Si deseas distinguir tus productos, servicios o ambos de los de otra empresa, es posible que necesites una marca o nombre comercial. Descubre qué son, en qué consiste su procedimiento de registro y qué implica.
Información sobre los plazos de presentación de solicitudes de transformación de marcas de la Unión Europea en marca nacional española. Más información
Si tienes un nuevo dispositivo, producto o procedimiento que resuelva un problema técnico o tenga una ventaja práctica, existen distintas formas de protegerlo en España y en otros países. Descubre cómo hacerlo.
¿Tu innovación reside en la estética, la ornamentación o la apariencia de tu producto? Protégela mediante un diseño industrial. Descubre qué derechos confiere el registro y cómo realizar la tramitación.
Las indicaciones geográficas protegen el nombre de un producto originario de una zona geográfica, a la cual le debe una determinada calidad, reputación u otra característica. Descubre qué son, en qué consiste su procedimiento de registro y qué beneficios conceden.
Las patentes publicadas en todo el mundo son una valiosa fuente de información científica, técnica y comercial.
Si eres emprendedor/a o una empresa y quieres potenciar y mejorar la rentabilidad de tu negocio protegiendo de forma adecuada los activos intangibles de tu organización, en este espacio encontrarás lo necesario.
77
resultados
Última actualización
12/05/2026 [07:41:00]
Resumen de: WO2026093095A1
Embodiments of the present disclosure relate to a method and apparatus for task planning, a device, and a computer program product. The method includes: generating, based on a current state of a task, a heterogeneous graph representation of a plurality of objects related to the task, where the heterogeneous graph representation includes object nodes indicating the plurality of objects and a first edge indicating a relationship between the plurality of objects in the current state. In addition, the method further includes generating a task action based on the heterogeneous graph representation by using a graph neural network. Therefore, according to the embodiments of the present disclosure, the task planning can be modeled by using heterogeneous graph data, and a task action of the task planning can be generated by using the graph neural network, thereby avoiding manual design of a learning strategy, reducing complexity of the task planning, and improving an effect of the task planning.
Resumen de: WO2026094919A1
This information processing device comprises an input unit to which a signal acquired by a magnetic sensor in a fusion reactor is input as input data, and an inference unit that uses a model having a plurality of neural networks to calculate a plasma magnetic flux from the input data.
Resumen de: WO2026095510A1
The present invention relates to a method for recommending a control operation, and an electronic device. The method may extract a first hashtag corresponding to first content. The method may generate a first embedding vector corresponding to the first hashtag on the basis of the first hashtag and the first content. The method may determine a first cluster corresponding to the first embedding vector among a plurality of clusters. The method may generate personalized classification information corresponding to the first hashtag on the basis of the first embedding vector and a first center point vector corresponding to a center point of the first cluster. The method may provide a recommendation application and a recommendation control operation from personalized classification information by using a neural network model. The first embedding vector may include a first hashtag vector corresponding to the first hashtag and a first content vector corresponding to the first content.
Resumen de: WO2026091165A1
The present invention relates to a vision-assisted millimeter wave beam prediction method for low-light environments, belonging to the field of wireless communications. Targeting millimeter wave communication systems, the method collects image data of a communication environment under low-light conditions by means of a camera installed at a base station to construct a millimeter wave beam prediction system model, and uses a deep neural network model and a course training policy to learn the collected image data so as to predict an optimal communication beam. The present invention can improve the performance of models in low-light environments and accelerate the convergence speed of network models, thereby improving the accuracy of beam prediction and enhancing the robustness and reliability of millimeter wave communication systems in low-light environments.
Resumen de: US20260128153A1
The present application provides a processing method and device of a hematoma aspiration decision-making system for intracerebral hemorrhage. A two-layer deep neural network is constructed for processing of a hematoma aspiration protocol, and a perception-decision-making-control method based on a time series is thus realized, which overcomes unpredictability of results of hematoma aspiration processes existing in the prior art, provides direct and convenient information transmission for surgeons to make decisions, can effectively improve the accuracy of aspiration treatment protocols for intracerebral hematoma, and provides digital, intelligent, and powerful support for clinical hematoma aspiration treatments.
