GENERAL STATEMENT OF THE CLASS SUBJECT MATTER
This is a generic class for artificial intelligence type computersand digital data processing systems and corresponding data processingmethods and products for emulation of intelligence (i.e., knowledgebased systems, reasoning systems, and knowledgeacquisition systems); and including systems forreasoning with uncertainty (e.g., fuzzylogic systems), adaptive systems, machinelearning systems, and artificial neural networks.
(1)Note. This class includes systems having a facultyof perception or learning.
(2)Note. This class also provides for data processingsystems and corresponding data processing methods for performingautomated mathematical or logic theorem proving.
(3)Note. This class accepts combinations of an art classdevice, or art class method, with artificial intelligencetechniques not elsewhere provided for in USPC. This can includemechanical systems, electromagnetic systems, acousticsystems, thermal systems, photonic systems, chemicalsystems, biological systems, nanomachines andquantum mechanical systems where data or signals are processed accordingto artificial intelligence methods. A searcher shouldalso consider the relevant art classes and at least the followingdata processing classes 700 Data processing: generic controlsystems or specific applications; 701 Data processing: vehicles, navigation, and relativelocation; 702 Data processing: measuring, calibrating, ortesting; 703 Data processing: structural design, modeling, simulation, andemulation.
(4)Note. This class can accept combinations of dataprocessing arts with artificial intelligence techniques not elsewhereprovided for in USPC. Data processing art in combinationwith AI can include internet systems, intranet systems, client-server systems, databasesystems, computer interface systems, multi agentcollaboration systems (e.g., societiesof agents, groupware), groupware perse, distributed intelligent systems, multi agentsystems distributed intelligences, blackboard collaborativesystems, social networking methods, hacker detection (e.g., spamdetection, data mining, data farming) andartificially intelligent action systems (e.g., webpage ranking systems, Eigentrust systems).When mandatory classification is in multiple classes, theORIGINAL classification may be in a class other than where the applicationwas assigned and examined.A searcher should consider at least the relevant related dataprocessing classes on a case by case basis such as: 700, DataProcessing: Generic Control Systems or Specific Applications; 704, DataProcessing: Speech Signal Processing, Linguistics, LanguageTranslation, and Audio Compression/Decompression; 705, DataProcessing: Financial, Business Practice, Management, orCost/Price Determination; 707, DataProcessing: Database, Data Mining, andFile Management or Data Structures; 709, ElectricalComputers and Digital Processing Systems: Multicomputer DataTransferring; 710, Electrical Computers and DigitalData Processing Systems: Input/Output; 712, ElectricalComputers and Digital Processing Systems: Processing ArchitecturesAnd Instruction Processing (e.g., Processors); 713, ElectricalComputers and Digital Processing Systems: Support; 714, ErrorDetection/Correction and Fault Detection/Recovery; 715, DataProcessing: Presentation Processing of Document, OperatorInterface Processing, and Screen Saver Display Processing; 716, DataProcessing: Design and Analysis of Circuit or SemiconductorMask; 717, Data Processing: SoftwareDevelopment, Installation, and Management; 718, Electrical Computersand Digital Processing Systems: Virtual Machine Task orProcess Management or Task Management/Control; 719, ElectricalComputers and Digital Processing Systems: InterprogramCommunication or Interprocess Communication (IPC); 726, InformationSecurity.
(5)Note. This class can accept combinations of dataprocessing arts with artificial intelligence techniques not elsewhereprovided for in USPC. Data processing art in combinationwith AI can include Human Computer Interface (HCI).HCI AI may include Telerobotics, Human Supervisory Control (e.g., WaypointNavigation), Brain-Computer Neural Interfaces (e.g., ThoughtControlled Devices, Brain Interfaces) and Chatbots (akaChatterbots) (e.g., AIML).
