If you want to distinguish your goods, services or both from those of another company, you may need a trademark or trade name. Find out what they are, what their registration procedure is and what it involves.
Information on the deadlines for filing applications for transformation of European Union trademarks into Spanish national trademarks. See more
If you have a new device, product or procedure that solves a technical problem or has a practical advantage, there are different ways to protect it in Spain and other countries. Find out how.
Does your innovation lie in the aesthetics, ornamentation or appearance of your product? Protect it through industrial design. Find out what rights registration confers and how to proceed.
Patents published worldwide are a valuable source of scientific, technical and commercial information.
reembolso del 75% del coste de Búsquedas e Informes Tecnológicos de Patentes. Más información*
If you are an entrepreneur or a company and you want to boost and improve the profitability of your business by adequately protecting the intangible assets of your organisation, in this space you will find what you need.
32
results.
Updated on
11/09/2024 [08:09:00]
Results 25 to 32 of 32
Absstract of: WO2024164508A1
Disclosed in the present application are a distributed deployment and inference method for a deep spiking neural network, and a related apparatus. The method comprises: acquiring a deep spiking neural network to be deployed, and splitting the deep spiking neural network into several sub-neural networks; compiling each of the several sub-neural networks, so as to obtain a generative model file corresponding to each sub-neural network; and sequentially deploying into a brain-inspired chip generative model files corresponding to the several sub-neural networks, so as to gradually perform inference on input data by means of the several sub-neural networks to obtain output data corresponding to the input data. In the present application, a deep spiking network is split into several sub-neural networks, and the individual sub-neural networks are separately compiled and deployed, such that the deep spiking neural network can be deployed into a brain-inspired chip, and the brain-inspired chip uses a large-scale deep spiking neural network to perform model inference, thereby expanding the scope of usage of the deep spiking neural network.
Absstract of: US2024270260A1
Systems and methods for predicting interactions between objects and predicting a trajectory of an object are presented herein. A system can obtain object data associated with a first object and a second object. The object data can have position data and velocity data for the first object and the second object. Additionally, the system can process the obtained object data to generate a hybrid graph using a graph generator. The hybrid graph can have a first node indicative of the first object and a second node indicative of the second object. Moreover, the system can process, using an interaction prediction model, the generated hybrid graph to predict an interaction type between the first node and the second node. Furthermore, the system can process, using a graph neural network model, the predicted interaction type between the first node and the second node to predict a trajectory of the first object.
Absstract of: US2024273370A1
Systems and methods, for selecting a neural network for a machine learning (ML) problem, are disclosed. A method includes accessing an input matrix, and accessing an ML problem space associated with an ML problem and multiple untrained candidate neural networks for solving the ML problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the ML problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the ML problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the ML problem. The method includes providing an output representing the selected at least one candidate neural network.
Absstract of: US2024273368A1
A circuit for performing neural network computations for a neural network comprising a plurality of layers, the circuit comprising: activation circuitry configured to receive a vector of accumulated values and configured to apply a function to each accumulated value to generate a vector of activation values; and normalization circuitry coupled to the activation circuitry and configured to generate a respective normalized value from each activation value.
Absstract of: US2024273349A1
Spiking events in a spiking neural network may be processed via a memory system. A memory system may store data corresponding to a group of destination neurons. The memory system may, at each time interval of a SNN, pass through data corresponding to a group of pre-synaptic spike events from respective source neurons. The data corresponding to the group of pre-synaptic spike events may be subsequently stored in the memory system.
Absstract of: US2024273369A1
A method of generating a neural network based open-domain dialogue model, includes receiving an input utterance from a device having a conversation with the dialogue model, obtaining a plurality of candidate replies to the input utterance from the dialogue model, determining a plurality of discriminator scores for the candidate replies based on reference-free discriminators, determining a plurality of quality score associated with the candidate replies, and training the dialogue model based on the quality scores.
Nº publicación: US2024273351A1 15/08/2024
Applicant:
BOE TECH GROUP CO LTD [CN]
BOE Technology Group Co., Ltd
Absstract of: US2024273351A1
A method of generating a negative sample set for predicting macromolecule-macromolecule interaction is provided. The method includes receiving a positive sample set including pairs of macromolecules of a first type and macromolecules of a second type having macromolecule-macromolecule interaction; generating a first similarity map of the macromolecules of the first type; generating a second similarity map of the macromolecules of the second type; generating vectorized representations of nodes in the first similarity map and vectorized representations of nodes in the second similarity map; and generating the negative sample set using the vectorized representations of nodes in the first similarity map and the vectorized representations of nodes in the second similarity map.
BOPI
Electronic site
