel tree using an ADNN, according to the invention, involves using ADNN adapted for point cloud processing with geodesic distance based point grouping. The invention concerns also a computer program products comprising a set of instruction that, when run on a computing system, cause it to realize the methods according to the invention. The invention concerns also a computer system adapted to realize methods according to the invention.

SELF-LEARNING IN DISTRIBUTED ARCHITECTURE FOR ENHANCING ARTIFICIAL NEURAL NETWORK

Resumen de: US20260268648A1

A vehicle having the first ANN model initially installed therein to generate outputs from inputs generated by one or more sensors of the vehicle. The vehicle selects an input based on an output generated from the input using the first ANN model. The vehicle has a module to incrementally train the first ANN model through unsupervised machine learning from sensor data that includes the input selected by the vehicle. Optionally, the sensor data used for the unsupervised learning may further include inputs selected by other vehicles in a population. Sensor inputs selected by vehicles are transmitted to a centralized computer server, which trains the first ANN model through supervised machine learning from sensor received inputs from the vehicles in the population and generates a second ANN model as replacement of the first ANN model previously incrementally improved via unsupervised machine learning in the population.

Computation Device, Updating Method, and Environment Recognition Device

Resumen de: US20260268143A1

A computation device comprising an inference unit that uses an inference model, which is a neural network model, and outputs an inference result corresponding to input data, and an updating unit that updates a weight of the inference model based on the inference result, and spatial information indicating at least a part of an area in the input data.

METHOD AND APPARATUS FOR RECOGNISING CONSTRUCTION PRODUCTS AND/OR PROCESSES AT A CONSTRUCTION SITE

Resumen de: US20260268659A1

The present invention relates to a method and an apparatus for recognizing construction products and/or construction processes at a construction site, wherein, by means of a sensor system, a construction product and/or process at the construction site is detected and a product- and/or process-specific sensor data record is provided which is evaluated by means of an artificial neural network to identify and/or characterize the construction product and/or process. It is proposed that the recognition of the construction products and/or processes is no longer carried out by a central artificial neural network and instead, for this, a plurality of separate artificial neural networks are used which have been trained differently from one another and only for different subsets of the construction products and/or construction processes provided at the construction site.

METHOD FOR DIAGNOSING CARDIOVASCULAR DISEASE AND DEVICE USING THE SAME

Resumen de: US20260269067A1

Disclosed are a method for diagnosing cardiovascular disease and a device using the same. A control method of a diagnostic device according to one embodiment may comprise: obtaining a retinal image of a subject; and obtaining cardiovascular disease diagnostic information about the subject by using a machine learning model based on the retinal image, wherein the machine learning model includes a first model and a second model, wherein the first model is a neural network model, and wherein the second model may be a regression-based machine learning model.

SYSTEMS AND METHODS FOR TRAINING NEURAL NETWORKS WITH SPARSE DATA

Resumen de: US20260268137A1

A method, computer readable medium, and system are disclosed for training a neural network model. The method includes the step of selecting an input vector from a set of training data that includes input vectors and sparse target vectors, where each sparse target vector includes target data corresponding to a subset of samples within an output vector of the neural network model. The method also includes the steps of processing the input vector by the neural network model to produce output data for the samples within the output vector and adjusting parameter values of the neural network model to reduce differences between the output vector and the sparse target vector for the subset of the samples.

AUTOMATED CLASSIFIER FOR DIAGNOSING ACUTE OTITIS MEDIA

Resumen de: AU2025228694A1

A system for assessing AOM includes a computing device having a processor apparatus, wherein the processor apparatus implements a diagnostic classifier component that comprises a. trained neural network, the processor apparatus being structured and configured to receive tympanic membrane image data representing one or more images of a tympanic membrane of the patient, provide the tympanic membrane image data to the diagnostic classifier component, and process the tympanic membrane image data in the diagnostic classifier component to determine: (i) a plurality of tympanic membrane features from the tympanic membrane image data, (ii) a diagnosis of whether the patient has AOM based on the plurality of tympanic membrane features, (iii) a. confidence in the diagnosis, and (iv) an identification one or more of the tympanic membrane features determined to be predominant contributing features that led to the diagnosis.

