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.
141
resultados
Última actualización
28/08/2026 [07:15:00]
Resumen de: EP4797760A1
0001 This application provides a model management method and an apparatus, and relates to the field of communication technologies, to reduce a data security risk of an AI model in the wireless field. The method includes: obtaining a first communication data set used for updating a first machine learning model, and sending a second communication data set to a model training function entity. The first processing policy includes a first loss threshold, and the first machine learning model is used for managing a wireless communication service. The second communication data set is obtained by performing a processing operation on suspicious data in the first communication data set according to the first processing policy, and the suspicious data is wireless communication data whose loss value is greater than the first loss threshold.
Resumen de: EP4796407A1
Embodiments of this application disclose an information processing method and a related device. The method may be applied to the autonomous driving field in artificial intelligence. The method includes: inputting, to a deep learning model, first information corresponding to a traffic scene around an ego vehicle, and obtaining second information corresponding to the first information, where the first information includes first text information, the second information is obtained based on the deep learning model, and the second information corresponds to any one of the following tasks: making a decision on behavior of the ego vehicle, planning a trajectory for the ego vehicle, or controlling the ego vehicle. Input information of the deep learning model provided in this application includes text information that facilitates understanding by a user. Therefore, interpretability of a running process of the deep learning model is improved, to be specific, a decision-making, trajectory planning, or control process of an autonomous vehicle is more transparent, so that the user can more intuitively understand behavior of the autonomous vehicle.
Resumen de: EP4797146A1
0001 A mathematical model obtaining method and device, and an operations optimization method are provided. In the method, an artificial intelligence technology may be used to obtain a mathematical model corresponding to an operations optimization problem. The method includes: outputting at least one problem, and obtaining an answer to each problem, where the answer to each problem is for obtaining first description information, the first description information is description information for describing the operations optimization problem, and a first problem is a problem for obtaining the description information of the operations optimization problem; and obtaining first information based on the first description information, and inputting the first information into a machine learning model, to obtain a mathematical model, where the mathematical model is for solving the operations optimization problem, and the mathematical model includes an objective function and a constraint. The solution greatly reduces manpower costs consumed in a process of obtaining the mathematical model, and can be adapted to obtaining mathematical models in various domains, and has high generalization.
Resumen de: EP4797015A1
0001 The disclosure relates to computer systems 10 and computer-implemented methods 200, 300 for performing a process having a binary output value and for optimizing controllable input parameters of such processes. One computer system 10 comprises at least one processor 11 and memory 12 configured to implement a parameter optimization module 102 for optimizing input parameters of a process having a binary output value, comprising: a probabilistic machine learning model 1021 trained to model a relationship between at least a first controllable input parameter of the process, a second controllable input parameter of the process and the binary output value of the process, the relationship defining an optimized pair of input parameter values 104 comprising an optimized value for the first input parameter and an optimized value for the second input parameter; and a batched binary Bayesian Optimizer 1022 configured to generate, from the trained probabilistic machine learning model 1021, a list 105 comprising a plurality of training pairs of input parameter values, each training pair comprising a value for the first input parameter and a value for the second input parameter. The parameter optimization module 102 is configured to output the optimized pair of input parameter values 104 and the list 105 of training pairs of input parameter values, and to receive binary output values of the process performed using the training pairs of input parameter values. The computer system 10 i
Resumen de: EP4797139A1
A computer-implemented method of processing input data, comprising receiving an encrypted model parameter update, wherein at least model parameters relating to personally identifiable information are encrypted using a homomorphic encryption algorithm, decrypting the encrypted model parameter update using the homomorphic encryption algorithm, applying the decrypted model parameter update to model parameters of a machine learning model, receiving, by the machine learning model, input data comprising personally identifiable information; processing, using a machine learning model, the input data to produce output data; and applying an explainability algorithm to the output data.
Resumen de: EP4797163A1
A computer-implemented method (100) for retrieving a response (3) to a query (1) from a machine learning/artificial intelligence model, ML/Al model (2), the method (100) comprising the steps of:• providing (110) the query (1) to the ML/Al model (2), thereby obtaining an initial response (3);• determining (120), from the initial response (3), instances of concepts (5*) of a given symbolic knowledge representation (4) that the initial response (3) relates to;• marking (130), in the symbolic knowledge representation (4), each concept (5*) that the initial response (3) relates to as instantiated;• determining (140), by a reasoning engine (6), concepts (5#) of the symbolic knowledge representation (4) that, given the set of presently instantiated concepts (5*), need to be instantiated as well;• determining (150) a supplemental query (1*) for information relating at least one concept (5#) that needs to be instantiated as well; and• providing (160) this supplemental query (1*) to the ML/Al model (2), thereby obtaining a supplemental response (3*) that augments the initial response (3).
