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.
162
resultados
Última actualización
24/10/2026 [07:17:00]
Resumen de: US20260289633A1
0000 Various embodiments are directed to apparatuses, methods, computer-readable media, computer program products, and systems related to detecting a process trigger that identifies a target entity; identifying, entity data associated with the target entity; generating, using a machine learning based prediction model, a predictive asset output based at least in part on the entity data, wherein the predictive asset output comprises at least one predicted asset; generating, using a dynamic contextualization model, a multifactor contextualized asset representation for the at least one predicted asset based at least in part on the at least one predicted asset and the entity data; and transmitting the multifactor contextualized asset representation to one or more computing devices via one or more communication channels.
Resumen de: US20260289351A1
Provided is a method of model training, an electronic device and a storage medium, relating to the field of data processing technologies, and in particular, to the technical fields of artificial intelligence, big data, large models and deep learning. The method includes: obtaining target sample data corresponding to N branches required in a current training period of distributed training; performing T update operations on network parameters of a preset model deployed on the N training nodes for the distributed training based on T data blocks corresponding to the branches to obtain first full parameter sets corresponding to the branches; performing model parameter fusion on the first full parameter sets to obtain a fused full parameter set; and updating the network parameters of the preset model deployed on the training nodes based on the fused full parameter set and proceeding to the next training period to obtain a target model.
Resumen de: US20260284519A1
0000 A deep-learning system enables a coach to guide interactions between a user and an avatar while adaptively refining behavioral characteristics associated with the avatar. During a session, a coach provides prompts, feedback, and scoring through a dedicated interface, and the system generates avatar-matched dialog based on predefined behavioral traits, objectives, and personality attributes. The system compares automated scoring with coach-provided evaluations to identify substantive differences and dynamically update adaptive model parameters used in future dialog generation. The invention further supports real-time response suggestions, behavioral reinforcement, avatar movement controls, voice transformation, and personality-based language modification. Training data generated during sessions may be reviewed and incorporated into ongoing deep-learning processes, enabling continuous improvement of avatar behavior modeling for coaching, simulation, education, therapeutic, or interactive communication environments.
Resumen de: US20260292652A1
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a transmitter node may predict a future state associated with a wireless channel at a future time instance using a machine learning model, wherein the future state is predicted based at least in part on one or more of weights associated with the machine learning model, a current state associated with the wireless channel, or one or more previous states associated with the wireless channel. The transmitter node may select one or more parameters for a transmission to occur at the future time instance based at least in part on the future state associated with the wireless channel. The transmitter node may perform the transmission using the one or more parameters. Numerous other aspects are described.
Resumen de: US20260289418A1
Embodiments relate to multi-domain process automation with user feedback integration. Some embodiments include a method performed by one or more computing devices. The one or more computing devices generate, using a machine learning (ML) model, predictions for records. The one or more computing devices receive at least one of single user feedback or multiple user feedback for the predictions. The one or more computing devices generate a user validated record pool based on the single user feedback or multiple user feedback. The one or more computing devices update the ML model using the user validated record pool.
Resumen de: US20260289401A1
A machine learning system can include a symbolic representation generator and an inference component. The symbolic representation can generate a symbolic memory network using input obtained from an environment. The inference component can generate a causal model by experimenting upon components, operations, and parameters of the symbolic representation generator. The causal model can influence the generation of the symbolic memory network. In particular, the machine learning system can identify an analog in the symbolic memory network as being entangled, identify a disentangled version of the identified entangled analog in the symbolic memory network, and, in response to the identification of the disentangled version of the identified entangled analog, release the identified entangled analog from the symbolic memory network and provide instructions to take an action in an environment based on the disentangled version of the identified entangled analog.
Resumen de: US20260289073A1
The sentence generation model generation device (10) includes a learning unit (11) configured to, in response to input of an input sentence, identify, from the input sentence, a claim and a premise that supports the claim from the same standpoint as the claim or from a different standpoint than the claim, perform at least one of labeling the claim and the premise and labeling a relation between the claim and the premise, and conduct machine learning using the input sentence as an explanatory variable and the text of the labeled portion as an objective variable, to perform training of a sentence generation model for generating and outputting a text sentence related to a structure of the input sentence and generate the sentence generation model.
Resumen de: US20260285373A1
0000 Embodiments of this disclosure 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 disclosure includes text information that facilitates understanding by a user.
Resumen de: US20260289400A1
A method for securely deploying a model, a device, and a storage medium are provided. The method includes: receiving, from a user side, a deployment request for a machine learning model, the deployment request including at least a model identification of the machine learning model that is encrypted and a key identification of a key used to encrypt the machine learning model; obtaining the machine learning model that is encrypted and the key based on the deployment request; decrypting, in a trusted execution environment, the machine learning model that is encrypted using the key to obtain the machine learning model; and deploying the machine learning model in the trusted execution environment.
Nº publicación: US20260289358A1 24/09/2026
Solicitante:
ASKIT AI INC [US]
AskIt-AI, Inc.
Resumen de: US20260289358A1
A method and system for simulating responses from virtual personas, the method comprising: using a computer processor, generating a plurality of target audience descriptions; converting the plurality of target audience descriptions into numerical embeddings; converting a pre-existing personality survey into numerical embeddings; determining, with a machine learning model, compatibility of the plurality of target audience descriptions with respondents of the pre-existing personality survey; forming an array of likelihood scores based on the compatibility, wherein columns of the array correspond to the target audience descriptions and rows correspond to the pre-existing personality survey; selecting a subset of the rows of the array with likelihood scores above a user-selected threshold; generating, from the subset, a set of virtual personas combining the plurality of target audience descriptions and the pre-existing personality survey respondents; and simulating responses using the set of virtual personas.
BOPI
Sede Electrónica
