Cooperative Patent Classification

  1. CPC Sections
  2. CPC Section G
  3. CPC Class G06
  4. CPC Subclass G06N
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Version: 2019.01
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CPC
COOPERATIVE PATENT CLASSIFICATION
G06N
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS [2018-08]
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
Computer systems based on biological models [2019-01]
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. computers simulating life} [2013-01]
G06N 3/006
. .
{based on simulated virtual individual or collective life forms, e.g. single "avatar", social simulations, virtual worlds or particle swarm optimisation} [2019-01]
G06N 3/008
. .
{based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. robots replicating pets or humans in their appearance or behavior} [2019-01]
G06N 3/02
.
using neural network models [2019-01]
G06N 3/04
. .
Architectures, e.g. interconnection topology [2013-01]
G06N 3/0409
. . .
{Adaptive resonance theory [ART] networks} [2019-01]
G06N 3/0418
. . .
{using chaos or fractal principles} [2013-01]
G06N 3/0427
. . .
{in combination with an expert system} [2013-01]
G06N 3/0436
. . .
{in combination with fuzzy logic} [2013-01]
G06N 3/0445
. . .
{Feedback networks, e.g. hopfield nets, associative networks} [2013-01]
G06N 3/0454
. . .
{using a combination of multiple neural nets} [2013-01]
G06N 3/0463
. . .
{Neocognitrons} [2013-01]
G06N 3/0472
. . .
{using probabilistic elements, e.g. p-rams, stochastic processors} [2013-01]
G06N 3/0481
. . .
{Non-linear activation functions, e.g. sigmoids, thresholds} [2013-01]
G06N 3/049
. . .
{Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs} [2013-01]
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 3/0635
. . . .
{using analogue means} [2013-01]
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 [2013-01]
G06N 3/082
. . .
{modifying the architecture, e.g. adding or deleting nodes or connections, pruning} [2013-01]
G06N 3/084
. . .
{Back-propagation} [2013-01]
G06N 3/086
. . .
{using evolutionary programming, e.g. genetic algorithms} [2013-01]
G06N 3/088
. . .
{Non-supervised learning, e.g. competitive learning} [2013-01]
G06N 3/10
. .
Simulation on general purpose computers [2013-01]
G06N 3/105
. . .
{Shells for specifying net layout} [2013-01]
G06N 3/12
.
using genetic models [2013-01]
G06N 3/123
. .
{DNA computers, i.e. information processing using biological DNA} [2013-01]
G06N 3/126
. .
{Genetic algorithms, i.e. information processing using digital simulations of the genetic system} [2013-01]
G06N 5/00
Computer systems using knowledge-based models [2019-01]
G06N 5/003
.
{Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound} [2019-01]
G06N 5/006
. .
{Automatic theorem proving} [2013-01]
G06N 5/02
.
Knowledge representation [2019-01]
G06N 5/022
. .
{Knowledge engineering; Knowledge acquisition} [2019-01]
G06N 5/025
. .
{Extracting rules from data} [2019-01]
G06N 5/027
. .
{Frames} [2013-01]
G06N 5/04
.
Inference methods or devices [2013-01]
G06N 5/041
. .
{Abduction} [2013-01]
G06N 5/042
. .
{Backward inferencing} [2013-01]
G06N 5/043
. .
{Distributed expert systems; Blackboards} [2019-01]
G06N 5/045
. .
{Explanation of inference steps} [2013-01]
G06N 5/046
. .
{Forward inferencing; Production systems} [2019-01]
G06N 5/047
. . .
{Pattern matching networks; RETE networks} [2019-01]
G06N 5/048
. .
{Fuzzy inferencing} [2013-01]
G06N 7/00
Computer systems based on specific mathematical models [2013-01]
G06N 7/005
.
{Probabilistic networks} [2013-01]
G06N 7/02
.
using fuzzy logic (computer systems based on biological models G06N 3/00; computer systems using knowledge-based models G06N 5/00) [2019-01]
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 3/0436)} [2013-01]
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 computers, i.e. computer systems based on quantum-mechanical phenomena [2019-01]
G06N 20/00
Machine learning [2019-01]
WARNING

  • Group G06N 20/00 is impacted by reclassification into groups G06N 20/10 and G06N 20/20.
    All groups listed in this Warning should be considered in order to perform a complete search.
G06N 20/10
.
using kernel methods, e.g. support vector machines [SVM] [2019-01]
WARNING

G06N 20/20
.
Ensemble learning [2019-01]
WARNING

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]
United States Patent and Trademark Office