Resumen de: US20260127883A1
0000 The present disclosure provides a control method and apparatus for a broadcast monitoring system, a computer device and a storage medium, and belongs to the field of image recognition and terminal broadcast monitoring technology. The control method includes: acquiring an image to be detected; dividing the image to be detected into a plurality of target sub-images according to a shooting visual angle of a shooting device for shooting the image to be detected; processing each of the target sub-images by using a pre-trained target neural network model to obtain a recognition result for the target sub-image; and obtaining a detection result of whether a target object exists in the image to be detected or not based on the recognition result corresponding to each target sub-image, and sending the detection result to a terminal so that the terminal determines a display state at least based on the detection result.
Resumen de: US20260125054A1
In various examples, systems and methods are disclosed that use one or more machine learning models (MLMs)—such as deep neural networks (DNNs)—to compute outputs indicative of an estimated visibility distance corresponding to sensor data generated using one or more sensors of an autonomous or semi-autonomous machine. Once the visibility distance is computed using the one or more MLMs, a determination of the usability of the sensor data for one or more downstream tasks of the machine may be evaluated. As such, where an estimated visibility distance is low, the corresponding sensor data may be relied upon for less tasks than when the visibility distance is high.
Resumen de: US20260128148A1
A computer-implemented method of classifying images comprising dose-response graphs obtained from dose-response experiments. The method comprises receiving, at a curve shape classifier model, an input comprising image data including a plurality of pixels, wherein the image data represents an image of a dose-response graph indicating a relationship between the concentration of a compound and its activity. The curve shape classifier model comprises a neural network model configured for classifying images of dose-response graphs into a plurality of dose-response graph categories relating to curve shape. The method further comprises generating, using the neural network model, a classification output for the image represented by the received image data, said generating comprising processing the image data using one or more layers of the neural network model in accordance with parameters associated with the one or more layers.
Resumen de: US20260127430A1
A method performed by a deep neural network (DNN) includes receiving, at a layer of the DNN during an inference stage, a layer input comprising content associated with a DNN input received at the DNN. The method also includes quantizing one or more parameters of a plurality of parameters associated with the layer based on the content of the layer input. The method further includes performing a task corresponding to the DNN input, the task performed with the one or more one quantized parameters.
Resumen de: US20260126856A1
0000 The invention discloses an emotion recognition method based on spatio-temporal multi-scale attention convolutional neural network, which comprises: collecting EEG data of subjects for preprocessing to obtain EEG data containing spatial dimension and temporal dimension; constructing a lightweight convolutional neural network including two-stream spatio-temporal feature construction layer, hybrid attention mechanism layer, high-order fusion layer and classification layer; wherein the two-stream spatio-temporal feature construction layer comprises a temporal feature extraction module and a parallel spatial feature extraction module; the high-order fusion layer is used to re-learn from the learned global convolution kernel to the representation of the local hemisphere convolution kernel; the trained lightweight convolutional neural network is used to identify EEG data, and the emotion recognition results of the subjects are obtained. By constructing a lightweight model with fewer parameters, the accuracy and efficiency of EEG-driven emotion recognition are improved.
Resumen de: US20260127494A1
In embodiments, a method for configuring an intelligent agent to do a task based on spatial-temporal magnetic imaging data of the brain of a worker is disclosed. The method includes generating a brain region parameter indicating an active neocortex region associated with visual processing during performance of the task based on the spatial-temporal magnetic imaging data. The method further includes selecting a convolutional neural network (CNN) component type in response to a match between the brain region parameter and an associated CNN component type. The method includes configuring the intelligent agent based on the selected CNN component and a neocortical processing flow parameter derived using the spatial-temporal magnetic imaging data, wherein the intelligent agent is configured to process image data using the CNN component and provide an output of the CNN to another AI component via a data connection created based on the neocortical processing flow parameter.
