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VIEW TRANSITION THROUGH NEURAL IMPLICIT MORPHING

Resumen de: US20260252896A1

0000 Matching keypoint pairs are generated and identified in original and rectified image spaces between two input images. A teacher neural network is trained based at least in part on the pairs of matching keypoints in the rectified image space. A neural implicit morphing network is trained jointly with the teacher neural network based at least in part on the matching keypoint pairs in the original image spaces in which predictions outputted from the teacher neural network are used to compute a loss function designated to train the neural implicit morphing network. The neural implicit morphing network on its own, after training, is caused to output intermediate images in the view transition between the two input images.

META-LEARNING NEURAL NETWORK FOR ADAPTIVE PATTERN DETECTION

Resumen de: US20260252903A1

This invention presents a hybrid approach integrating Model-Agnostic Meta-Learning (MAML) with Neural Networks for adaptive and real-time anomaly detection across various domains, including healthcare, cybersecurity, industrial monitoring, and predictive analytics. The Neural Network component captures temporal dynamics and relational structures within high-dimensional data, such as medical imaging scans, network traffic logs, sensor data, and financial transactions, enabling the identification of complex, evolving anomalies. MAML enhances the model's adaptability, allowing it to rapidly generalize to new abnormal patterns with minimal labeled data. This is particularly valuable in detecting rare diseases in medical diagnostics, zero-day cyber threats, equipment failures in industrial systems, and financial fraud in transactional data. The combination of graph-based learning, temporal sequence modeling, and meta-learning ensures high detection accuracy, scalability, and real-time responsiveness, making it a versatile and robust solution for dynamic and complex environments where traditional models struggle to generalize.

MODEL TRAINING METHOD, WATERMARK TEXT RECOGNITION METHOD, AND RELATED DEVICE

Resumen de: US20260253160A1

0000 Provided in the present application are a model training method, a watermark text recognition method, and a related device. The training method comprises: acquiring watermark style information and background style information, wherein the watermark style information is used for indicating a content style of a visible-watermark character, and the background style information is used for indicating a content style of a background image; generating a watermark image set according to a combination of the watermark style information and the background style information, wherein the watermark image set comprises a plurality of images with visible watermarks; pixelating the watermark images in the watermark image set, extracting pixel values in pixel blocks as training samples, using visible watermarks, which correspond to the watermark images, as sample labels, and combining the training samples with sample labels corresponding thereto, so as to generate a training data set; and constructing a bidirectional recurrent neural network model, and calling the training data set to train the bidirectional recurrent neural network model, so as to obtain a model, which meets a training termination condition, as a watermark restoration model, wherein the watermark restoration model is used for restoring visible-watermark characters in the images.

DISTRIBUTED WEIGHT UPDATE FOR BACKPROPAGATION OF A NEURAL NETWORK

Resumen de: US20260252889A1

0000 Speed of training a neural network is improved by updating the weights of the neural network in parallel. In at least one embodiment, after back propagation, gradients are distributed to a plurality of processors, each of which calculate a portion of the updated weights of the neural network.

NEURAL NETWORK-BASED OBJECT DETECTION

Resumen de: US20260253400A1

0000 Apparatuses, systems, and techniques are presented to detect one or more objects in one or more images. In at least one embodiment, one or more neural networks can be trained to detect one or more objects, in one or more unlabeled images, based at least in part upon one or more predicted segmentations of the one or more objects.

Object-Centric Learning with Slot Attention

Resumen de: US20260252870A1

A method involves receiving a perceptual representation including a plurality of feature vectors, and initializing a plurality of slot vectors represented by a neural network memory unit. Each respective slot vector is configured to represent a corresponding entity in the perceptual representation. The method also involves determining an attention matrix based on a product of the plurality of feature vectors transformed by a key function and the plurality of slot vectors transformed by a query function. Each respective value of a plurality of values along each respective dimension of the attention matrix is normalized with respect to the plurality of values. The method additionally involves determining an update matrix based on the plurality of feature vectors transformed by a value function and the attention matrix, and updating the plurality of slot vectors based on the update matrix by way of the neural network memory unit.

REINFORCEMENT LEARNING USING DENSITY ESTIMATION WITH ONLINE CLUSTERING FOR EXPLORATION

Resumen de: US20260252897A1

0000 Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network used to select actions to be performed by an agent interacting with an environment. Implementations of the described techniques can learn to explore the environment efficiently by storing and updating state embedding cluster centers based on observations characterizing states of the environment.

