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.
Les indicacions geogràfiques protegeixen el nom d’un producte originari d’una zona geogràfica, a la qual li deu una determinada qualitat, reputació o una altra característica. Descobreix què són, en què consisteix el seu procediment de registre i quins beneficis atorguen.
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.
91
resultados
Última actualización
29/11/2025 [07:26:00]
Resumen de: US2025365588A1
Provided are a method and an apparatus for performing beam management in a wireless communication system. The method of a terminal may include triggering at least one beam failure recovery (BFR) for a cell that performs beam management using an artificial intelligence and/or machine learning model, deactivating the artificial intelligence and/or machine learning model based on the number of the at least one beam failure recovery triggered during a specific time duration, and transmitting deactivation information of the artificial intelligence and/or machine learning model to a base station. The method of the base station may include transmitting, to the terminal, configuration information related to the artificial intelligence and/or machine learning model, receiving, from the terminal, the deactivation information of the artificial intelligence and/or machine learning model, and, based on the received deactivation information, stopping beam generation related to the artificial intelligence and/or machine learning model.
Resumen de: US2025359937A1
Methods, non-transitory computer readable media, and surgical computing devices are illustrated that improve surgical planning using machine learning. With this technology, a machine learning model is trained based on historical case log data sets associated with patients that have undergone a surgical procedure. The machine learning model is applied to current patient data for a current patient to generate a predictor equation. The current patient data comprises anatomy data for an anatomy of the current patient. The predictor equation is optimized to generate a size, position, and orientation of an implant, and resections required to achieve the position and orientation of the implant with respect to the anatomy of the current patient, as part of a surgical plan for the current patient. The machine learning model is updated based on the current patient data and current outcome
Resumen de: US2025359785A1
A method for estimating a concentration of a respiratory gas in the blood of a patient comprises: receiving measurement data, which indicate a volume-dependent course of a concentration of the respiratory gas in a respiratory airflow exhaled by the patient depending on a respiratory air volume exhaled by the patient; generating input data from the measurement data, the input data comprising a matrix of values for various parameters with respect to the volume-dependent course; inputting the input data into a machine learning module which was trained to convert the input data into output data, which indicate a concentration of the respiratory gas in the blood of the patient; outputting the output data by way of the machine learning module.
Resumen de: US2025363524A1
The present disclosure relates to ranking electronic advertisements using one or more machine learning algorithms for a targeted audience associated with an aircraft flight. For example, one or more embodiments described herein include a computer-implemented method comprising executing a learn-to-rank algorithm to train a machine learning model on a training dataset that includes electronic advertisements with associated scores characterizing a relevancy between the electronic advertisements and a defined query. The computer-implemented method can also comprise applying the trained machine learning model to rank a set of electronic advertisements based on a feature vector characterizing input data that includes flight details of an aircraft.
Resumen de: WO2025244724A1
A computing system (10) including one or more processing devices (12) configured to receive a prompt (20). At a machine learning model (30) that has an output token vocabulary (40) including candidate output tokens (42), the one or more processing devices are further configured to compute output token probabilities (34) over the output token vocabulary based at least in part on the prompt. At a decoder plugin (60), the one or more processing devices are further configured to compute a constrained output token vocabulary (64) as a proper subset of the output token vocabulary. The one or more processing devices are further configured to select output tokens (52) based at least in part on the computed output token probabilities. The output tokens are selected from among the candidate output tokens included in the constrained output token vocabulary. The one or more processing devices are further configured to transmit an output (50) including the output tokens to an additional computing process (54).
Resumen de: WO2025245236A1
A method performed by one or more computers. The method comprises receiving a natural language query specifying requirements for a compound; processing the natural language query using a language policy to generate a plurality of representations of candidate compounds that each satisfy at least a subset of the requirements specified in the natural language query. Each representation specifies at least a chemical formula of the corresponding candidate compound. The method further comprises, for each representation in a subset of the representations, using a generative machine learning model conditioned on the representation to generate one or more candidate chemical structures, each candidate chemical structure comprising a respective spatial location for each of the atoms of the corresponding candidate compound; and selecting a chemical structure and corresponding compound from the plurality of candidate chemical structures.
Resumen de: US2025363511A1
In an embodiment, a method for segmenting a large dataset into distinct segments using artificial intelligence (AI) is disclosed. The method includes receiving aggregated datasets including user data and user IDs assigned thereto, processing the datasets to extract user data characteristics, and creating distinct segments according to a segmentation pipeline based on the extracted user data characteristics. The method further includes predicting segment membership using explainable AI and assigning users into given ones of the distinct segments according to an ensemble machine learning-based segmentation model and the extracted user data characteristics. The method further includes receiving additional user data, refining the segmentation model according to the additional user data, and updating a set of the distinct segments according to the refined segmentation model.