Resumen de: US20260127955A1
0000 Systems, methods and apparatus of drowsiness detection for vehicle control. For example, a vehicle includes: a camera configured to face a driver of the vehicle and generate a sequence of images of the driver driving the vehicle; an artificial neural network configured to analyze the sequence of images and classify, based on the sequence of images, whether the driver is in a drowsy state; and an infotainment system configured to provide instructions to the driver in response to a classification by the artificial neural network that the driver is in the drowsy state.
Resumen de: US20260127446A1
An acceleration method for heterogeneous graph neural networks based on meta-path graphs is provided, including: constructing a meta-path graph by arranging all meta-path instances in graph form, based on a given heterogeneous graph and specified types of meta-paths; partitioning the meta-path graph to obtain multiple meta-path subgraphs, which are further divided by workload; performing layer-based encoding on the meta-path instances according to the workload distribution; merging all meta-path instance slice encodings based on inter-layer relationships in the meta-path graph to obtain meta-path instance encodings; and performing intra-meta-path aggregation and inter-meta-path aggregation to compute the final features of the target vertices. The present method significantly reduces redundant computations in heterogeneous graph neural network, thereby improving model inference efficiency.
Resumen de: WO2026091782A1
The present application relates to the technical field of artificial intelligence. Disclosed are a secure federated learning method, a device, a medium, and a product. In the present application, after receiving a model update gradient sent by each participant, a central server calculates a credibility score for each participant on the basis of a plurality of calculation indexes, i.e., the similarities between a model feature parameter set of each participant and model feature parameter sets of other participants, and the similarity between a last-layer neural network before global model training and the last-layer neural network after global model training; and the central server uses each credibility score as a basis for determining a weighting coefficient during the process of performing weighted aggregation update on the basis of the model update gradient of each participant, wherein a higher credibility score corresponds to a higher weighting coefficient. In addition, a weighting coefficient during weighted aggregation is determined by means of a credibility score, such that a participant with a low credibility score has no or little impact on the update of a global model, thereby defending against the attack of a poisoning model and improving the robustness of federated learning.
Resumen de: US20260127432A1
A heterogeneous processor includes a first processor and a second processor of a different type. The heterogeneous processor operates in either a low-power mode or a full-power mode. The first processor is configured to operate in the low-power mode, process sensing data from a sensor using a trained neural network model, and generate a wake-up signal when an output of the trained neural network model satisfies a predefined criterion. The wake-up signal is provided to the second processor during the low-power mode. The second processor remains in a powered-down state during the low-power mode and transitions to the full-power mode in response to the wake-up signal.
Resumen de: US20260127961A1
0000 A method for detecting an infringement by vehicle operator is described. The method comprises detecting a vehicle; receiving one or more image of at least a part of the vehicle operator; automatically analysing with a neural network the one or more captured received image to detect an infringing act; and providing the one or more captured received images comprising the detected infringing act to thereby detect the infringement. Also described are a system, a device, a computer system and a computer program product all for detecting an infringement by a vehicle operator. The device may comprise one or more flash for illuminating the vehicle or a part thereof with light at a narrow band and one or more camera comprising a narrow band filter that lets through only the wavelengths of light produced by the one or more flash.
Resumen de: WO2026092345A1
The present application relates to the technical field of industrial intelligence, and discloses a fault diagnosis method and apparatus for a large die forging press, a device, and a medium. The method comprises: acquiring multi-modal sensor data of a large die forging press; preprocessing the multi-modal sensor data; inputting the preprocessed multi-modal sensor data into a multi-modal attention convolutional neural network model to perform feature extraction so as to obtain multi-modal features; and performing fusion processing on the multi-modal features; and inputting the fused multi-modal features into a classifier to perform classification so as to obtain a fault diagnosis result.
