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FEATURE FUSION CLASSIFICATION METHOD FOR MULTIPLE TYPES OF PACKAGING BAG

Resumen de: WO2026064957A1

Disclosed is a feature fusion classification method for multiple types of packaging bag, relating to the technical field of classification of multiple types of packaging bag. On the basis of a random forest concept, the present invention provides a feature fusion classification algorithm for multiple types of packaging bag. By means of three different classification methods: a support vector machine, template matching, and a neural network, denoising processing is performed on images of various packaging bags transmitted from a camera using a median filter, and on the basis of a homomorphic filtering algorithm, enhancement processing is performed on the denoised images. The images are classified by separately using a support vector machine model, a template matching algorithm, and a neural network model, and a majority rule-based voting mechanism is implemented for prediction results of the three methods, to obtain a final result. The voting mechanism-based feature fusion classification algorithm for multiple types of packaging bag of the present invention provides high accuracy, reduces error generation, and obtains more accurate results.

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM

Resumen de: WO2026069497A1

This information processing device performs predetermined processing using a neural network model and comprises an inference unit that performs the predetermined processing using the model. The model includes a positional encoding unit that calculates relative positional information of each token in a token string using a wavelet function, and an attention mechanism that calculates a latent representation of the token string using the positional information.

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.

METHOD FOR CONDENSING TRAINING DATASET, AND IMAGE PROCESSING DEVICE

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.

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.

NEURAL NETWORK QUANTIZATION PARAMETER DETERMINATION METHOD AND RELATED PRODUCTS

Nº publicación: EP4718326A2 01/04/2026

Solicitante:

SHANGHAI CAMBRICON INF TECH CO LTD [CN]

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.