METHODS AND SYSTEMS FOR IMPLEMENTING SECURE AND TRUSTWORTHY ARTIFICIAL INTELLIGENCE

Resumen de: US20260268024A1

Provided herein are systems, methods, computer-readable media, and techniques for providing trusted artificial intelligence (AI) using Fully Homomorphic Encryption (FHE), comprising: (A) providing a deep neural network (DNN)-based model with modified architecture, wherein the modified architecture at least (i) uses a Gaussian function as an activation function and (ii) removes one or more pooling layers; (B) obtaining encrypted data, wherein the encrypted data are generated by applying the FHE to plaintext data; and (C) generating an inference with the DNN-based model based on the encrypted data. Further provided herein are systems, methods, computer-readable media, and techniques for providing trusted AI using stochastic computing, using noise based computing, using an artificial immune system, and with secure multi-party computation (SMPC) based at least in part on watermarking.

ACCELERATED PHASE SPACE EXPLORATION OF MOLECULAR SYSTEMS USING NEURAL NETWORKS

Resumen de: US20260269024A1

A computer-implemented method for accelerating the simulation of a molecular system is provided. The method includes learning a first set of parameters by executing a Hamiltonian Monte Carlo method on the molecular system based on a set of initial conditions. A simulation of the molecular system is executed based on the first set of parameters. Thermodynamics expectation values of the molecular system are predicted based on the executed simulation. The predicted thermodynamics expectation values are provided for a downstream machine learning (ML) task. The method has applications including, but not limited to use cases in artificial intelligence (AI), drug development, medical diagnostics/applications, healthcare, material design catalyst design and high performance computing, to optimize predictions, improve model (e.g., neural network) performance or support decision making.

FUNCTIONAL ACTIVATION-BASED ANALYSIS OF DEEP NEURAL NETWORKS

Resumen de: US20250148285A1

0000 Functional activation-based analysis of deep neural networks uses a structured set of inputs (e.g., input datasets corresponding to different knowledge or datatype domains) are sequentially provided to a pretrained neural network (e.g., according to a block-sequence). The output values for each node in the neural network are recorded and stored as a time-series of layer output values. A statistical analysis of the time-series of layer output values may be fit as a function of the structured set of inputs to generate neural network analysis data that indicate activations of layers within the neural network based on the inputs.

GENERATING AUDIO USING AUTO-REGRESSIVE GENERATIVE NEURAL NETWORKS

Resumen de: EP4804179A2

0001 Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal; obtaining a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.

RISK ASSESSMENT OF ROTOR ANGLE INSTABILITY IN A POWER NETWORK

Resumen de: EP4804364A1

Rotor angle instability is a key criterion of dynamic stability in power networks. State-of-the-art machine-learning approaches are difficult to scale and have limited inputs with which to make predictions as to rotor angle instability. Accordingly, disclosed embodiments utilize a machine-learning model that is applied to bus voltage angles, which are local quantities available at every bus in the power network, to derive a prediction of the risk of rotor angle stability in the power network. These predictions may be biased in order to avoid false negatives. The machine-learning model may be a message-passing neural network. The resulting predictor is capable of quickly and reliably flagging potential rotor instability within a power network.

MACHINE PERCEPTION

Resumen de: US20260260498A1

A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

INTERFACE TRANSLATION USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260260475A1

0000 Apparatuses, systems, and techniques are presented to generate one or more interfaces. In at least one embodiment, one or more neural networks are used to generate one or more second graphical user interfaces based, at least in part, on one or more functional features of one or more first graphical user interfaces.

NETWORK BASED IMAGE FILTERING FOR VIDEO CODING

Resumen de: US20260261668A1

0000 A method and an apparatus for image filtering in video coding using a neural network are provided. The method includes generating a deblocking strength map indicating boundaries of prediction blocks or partition blocks. The deblocking strength map is input into a neural network, and the neural network filters an input frame based on the deblocking strength map.

DECISION MANAGEMENT SYSTEM

Resumen de: WO2026180856A1

A computer implemented method for decision management, which utilizes the decision management system as described herein. In the computer implemented method a problem statement input is received. A large language model business knowledge base is searched for a set of top relevant results. The large language model business knowledge base is a trained neural network aggregating proprietary business information and non-proprietary business information from multiple sources. At least a partial feasibility report is automatically constructed responsive to the problem statement for the top relevant results using the large language model business knowledge base.

OUTLIER CORRECTION SERVER AND METHOD USING WINDOW CORRELATION MATRIX, AND SYSTEM INCLUDING SAME

Resumen de: WO2026182308A1

The present invention relates to an outlier correction server and method using a data correction technique utilizing a long short-term memory (LSTM)-based variational autoencoder-generative adversarial network (VAE-GAN) model to effectively improve the quality of multivariate time-series data, and to a system including same. The outlier correction server comprises a memory storing at least one instruction and at least one processor that executes the at least one instruction, wherein the processor collects input data, preprocesses the input data to generate a preprocessing result, and detects an outlier from the preprocessing result by using a pre-trained neural network model.