Resumen de: US20260127702A1
A mechanism is described for detecting, at training time, information related to one or more tasks to be performed by the one or more processors according to a training dataset for a neural network, analyzing the information to determine one or more portions of hardware of a processor of the one or more processors that is configurable to support the one or more tasks, configuring the hardware to pre-select the one or more portions to perform the one or more tasks, while other portions of the hardware remain available for other tasks, and monitoring utilization of the hardware via a hardware unit of the graphics processor and, via a scheduler of the graphics processor, adjusting allocation of the one or more tasks to the one or more portions of the hardware based on the utilization.
Resumen de: US20260127714A1
An auto-exposure control is proposed for high dynamic range images, along with a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. Corresponding method and system for high dynamic range object detection are also provided.
Resumen de: US20260127869A1
An apparatus configured to perform a perception task may generate sensor features from data from one or more sensors. process the sensor features with a time-continuous recurrent neural network (RNN) to produce time-continuous features, and perform the perception task using the time-continuous features. The time-continuous features may be defined by a first feature vector value corresponding to a first observation time of the one or more sensors, a prediction of a steady state feature vector value, and estimated feature vector values between the first feature vector value and the steady state feature vector value, the estimated feature vector values being defined by a function.
Resumen de: EP4738380A1
A computer-implemented method of prediction of a cardiovascular, CV, event of a patient, based on a retinal image from the patient, is provided. The method comprises extracting a feature data set from the retinal image using a neural network, the neural network being pretrained to output feature data sets based on retinal images; inputting the extracted feature data set to a machine learning model, ML model, the ML model being pretrained to output a CV event indicator, the pretraining being based on the following data: training feature data sets extracted from retinal images; and training CV event indicators, one training CV event indicator per each one of the training feature data sets; and obtaining a CV event indicator of the patient from the ML model. A computer-implemented method of training a deep learning model based on retinal images and CV event indicators wherein the deep learning model comprises a neural network, NN, and a machine learning, ML, model, is provided. A data processing system, a training data processing system and a computer program product are also provided.
Resumen de: EP4738826A1
0001 A method and an apparatus for encoding a multi-layer feature map, and a recording medium, of the present disclosure may comprise the steps of: extracting, from an input image, a multi-layer feature map including a plurality of feature maps in a hierarchical form; outputting a single-layered fusion latent representation by sequentially encoding the multi-layer feature map through consecutive encoding blocks; and encoding the single-layered fusion latent representation into a bit stream.
Resumen de: WO2026089955A1
A video encoder for machine-based video applications is provided which includes a split neural network front end receiving an input image signal and generating a plurality of feature maps comprising a plurality of feature tensors representing the input image signal. A feature reduction module receives the feature maps and generates at least one reduced feature map representing the original plurality of feature maps. A feature conversion module converts the reduced feature maps to a video format. A block segmentation and truncation module performs a block truncation method on blocks identified with low variability, an indication of lower importance of the information in the block, to further reduce feature map complexity. An inner encoder follows block segmentation and truncation and generates an encoded bitstream representing the plurality of feature maps.
Resumen de: US20260120481A1
A computer-implemented method for classifying a traffic sign in an image, a computing device and vehicle thereof is disclosed. The method includes obtaining the image depicting at least a portion of a surrounding environment of the vehicle; identifying a region in the image corresponding to a traffic sign, by processing the image through a first machine learning model configured to output detections of traffic signs in input images; extracting a crop corresponding to the identified region, wherein the crop has a native resolution based on a size of the identified region in relation to the obtained image; and determining classification data of the traffic sign by processing the crop, at the native resolution, through a second machine learning model, wherein the second machine learning model is an attention-based neural network, trained to process input images of traffic signs of varying resolution and to generate corresponding classification data.
Nº publicación: US20260120246A1 30/04/2026
Solicitante:
TORC CND ROBOTICS INC [CA]
TORC CND ROBOTICS, INC.
Resumen de: US20260120246A1
0000 An auto-exposure control is proposed for high dynamic range images, along with a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. Corresponding method and system for high dynamic range object detection are also provided.
BOPI
Sede Electrónica