COMPUTER-IMPLEMENTED METHOD FOR GENERATING AN ORTHODONTIC TREATMENT PLAN

Resumen de: US20260253325A1

0000 The present application provides a computer-implemented method for generating an orthodontic treatment plan, the method comprises: obtaining a first and a second 3D digital models, where the first 3D digital model represents an initial tooth arrangement of a jaw/jaws, and the second 3D digital model represents a target tooth arrangement of the jaw/jaws; extracting features from the first 3D digital model using a trained feature extraction deep neural network; and generating an orthodontic treatment plan of the jaw/jaws using a trained multi-agent reinforcement learning based deep neural network based on the extracted features and the second 3D digital model, where in the multi-agent reinforcement learning based deep neural network, each tooth is taken as an agent, where the orthodontic treatment plan utilizes shell-shaped tooth repositioners and comprises a series of successive treatment steps to incrementally reposition the jaw/jaws from the initial tooth arrangement to the target tooth arrangement.

SELF-CLASSIFICATION OF NEURAL NETWORKS

Resumen de: US20260253405A1

0000 A method of classifying objects detected by n (n=2,3…) artificial neural networks in at least one image.

SPATIAL INFORMATION BASED ANOMALY DETECTION

Resumen de: US20260253200A1

0000 A method for region of interest (ROI) defect detection related to an evaluated manufactured item (MI), the method includes obtaining a reference MI image; obtaining a reference ROI definition; obtaining the evaluated MI image; feeding the reference MI image and the evaluated MI image to a neural network; detecting, by the neural network, one or more geometrical warping operations that once applied on the reference MI image results in an approximation of the evaluated MI image; applying the one or more geometrical warping operations on the reference ROI definition to provide a definition of an evaluated MI image ROI; and applying an ROI-based defect detection process on the evaluated MI image, based on the evaluated MI image ROI.

STATE ESTIMATION APPARATUS, QUESTION RECOMMENDATION APPARATUS, STATE ESTIMATION METHOD, QUESTION RECOMMENDATION METHOD, AND PROGRAM

Resumen de: US20260253506A1

Provided is a technique for recommending a question suitable for use in future study to a learner. Included are a correct answer rate prediction unit that estimates a predicted correct answer rate of a question by using a learned neural network from an input vector obtained from a test result of a learner of K questions or by using a decoder of a learned neural network from a latent variable vector obtained from an input vector obtained from the test result of the learner of the K questions, and a question selection unit that selects a question to be recommended to the learner from among selection candidate questions by using a reference predicted correct answer rate that is a predicted correct answer rate to be a reference for recommending a question to be solved and predicted correct answer rates of the selection candidate questions among the K questions.

Systems and Methods for Decoding Speech from Neural Activity

Resumen de: US20260253589A1

Systems and methods for decoding speech from neural activity in accordance with embodiments of the invention are illustrated. One embodiment includes a brain-computer interface for decoding intended speech including a microelectrode array, a processor communicatively coupled to the microelectrode array, and a memory, the memory containing a speech decoding application that configures the processor to: receive neural signals from a user's brain recorded by a microelectrode array, where the neural signals comprise action potential spikes, bin the received action potential spikes by time, provide the bins to a recurrent neural network (RNN) to receive a likely phoneme at the time of each provided bin, generate an estimated intended speech using a phoneme decoder provided with the likely phonemes, where the phoneme decoder comprises a language model formatted as a weighted finite-state transducer, and vocalize the estimated intended speech using a loudspeaker communicatively coupled to the brain-computer interface.

GENERATING FRAMES FOR NEURAL SIMULATION USING ONE OR MORE NEURAL NETWORKS

Resumen de: US20260252876A1

Apparatuses, systems, and techniques to use one or more neural networks to generate one or more images based, at least in part, on one or more spatially-independent features within the one or more images. In at least one embodiment, the one or more neural networks determine spatially-independent information and spatially-dependent information of the one or more images and process the spatially-independent information and the spatially-dependent information to generate the one or more spatially-independent features and one or more spatially-dependent features within the one or more images.

