US5659666 - Device for the autonomous generation of useful information, 08/19/1997.
Abstract - A device for simulating human creativity employing a neural network trained to produce input-output maps within some predetermined knowledge domain, an apparatus for subjecting the neural network to perturbations that produce changes in the predetermined knowledge domain, the neural network having an optional output for feeding the outputs of the neural network to a second neural network that evaluates and selects outputs based on training within the second neural network. The device may also include a reciprocal feed back connection from the output of the second neural network to the first neural network to further influence and change what takes place in the aforesaid neural network.
AU689677B2 - Device for the autonomous generation of useful information, 04/02/1998.
Abstract - A device for simulating human creativity employing a neural network trained to produce input-output maps within some predetermined knowledge domain, an apparatus for subjecting the neural network to perturbations that produce changes in the predetermined knowledge domain, the neural network having an optional output for feeding the outputs of the neural network to a second neural network that evaluates and selects outputs based on training within the second neural network. The device may also include a reciprocal feed back connection from the output of the second neural network to the first neural network to further influence and change what takes place in the aforesaid neural network.
US5845271 - Non-Algorithmically implemented artificial neural networks and components thereof, 12/22/1998.
Abstract - Constructing and simulating artificial neural networks and components thereof within a spreadsheet environment results in user friendly neural networks which do not require algorithmic based software in order to train or operate. Such neural networks can be easily cascaded to form complex neural networks and neural network systems, including neural networks capable of self-organizing so as to self-train within a spreadsheet, neural networks which train simultaneously within a spreadsheet, and neural networks capable of autonomously moving, monitoring, analyzing, and altering data within a spreadsheet. Neural networks can also be cascaded together in self training neural network form to achieve a device prototyping system.
US5852816 - Neural network based database scanning system, 12/22/1998. A major advance in datamining, whether in spreadsheets or more linear code.
Abstract - Constructing and simulating artificial neural networks and components thereof within a spreadsheet environment results in user friendly neural networks which do not require algorithmic based software in order to train or operate. Such neural networks can be easily cascaded to form complex neural networks and neural network systems, including neural networks capable of self-organizing so as to self-train within a spreadsheet, neural networks which train simultaneously within a spreadsheet, and neural networks capable of autonomously moving, monitoring, analyzing, and altering data within a spreadsheet. Neural networks can also be cascaded together in self training neural network form to achieve a device prototyping system.
US05852815 - Neural network based prototyping system and method, 12/22/1998. A major advance in building complex cascades of neural nets that learn in situ and utilize what are now considered deep learning techniques.
Abstract - Constructing and simulating artificial neural networks and components thereof within a spreadsheet environment results in user friendly neural networks which do not require algorithmic based software in order to train or operate. Such neural networks can be easily cascaded to form complex neural networks and neural network systems, including neural networks capable of self-organizing so as to self-train within a spreadsheet, neural networks which train simultaneously within a spreadsheet, and neural networks capable of autonomously moving, monitoring, analyzing, and altering data within a spreadsheet. Neural networks can also be cascaded together in self training neural network form to achieve a device prototyping system.
GB2336227B - Device for the autonomous generation of useful information, 12/29/1999.
Device 20 for simulating human creativity employs a first neural network 22 which has been trained to produce input-output maps within some predetermined knowledge domain, and is subjected to progressive perturbations that produce changes in the knowledge domain so that the network produces a stream of output "concepts" accordingly. A second neural network 24 monitors the outputs from the first neural network and, according to its own training, identifies "useful" concepts, e.g. designs which satisfy certain criteria. A feedback connection 28 may be made from the second to the first neural network, to further influence and change the first neural network. The perturbations applied to the first neural network may be random, and may be external (changed input patterns) or internal (perturbed weights, biases or internal activations). Certain design constraints can be applied to the perturbations. Device 20 can be applied to the design of products, musical composition problem solving, or developing manufacturing processes for example. Selected "concepts" can be displayed/played back etc. for the user.
GB2308476B - Device for the autonomous generation of useful information, 12/29/1999.
Abstract - A device (20) for simulating human creativity employing a neural network (22) trained to produce input-output maps within some predetermined knowledge domain, an apparatus for subjecting the neural network to perturbations that produce changes in the predetermined knowledge domain, the neural network (22) having an optional output (26) for feeding the outputs of the neural network (22) to a second neural network (24) that evaluates and selects outputs based on training within the second neural network (24). The device may also include a reciprocal feed back connection (28) from the output of the second neural network (24) to the first neural network (22) to further influence and change what takes place in the aforesaid neural network.
