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CPC
COOPERATIVE PATENT CLASSIFICATION
G06N
COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS [2022-01]
WARNING

  • In this subclass non-limiting references (in the sense of paragraph 39 of the Guide to the IPC) may still be displayed in the scheme.
G06N 3/00
Computing arrangements based on biological models [2022-01]
G06N 3/002
.
{Biomolecular computers, i.e. using biomolecules, proteins, cells (using DNA G06N 3/123; using neurons G06N 3/061)} [2013-01]
G06N 3/004
.
Artificial life, i.e. computing arrangements simulating life [2023-01]
G06N 3/006
. .
based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] [2023-01]
G06N 3/008
. .
based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour [2023-01]
G06N 3/02
.
Neural networks [2023-01]
G06N 3/04
. .
Architecture, e.g. interconnection topology [2023-01]
WARNING

G06N 3/0409
. . .
{Adaptive resonance theory [ART] networks} [2019-01]
G06N 3/0418
. . .
{using chaos or fractal principles} [2013-01]
G06N 3/042
. . .
Knowledge-based neural networks; Logical representations of neural networks [2023-01]
G06N 3/043
. . .
based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS] [2023-01]
G06N 3/044
. . .
Recurrent networks, e.g. Hopfield networks [2023-01]
WARNING

G06N 3/0442
. . . .
characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] [2023-01]
WARNING

G06N 3/045
. . .
Combinations of networks [2023-01]
WARNING

G06N 3/0455
. . . .
Auto-encoder networks; Encoder-decoder networks [2023-01]
WARNING

G06N 3/0463
. . .
{Neocognitrons} [2013-01]
G06N 3/0464
. . .
Convolutional networks [CNN, ConvNet] [2023-01]
WARNING

G06N 3/047
. . .
Probabilistic or stochastic networks [2023-01]
WARNING

G06N 3/0475
. . .
Generative networks [2023-01]
WARNING

G06N 3/048
. . .
Activation functions [2023-01]
G06N 3/049
. . .
Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs [2023-01]
G06N 3/0495
. . .
Quantised networks; Sparse networks; Compressed networks [2023-01]
WARNING

G06N 3/0499
. . .
Feedforward networks [2023-01]
WARNING

G06N 3/06
. .
Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons [2013-01]
G06N 3/061
. . .
{using biological neurons, e.g. biological neurons connected to an integrated circuit} [2013-01]
G06N 3/063
. . .
using electronic means [2013-01]
G06N 3/065
. . . .
Analogue means [2023-01]
G06N 3/067
. . .
using optical means [2013-01]
G06N 3/0675
. . . .
{using electro-optical, acousto-optical or opto-electronic means} [2013-01]
G06N 3/08
. .
Learning methods [2023-01]
WARNING

G06N 3/082
. . .
modifying the architecture, e.g. adding, deleting or silencing nodes or connections [2023-01]
G06N 3/084
. . .
Backpropagation, e.g. using gradient descent [2023-01]
G06N 3/086
. . .
using evolutionary algorithms, e.g. genetic algorithms or genetic programming [2023-01]
G06N 3/088
. . .
Non-supervised learning, e.g. competitive learning [2023-01]
G06N 3/0895
. . .
Weakly supervised learning, e.g. semi-supervised or self-supervised learning [2023-01]
WARNING

G06N 3/09
. . .
Supervised learning [2023-01]
WARNING

G06N 3/091
. . .
Active learning [2023-01]
WARNING

G06N 3/092
. . .
Reinforcement learning [2023-01]
WARNING

G06N 3/094
. . .
Adversarial learning [2023-01]
WARNING

G06N 3/096
. . .
Transfer learning [2023-01]
WARNING

G06N 3/098
. . .
Distributed learning, e.g. federated learning [2023-01]
WARNING

G06N 3/0985
. . .
Hyperparameter optimisation; Meta-learning; Learning-to-learn [2023-01]
WARNING

G06N 3/10
. .
Interfaces, programming languages or software development kits, e.g. for simulating neural networks [2023-01]
G06N 3/105
. . .
{Shells for specifying net layout} [2013-01]
G06N 3/12
.
using genetic models [2013-01]
G06N 3/123
. .
DNA computing [2023-01]
G06N 3/126
. .
Evolutionary algorithms, e.g. genetic algorithms or genetic programming [2023-01]
G06N 5/00
Computing arrangements using knowledge-based models [2022-01]
G06N 5/01
.
Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound [2023-01]
G06N 5/013
. .
{Automatic theorem proving} [2023-01]
G06N 5/02
.
Knowledge representation; Symbolic representation [2023-01]
G06N 5/022
. .
Knowledge engineering; Knowledge acquisition [2023-01]
G06N 5/025
. . .
Extracting rules from data [2023-01]
G06N 5/027
. .
{Frames} [2013-01]
G06N 5/04
.
Inference or reasoning models [2023-01]
G06N 5/041
. .
{Abduction} [2013-01]
G06N 5/042
. .
{Backward inferencing} [2013-01]
G06N 5/043
. .
Distributed expert systems; Blackboards [2023-01]
G06N 5/045
. .
Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence [2023-01]
G06N 5/046
. .
Forward inferencing; Production systems [2023-01]
G06N 5/047
. . .
Pattern matching networks; Rete networks [2023-01]
G06N 5/048
. .
Fuzzy inferencing [2023-01]
G06N 7/00
Computing arrangements based on specific mathematical models [2022-01]
G06N 7/01
.
Probabilistic graphical models, e.g. probabilistic networks [2023-01]
G06N 7/02
.
using fuzzy logic (computing arrangements based on biological models G06N 3/00; computing arrangements using knowledge-based models G06N 5/00) [2022-01]
G06N 7/023
. .
{Learning or tuning the parameters of a fuzzy system} [2013-01]
G06N 7/026
. .
{Development tools for entering the parameters of a fuzzy system} [2013-01]
G06N 7/04
. .
Physical realisation [2013-01]
G06N 7/043
. . .
{Analogue or partially analogue implementation} [2013-01]
G06N 7/046
. . .
{Implementation by means of a neural network (neural networks using fuzzy logic G06N 3/043)} [2023-01]
G06N 7/06
. .
Simulation on general purpose computers [2013-01]
G06N 7/08
.
using chaos models or non-linear system models [2013-01]
G06N 10/00
Quantum computing, i.e. information processing based on quantum-mechanical phenomena [2022-01]
WARNING

G06N 10/20
.
Models of quantum computing, e.g. quantum circuits or universal quantum computers [2022-01]
WARNING

G06N 10/40
.
Physical realisations or architectures of quantum processors or components for manipulating qubits, e.g. qubit coupling or qubit control [2022-01]
WARNING

G06N 10/60
.
Quantum algorithms, e.g. based on quantum optimisation, quantum Fourier or Hadamard transforms [2022-01]
WARNING

G06N 10/70
.
Quantum error correction, detection or prevention, e.g. surface codes or magic state distillation [2022-01]
WARNING

G06N 10/80
.
Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computers; Platforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing [2022-01]
WARNING

G06N 20/00
Machine learning [2021-08]
G06N 20/10
.
using kernel methods, e.g. support vector machines [SVM] [2021-08]
G06N 20/20
.
Ensemble learning [2021-08]
G06N 99/00
Subject matter not provided for in other groups of this subclass [2013-01]
G06N 99/007
.
{Molecular computers, i.e. using inorganic molecules (using biomolecules G06N 3/002)} [2013-01]