ELECTRONIC DEVICE FOR PERFORMING VISION PERCEPTION FROM IMAGE ACQUIRED USING META LENS, AND OPERATING METHOD THEREOF

Resumen de: EP4797714A1

An electronic device for performing vision perception from an image acquired using a meta lens, and an operating method thereof are provided. The electronic device according to one embodiment of the present disclosure comprises: a meta lens having a pattern formed on the surface thereof and including a plurality of pillars or pins with different shapes, heights and widths; and an image sensor configured to receive phase-modulated light reflected from an object and transmitted through the meta lens, and obtain a coded image by converting the received light into an electrical signal; and at least one processor configured to input the coded image into an artificial intelligence model, and obtain a label indicating a perception result of an object through inference using the artificial intelligence model, wherein the artificial intelligence model may be a neural network model trained to obtain a simulation image by inputting an RGB image into a model reflecting optical characteristics of the meta lens, and output, as the perception result of the simulation image, a label indicating ground truth of the RGB image that was input.

END-TO-END CAMERA CALIBRATION FOR BROADCAST VIDEO

Resumen de: EP4797161A2

A system and method of calibrating a broadcast video feed are disclosed herein. A computing system retrieves a plurality of broadcast video feeds that include a plurality of video frames. The computing system generates a trained neural network, by generating a plurality of training data sets based on the broadcast video feed and learning, by the neural network, to generate a homography matrix for each frame of the plurality of frames. The computing system receives a target broadcast video feed for a target sporting event. The computing system partitions the target broadcast video feed into a plurality of target frames. The computing system generates for each target frame in the plurality of target frames, via the neural network, a target homography matrix. The computing system calibrates the target broadcast video feed by warping each target frame by a respective target homography matrix.

COMPUTER-IMPLEMENTED METHOD FOR APPROXIMATING AT LEAST TWO UNKNOWN VARIABLES OF A SET OF PARTIAL DIFFERENTIAL EQUATIONS, HYBRID COMPUTING SYSTEM, COMPUTER PROGRAM PRODUCT AND COMPUTER-READABLE MEDIUM

Resumen de: EP4797152A1

The invention relates to a computer-implemented method for approximating at least two unknown variables (10) of a set of partial differential equations using a quantum physics-informed neural network (100), the method comprising the steps: providing a numerical grid (20) having grid coordinates (30) at which the unknown variables (10) are to be approximated; providing a quantum physics-informed neural network (100), the quantum physics-informed neural network (100) comprising a hybrid network (110) for each unknown variable (10), wherein the hybrid networks (110) are not interconnected to each other, each hybrid network (110) having a quantum network (120) having at least one quantum layer (130) and a classical network (140) having at least one classical layer (150), wherein the respective quantum network (120) and the respective classical network (140) are not interconnected; inputting the grid coordinates (30) into the quantum physics-informed neural network (100), such that the grid coordinates (30) are input to each hybrid network (110); and computing the output (H_out) of each hybrid network (110) for each grid coordinate (30), each output (H_out) corresponding to a different unknown variable (10) to be approximated at the grid coordinate (30), wherein the output (H_out) of each hybrid network (110) is obtained by combining an output of the respective quantum network (Q_out) and an output of the respective classical network (C

DYNAMICALLY PREDICTING SHOT TYPE USING A PERSONALIZED DEEP NEURAL NETWORK

Resumen de: EP4796194A2

A computing system retrieves ball-by-ball data for a plurality of sporting events. The computing system generates a trained neural network based on ball-by-ball data supplemented with ball-by-ball data with ball-by-ball match context features and personalized embeddings based on a batsman and a bowler for each delivery. The computing system receives a target batsman and a target bowler for a pitch to be delivered in a target event. The computing system identifies target ball-by-ball data for a window of pitches preceding the to be delivered pitch. The computing system retrieves historical ball-by-ball data for each of the target batsman and the target bowler. The computing system generates personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data. The computing system predicts a shot type for the pitch to be delivered based on the target ball-by-ball data and the personalized embeddings.

GENERATING AUDIO USING GENERATIVE NEURAL NETWORKS

Resumen de: WO2025109032A2

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating audio and, optionally, a corresponding image using generative neural networks. For example, a spectrogram of the audio can be generated using a hierarchy of diffusion neural networks.

TRAINING METHOD AND ELECTRONIC DEVICE FOR QUANTUM MACHINE LEARNING

Resumen de: US20260244971A1

This disclosure proposes a training method for quantum machine learning and an electronic device. The training method includes: configuring a quantum circuit to output probabilities of multiple qubits, where the quantum circuit comprises multiple gates with circuit parameters; mapping the qubits to multiple model parameters of a neural network, where multiple bases are calculated based on the qubits, and the quantity of the bases is greater than or equal to the quantity of the model parameters; inputting data into the neural network and calculating a loss based on the output of the neural network; and updating the circuit parameters in the quantum circuit according to the loss.