Resumen de: WO2026173714A1
A computer system for labeling anomalous data for re-training a scoring machine-learning model is provided. The computer system includes a processor programmed to: receive transaction data associated with a plurality of declined transactions; apply a scoring model to the transaction data for the plurality of declined transactions; rank the plurality of declined transactions from low probability to high probability of fraud; apply a labeling model to the transaction data of a set of the plurality of declined transactions, the set including a batch of the declined transactions having higher probability scores assigned thereto; generate, using the labeling model, a precision percentage for the set of the plurality of declined transactions representing a ratio of the declined transactions labeled as fraud by the labeling model relative to the total number of declined transactions included in the set of declined transactions; and refine the precision percentage by examining subsets of the set.
Resumen de: WO2026170718A1
The present application relates to the technical field of machine learning, and discloses a model file loading method and system, a computer device, and a storage medium. The method comprises: when an inference service starts, acquiring an update request; if it is detected that the update request carries a local storage path of a model file, using the local storage path as a target storage volume of a target scheduling unit; mounting the target storage volume in an init container of the target scheduling unit, and generating a target mount item of the init container; generating response information on the basis of the target storage volume and the target mount item, wherein the response information is used for updating the target scheduling unit to establish a communication link between the updated target scheduling unit and a local directory; and sending the response information to a first slave node, so that the first slave node updates the target scheduling unit, and loads the model file on the basis of the communication link. The present application can solve problems such as high transmission delay, redundant occupation of hard disk resources, and namespace limitations during model file loading.
Resumen de: US20260244950A1
Methods, systems, apparatuses, devices, and computer program products are described. In a group-based communication system, a user may save posts for later (e.g., to reply to a message at a later time, to complete a task associated with a message at a later time). The system may use a machine learning model to determine to automatically mark a post for later for a user, for example, based on a set of features including at least a semantic embedding of the post. Additionally, or alternatively, the system may use a machine learning model to determine an order for displaying items (e.g., posts, reminders, files) within a user view (e.g., a later tab, a drafts tab, a threads tab, a files tab) for a user via a user interface. The system may update one or more machine learning models based on how users interact with the posts, user views, or both.
Resumen de: US20260245740A1
A system, comprising at least one cardiac sensor adapted to measure a hemodynamic profile of a patient; and a computing node operatively coupled to the cardiac sensor and configured to perform the steps of reading a hemodynamic profile of a patient; based on the hemodynamic profile, tuning a plurality of parameters of a cardiovascular model to create a digital twin of the patient; augmenting the hemodynamic profile of the patient with at least one parameter generated from the digital twin; providing the augmented hemodynamic profile to a pretrained machine learning model and receiving therefrom a patient profile; and outputting the patient profile for clinical decision support.
Resumen de: AU2025271014A1
Aspects of the present disclosure relate to automated analytical content generation. Embodiments include receiving data from one or more data sources. Embodiments further include extracting trends from the data using a heuristic algorithm. Embodiments further include providing an input based on the extracted trends to a generative machine learning model that has been configured to generate content based on extracted trends. Embodiments further include receiving, from the generative machine learning model based on the input, content that represents the extracted trends. Embodiments further include displaying the content via a user interface. ov o v RECEIVE DATA FROM ONE OR MORE DATA SOURCES EXTRACT TRENDS FROM THE DATA USING A HEURISTIC ALGORITHM PROVIDE AN INPUT BASED ON THE EXTRACTED TRENDS TO A GENERATIVE MACHINE LEARNING MODEL THAT HAS BEEN CONFIGURED TO GENERATE CONTENT BASED ON EXTRACTED RECEIVE, FROM THE GENERATIVE MACHINE LEARNING MODEL BASED ON THE INPUT, CONTENT THAT REPRESENTS THE EXTRACTED DISPLAY THE CONTENT VIA A USER INTERFACE RECEIVE DATA FROM ONE OR MORE DATA SOURCES ov o v
Resumen de: WO2026171953A1
The present invention disclosure provides systems and methods for efficiently managing machine learning (ML) operations in communication networks using cell- independent and cell-dependent identification types. ML IDs, encompassing ML condition IDs, model IDs, and dataset IDs, are mapped to these identifiers to enable dynamic adaptability and optimal performance. A mapping relation table is utilized to associate ML IDs with cell-specific and network-wide configurations, facilitating seamless operation across cell boundaries. The invention also includes techniques for periodic and non-periodic feedback-based performance monitoring and signaling flow for mapping relation updates, ensuring enhanced resource utilization and reduced signaling overhead. These configurations enable flexible ML operation management, supporting UE mobility and adaptive decision-making across varying network conditions.
