Si desitges distingir els teus productes, serveis o tots dos dels d'una altra empresa, és possible que necessitis una marca o nom comercial. Descobreix què són, en què consisteix el seu procediment de registri i què implica.
Información sobre los plazos de presentación de solicitudes de transformación de marcas de la Unión Europea en marca nacional española. Más información
Si tens un nou dispositiu, producte o procediment que resolgui un problema tècnic o tingui un avantatge pràctic, existeixen diferents formis de protegir-ho a Espanya i en altres països. Descobreix com fer-ho.
La teva innovació resideix en l'estètica, l'ornamentació o l'aparença del teu producte? Protegeix-la mitjançant un disseny industrial. Descobreix quins drets confereix el registri i com realitzar la tramitació.
Els patents publicades a tot el món són una valuosa font d'informació científica, tècnica i comercial.
El Bono 3 del Fondo para PYMEs 2025, que cubre la elaboración de Informes Tecnológicos de Patentes (ITP) y Búsquedas Retrospectivas se cerrará el lunes 10 de febrero de 2025 al cierre de la jornada laboral. Próximamente se informará sobre su reapertura. Puede consultar más información aquí.
Si ets emprenedor/a o una empresa i vols potenciar i millorar la rendibilitat del teu negoci protegint de forma adequada els actius intangibles de la teva organització, en aquest espai trobaràs el necessari.
32
resultados
Última actualización
09/03/2025 [07:37:00]
Resumen de: US2025078809A1
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating speech from text. One of the systems includes one or more computers and one or more storage devices storing instructions that when executed by one or more computers cause the one or more computers to implement: a sequence-to-sequence recurrent neural network configured to: receive a sequence of characters in a particular natural language, and process the sequence of characters to generate a spectrogram of a verbal utterance of the sequence of characters in the particular natural language; and a subsystem configured to: receive the sequence of characters in the particular natural language, and provide the sequence of characters as input to the sequence-to-sequence recurrent neural network to obtain as output the spectrogram of the verbal utterance of the sequence of characters in the particular natural language.
Resumen de: US2025078998A1
According to various embodiments, a machine-learning based system for mental health disorder identification and monitoring is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs. The processors are further configured to train at least one neural network based on raw physiological data augmented with synthetic data and subjected to a grow-and-prune paradigm to generate at least one mental health disorder inference model. The processors are also configured to output a mental health disorder-based decision by inputting the received physiological data into the generated mental health disorder inference model.
Resumen de: US2025077877A1
A computer-implemented method for improving entity matching in a probabilistic matching engine can train a graph neural network (GNN) model on an output of a probabilistic matching engine to perform entity matching and determine counterfactual explanations for non-matches of entities. A list of data transformations can be identified by actionable recourse using the GNN model. The list of data transformations can be ranked, using the GNN model, based on computational overhead and an estimated improvement in entity matching within the probabilistic matching engine.
Resumen de: US2025077898A1
A computer-implemented method comprising a mathematical formulation of the social energy flow of Karma and a machine learning framework combining mathematical graph computation, which form a global sentiment aggregator for the measurement of social energy flows and which is formulated as a data processing, filtering and sampling framework which fuses structural, interpretable graph machine learning with graph neural networks and introduces a Graph Attention Mechanism (GAM) whereby machine learning guides an interpretable graph computation substrate in order to generate general, structured predictions for the past, present and future. This enables the framework to handle large data sets on a global scale in the graph neural network while affording interpretability within the less scalable graph computational substrate which in turn optimizes the use of memory, computer storage and processing power over traditional designs, thereby making global-scale computations on commodity hardware feasible.
Resumen de: WO2025045319A1
The invention relates to a method for explaining and/or verifying a behaviour of a neural network trained with a training database, said method having the following steps: - determining sensor information by means of a sensor, so that sensor information is available, - applying the neural network to the sensor information, so that target information is available, - determining similarity information, wherein a comparison is made of the sensor information or part of the sensor information with an information database, so that the similarity information is available, linking the similarity information with the target information, so that a target information tuple is available, on the basis of which the explanation and/or verification of the behaviour of the neural network can be realised.
Resumen de: WO2025047998A1
A processor of an electronic device according to an embodiment may be configured to acquire, from a first neural network to which a speech signal received via a microphone has been input, a first sequence of feature information about portions, corresponding to designated frame units, of the speech signal. The processor may be configured to acquire, from a second neural network to which a designated text has been input, a second sequence of one or more phonetic symbols for the designated text. The processor may be configured to acquire, from a third neural network to which the first sequence and the second sequence have been input, a first parameter indicating the degree of correspondence between the voice signal and the designated text. The processor may be configured to acquire one or more second parameters indicating the degree of correspondence the portions of the voice signal have with respect to each of the one or more phonetic symbols included in the second sequence.
