Alerta

SYSTEMS, APPARATUSES, AND METHODS FOR DISEASE PREDICTION

Resumen de: WO2026155960A1

A system for training a graph neural network (GNN) for disease prediction 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 first parameter values of parameters from individuals, determine a causal structure indicative of one or more causal relationships between the plurality of parameters based on the plurality of parameters, and train the at least one GNN with the one or more causal relationships to create a trained GNN, the trained GNN configured to output a disease prediction based on second parameter values, the second parameter values being parameter values of parameters from a patient.

MACHINE LEARNING MODELS FOR BEHAVIOR UNDERSTANDING

Resumen de: US20260212162A1

0000 A method for performing one or more tasks, wherein each of the one or more tasks includes predicting behavior of one or more agents in an environment, the method comprising: obtaining a three-dimensional (3D) input tensor representing behaviors of the one or more agents in the environment across a plurality of time steps; generating an encoded representation of the 3D input tensor by processing the 3D input tensor using an encoder neural network, wherein 3D input tensor comprises a plurality of observed cells and a plurality of masked cells; and processing the encoded representation of the 3D input tensor using a decoder neural network to generate a 4D output tensor.

AUTOMATED ANEUPLOIDY SCREENING USING ARBITRATED ENSEMBLES

Resumen de: AU2026205299A1

Abstract Systems and methods are provided for fully automated screening for aneuploidy in a human embryo. An image of the embryo is obtained at an associated imager and provided to a neural network to generate a first clinical parameter. A set of at least one parameter representing one of biometric parameters of one of a patient receiving the embryo, an egg utilized to produce the human embryo, a sperm used to create the embryo, a sperm donor who provided sperm used to create the embryo, and an egg donor who provided the egg is retrieved, and a second clinical parameter is generated from the set of at least one parameter at a predictive model. A composite parameter, representing a likelihood of aneuploidy in the embryo, is generated from the first clinical parameter and the second clinical parameter. Abstract ul u l b s t r a c t

TRAINING REINFORCEMENT LEARNING AGENTS TO LEARN FARSIGHTED BEHAVIORS BY PREDICTING IN LATENT SPACE

Resumen de: US20260208355A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network used to select an action to be performed by an agent interacting with an environment. In one aspect, a method includes: receiving a latent representation characterizing a current state of the environment; generating a trajectory of latent representations that starts with the received latent representation; for each latent representation in the trajectory: determining a predicted reward; and processing the state latent representation using a value neural network to generate a predicted state value; determining a corresponding target state value for each latent representation in the trajectory; determining, based on the target state values, an update to the current values of the policy neural network parameters; and determining an update to the current values of the value neural network parameters.

Machine-Learning Techniques For Monotonic Neural Networks

Resumen de: AU2026205315A1

Abstract In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions. Abstract ul b s t r a c t u l

SYSTEM AND METHOD FOR PROCESSING ULTRASOUND IMAGES

Resumen de: US20260212999A1

0000 A computer-implemented method provides real-time visual guidance for orienting an ultrasound probe toward a canonical ultrasound view. The method involves receiving successive ultrasound images acquired by the probe during a scanning procedure. A trained neural network generates a spatial mapping from the received images without reliance on external position or motion sensors. This mapping encodes adjustments in probe position and rotation needed to achieve a target spatial pose associated with the canonical view. The spatial mapping is then transformed into visual guidance data. One or more visual guidance elements, derived from this data, are generated and displayed on a graphical user interface to instruct a user on how to manipulate the probe. The visual guidance elements are continuously updated until the canonical ultrasound view is achieved, helping non-experts capture high-quality diagnostic images.

LIGHT RENDERING METHOD AND APPARATUS, COMPUTER DEVICE, AND COMPUTER-READABLE STORAGE MEDIUM

Resumen de: US20260212595A1

A light rendering method includes: performing light effect detection on a rendered image, to obtain a detection result; in response to the detection result indicating that a first object vertex not meeting a light rendering condition exists in the rendered image, obtaining, along a light path on which the first object vertex is located, light information of a second object vertex that is in the rendered image and that meets the light rendering condition; training a neural network based on the light information of the second object vertex, to obtain a trained neural network; extracting light information of object vertexes including the first object vertex in a target image by using the trained neural network, to obtain the light information of the object vertexes; and performing light rendering on the target image based on the light information of the object vertexes.

ABSTRACTION LIBRARY TO ENABLE SCALABLE DISTRIBUTED MACHINE LEARNING

Resumen de: US20260212440A1

One embodiment provides for a non-transitory machine readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising providing an interface to define a neural network using machine-learning domain specific terminology, wherein the interface enables selection of a neural network topology and abstracts low-level communication details of distributed training of the neural network.