US6014653 - Non-Algorithmically implemented artificial neural networks and components thereof, 01/11/2000.
Abstract - Constructing and simulating artificial neural networks and components thereof within a spreadsheet environment results in user friendly neural networks which do not require algorithmic based software in order to train or operate. Such neural networks can be easily cascaded to form complex neural networks and neural network systems, including neural networks capable of self-organizing so as to self-train within a spreadsheet, neural networks which train simultaneously within a spreadsheet, and neural networks capable of autonomously moving, monitoring, analyzing, and altering data within a spreadsheet. Neural networks can also be cascaded together in self training neural network form to achieve a device prototyping system.
US6018727A - Device for the autonomous generation of useful information, 01/25/2000.
Abstract - A device for generating useful information employing a first neural network trained to produce input-output maps within a predetermined initial knowledge domain, an apparatus for subjecting the neural network to perturbations which may produce changes in the predetermined knowledge domain, the neural network having an optional output for feeding the outputs of the first neural network to a second neural network that evaluates the outputs based on training within the second neural network. The device may also include a reciprocal feed back connection from the output of the second neural network to the first neural network to further influence and change what takes place in the aforesaid neural network.
AU1997021120 - Non-Algorithmically implemented artificial neural networks and components thereof, 03/02/2000.
Abstract - Constructing and simulating artificial neural networks and components thereof within a spreadsheet environment results in user friendly neural networks which do not require algorithmic based software in order to train or operate. Such neural networks may be easily cascaded to form complex neural networks and neural network systems, including neural networks capable of self-organizing so as to self-train within a spreadsheet, neural networks which train simultaneously within a spreadsheet, and neural networks capable of autonomously moving, monitoring, analyzing, and altering data within a spreadsheet. Neural networks can also be cascaded together in self-training neural network form to achieve a device prototyping system.
US6115701 - Neural network-based target seeking system, 09/05/2000.
Abstract - A system and process for readily determining, for a specified knowledge domain in a given field of endeavor, perturbations applicable to an artificial neural network embodying such a specified knowledge domain that will produce a desired output, comprising a first, previously trained, artificial neural network containing training in some problem domain, which neural network is responsive to the presentment of a set of data inputs at the input portion thereof to produce a set of data outputs at the output portion thereof, a monitoring portion which constantly monitors the outputs of the first neural network to identify the desired outputs, and a network perturbation portion for effecting the application of perturbations, either externally or internally, to the first neural network to thereby effect changes in the output thereof. The perturbations may be effected by any number of different means, including by, but not limited to, presentment of new, varied data inputs, alteration or fixed or previously applied data inputs, such as by the introduction of noise to the inputs, relaxation or degradation of the network, and so forth, either randomly or systematically, and may be accomplished autonomously or upon specific external authorization or control. Identification of a desired output establishes an input/perturbation/output mapping relationship from which data inputs (external perturbations) and/or knowledge domain alterations (internal perturbations) that produce the desired output can be determined. The system and process can be employed in some instances and in some embodiments as a target seeking system for use with various design or problem solving applications, and can, in some embodiments, comprise or be comprised of a system and process for autonomously producing and identifying desirable design concepts through utilization of such a target seeking system.
US6115701 - Neural network-based target-seeking system, 09/05/2000.
Abstract - A system and process for readily determining, for a specified knowledge domain in a given field of endeavor, perturbations applicable to an artificial neural network embodying such a specified knowledge domain that will produce a desired output, comprising a first, previously trained, artificial neural network containing training in some problem domain, which neural network is responsive to the presentment of a set of data inputs at the input portion thereof to produce a set of data outputs at the output portion thereof, a monitoring portion which constantly monitors the outputs of the first neural network to identify the desired outputs, and a network perturbation portion for effecting the application of perturbations, either externally or internally, to the first neural network to thereby effect changes in the output thereof. The perturbations may be effected by any number of different means, including by, but not limited to, presentment of new, varied data inputs, alteration or fixed or previously applied data inputs, such as by the introduction of noise to the inputs, relaxation or degradation of the network, and so forth, either randomly or systematically, and may be accomplished autonomously or upon specific external authorization or control. Identification of a desired output establishes an input/perturbation/output mapping relationship from which data inputs (external perturbations) and/or knowledge domain alterations (internal perturbations) that produce the desired output can be determined. The system and process can be employed in some instances and in some embodiments as a target seeking system for use with various design or problem solving applications, and can, in some embodiments, comprise or be comprised of a system and process for autonomously producing and identifying desirable design concepts through utilization of such a target seeking system.