(6)Note. Artificial Intelligence methods include, butare not limited to: Supervised learning classifiers, unsupervisedlearning classifiers, reinforcement learning, statisticallearning, theorem proving, boosting classifiers, dimensionalityreduction, multiresolution analysis, wavelets, quantumAI systems, nanotechnology AI systems, augmentedreality systems, pattern recognition systems and automatedplanning systems.
(7)Note. Artificial Intelligence preprocessing methodsinclude Dimensionality Reduction (reduced feature space, subspace) via PrincipalComponent Analysis (PCA), Kernel PrincipalComponent Analysis (KPCA), NonlinearPrincipal component analysis, Independent component analysis (ICA), SingularValue Decomposition (SVD), Eigenface, KernelEigenface, Eigenvoice, Kernel Eigenvoice, SelfOrganizing Map (SOM), Growing Self OrganizingMap, Linear Discriminant Analysis (LDA), Fisher'slinear discriminant, Linear-Nonlinear Poisson (LNP) Cascade, MultifactorDimensionality Reduction, Data fusion, Sensorfusion, Image fusion.
(8)Note. Multiresolution Analysis methods include Wavelettransforms, Wavelet series, Wavelet packet, FastWavelet Transform, Pyramid generation.
(9)Note. Artificial Intelligence Learning methods fallinto three broad categories, namely, SupervisedLearning, Unsupervised Learning, and ReinforcementLearning.
(10)Note. Inventive combinations or subcombinations forSupervised Learning Classifiers that may be classified in this classinclude k-Nearest Neighbor Systems, Fuzzy Logic (e.g., Possibilitytheory), Neural Networks, Spin GlassAnalog Systems, Simulated Annealing, BoltzmannMachines, Vector Quantization, Restricted CoulombEnergy (RCE), Kohonen, NeuralGas, Growing Neural Gas, Pulsed Neural Nets, Support VectorMachines, Maximum Margin Classifiers, Hill-Climbing, InductiveLogic Systems, Bayesian Networks, Belief Networks, Dempster-ShaferTheoretic Network Systems, Gaussian Mixtures, Kriging, Petri Nets (e.g., FiniteState Machines, Mealy Machines, Moore Finite StateMachines) and Ensembles of Classifiers (e.g., Bagging Systems, BoostingSystems ADABOOST, Classifier trees (e.g., Perceptrontrees, Support vector trees, Markov trees, Decision TreeForests, Random Forests) and Pandemonium Modelsand Systems.
(11)Note. Inventive combinations or subcombinations forUnsupervised Learning Classifiers that may be classified in thisclass include Evolutions strategie, Evolutionary Systems, Clustering, BlindSource Separation, Blind Signal Separation, BlindDeconvolution, Self-organizing Maps, Tabu Search.
(12)Note. Inventive combinations or subcombinations forReinforcement Learning Methods that may be classified in this class includeReinforcement Neural Networks.
(13)Note. Inventive combinations or subcombinations forArtificial Intelligence Learning Hardware can include Memistors, Memristors, Transconductanceamplifiers, Pulsed Neural Circuits, ArtificiallyIntelligent Nanotechnology Systems (e.g., Autonomousnanomachines) or Artificially Intelligent Quantum MechanicalSystems (e.g., Quantum NeuralNetworks).
(14)Note. Inventive combinations or subcombinations forArtificial Intelligence Automatic Pattern Recognition Systems can includeMachine Vision Systems, Acoustic Recognition Systems, HandwritingRecognition Systems, Data Fusion Systems, SensorFusion Systems, Soft Sensors. Machine VisionSystems can also include Content Based Image Retrieval, OpticalCharacter Recognition, Augmented Reality, Egomotion, Trackingor Optical flow.
(15)Note. Inventive combinations or subcombinations forAutomatic Planning and Decision Support Systems can include Emergentsystems, Artificially Intelligent Planning, DecisionTrees, Petri Nets, Artificially Intelligent Forecasting, whichmay be properly classifiable in this class.