Resumen de: WO2025049978A1
Presented herein are systems and methods for the use and/or automated design of neural networks (NNs) with layer shared architecture (e.g., an intelligent layer shared (ILASH) neural architecture). In certain embodiments, the NN with layer shared architecture comprises a base set of layers (e.g., base layer shared model) and one or more branches extending from the base set of layers. Each branch may include one or more layers from the base set of layers and one or more additional layers different from the base set of layers, each branch designed and trained to perform a particular unique task on a common set of input data. As a result, the NN will share some layers among multiple tasks. Moreover, presented herein are techniques for using a predictive neural network search algorithm to create the branched network of the layer shared architecture.
Resumen de: WO2025048121A1
A processor of an electronic device according to an embodiment may be configured to acquire, from a first neural network to which a speech signal received via a microphone has been input, a first sequence of feature information about portions of the speech signal corresponding to designated frame units. The processor may be configured to acquire, from a second neural network to which the designated text has been input, a second sequence of one or more phonetic symbols for the designated text. The processor may be configured to acquire, from a third neural network to which the first sequence and the second sequence have been input, a first parameter indicating the degree of correspondence between the speech signal and the designated text. The processor may be configured to acquire one or more second parameters indicating the degree of correspondence between the portions of the speech signal and each of the one or more phonetic symbols included in the second sequence.
Resumen de: WO2025045736A1
A computer-implemented method for improving entity matching in a probabilistic matching engine can train a graph neural network (GNN) model on an output of a probabilistic matching engine to perform entity matching and determine counterfactual explanations for non-matches of entities. A list of data transformations can be identified by actionable recourse using the GNN model. The list of data transformations can be ranked, using the GNN model, based on computational overhead and an estimated improvement in entity matching within the probabilistic matching engine.
Resumen de: EP4517581A1
A modular artificial intelligence (AI) platform operates by: receiving media input that includes image data and text data; generating encoded text data via a text encoder module that includes first language processing AI; generating encoded image data via an image encoder module that includes a plurality of neural networks and a long short-term memory; generating concept structure data via a concept identification module that includes graph-based learning AI; generating decoded text data via a text decoder module that includes language processing AI; generating decoded image data, via an image decoder module that includes a plurality of neural networks and a long short-term memory; and combining the decoded image data and the decoded text data to generate media output data.
Resumen de: US2025068837A1
Systems, methods, and computer-readable storage devices are disclosed for improved table identification in a spreadsheet. One method including: receiving a spreadsheet including at least one table; identifying, using machine learning, one or more classes of a plurality of classes for each cell of the received spreadsheet, wherein the plurality of classes include corners and not-a-corner; and inducing at least one table in the received spreadsheet based on the one or more identified classes for each cell of the received spreadsheet.
Resumen de: US2025068938A1
A processor-implemented method includes obtaining a benchmark execution result, receiving input data comprising a neural network model subject to prediction and analysis requirement information, receiving information on hardware of a device in which the neural network model is run, building a prediction model based on the benchmark execution result and the hardware information, extracting layer information respectively corresponding to a plurality of layers configuring the neural network model, and predicting either one or both of operation performance information and energy efficiency information respectively corresponding to the plurality of layers by inputting the analysis requirement information and the layer information to the prediction model.
Resumen de: US2025069358A1
Apparatuses, systems, and techniques to use data obtained from an inferred object to determine whether to re-infer the same inferred object. In at least one embodiment, one or more objects are identified in one or more images. A size of the one or more objects in one image is compared to a size of the one or more objects in another image. The one or more objects are re-inferenced based, at least in part, on the comparison.
Resumen de: US2025068913A1
Systems, methods, and computer readable media related to information retrieval. Some implementations are related to training and/or using a relevance model for information retrieval. The relevance model includes an input neural network model and a subsequent content neural network model. The input neural network model and the subsequent content neural network model can be separate, but trained and/or used cooperatively. The input neural network model and the subsequent content neural network model can be “separate” in that separate inputs are applied to the neural network models, and each of the neural network models is used to generate its own feature vector based on its applied input. A comparison of the feature vectors generated based on the separate network models can then be performed, where the comparison indicates relevance of the input applied to the input neural network model to the separate input applied to the subsequent content neural network model.
Resumen de: US2025068909A1
A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.
Resumen de: US2025069593A1
A method includes obtaining, from a user device associated with a user, an audio signature, the audio signature extracted from audio data corresponding to a query spoken by the user. The method also includes processing, using a neural network, the audio signature to identify document tokens that match the audio signature within a shared embedding space, the neural network trained to jointly embed audio signatures and document tokens in the shared embedding space. The method also includes retrieving, using the document tokens and the shared embedding space, a set of search results for the query, and providing, for output from the user device, one or more search results from the set of search results to the user.
