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.
113
resultados
Última actualización
19/07/2026 [07:23:00]
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.
Resumen de: US20260205558A1
0000 Apparatuses, systems, and techniques to enhance video are disclosed. In at least one embodiment, one or more neural networks are used to create, from a first video, a second video having one or more additional video frames.
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.
Resumen de: US20260203013A1
0000 A set of measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In other embodiment, helper networks can be used to filter identification inputs to improve the accuracy of the models that use encrypted inputs for classification.
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.
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.
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.
Resumen de: US20260203014A1
0000 Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.
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.
Resumen de: US20260203466A1
Systems and methods disclosed relate to generating training data. In one embodiment, the disclosure relates to systems and methods for generating training data to train a neural network to detect and classify objects. A simulator obtains 3D models of objects, and simulates 3D environments comprising the objects using physics-based simulations. The simulations may include applying real-world physical conditions, such as gravity, friction, and the like on the objects. The system may generate images of the simulations, and use the images to train a neural network to detect and classify the objects from images.
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.
Resumen de: US20260202568A1
0000 Provided are a frequency and amplitude reduction regulation and control method and system for multi-source dynamic disturbances in deep tunnels. The method includes: acquiring first information and a wave-absorbing material dataset; determining mounting positions of sensors according to the first information, and acquiring second information, wherein the second information includes disturbance wave data collected by the sensors; constructing a first feature diagram according to the second information; determining third information according to the first feature diagram, wherein the third information includes types of disturbance waves; dividing the wave-absorbing material dataset according to the third information to obtain at least one divided wave-absorbing material dataset; and based on the divided wave-absorbing material dataset, training a neural network to obtain at least one trained neural network model, wherein the trained neural network model is configured to output wave-absorbing material mixture ratios corresponding to different types of disturbance waves.
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.
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.
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.
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.
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.
Resumen de: US20260203580A1
Methods and systems are described herein for generating recommendations for counterfactual explanations to computer alerts that are automatically detected by a machine learning algorithm. The methods and systems use an artificial neural network architecture that trains a hybrid classifier and autoencoder. For example, one model (or artificial neural network), which is a classifier, is trained to make predictions. A second model (or artificial neural network), which is an autoencoder, is trained to reconstruct its inputs. As the second model is trained to reconstruct its inputs means, the second model is implicitly trained to determine what in-sample data looks like. By combining these networks and train them jointly, the system generates predictions (e.g., counterfactual explanations) that are in-sample.
Resumen de: EP4776560A1
Embodiments of this specification provide a method and an apparatus for model inference using a cryptographic neural network. The cryptographic neural network includes a plurality of linear layers. The method includes: receiving ciphertext data and key data of a user, where the key data is partial data of an evaluation key of a target format, and the evaluation key of the target format includes a plurality of extended ciphertexts; and inputting the ciphertext data into the cryptographic neural network for cryptographic processing, where the cryptographic processing includes: performing, at any target linear layer in the plurality of linear layers, a ciphertext rotation operation by using a first data part corresponding to a first ciphertext level t in the evaluation key, where the first data part includes only partial data of a single extended ciphertext.
Resumen de: NL4001803A
The present invention relates to the technical field of educational informatization, and particularly relates to a precise analysis and guidance system and method for ideological and political education of college students. The system comprises a data acquisition layer, a data fusion layer, an intelligent analysis layer, and an application service layer connected in sequence. The system acquires full-dimensional data covering student family background, growth experience, on-campus teaching, student activities, and daily life. After cleaning, standardization, and knowledge graph construction, a student holographic profile is formed. Algorithms such as causal inference, graph neural networks, and long short-term memory networks are adopted to extract multi-dimensional ideological and political features, thereby realizing dynamic assessment, risk warning, and trend prediction of students’ ideological states. Finally, personalized ideological guidance schemes and visualized decision support are generated. The present invention can significantly improve the scientificity, timeliness, and precision of ideological and political work in colleges and universities.
Resumen de: US20260197476A1
0000 Information processing with improved inferencing for machine learning is disclosed. In one example, a neural network is analyzed before inferencing is performed and generates control information for controlling compression and decoding of a feature amount processed by the neural network. Inferencing is performed using input data and the neural network and a processing result obtained by processing the feature amount is output as a computing result. The feature amount is compressed on the basis of the control information and recorded as a compressed feature amount. A decoder decodes the compressed feature amount temporarily recorded in the memory on the basis of the control information and outputs the decoded feature amount to the computing unit.
Resumen de: WO2026147908A1
A computational pupillometry system comprises an imaging device configured to capture video frames of a subject's eye and processors executing instructions to perform advanced pupillary assessment. The system employs multi-frame integration techniques, including super-resolution algorithms that utilize sub-pixel shifts between frames, temporal averaging for noise reduction, and parallax-based artifact mitigation to enhance measurement accuracy. Artificial intelligence models, including temporal neural networks, analyze the enhanced pupillary data to determine pupillary parameters and calculate a light-invariant Pupil Reactivity (PuRe) score. The system processes ambient lighting conditions through computational models that analyze video frames before and after controlled stimulation, enabling consistent scoring across varying environmental conditions. Quality assurance mechanisms provide prerecording and post-recording validation with real-time feedback. The system integrates with electronic medical records through standardized healthcare protocols and supports synchronized, multi-device deployment across healthcare networks.
Resumen de: WO2026146838A1
The present invention relates to a method for generating a foundation model for a hyperspectral image by using artificial intelligence. The method for generating a foundation model for a hyperspectral image by using artificial intelligence: masks a masking patch area randomly determined for a hyperspectral image to be learned; randomly rearranges the positions of some of unmasked patches or adds noise; extracts feature information by inputting the unmasked patches to an encoder; inserts masking feature information into the extracted feature information and inputs same to a decoder to generate a reconstructed hyperspectral image in which the hyperspectral image is reconstructed; and trains the encoder and the decoder so as to minimize the difference between the hyperspectral image and the reconstructed hyperspectral image, thereby making it possible to train and generate a foundation model including an artificial neural network-based encoder and decoder.
Resumen de: US20260196020A1
Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, a set of exemplars corresponding to a class is accessed, and the set of exemplars is blended to generate a blended exemplar. The blended exemplar is aggregated with a noise sample to generate a noisy exemplar. An output corresponding to the class is generated based on processing the noisy exemplar using a generator neural network. The output is output.
Nº publicación: US20260196015A1 09/07/2026
Solicitante:
NVIDIA CORP [US]
NVIDIA Corporation
Resumen de: US20260196015A1
0000 Apparatuses, systems, and techniques to update lower resolution images. In at least one embodiment, color information from one or more upsampled images may be obtained so that the color information from the one or more upsampled images may be caused to be applied to one or more subsequent lower resolution images.
BOPI
Sede Electrónica
