Neural networks

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

Absstract of: 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

Absstract of: 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.

END-TO-END CAMERA CALIBRATION FOR BROADCAST VIDEO

Absstract of: 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.

GENERATING AUDIO USING GENERATIVE NEURAL NETWORKS

Absstract of: 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.

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

Absstract of: 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.

INTERSECTION REGION DETECTION AND CLASSIFICATION FOR AUTONOMOUS MACHINE APPLICATIONS

Absstract of: 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.

PREDICTING ANIMAL EMOTIONS USING ANIMAL EMOTION KNOWLEDGE GRAPH AND GRAPH NEURAL NETWORK

Absstract of: US20260245403A1

0000 This disclosure relates generally to a method and system for predicting animal emotions using animal emotion knowledge graph and graph neural network. Current available methods focus only on visual and language data captured from animals, and lacks real time adaptability. The method disclosed generates an animal emotion knowledge graph (AEKG) that combines human and animal neurobiological data, behavioral studies, and the human wheel of emotions. Further real time graphs are generated from multimodal input data captured from the animal. These real time graphs are used for predicting primary, secondary, and tertiary emotions of the animals using a trained graph neural network-Transformer model. This model is trained using the AEKG. Using temporal graph analysis, the method predicts future emotions and generates real-time recommendations based on generative artificial intelligence techniques. Predicting the emotions of animals in real time helps to grasp their emotional well-being to improve their care and management effectively.

Computer-implemented method for monitoring a deep neural network and applications thereof

Absstract of: US20260241941A1

A computer-implemented method for monitoring an artificial deep neural network comprises supplying input data to the trained deep neural network monitored, in order to obtain therefrom output data activation map data, and supplying the input data, the output data and the activation map data to a computer-implemented network observer. The network observer generates masking data from the activation map data and/or the input data and/or the output data; masks the activation map data using the masking data in order to obtain masked activation map data, wherein the masked activation map data contain unmasked activation values and masked activation values; and determines an outlier score for the output data using the masked activation map data, wherein merely the unmasked values are taken into account when determining the outlier score. The outlier score is a numerical value and indicates the extent to which the determined output deviates from a typical case.

SYSTEMS AND METHODS FOR CONTENT ADAPTIVE MULTI-SCALE FEATURE LAYER FILTERING AND REDUNDANT CHANNEL PROCESSING

Absstract of: US20260246928A1

0000 Systems and methods are provided for encoding and decoding video for machine consumption in which bandwidth is reduced by filtering feature layers and filtering channels at the encoder site that are determined to be redundant or of reduced relevance. A video encoder includes a neural network front end which receives image data and generates a plurality of feature layers. The relevance of the plurality of feature layers to a machine task at the decoder site is determined and redundant layers can be removed. Channels in at least one feature layer can be evaluated for redundancy and redundant channels also removed prior to encoding.

DATA PROCESSING METHOD AND APPARATUS, DEVICE, AND MEDIUM

Absstract of: US20260244898A1

Provided in the present disclosure are a data processing method and apparatus, device, and medium. The method includes: inputting data to be processed into a target neural network for processing to obtain a processing result of the data to be processed, at least one convolution layer of the target neural network being an attention convolution layer based on a first attention mechanism, and/or, performing feature fusion between at least two levels of convolution layers of the target neural network on the basis of a second attention mechanism, the first attention mechanism including a self-attention mechanism for a local area of a feature, and the second attention mechanism including an attention mechanism for a local area of an output feature between output features of different scales.

SYSTEMS AND METHODS FOR ENTROPY-BASED PRUNING OF NEURAL NETWORK MODELS

Absstract of: US20260244926A1

Embodiments described herein provide a method for hardware resource allocation during the operation of a generative neural network model. The method includes receiving a set of input data at a neural network-based model implemented on one or more hardware processors, where the model comprises a plurality of sequentially connected blocks. The method involves computing respective input and output intermediate values for at least one block during forward passes of the model, and calculating a change in entropy for each block based on the difference between entropy estimates for the input and output intermediate values. Blocks are pruned based on their respective changes in entropy, and hardware resources allocated to the pruned model are adjusted accordingly. The pruned neural network model is then operated using the adjusted hardware resources.

