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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.

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.

METHODS AND APPARATUSES FOR CHARACTERIZING CHEMICAL SUBSTANCES, MEASURING PHYSICOCHEMICAL PROPERTIES AND GENERATING CONTROL DATA FOR SYNTHESIZING CHEMCIAL SUBSTANCES

Resumen de: US20260212962A1

Provided is a method for generating a digital representation of a chemical substance. This may involve training aspects of neural networks to represent chemical sub-stances. Provided further relates to applications of the digital representation including a computer program product, a database search engine for identifying chemical sub-stances, apparatuses for generating measurement data associated with chemical substances and control data associated with synthesis specifications for chemical substances.

METHOD AND APPARATUS FOR COMPRESSING A NEURAL NETWORK, ELECTRONIC DEVICE, AND AIRCRAFT

Resumen de: US20260212183A1

A method and an apparatus for compressing a neural network. Circuitry receives a neural network comprising a set of parameters being in at least one floating-point number format of a first precision. The circuitry also applies at least one mathematical model on the neural network to determine errors introduced by using arithmetic of a second precision lower than the first precision and propagation through network layers of the neural network due to using the arithmetic of the second precision instead of the first precision. The circuitry then applies a solver to an optimization problem formulated based on the errors determined by the at least one mathematical model to determine a set of optimized parameters. Finally, the circuitry compresses the neural network based on the set of optimized parameters to output a compressed neural network.

METHOD AND SYSTEM OF IMAGE CLASSIFICATION USING A DEEP NEURAL NETWORK EMBEDDED WITH MULTI-SCALE SPATIAL ATTENTION MECHANISM

Resumen de: US20260212659A1

0000 The present invention provides a method for image classification by incorporating a deep neural network embedded with multiscale spatial attention mechanism (MSSAM). The method according to the present invention comprises various stages: Stage I—Data preparation stage; Stage II—Model training stage; Stage III—Evaluation and Testing stage; Stage IV—Iterative optimization stage. During Data preparation stage, data is collected from a large and diverse dataset of images relevant to specific classification task. During the Model training stage, the model architecture is established, appropriate loss function is selected, an optimizer and an initial learning rate is chosen, the model is trained on training dataset, monitoring validation performance and experiments are performed with hyperparameters. During the Evaluation and Testing stage, model's performance is evaluated on the validation set using metrics and during Iterative optimization stage, the optimization process is iterated and continuously monitored for best results.

MODULAR CONCEPT-BASED LANGUAGE PROCESSING WITH TRACE-DRIVEN RESOURCE TIERING AND DYNAMIC CACHING

Resumen de: US20260211817A1

0000 Modular systems and methods for concept-based processing of natural language are provided. An ontology dictionary stores concepts each having a concept identifier, attributes, and typed relationships. Natural language inputs are mapped to concept identifiers, optionally by deriving intermediate units using transforms or statistical analysis and mapping the intermediate units to concepts, or by mapping raw tokens to concepts. A neural network model processes the concept identifiers to generate outputs. During inference and/or training, a trace engine records activation information associated with concepts and stores activation information or derived statistics in a trace data structure. A resource controller uses the trace data structure to reconfigure, during inference for subsequent inputs, a resource allocation policy that allocates frequently used concept data to a faster memory tier and allocates infrequently used concept data to a slower tier or specialized tail handling. Predictive prefetching and relational group caching may be performed.

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.

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.

FPGA-ORIENTED DSP PLACEMENT OPTIMIZATION METHOD FOR CNN ACCELERATORS

Resumen de: US20260212104A1

An FPGA-oriented DSP placement optimization method for CNN accelerators includes the following steps: S1. DSP path information extraction: converting a designed netlist into a graph representation, and carrying out data-path DSP node identification and data-path DSP graph building; and S2. datapath-driven DSP placement: distributing data-path DSP nodes to specific positions on an FPGA according to an extracted data-path DSP graph. According to the invention, automated extraction and building of data-path DSP graphs are carried out by means of graph neural network (GCN)-based DSP node classification and min-cost flow (MCF) model optimization algorithms, and compact placement and cascade constraint optimization are used in combination, thereby greatly improving the timing performance and computing efficiency of the placement and also significantly improving the clock frequency and throughput. Therefore, the method provides a universal and efficient FPGA placement solution for multiple CNN accelerator architectures.

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.

AUTOMATIZED DETECTION OF INTESTINAL INFLAMMATION IN CROHN'S DISEASE USING CONVOLUTIONAL NEURAL NETWORK

Resumen de: US20260212495A1

0000 The invention relates to system and methods for predicting and/or diagnosing of IBD from ultrasound images according to one or more of diagnostic signs.

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

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.

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.

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.

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 DEVICE FOR DETERMINING LANE OF TRAVELING VEHICLE BY USING ARTIFICIAL NEURAL NETWORK, AND NAVIGATION DEVICE INCLUDING SAME

Resumen de: US20260204081A1

0000 An embodiment provides a lane determination apparatus for a driven vehicle using an artificial neural network comprising an image information collection module configured to acquire driving image information of a vehicle from at least one camera module installed in the vehicle, a pre-trained lane prediction artificial neural network module, with the driving image information as input information and with lane prediction information of the vehicle and confidence information for the lane prediction information as output information, an output information distribution calculation module configured to calculate a data distribution map of the output information to thereby generate a first data distribution map, a reference information distribution calculation module configured to collect reference information for actual traveling lane prediction information of the vehicle, to calculate a data distribution map of the reference information, and to thereby generate a second data distribution map and a confidence calibration module configured to update parameters of the artificial neural network module so as to reduce a difference between the first data distribution map and the second data distribution map based on the second data distribution map.

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.

METHOD AND APPARATUS FOR MICRO-NANO PARTICLE MORPHOLOGY DETECTION AND SLURRY-FORMING PERFORMANCE PREDICTION, AND DEVICE

Resumen de: WO2026149240A1

The present invention relates to the technical fields of energy materials and fluid engineering. Provided in the present invention are a method and apparatus for micro-nano particle morphology detection and slurry-forming performance prediction, and a device. The method comprises: preprocessing a slurry fuel sample, and collecting microscopic morphology images and particle size distribution data of particles in the slurry fuel sample, in order to form a raw dataset; on the basis of a region-based convolutional neural network algorithm, performing segmentation and feature extraction on the microscopic morphology images in the raw dataset, in order to identify particle morphology parameters in the microscopic morphology images; establishing a dynamic database including the particle morphology parameters and the particle size distribution data; on the basis of data in the dynamic database, using a grey correlation method to quantify the degree of correlation between the particle morphology parameters and corresponding slurry-forming performance metrics, and performing linear fitting; and constructing a slurry-forming performance prediction model, in order to obtain predicted slurry-forming performance values. The present invention can be directly applied to quality control and process optimization in slurry fuel production, thereby helping improve the production efficiency and the product quality consistency.

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

Nº publicación: US20260203557A1 16/07/2026

Solicitante:

VALITACELL LTD [IE]
ValitaCell Limited

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.