G06V 10/82 using neural networks
Introduced: January 2022
Full Title
Full titles differ between systems:
Arrangements for image or video recognition or understanding > using pattern recognition or machine learning > using neural networks
Arrangements for image or video recognition or understanding (character recognition in images or video G06V30/10) > using pattern recognition or machine learning (optical pattern recognition or electronic computations therefor G06V10/88) > using neural networks
Additional Content IPC
AE auto-encoder network AlexNet CNN designed by Alex Krizhevsky et al. Backprop backpropagation, an algorithm for computing the gradient of the weights of an artificial neural network BERT bidirectional encoder representations from transformers, a transformer based artificial neural network CNN convolutional neural network, an artificial neural network that includes convolutional layers DNN deep neural network FCL fully connected layer of an artificial neural network FCNN fully convolutional neural network GAN generative adversarial network GoogLeNet deep convolutional neural network Inception convolutional neural network which concatenates several filters of different sizes at the same level of the network LeNet early CNN that firstly demonstrated the performance of CNNs on handwritten character recognition LSTM long short-term memory, a recurrent neural network MLP multi-layer perceptron MS COCO annotated image dataset Perceptron simple feed-forward neural network RBF radial basis function R-CNN convolutional neural network using a region proposal algorithm for object detection (variants: fast R-CNN, faster R-CNN, cascade R-CNN) Res-Net residual neural network, an artificial neural network having shortcuts / skip connections between different layers SOM self-organising maps, an algorithm for generating a low-dimensional representation of data while preserving the topological structure of the data SSD single shot (multibox) detector, a neural network for object detection U-Net neural network having a specific layer structure YOLO you only look once, an artificial neural network used for object detection (comes in various versions: YOLO v2, YOLO v3 etc.)
No child classifications to compare. This is a leaf node in both IPC and CPC.
Top Applicants
Top Applicants (IPC)
Class G06,2013–2023, worldwide · Source: EPO PATSTAT
- SAMSUNG ELECTRONICS COMPANY KR 66,669
- IBM (INTERNATIONAL BUSINESS MACHINES CORPORATION) US 62,313
- MICROSOFT TECHNOLOGY LICENSING US 41,918
- GOOGLE US 32,969
- SGCC(STATE GRID CORPORATION OF CHINA) 30,822
- INTEL CORPORATION US 30,010
- TENCENT TECHNOLOGY (SHENZHEN) COMPANY 28,235
- HUAWEI TECHNOLOGIES COMPANY CN 26,079
- APPLE US 21,891
- HUAWEI TECHNOLOGIES COMPANY 20,505
Top Applicants (CPC)
Class G06,2013–2023, worldwide · Source: EPO PATSTAT
- SAMSUNG ELECTRONICS COMPANY KR 76,952
- IBM (INTERNATIONAL BUSINESS MACHINES CORPORATION) US 62,841
- MICROSOFT TECHNOLOGY LICENSING US 44,778
- GOOGLE US 35,735
- INTEL CORPORATION US 32,087
- HUAWEI TECHNOLOGIES COMPANY CN 30,572
- TENCENT TECHNOLOGY (SHENZHEN) COMPANY 25,023
- APPLE US 23,482
- SGCC(STATE GRID CORPORATION OF CHINA) 22,548
- HUAWEI TECHNOLOGIES COMPANY 20,917