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

Absstract of: 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

Absstract of: 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.

SYNTHETIC EYE IMAGE GENERATION USING NEURAL NETWORKS

Absstract of: 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.

DEPTH-MAP BASED DETECTION OF ITEMS ON CONVEYOR BELTS

Absstract of: US20260245223A1

0000 A system receives 2D depth-map images of an empty conveyor belt. The system generates a training set of labelled 2D depth-map images based on the 2D depth-map images. The system trains or fine-tunes a neural network using the training set to form trained parameters that cause the neural network to detect items on the conveyor belt using 2D depth-map images of the items on the conveyor belt. In an alternative embodiment, a pre-trained neural network is configured to select feature map channels generated from 2D depth-map images of items on a conveyor belt. The selected channels are resized and combined to provide relevant data for generating a segmentation mask based of the items using a binarization threshold that is automatically determined. The system may include a pretrained image segmentation model that generates at least one instance segmentation mask from the 2D depth-map images.

QUERY-LESS SPEAKER TARGETING

Absstract of: US20260245547A1

Disclosed are apparatuses, systems, and techniques for implementing efficient transcription of multi-speaker speech with overlapping utterances using speaker activity detection. The techniques include processing, using a first set of neural network (NN) layers of an automatic speech recognition (ASR) model, speech data for the multi-speaker speech to generate an intermediate feature (IF) representative of the speech data. The techniques further include modifying, using a second set of NN layers of the ASR model, the IF to obtain modified IFs using speaker activity data, which identifies times when various speakers speak in the multi-speaker speech. The techniques further include processing the modified IFs to obtain a plurality of transcriptions identifying content of speech of the plurality of speakers, and generating, using the plurality of transcriptions, a transcript of the multi-speaker speech.

CONTRASTIVE-LEARNING-BASED MEDICAL IMAGE CLASSIFICATION METHOD AND SYSTEM, AND STORAGE MEDIUM

Absstract of: WO2026170664A1

The present invention relates to the technical field of long-tailed medical image classification. Disclosed are a contrastive-learning-based medical image classification method and system, and a storage medium. The method comprises: dividing a long-tailed medical image data set into a training set and a test set, and on the basis of a preset scheme and in the form of batch processing, separately performing weak data augmentation and strong data augmentation on images in the training set; by means of a deep convolutional neural network, performing a contrastive learning task on obtained weakly data-augmented images and obtained strongly data-augmented images, and learning network parameters to obtain a parameter-optimized deep convolutional neural network; and classifying long-tailed medical images in the test set. In the present application, by means of a prototype-enhanced contrastive learning strategy, learnable-class prototypes are generated and subjected to data augmentation, so as to obtain a balanced implicitly-augmented contrastive learning loss, thereby achieving the advantages of high precision, high efficiency, low costs, wide applicability, etc.

IMAGE PROCESSING DEVICE AND OPERATION METHOD THEREFOR

Absstract of: EP4793878A1

Provided are an image processing device and an operating method of the same. The image processing device includes a memory storing one or more instructions, and at least one processor including processing circuitry, and memory storing one or more instructions that, when executed by the at least one processor individually or collectively, cause the image processing device to obtain a neural network model corresponding to a quality of an input image and viewing information related to the input image. The at least one processor is configured to generate training data, based on the quality of the input image and the viewing information. The at least one processor is configured to train the neural network model by using the training data. The at least one processor is configured to obtain an image quality processed output image from the input image, based on the trained neural network model.

