Alerta

ELECTRONIC NOSE INSTRUMENT OPERABLE IN BOTH SCHEDULED AND ON-DEMAND MODES AND METHOD FOR ONLINE REAL-TIME DETECTION AND ANALYSIS OF MULTI-COMPONENT ODORS

Resumen de: WO2026065613A1

An electronic nose instrument operable in both scheduled and on-demand modes and a method for online real-time detection and analysis of multi-component odors. A hardware unit of the electronic nose instrument mainly comprises: a gas-sensitive sensor array module (I), a headspace sampling module (II), a pressurization cylinder (III), a computer control and analysis module (IV), a backup power supply (V), and a clean air cylinder (VI). A main housing integrates the first four components. Within a cycle time T=180-600 s, the pressurization cylinder (III) significantly increases a gas-sensitive response by means of short-term pressure multiplication. The gas-sensitive sensor array obtains a 50-dimensional sensing sample for single detection. A large odor dataset X comprises online detection data from the electronic nose instrument, and offline detection data from olfactometry and chromatography etc. The detection data is decomposed into multiple single-concentration sub-tasks. A machine learning cascade model is formed by multiple learning groups consisting of single neurons, and shallow and deep neural networks. The electronic nose instrument can flexibly achieve online real-time identification of odor pollutants and multi-component concentration estimation and prediction.

METHOD FOR PREDICTING PRICE OF CRYPTOCURRENCY ON BASIS OF ARTIFICIAL NEURAL NETWORK

Resumen de: WO2026071683A1

According to an embodiment of the present disclosure, disclosed is a method for predicting the price of cryptocurrency on the basis of an artificial neural network. The method may comprise the steps of: acquiring monitoring reference information from a user terminal; generating a chart image according to the monitoring reference information; generating a pattern prediction result corresponding to the chart image on the basis of an artificial neural network-based pattern prediction model; and transmitting, to the user terminal, notification information generated on the basis of the pattern prediction result.

GENERATING CONTROLLER LOGIC USING LLMS

Resumen de: EP4718233A1

0001 Embodiments of the present disclosure relate to generating controller logic. Indication of a controller logic generation request associated with an asset identifier may be received. A prompt template set associated with a controller logic generation workflow may be identified based on the asset identifier. The prompt template of the prompt template set may comprise one or more instruction sets. The prompt template set may be input into a large language model comprising one or more transformer neural networks and configured to generate a controller logic configuration file for the asset identifier based on the prompt template set and intent classification associated with each prompt template. The controller logic configuration file may be received from the large language model. Performance of one or more prediction-based actions may be initiated based on the controller logic configuration file.

ELECTRONIC DEVICE FOR EXECUTING NEURAL NETWORK MODEL INCLUDING NON-LINEAR OPERATION AND OPERATION METHOD THEREOF

Resumen de: EP4718327A1

0001 An electronic device for executing a neural network model including a non-linear operation and an operation method thereof are provided. The operation method of the electronic device includes obtaining data to be inferred and obtaining an inference result of the data output from the neural network model as the data is input to the neural network model including a plurality of nodes, wherein, in an inference process, a first weight applied when a value of a first node among the plurality of nodes is transmitted to a second node may be updated based on a value of a first reference node, which is any one of the plurality of nodes.

NEURAL NETWORK QUANTIZATION PARAMETER DETERMINATION METHOD AND RELATED PRODUCTS

Resumen de: EP4718326A2

0001 The technical solution involves a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus, respectively; the storage component is used to store data; the interface apparatus is used to implement data transfer between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card is used to perform an artificial intelligence operation.

METHOD FOR CONDENSING TRAINING DATASET, AND IMAGE PROCESSING DEVICE

Nº publicación: EP4718328A1 01/04/2026

Solicitante:

SAMSUNG ELECTRONICS CO LTD [KR]

Resumen de: EP4718328A1

0001 A method of condensing a training dataset and an image processing device are provided. The method of condensing the training dataset includes generating a cluster set by clustering the training dataset, generating an initial condensed high-resolution (HR) dataset by selecting, for each cluster included in the cluster set, some of images included in each cluster, obtaining a first loss of a first neural network model based on the training dataset and obtaining a second loss of a second neural network model based on the initial condensed HR dataset, and generating a condensed HR dataset by updating, based on the first loss and the second loss, pixels in each of images included in the initial condensed HR dataset.