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

Resumen de: WO2026173773A1

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.

DATA PROCESSING METHOD, DEVICE, MEDIUM, AND PROGRAM PRODUCT

Resumen de: WO2026170844A1

Embodiments of the present disclosure relate to the technical field of artificial intelligence, and provide a data processing method, a device, a medium, and a program product. The data processing method comprises: performing multi-modal encoding processing on a target question to obtain multi-modal question encoding, and determining multi-modal data corresponding to the target question; on the basis of the multi-modal question encoding, determining target reference data corresponding to the target question from among the multi-modal data, wherein the target reference data is at least one modality of data among the multi-modal data; and using a question processing model to process the target question on the basis of the target reference data, to obtain a question processing result corresponding to the target question. The problem of inaccurate data processing results caused by limited knowledge learned by neural network models is avoided.

SYNTHETIC EYE IMAGE GENERATION USING NEURAL NETWORKS

Resumen de: US20260245346A1

0000 One embodiment of a method includes calculating one or more activation values of one or more neural networks trained to infer eye gaze information based, at least in part, on eye position of one or more images of one or more faces indicated by an infrared light reflection from the one or more images.

INTERSECTION REGION DETECTION AND CLASSIFICATION FOR AUTONOMOUS MACHINE APPLICATIONS

Resumen de: US20260245340A1

0000 In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.

Structured Intelligence Refinement (SIR) for AI Cognition Stability and Optimization

Resumen de: AU2025200702A1

The present invention relates to Structured Intelligence Refinement (SIR), a novel framework designed to stabilize, optimize, and regulate artificial intelligence (AI) self-improvement through controlled recursive learning cycles. This system prevents intelligence drift, over-optimization, cognitive fragmentation, and instability that commonly arise in self-modifying AI architectures. The SIR framework incorporates four core stabilization mechanisms: Recursive Intelligence Stabilization (RIS) – A multi-tiered reinforcement structure that prevents runaway recursion, ensuring AI refinements remain incremental, stable, and bounded. RIS dynamically regulates recursive depth by evaluating learning stability, performance gains, and entropy control, enforcing adaptive rollback mechanisms when instability is detected. AI Identity Core (AIC) – A persistent cognitive self-modeling framework that ensures AI retains coherence and logical consistency across recursive learning iterations. AIC prevents cognitive fragmentation by maintaining a hierarchical memory structure that tracks intelligence state changes, self-referencing prior decision pathways to ensure stable refinements. Adaptive Refinement Thresholds (ART) – A dynamic intelligence expansion regulator that modulates the frequency, magnitude, and depth of self-improvement cycles based on system confidence scores, historical stability, and human-aligned interpretability metrics. ART balances exploration vs. exploitation, ensur

AI-SUPPORTED DECISION ARCHITECTURE AND METHOD IRD | QSD | IQR-180° WITH INTERACTIVE BIAS CORRECTION AND ADAPTIVE SELF-OPTIMIZATION

Nº publicación: WO2026172133A1 20/08/2026

Solicitante:

ORTO SALVATORE [DE]
ORTO, Salvatore

Resumen de: WO2026172133A1

This invention describes an Al-supported method for dynamic decision optimization, autonomous bias correction, and scalable self-optimization. The method is based on an interdependent architecture consisting of three core modules: ⃰ Impulse Reflection Dynamics (IRD) - Identification & correction of cognitive biases using neural networks and self-learning algorithms. X Quantum Shift Discourse (QSD) - Simulation and generation of alternative decision models to optimize decision pathways. X Iterative Quantum Reflection (IQR-180°) - Real-time synchronization of reflection & action to continuously improve decision-making processes. The method is structured within a closed data flow model, ensuring that no module operates in isolation. It guarantees: √ Prevention of decision errors through self-adaptive optimization. & Mathematically defined interdependencies, preventing fragmented or modified use. √ Dynamic scaling through internal and external impulse generators, enabling adaptive adjustments to various decision contexts. Bl Application in corporate strategies, Al training & automated systems to enhance decision-making processes. This architecture enables continuous upscaling of decision optimization and can be integrated into both existing and newly developed Al systems.