MACHINE-LEARNING APPARATUS AND METHOD FOR IMPLEMENTING ARTIFICIAL INTELLIGENCE EDGE DEVICE BY USING RESISTIVE ELEMENTS, AND ANALYSIS APPARATUS AND METHOD USING SAME

Resumen de: US20260260106A1

A learning apparatus and method for implementing an edge device using a resistive element and an analysis apparatus and method using the same are disclosed. The learning apparatus for implementing an edge device using a resistive element according to an embodiment of the present application may include a first learning unit determining a weight of an artificial neural network through learning based on first training data and reflecting the determined weight in a first resistive element, and a second learning unit updating the weight of the artificial neural network through learning based on second training data collected through the device and reflecting the updated weight in the second resistive element.

Systems and Methods for Digitally Transforming Economic, Organizational and/or Industrial Content and/or Processes

Resumen de: US20260260401A1

Various embodiments of the teachings herein include systems for automatically transforming economic, organizational, and/or industrial content and/or processes capturable by natural language into a digital representation. An example includes: modules for capturing and/or recording user-specific data; processors to process captured data for forwarding to an AI and create digital representations in a recording language; an interface to a second processor associated with a neural network having an AI trained to carry out pattern analysis, pattern recognition, and/or pattern prediction on the basis of the processed user-specific recording data; and a second interface to transmit results from the data editing of the AI to the first processor to generate a digital representation made available to the user via a display module.

TARGET DETECTION METHOD AND APPARATUS, DEVICE, AND STORAGE MEDIUM

Resumen de: US20260260447A1

The present application relates to a target detection method and apparatus, a device, and a storage medium. A main technical solution includes: acquiring video set data, inputting the video set data to a backbone network to obtain video frame feature data, inputting the video frame feature data to a convolutional neural network to obtain candidate box data of a target object, optimizing the candidate box data of the target object according to a preset uncertainty estimation loss model to obtain candidate box feature data, and inputting the candidate box feature data to a preset cross-frame and cross-view model for updating and then outputting to obtain a target box and corresponding target detection data.

NEURAL NETWORK TRAINING METHOD AND RELATED DEVICE

Resumen de: US20260260129A1

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, 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, such that each first device can implement efficient data processing.

Tunable Hybrid Neural Video Representations

Resumen de: US20260261689A1

0000 There is provides a neural network-based video decoding method including receiving a latent feature corresponding to a compressed version of a first video frame of a first plurality of a plurality of video frames included in a video content, the latent feature being a combination of a weighted frame-specific embedding of the first video frame with weighted one or more group-of-pictures (GOP) features of the first plurality of the plurality of video frames, wherein the weighted frame-specific embedding is a product of applying a first weight to a frame-specific embedding and the weighted one or more GOP features are products of applying a second weight to the one or more GOP features, wherein the first weight and the second weight are selected as levers for content-specific fine-tuning. The method also including decoding the latent feature to provide an uncompressed video frame corresponding to the first video frame

IMAGE PROCESSING METHOD USING NEURAL NETWORK MODEL, AND ELECTRONIC DEVICE FOR PERFORMING SAME

Resumen de: US20260260316A1

An image processing method using a neural network model, and an electronic device are provided. The method may comprise: acquiring a low-resolution image; extracting, from the low-resolution image, luminance information through a luminance channel; acquiring a first feature vector on the basis of the luminance information by using the neural network model to which a first weight is applied; acquiring an output image from the first feature vector by using the neural network model to which a second weight is applied; and generating, on the basis of the output image, a high-resolution image with respect to the low-resolution image. The first weight and the second weight can be different.

CONTROL SYSTEM TESTING UTILIZING RULEBOOK SCENARIO GENERATION

Nº publicación: US20260257689A1 03/09/2026

Solicitante:

MOTIONAL AD LLC [US]
Motional AD LLC

Resumen de: US20260257689A1

0000 Provided are methods for testing of a control system of a vehicle using generated rulebook based scenarios, which can include determining a simulated environment, receiving a hierarchical plurality of autonomous vehicle rules, determining a trajectory of a simulated vehicle within the simulated environment, generating a plurality of simulated scenarios for the simulated vehicle, identifying at least one violation of at least one autonomous vehicle rule by the simulated vehicle in a set of the simulated scenarios, determining a scenario score for each simulated scenario based on the violations, and identifying at least one simulated scenario for a trained neural network of a vehicle based on the scenario scores.