EXPLOITING INPUT DATA SPARSITY IN NEURAL NETWORK COMPUTE UNITS

Absstract of: EP4793824A2

0001 A hardware circuit for implementing a neural network comprising a plurality of neural network layers comprises a controller. The controller is configured to analyze output activations computed by a first compute system for a first neural network layer, where the output activations are provided on an output activation bus. The controller is further configured to determine which of the output activations have a non-zero value, generate an additional representation of the output activations that identifies only the output activations having a non-zero value, and use the additional representation to supply only the output activations having a non-zero value as input activations to a subsequent, second compute system for a second neural network layer.

ELECTRONIC APPARATUS AND METHOD FOR CONTROLLING THEREOF

Absstract of: EP4793903A2

A method of controlling an electronic apparatus includes acquiring an image and depth information of the acquired image; inputting the acquired image into a neural network model trained to acquire information on objects included in the acquired image; acquiring an intermediate feature value output by an intermediate layer of the neural network model; identifying a feature area for at least one object among the objects included in the acquired image based on the intermediate feature value; and acquiring distance information between the electronic apparatus and the at least one object based on the feature area for the at least one object and the depth information.

DATA PROCESSING METHOD AND APPARATUS BASED ON MULTI-MODAL FUSION

Absstract of: EP4793799A1

The present application provides a data processing method and apparatus based on multimodal fusion, pertaining to the technical field of data processing, where the method includes: acquiring one-dimensional data and image data; converting the one-dimensional data into two-dimensional data based on a dimension of the image data; performing zero-padding processing on vacant positions in the two-dimensional data; performing stacking processing on the zero-padded two-dimensional data and the image data to obtain a multilayer stacked input feature map; performing fusion processing on the multilayer stacked input feature map through a neural network to obtain a fused feature map; and performing data processing based on the fused feature map. The present invention can unify the data formats of different modalities, enabling them to be processed in the same feature space, significantly simplifying the alignment process between heterogeneous data.

METHOD AND SYSTEM FOR PROFILING PARTICLES IN TAXAS

Absstract of: EP4793908A1

0001 Disclosed is a computer-implemented method for profiling particles in a taxa using a sequence of input data images. The process involves identifying and categorizing suspended particles in each image to obtain bounding boxes and classification data. These particles are then tracked across subsequent images using the bounding boxes to compile tracking data. The method uses this data to output a profile of the suspended particles, incorporating taxonomic identification and possibly using convolutional operations. It employs two neural networks: one for identifying regions of interest and another for categorizing the particles based on these regions. The profile may include biomass calculations and assessments of ecosystem status, integrating sensor metadata such as depth, chlorophyll-a, salinity, and temperature. Non-particle elements like bubbles and damaged areas are excluded from tracking. The method also encompasses a system setup with a camera and processor, and a computer program that enables the execution of these methods.

TRAINING METHOD AND APPARATUS, VEHICLE SAFETY FUNCTION CONTROL METHOD AND APPARATUS, AND VEHICLE

Absstract of: EP4793797A1

The present disclosure relates to the field of control, and provides a training method and apparatus, a vehicle safety function control method and apparatus, and a vehicle. The training method comprises : acquiring a signal sample image of a vehicle, wherein the signal sample image comprises a safety function normal image and a safety function failure image; using the signal sample image to train a deep neural network model, and using the trained deep neural network model to perform data augmentation processing on the signal sample image to obtain training sample images; and using the training sample images to train a safety function failure identification classifier, wherein the safety function failure identification classifier is used for identifying a safety function state during vehicle operation, and the safety function state includes a safety function normal state or a safety function failure state.

THREE-DIMENSIONAL BASE CALLING IN NEXT GENERATION SEQUENCING ANALYSIS

Absstract of: AU2025214687A1

Disclosed herein are sequencing systems and sequencing methods for training neural networks and for utilizing the trained neural networks for sequencing analysis after acquiring flow cell images using the sequencing systems. The sequencing systems disclosed herein can include Field-Programmable Gate Array (FPGAs), artificial intelligence (AI) chips, or a combination thereof.

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

Nº publicación: US20260237189A1 13/08/2026

Applicant:

QUALCOMM INCORPORATED [US]
QUALCOMM Incorporated

Absstract of: 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.