PERFORMING TASKS USING GENERATIVE NEURAL NETWORKS

Resumen de: US20260212167A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing tasks. One of the methods includes obtaining a sequence of input tokens, where each token is selected from a vocabulary of tokens that includes text tokens and audio tokens, and wherein the sequence of input tokens includes tokens that describe a task to be performed and data for performing the task; generating a sequence of embeddings by embedding each token in the sequence of input tokens in an embedding space; and processing the sequence of embeddings using a language model neural network to generate a sequence of output tokens for the task, where each token is selected from the vocabulary.

MULTI-VIEW CONVOLUTIONAL NEURAL NETWORKS FOR VIDEO PROCESSING

Resumen de: WO2025058768A1

The present disclosure relates to processing video data. Some aspects involve partitioning input video data into two or more clips, each clip comprising a number of T frames, wherein each frame comprises a frame height, a frame width and a frame channel dimension Cin. Each clip is encoded into S encoded representations comprising a code height, a code width, and a code channel dimension, wherein T and S are integers with T ≥ S > 1. Encoding each clip into the S encoded representations may comprise concatenating all T frames of the clip into an input tensor along the frame channel dimension and encoding the input tensor into the S encoded representations using a convolutional neural network (CNN) encoder.

LEARNING A GENERATIVE FUNCTION CONFIGURED TO GENERATE A B-REP GIVEN A CONDITIONING SIGNAL REPRESENTING A GEOMETRY

Resumen de: EP4779510A1

The disclosure notably relates to a computer-implemented method of machine-learning, for learning a generative function configured to generate a B-rep given a conditioning signal representing a geometry. The generative function comprises a vertex neural network, an edge neural network, and a face neural network. The edge and face neural network each further comprise a topological model and a geometrical model. Each of the neural network is configured to generate the respective parts of the B-rep (vertices, edges, and faces) respecting the conditioning signal. This provides an improved solution for designing B-reps given a conditioning signal representing a geometry.

NEURAL NETWORK INFERENCE USING A QUANTIZED KEY-VALUE CACHE

Resumen de: WO2025068440A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a Transformer-based neural network to generate output sequences. To generate the output sequences, the Transformer-based neural network is configured to perform quantized inference.

Method and system for cognitive enhancement of artificial intelligence language models

Resumen de: GB2703359A

A system for enhancing artificial intelligence language models includes a Neurotransmitter Simulation Module (NSM) configured to simulate dynamics of multiple neurotransmitters. The system further includes a State Interpreter configured to generate a multi-dimensional cognitive-emotional state vector based on analysing neurotransmitter levels and an Adaptive Parameter Adjustment Module (APAM) configured to adjust language model parameters based on the cognitive-emotional state vector. A language model is configured to generate natural language outputs using the adjusted parameters. A feedback loop mechanism is configured to evaluate quality of the natural language outputs and provide feedback signals to at least one of the NSM or the APAM. The system may further include a Dynamic Neurotransmitter Balancer to maintain balance among the neurotransmitters. The neurotransmitters may include serotonin, dopamine, norepinephrine, acetylcholine, and gamma-aminobutyric acid (GABA). The state interpreter may comprise a neural network trained to map the neurotransmitter levels to generate the multi-dimensional cognitive-emotional state vector. Figure 1

INTELLIGENT TRANSPORTATION SYSTEMS

Resumen de: EP4778450A2

0001 Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

METHOD AND SYSTEM FOR MULTIMODAL FUSION PREDICTION OF MARINE ENVIRONMENT BASED ON DIGITAL TWINNING

Resumen de: US20260203606A1

0000 This disclosure relates to the technical field of marine environment prediction, and in particular, to a method and system for multimodal fusion prediction of a marine environment based on digital twinning. The method includes the following steps: capturing spatiotemporal correlation features of multimodal data of the marine environment based on a dynamic multimodal graph neural network; performing multi-scale feature fusion on the spatiotemporal correlation features by using a multi-scale gating unit to obtain a comprehensive feature representation; predicating the comprehensive feature representation by using a hybrid time-series prediction framework to obtain preliminary marine environment prediction data, including short-term dynamic modeling and long-term trend modeling; and performing noise fitting on the preliminary marine environment prediction data by using a generative adversarial network to generate the final marine environment prediction data.

Learning Unified Embedding

Resumen de: US20260203581A1

0000 A computer-implemented method for generating a unified machine learning model using a neural network on a data processing apparatus is described. The method includes the data processing apparatus determining respective learning targets for each of a plurality of object verticals. The data processing apparatus determines the respective learning targets based on two or more embedding outputs of the neural network. The method also includes the data processing apparatus training the neural network to identify data associated with each of the plurality of object verticals. The data processing apparatus trains the neural network using the respective learning targets and based on a first loss function. The data processing apparatus uses the neural network trained to generate a unified machine learning model, where the model is configured to identify particular data items associated with each of the plurality of object verticals.

BUFFERS SQUEEZING AND SOURCE CODE SYNTHESIS FOR REDUCED INFERENCE COST ON RESOURCE-CONSTRAINED SYSTEMS

Resumen de: US20260203615A1

0000 A processor-implemented method for performing inference tasks on resource limited device include receiving, by an artificial neural network (ANN), an input. The ANN includes one or more fused layers. The input is processed using the one or more fused layers to generate a fused output. The ANN generates an inference using the fused output.