Resumen de: US2025069704A1
A system, a method and a non-transitory computer readable storage medium for base calling are described. The base calling method includes processing through a neural network first image data comprising images of clusters and their surrounding background captured by a sequencing system for one or more sequencing cycles of a sequencing run. The base calling method further includes producing a base call for one or more of the clusters of the one or more sequencing cycles of the sequencing run.
Resumen de: AU2023324756A1
In aspects, the present approaches allow neural networks to be taught to understand patterns of human behavior without the need of expert data labeling or laboratory studies. First and second neural networks are trained to understand these patterns without labeling. Once trained, the neural networks can be deployed with a trained classifier to determine or classify human activity based upon received sensor inputs.
Resumen de: WO2025042692A1
Provided herein are systems, methods, computer-readable media, and techniques for providing trusted artificial intelligence (AI) using Fully Homomorphic Encryption (FHE), comprising: (A) providing a deep neural network (DNN)-based model with modified architecture, wherein the modified architecture at least (i) uses a Gaussian function as an activation function and (ii) removes one or more pooling layers; (B) obtaining encrypted data, wherein the encrypted data are generated by applying the FHE to plaintext data; and (C) generating an inference with the DNN-based model based on the encrypted data. Further provided herein are systems, methods, computer-readable media, and techniques for providing trusted AI using stochastic computing, using noise based computing, using an artificial immune system, and with secure multi-party computation (SMPC) based at least in part on watermarking.
Resumen de: EP4513385A1
The present disclosure relates to a method and apparatus for determining a relative energy between systems, an electronic device, a computer-readable storage medium, and a computer program product. The method (300) includes: for a chemical system, performing a plurality of iteration rounds using a neural network variational Monte Carlo method (310); acquiring a linear relationship between energy errors and energy variances, which are obtained in the plurality of iteration rounds (320); determining a first energy error at a position where the energy variance is zero based on the linear relationship (330); and determining the relative energy between the chemical system and a further system based on the first energy error (340). It can be seen that, since there is no need to wait for complete convergence of training in the solution, the required time is less, and the efficiency is higher.
Resumen de: US2025061043A1
Methods and systems for an intelligent technical debt helper may include receiving, via a processor, a level of technical debt associated with a technical debt of a computer program code and determining, via the processor, whether the level of technical debt is greater than a technical debt threshold. The method may also include generating, using an artificial intelligence neural network model communicatively coupled to the processor and based on the computer program code, an automated code recommendation to address the technical debt of the computer program code when the level of technical debt is greater than the technical debt threshold.
Resumen de: US2025061577A1
Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.
Resumen de: US2025061153A1
A generative model can be used for generation of spatial layouts and graphs. Such a model can progressively grow these layouts and graphs based on local statistics, where nodes can represent spatial control points of the layout, and edges can represent segments or paths between nodes, such as may correspond to road segments. A generative model can utilize an encoder-decoder architecture where the encoder is a recurrent neural network (RNN) that encodes local incoming paths into a node and the decoder is another RNN that generates outgoing nodes and edges connecting an existing node to the newly generated nodes. Generation is done iteratively, and can finish once all nodes are visited or another end condition is satisfied. Such a model can generate layouts by additionally conditioning on a set of attributes, giving control to a user in generating the layout.
Resumen de: US2025061318A1
One embodiment provides for a machine-learning accelerator device a multiprocessor to execute parallel threads of an instruction stream, the multiprocessor including a compute unit, the compute unit including a set of functional units, each functional unit to execute at least one of the parallel threads of the instruction stream. The compute unit includes compute logic configured to execute a single instruction to scale an input tensor associated with a layer of a neural network according to a scale factor, the input tensor stored in a floating-point data type, the compute logic to scale the input tensor to enable a data distribution of data of the input tensor to be represented by a 16-bit floating point data type.
Nº publicación: US2025061149A1 20/02/2025
Solicitante:
NEW YORK UNIV [US]
NEW YORK UNIVERSITY
Resumen de: US2025061149A1
A method of treating a subject comprises administering a treatment to a subject identified as having a high probability of distant metastatic recurrence, wherein the probability of distant metastatic recurrence was determined by a process, comprising acquiring at least one image of a tissue sample comprising a plurality of cells, taken from a subject, classifying each of the plurality of cells into categories, dividing the at least one image into a plurality of patches, calculating values for a plurality of morphological features based on the patches, and calculating a distant metastatic recurrence probability based on the values. A computer-implemented method of training a neural network and a system for characterizing a cancer in a subject are also described.
BOPI
Sede Electrónica
