using pattern recognition or machine learning (optical pattern recognition or electronic computations therefor G06V10/88)

Introduced: January 2022

Title

Titles differ between systems:

IPC: using pattern recognition or machine learning

CPC: using pattern recognition or machine learning (optical pattern recognition or electronic computations therefor G06V10/88)

Full Title

Full titles differ between systems:

IPC:

Arrangements for image or video recognition or understanding > using pattern recognition or machine learning

CPC:

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)

Additional Content IPC

Glossary

AE auto-encoder network AlexNet CNN designed by Alex Krizhevsky et al. Backprop backpropagation, an algorithm for adjusting the weights of an artificial neural network BERT bidirectional encoder representations from transformers, a transformer based artificial neural network C4.5 an algorithm for learning decision trees CART classification and regression trees CNN convolutional neural network, an artificial neural network that includes convolutional layers CPD coherent point drift, an algorithm for matching point clouds DAG directed acyclic graph DBSCAN density-based spatial clustering of applications with noise, a non-parametric clustering algorithm which does not require specifying the number of clusters in advance. DNN deep neural network EMD earth mover’s distance/Wasserstein metric FCL fully connected layer of an artificial neural network FCNN fully convolutional neural network GAN generative adversarial network GMM Gaussian mixture model GoogLeNet deep convolutional neural network ICA independent component analysis ICP iterative closest point, an algorithm for matching point clouds ID3 iterative Dichotomiser 3, an algorithm for learning decision trees Inception convolutional neural network which concatenates several filters of different sizes at the same level of the network. IoU intersection over union, a measure for quantifying the accuracy of an object detection algorithm KDE kernel density estimation, an algorithm for estimating the probability density function of a random variable kernel function which expresses an inner product of two inputs in another feature space. KLT Karhunen-Loève transform K-Means data clustering algorithm KNN K-nearest neighbour; a classification algorithm which, for a given data sample, chooses the k most similar samples from a training set, retrieves their respective class labels, and assigns a class label to the data sample by majority decision; variant: 1NN, which is KNN for k=1. LASSO least absolute shrinkage and selection operator LDA linear discriminant analysis LeNet early CNN that firstly demonstrated the performance of CNNs on handwritten character recognition. LSTM long short-term memory, a recurrent neural network LVQ learning vector quantisation MDS multi-dimensional scaling MLP multi-layer perceptron MRF Markov random field MS COCO annotated image dataset overfitting trained model suffers from overfitting if it performs well on the training data, but generalises poorly on new test data. PASCAL VOC collection of datasets for object detection PCA principal component analysis PDF probability density function Perceptron simple feed-forward neural network RANSAC random sample consensus, a popular regression algorithm RBF radial basis function Res-Net residual neural network, an artificial neural network having shortcuts / skip connections between different layers R-CNN convolutional neural network using a region proposal algorithm for object detection (variants: fast R-CNN, faster R-CNN, cascade R-CNN) ROC receiver-operating characteristics RPM robust point matching, an algorithm for matching point clouds RVM relevance vector machine 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 SVD singular value decomposition SVM support vector machine test data data set different from the training data, used for testing the performance of a trained model training data data set used for adjusting the parameters of the model during training transformer transformers deep learning model that uses attention to give different weights to individual parts of the input data. U-Net neural network having a specific layer structure validation data data set used for testing the performance of the model during training YOLO you only look once, an artificial neural network used for object detection (comes in various versions: YOLO v2, YOLO v3 etc.)

Limiting references

Pattern recognition performed by an arrangement of optical devices rather than by machine learning

Application references

Scenes; Scene-specific elements Character recognition Image or video recognition or understanding of human-related, animal-related or biometric patterns in image or video data

Of 11 combined children, 9 exist in both systems.

2 codes are CPC-only extensions.

IPC defines codes here since 2022.

Child Classifications

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G06V 10/72 Data preparation, e.g. statistical preprocessing of image or video features since 2022 IPC+CPC Available in IPC and CPC
G06V 10/74 Image or video pattern matching; Proximity measures in feature spaces since 2022 +1 CPC IPC+CPC Available in IPC and CPC
G06V 10/762 using clustering, e.g. of similar faces in social networks since 2022 +3 CPC IPC+CPC Available in IPC and CPC
G06V 10/764 using classification, e.g. of video objects since 2022 +1 CPC IPC+CPC Available in IPC and CPC
G06V 10/766 using regression, e.g. by projecting features on hyperplanes since 2022 IPC+CPC Available in IPC and CPC
G06V 10/768 CPC only CPC only
G06V 10/77 Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation since 2022 +1 CPC IPC+CPC Available in IPC and CPC
G06V 10/82 using neural networks since 2022 IPC+CPC Available in IPC and CPC
G06V 10/84 using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks since 2022 +1 CPC IPC+CPC Available in IPC and CPC
G06V 10/86 using syntactic or structural representations of the image or video pattern, e.g. symbolic string recognition; using graph matching since 2022 IPC+CPC Available in IPC and CPC
G06V 10/87 CPC only CPC only

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