Resumen de: US2025363550A1
A computer-implemented method of generating a graphic for a vehicle item may include: causing a user device to display a user interface indicative of one or more vehicles; receiving, from the user device, vehicle selection information, the vehicle selection information indicative of a vehicle selected by a user; obtaining, from a database, user data corresponding to the user; generating, using a machine learning model, a first score corresponding to a first vehicle item based on the user data; determining whether the first score exceeds a first predetermined score threshold; generating, in response to a determination that the first score exceeds the first predetermined score threshold, a first graphic indicative of the first vehicle item; and causing the user device to display the first graphic via the user interface.
Resumen de: US2025363400A1
Apparatus and methods for proactively and preemptively communicating with a user interacting with a software application are provided. The apparatus and methods may include an artificial intelligence/machine learning communication engine monitoring and tracking a user's interactions. The apparatus and methods may include the communication engine determining if the user requires further training, if the interaction is fraudulent, and pre-empting requests for information the user may commence. The apparatus and methods may include the communication engine creating and displaying training materials for the user to complete, revoking access if fraud is present, and proactively providing information before the user requests the information.
Resumen de: US2025363185A1
A system and method include generating synthetic data by generating a first set of hyperparameters for a first trained machine learning model and a second set of hyperparameters for a second trained machine learning model, generating a plurality of synthetic data vectors using the first and second trained machine learning models, computing an error function for the first and second set of hyperparameters using a third machine learning model, computing an objective function value, responsive to determining that the objective function value is not an optimal value, updating the first set of hyperparameters and the second set of hyperparameters or responsive to determining that the objective function value is an optimal value outputting the plurality of synthetic data vectors as a set of synthetic data.
Resumen de: US2025362945A1
The disclosure relates to a computer-implemented method for simulating performance of a web application. The technical problem solved by the disclosure is to identify aspects of a web application that greatly affect user retention and to quantify user retention by modifying the identified aspects of the web application. This is solved by a collecting monitoring data associated with interactions between users and the web application; training a machine learning model with training data; generating virtual performance metrics for the web application; simulating a rate of users leaving the web application based on the virtual performance metrics; identifying at least one modified performance metric causing a change in user retention; and outputting a recommendation specific to the web application.
Resumen de: US2025364120A1
A computer-implemented method for predicting a clinical outcome of a patient, the method comprising: obtaining a pathology image associated with the patient; processing the pathology image including: determining a salient region of the pathology image; and segmenting the pathology image into a plurality of tiles; providing the processed pathology image as an input to a machine learning model configured to annotate the pathology image; obtaining, as an output of the machine learning model, the annotated pathology image associated with the patient, wherein the annotated pathology image includes annotations for different classes of tissues including a tumor regression; and determining, based on the annotated pathology image, a score indicative of the clinical outcome of the patient.
Resumen de: WO2025244723A1
A computing system (10) is provided that receives a tokenized prompt (33) at a machine learning model (18), generates a model-generated content portion (40B) of an output sequence (38) of output tokens (40) in response to the tokenized prompt, identifies provenance metadata (29) for a grounded data source (44) in the model-generated content portion of the output sequence. Upon identification of the provenance metadata, the computing system at least temporarily ceases token-wise probabilistic generation of the output sequence with the machine learning model, retrieves grounded content (52) from the grounded data source using the provenance metadata, writes output tokens corresponding to the grounded content to a grounded content portion (40A1) of the output sequence, and transmits the output sequence to an additional computing process, for display, storage, or additional downstream processing, for example.
Resumen de: EP4654095A1
A more versatile technique is provided for constructing a regression model having a correspondence relationship with variation of an explanatory variable and variation of a response variable. A regression analysis method includes: retrieving, by a computer, training data from a storage device storing the training data, the training data being used as a response variable and an explanatory variable of a regression model; and performing, by the computer, machine learning by the regression model using the training data to minimize a cost function including a regularization term. The regularization term includes a first term that increases a cost more in an interval where a coefficient is positive than in an interval where the coefficient is negative, and a second term that increases the cost more in the interval where the coefficient is negative than in the interval where the coefficient is positive.
Resumen de: EP4654595A1
Example methods disclosed herein include accessing common homes data for a group of common homes, the common homes data including return path data and panel meter data. Disclosed example methods also include accessing common homes data for a group of common homes, the common homes data including first return path data and corresponding panel meter data associated with respective ones of the common homes, grouping the common homes data into view segments, classifying the view segments based on whether the return path data in respective ones of the view segments has matching panel meter data to determine labeled view segments, generating features from the labeled view segments, training a machine learning algorithm based on the features, and applying second return path data to the trained machine learning algorithm to determine whether a media device associated with the second return path data is on or off.
Resumen de: EP4654022A2
The disclosure relates to a computer-implemented method for simulating performance of a web application. The technical problem solved by the disclosure is to identify aspects of a web application that greatly affect user retention and to quantify user retention by modifying the identified aspects of the web application. This is solved by a collecting monitoring data associated with interactions between users and the web application; training a machine learning model with training data; generating virtual performance metrics for the web application; simulating a rate of users leaving the web application based on the virtual performance metrics; identifying at least one modified performance metric causing a change in user retention; and outputting a recommendation specific to the web application.