Resumen de: US20260240490A1
0000 Techniques for configuring one or more applications based on a detected wakefulness state of a user are disclosed. A system trains and applies a machine learning model to wakefulness data to compute a wakefulness state of a user. The system obtains the wakefulness data from wearable devices worn by the user and environmental devices in a user's environment. The system configures applications and/or devices based on the computed wakefulness state of the user. The system configures the ability of devices or applications to generate visual, audible, or tactile notifications in response to determining that a user is awake or asleep.
Resumen de: US20260244985A1
There is provided a user equipment apparatus that includes at least one processor and at least one memory storing instructions. The instructions, when executed by the at least one processor, cause the user equipment apparatus at least to: access a usable machine learning (ML) model; receive, from a network apparatus, a freeze-to-adaptive ratio value; determine, based on the freeze-to-adaptive ratio, a frozen portion of the current ML model to not train and an adaptive portion of the usable ML model to train; access a performance measure for the usable ML model; retrain the adaptive portion of the usable ML model to provide a retrained ML model; determine a performance measure for the retrained ML model; and select one of the retrained ML model or the usable ML model based on the performance measure of the usable ML model and the performance measure of the retrained ML model.WO
Resumen de: WO2026171971A1
The present disclosure describes a novel method of using the pre-configured AI/ML (artificial intelligence/machine learning) with cross-RAT model compatibility in wireless mobile communication system including base station (e.g., gNB, TRP, TN, NTN) and mobile station (e.g., UE). With AI/ML model applied to radio access network, compatibility of supporting the configured two-sided models is challenging for a single or multiple UEs having different ML operational capabilities and environments. Therefore, model operation (e.g., model training, inferencing, monitoring, updating, etc.) can be set up between network and UE by configuring cross-RAT model compatibility.
Resumen de: US20260244949A1
0000 Apparatus for generating structured data outputs and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive entity data associated with an entity, the entity data including projection data and location-based data, determine at least a selection criterion as a function of the entity data, receive from a data repository a plurality of metrics as a function of the at least a selection criterion, select at least an output parameter by applying the at least a selection criterion to a plurality of output parameters, as a function of the plurality of metrics, and synthesize, using an output generation machine-learning model trained on output generation training data, a structured data output as a function of the at least an output parameter, wherein the structured data output includes a plurality of event handler graphics.
Resumen de: US20260246526A1
The system described herein relates to operating a beam device for obtaining information about an object. Moreover, the invention relates to a computer program product having a program code, which, when executed, controls the beam device in such a way that the method for operating the beam device is carried out. Additionally, the invention relates to a method for generating a training data set for a processing unit and/or for a machine learning model. Furthermore, the invention relates to a method for training a machine learning model of a beam device. The processing unit determines which machine learning model of a plurality of machine learning models is to be used for determining control values of control parameters. The control values of the control parameters are used to operate the control unit for generating the information about the object.
Resumen de: US20260245733A1
0000 In an aspect, a method for predicting, for a subject, a recovery time from an acute or debilitating event is disclosed. The method may comprise (i) retrieving wearable sensor data from a first time period and a second time period. The first time period may be prior to the acute or debilitating event. The second time period may be after the acute or debilitating event. The method also may comprise (ii) determining the recovery time for the acute or debilitating event at least in part by processing said wearable sensor data from the first time period and the second time period with a trained machine learning algorithm.
Resumen de: WO2026172331A1
Various aspects of the present disclosure relate to a node for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the node to: obtain a machine learning (ML) model comprising of a set of model parameters; for each model parameter in the set of model parameters, determine a sensitivity of the model parameter, and provide error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value; and transmit the error protected model parameters to a further node.