Neural Network Techniques for Appliance Creation in Digital Oral Care

Resumen de: US20260199058A1

0000 Systems and methods are disclosed for generating a three-dimensional (3D) representation of oral care data for use in oral care treatment. The systems and methods involve receiving an input 3D representation of a patient's dentition and encoding the 3D representation into a lower-dimensional first latent representation using a trained first machine learning (ML) module. Subsequently, a trained second ML module, comprising a trained transformer encoder model or a trained transformer decoder model, is executed to generate a second latent representation using the first latent representation. The second latent representation is then reconstructed into a 3D oral care representation (e.g., a tooth restoration design, an appliance component, a fixture model component, etc.) by a decoder. Finally, the processing circuitry outputs the reconstructed 3D representation of oral care data. These systems and methods enable efficient and accurate generation of oral care data, facilitating improved oral care appliance generation, treatment planning and analysis.

A METHOD FOR EVALUATING TRUST OF CONVOLUTIONAL NEURAL NETWORK OUTPUTS

Resumen de: US20260203557A1

Disclosed is a system that includes a CNN is configured to predict an output for each input, and generate an FMCM for each input, thereby generating a plurality of training FMCMs for a plurality of training inputs that has been used to train the CNN, and form a training feature map covariance space based on the plurality of training FMCMs. The system further includes an out of domain classifier built based on the training feature map covariance space, and configured to run on a new input to classify the new input in or out of domain of the CNN, based on whether corresponding new FMCM is in or out of the training feature map covariance space.

GEOLOCATION ERROR DETECTION METHOD AND SYSTEM FOR SYNTHETIC APERTURE RADAR IMAGES

Resumen de: US20260202539A1

Methods, systems, and techniques for detecting geolocation error in a synthetic aperture radar (SAR) image. A SAR image purportedly depicting the geographical area is obtained. At least one reference image of the geographical area is also obtained. Data based on the SAR image and the at least one reference image are input into an artificial neural network trained as a classifier to determine that the SAR image and the reference image are of different areas, which results in a finding that the SAR image suffers from geolocation error.

Vision Based System and Methods for Targeted Spray Actuation

Resumen de: US20260198478A1

A computer implemented method including: capturing, with a first image sensor of a camera that is disposed on an implement, a first sequence of images while the implement travels through an agricultural field; capturing, with a second image sensor of the camera, a second sequence of images while the implement travels through the agricultural field; training a neural network (NN) model with image data from at least one channel of the first image sensor and one channel of the second image sensor; and providing weed size as NN training target output channel.

DYNAMIC DATA PIPELINE FOR TRAINING GENERATIVE NEURAL NETWORKS

Resumen de: WO2026151874A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for fine-tuning a generative neural network. For example, the system can fine-tune the generative neural network to more effectively generate data items that have a target property.

METHOD, APPARATUS AND SYSTEM FOR ENCODING AND DECODING TENSORS

Resumen de: WO2026148372A1

A system and method of decoding a bitstream to produce tensors for use by a network portion. The method comprises decoding a plurality of pictures from the bitstream, wherein each picture contains a feature map for the network portion and the decoding may or may not conform to behaviour of a particular implementation of neural network operations, with associated output from the decoder indicating the conformance status of the decoder output.

MULTI-MODEL SYSTEM FOR ELECTRONIC TRANSACTION AUTHORIZATION AND FRAUD DETECTION

Resumen de: US20260203764A1

A method receives an electronic image and uses the image as an input to a neural network. Based on a determination that the image represents a document, the method uses the image as an input to another neural network to identify a portion of the document containing an identifier. The method extracts the identifier by performing character recognition on the identified portion and determines whether the identifier is valid by using a validation API to determine whether the identifier is associated with a valid account at an institution. Based on a determination that the identifier is associated with a valid account, the method authorizes a transaction associated with the identifier. Based on a determination that the identifier is not associated with a valid account, the method denies the transaction. The first neural network classifies the electronic image into one of multiple valid document types and an invalid document type.

SPARSE AND DIFFERENTIABLE MIXTURE OF EXPERTS NEURAL NETWORKS

Nº publicación: US20260203553A1 16/07/2026

Solicitante:

GOOGLE LLC [US]
Google LLC

Resumen de: US20260203553A1

0000 A system including a main neural network for performing one or more machine learning tasks on a network input to generate one or more network outputs. The main neural network includes a Mixture of Experts (MoE) subnetwork that includes a plurality of expert neural networks and a gating subsystem. The gating subsystem is configured to: apply a softmax function to a set of gating parameters having learned values to generate a respective softmax score for each of one or more of the plurality of expert neural networks; determine a respective weight for each of the one or more of the plurality of expert neural networks; select a proper subset of the plurality of expert neural networks; and combine the respective expert outputs generated by the one or more expert neural networks in the proper subset to generate one or more MoE outputs.