Resumen de: WO2025238443A1
The present disclosure relates to systems, methods, and program applications for identifying separation-related problems in a pet. The methods, for example, can include identifying the presence or absence of multiple behavioral signs exhibited by a pet where each of the multiple behavioral signs are given a sign score based on binary annotations representing either the presence or the absence of each of the behavioral signs, and grouping subsets of the multiple behavioral signs into one of multiple principal component behavioral groupings using the binary annotations to generate principal component scores for each of the multiple principal component behavioral groupings. Methods can also include using one or more machine-learning algorithms under the control of at least one processor for accessing and correlating the principal component scores for each of the multiple principal component behavioral groupings with a population cluster associated with a type of separation-related problem.
Resumen de: US2025355960A1
The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model. Based on the digital connections, the disclosed systems can surface one or more digital content suggestions to a user interface of a client device.
Resumen de: WO2025239978A1
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, an input prompt for machine learning is received, and the input prompt is decomposed to generate a set of sub-prompts. A sequence of requests for sub-prompts of the set of sub-prompts that have sequential dependency is generated, and a parallel request for sub-prompts of the set of sub-prompts that do not have sequential dependency is generated. Based on evaluating the sequence of requests and the parallel request, an execution plan for using one or more machine learning models to generate a response to the input prompt is generated. The response to the input prompt is output according to the execution plan.
Resumen de: AU2024267218A1
An optical coherence tomography (OCT) device includes artificial intelligence for recommending a treatment plan for a patient with a retinal or macular disease such as age-related macular degeneration (AMD). The OCT device includes a sensor configured to quantify an initial level of macular edema or retinal exudation. The OCT device receives treatment information for a series of anti-vascular endothelial growth factor (anti-VEGF) injections to the patient. The OCT device performs OCT on the patient subsequent to each anti-VEGF injection to determine subsequent levels of edema or retinal exudation. The OCT device collects a set of training data including: the initial and subsequent levels of edema or exudation, patient information, and treatment information. The OCT device applies the training data to a machine-learning model trained on training data for a plurality of patients to determine a treatment plan for the retinal or macular disease of the patient.
Resumen de: US2025358769A1
Aspects presented herein may enable a consistency between multiple network entities in artificial intelligence (AI) or machine learning (ML) (AI/ML) related positioning training and inference. In one aspect, a first network entity transmits, to a second network entity, a request for an identifier (ID) to be used for indexing a set of datasets associated with at least one AI/ML model related to positioning. The first network entity receives, from the second network entity based on the request, the ID to be used for indexing the set of datasets associated with the at least one AI/ML model related to positioning. The first network entity stores, based on the ID, at least one of a set of positioning configurations or a set of radio statistics associated with the first network entity. The first network entity indexes the set of datasets with the ID.
Resumen de: US2025355683A1
A system displays a first set of generative interfaces in a user interface. Each generative interface includes user interface elements that contain content specifying information of the generative interface. Responsive to receiving a user interaction with a user interface element, the system activates a dynamic input phase that dynamically generates responses during runtime of receiving user inputs to the user interface. The system receives a second user input and applies a machine learning model to the generative interface comprising the interacted user interface element, the content contained in the interacted user interface element and the content from the second user input. The system receives content as an output and updates the user interface to display a second set of generative interfaces. The second set of generative interfaces may include one or more runtime-determined user interface elements, and each runtime-determined user interface element include information associated with the received content.
Resumen de: WO2025237631A1
Improved systems and methods for constructing a machine-learning model associated with lithography are disclosed. The method may include accessing a first set of data comprising metrology data, training a machine-learning model iteratively based on the first set of data, the machine-learning model associated with a lithography process, obtaining information generated by the machine- learning model from each of multiple iterations during the training, and identifying outlier data from the first set of data based on the obtained information.
Resumen de: WO2025240170A1
Aspects presented herein may enable a consistency between multiple network entities in artificial intelligence (AI) or machine learning (ML) (AI/ML) related positioning training and inference. In one aspect, a first network entity transmits, to a second network entity, a request for an identifier (ID) to be used for indexing a set of datasets associated with at least one AI/ML model related to positioning. The first network entity receives, from the second network entity based on the request, the ID to be used for indexing the set of datasets associated with the at least one AI/ML model related to positioning. The first network entity stores, based on the ID, at least one of a set of positioning configurations or a set of radio statistics associated with the first network entity. The first network entity indexes the set of datasets with the ID.
Nº publicación: WO2025237802A1 20/11/2025
Solicitante:
BUNDESDRUCKEREI GMBH [DE]
BUNDESDRUCKEREI GMBH
Resumen de: WO2025237802A1
Disclosed is a method for training a machine learning model for generating synthetic data, the method comprising: Providing an encrypted data set, resulting from encryption of an original data set, to a server, the encrypted data set comprising a set of entries, wherein each entry of the encrypted data set comprises values for a set of attributes; application of a homomorphic machine learning algorithm for data synthesis comprising a set of homomorphic group operations on the encrypted data set, such a homomorphic algorithm as understood herein encompasses any algorithm the execution of which causes the decrypted results to coincide with the results of applying the same homomorphic group operations on the original data set.
BOPI
Sede Electrónica