Resumen de: WO2026171902A1
A computer implemented method for generating a machine learning medical differential diagnosis using a differential diagnosis coordination module that is in bidirectional communication with a plurality of agent modules that each perform a specific task. The method is performed by the differential diagnosis coordination module. The method comprises receiving patient profile data and performing a series of iterations that are carried out until a predetermined condition is satisfied. Each iteration comprises selecting an agent module from the plurality of agent modules, generating agent specific instructions that cause the selected agent module to perform its specific task according to the agent specific instructions, logging iteration attribute data that relates to attributes of a current iteration and that includes an output of the selected agent module generated according to the agent specific instructions, and updating the patient profile data based on the logged iteration attribute data. When the predetermined condition is satisfied, the method comprises outputting a medical differential diagnosis based on the logged iteration attribute data. The present disclosure can be used in a variety of applications including, but not limited to, several anticipated use cases in medical diagnostics/applications and in healthcare. The present disclosure can also help in patient/physician decision making and can be used with machine learning.
Resumen de: US20260245109A1
Provided are computer-implemented methods and systems for generating a prediction using a cold-start model, including: providing at least one data set from at least one data source, the at least one data set comprising historical transactional data for a first plurality of individuals, and contextual data for a second plurality of individuals, the first plurality of individuals comprising a subset of the second plurality of individuals; determining at least one activity from the at least one data set, the at least one activity comprising at least one feature of the corresponding data set; generating a cold start prediction comprising at least one candidate identifier; generating at least one attribution value based on the at least one feature of the at least one activity; and generating an explainable prediction. Also provided are computer-implemented methods and systems for generating a cold-start model.
Resumen de: US20260245097A1
A method and apparatus for fraud detection during transactions using identity graphs are described. A method includes receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method also includes determining, by the commerce platform system, an identity associated with the user associated with additional transaction attributes not received with the transaction. Furthermore, the method includes accessing a feature set associated with the initial transaction attributes and the additional transaction attributes that includes machine learning (ML) model features for detecting transaction fraud. The method also includes performing, by the commerce platform system, a machine learning model analysis using the feature set and the transaction data to determine a likelihood that the transaction is fraudulent, and performing, by the commerce platforms system, the transaction when the likelihood that the transaction is fraudulent does not satisfy a transaction fraud threshold.
Resumen de: WO2026171696A1
The method (900) for predicting Eimeria maxima infection or prevalence in animals comprises the steps of: - providing (905) a plurality of features and a plurality of empirically measured biomarker data; - training (910), using as input the plurality of features and historical biomarker data, a machine learning model to associate predetermined labels indicating whether the set of animals have Eimeria maxima infection or prevalence to said input; - receiving (915), measured biomarker data corresponding to one or more animals, wherein the measured biomarker data indicates blood concentrations of one or more biomarkers in the one or more animals; providing (920), the biomarker data as input to the trained machine learning model; receiving (925) at least one predetermined label indicating whether the one or more animals are positive for Eimeria maxima infection or prevalence or susceptible to mMX prevalence; and providing (930), upon a computer interface, at least one predetermined label received.
Resumen de: US11966704B1
The techniques described herein relate to techniques for verifying a veracity of machine learning outputs. An example method includes receiving a first output generated by a first model responsive to a first input, the first output comprising one or more verifiable statements in text, verifying, using a second model and first reference data stored in at least one first datastore, the one or more verifiable statements to produce first verification results indicating which of them has been verified, when it is determined that at least one of them remains unverified based on the first verification results, identifying, using at least one of the first or second models, at least one second datastore having second reference data attesting to veracity of the first output; and verifying, using the second model and the second reference data, the at least one unverified statement to produce second verification results to be provided as output.
Nº publicación: EP4792262A1 19/08/2026
Solicitante:
GOODER AI INC [US]
Gooder AI, Inc.
Resumen de: WO2025080778A1
A universal system and method for dynamically evaluating and visualizing the performance of any predictive model, including machine learning models. The system and method compute performance metrics based on test set data and display visual representations in real-time, allowing users to interactively explore model performance by adjusting parameters that reflect model-deployment scenarios. Key features include model-agnostic design, support for both technical and business metrics, and the ability to compare multiple models. The system and method's extensible architecture enables custom metrics and visualizations, making them scalable across various modeling use cases and industries. By providing intuitive, real-time visual feedback, embodiments of the invention empower both technical and non-technical stakeholders to gain deeper insights into model behavior, leading to more informed decisions about deployment and optimization.
BOPI
Sede Electrónica