EP 0786106 - Device for the autonomous generation of useful information, 12/06/2001. This is the European equivalent of US05659666.
AU2000010103 - Neural network based database scanning system and method, 01/24/2002. This is the Australian equivalent of US05852816.
US6356884 - System for the autonomous generation of useful information, 03/12/2002.
Abstract - An artificial neural network-based system and method for determining desired concepts and relationships within a predefined field of endeavor, including a neural network portion, which neural network portion includes an artificial neural network that has been previously trained in accordance with a set of given training exemplars, a monitor portion associated with the neural network portion to observe the data outputs produced by the previously trained artificial neural network, and a perturbation portion for perturbing the neural network portion to effect changes, subject to design constraints of the artificial neural network that remain unperturbed, in the outputs produced by the neural network portion, the perturbation portion operable such that production of an output by the neural network portion thereafter effects a perturbation of the neural network portion by the perturbation portion, the monitor portion responsive to detection of the data outputs being produced by the previously trained neural network, whereby the system is operable to derive over a period of time a plurality of input/perturbation/output mapping relationships that differ from the input/perturbation/mapping relationships of the training exemplars.
DE69525059- Artificial neural network emulates human creativity, 10/2/2002.
IN193381 - Device for the autonomous generation of useful information, 07/17/2004. This is the Indian equivalent of US05659666.
CA2199969 - Device for the autonomous generation of useful information, 04/15/2008. This is the Canadian equivalent of US05659666.
US07454388 - Device for the autonomous bootstrapping of useful information, 11/18/2008.
Abstract - A discovery system employing a neural network, training within this system, that is stimulated to generate novel output patterns through various forms of perturbation applied to it, a critic neural network likewise capable of training in situ within this system, that learns to associate such novel patterns with their utility or value while triggering reinforcement learning of the more useful or valuable of these patterns within the former net. The device is capable of bootstrapping itself to progressively higher levels of adaptive or creative competence, starting from no learning whatsoever, through cumulative cycles of experimentation and learning. Optional feedback mechanisms between the latter and former self-learning artificial neural networks are used to accelerate the convergence of this system toward useful concepts or plans of action.
JP4282760 - Device for the autonomous generation of useful information, 04/15/2008.
JP2012108950 - Device that autonomously bootstraps useful information, 06/07/2012.
Abstract - To provide a device which starts with a non-learning state and progressively bootstraps itself to higher level of adaptive or creative capability through cumulative cycles of experiment and learning.SOLUTION: There is provided a discovery system using a neural network which executes training in the discovery system to be stimulated so as to generate a new output pattern through perturbations in various forms applied to itself, and an evaluating function neural network which performs training on the spot in the system similarly and can relate availability or value thereof to useful or more important new pattern among those patterns in the former network while actuating reinforced learning of such patterns. A feedback mechanism as an option between the former and latter self-learning artificial neural networks is used to accelerate convergence of this system on a useful concept or action plan.
US10423875 - Electro-optical device and method for identifying and inducing topological states formed among interconnecting neural modules, 09/24/2019.
A system for monitoring an environment may include an input device for monitoring and capturing pattern-based states of a model of the environment. The system may also include a 5 thalamobot embodied in at least a first processor, in which the first processor is in communication with the input device. The thalamobot may include at least one filter for monitoring captured data from the input device and for identifying at least one state change within the captured data. The system may also include at least one critic and/or at least one recognition system.
US11727251 - Electro-optical devices and methods for identifying and inducing topological states formed among interconnecting neural modules, 08/15/2023.
A system for monitoring an environment may include an input device for monitoring and capturing pattern-based states of a model of the environment. The system may also include a thalamobot embodied in at least a first processor in communication with the input device. The thalamobot may include at least one filter for monitoring captured data from the input device and for identifying at least one state change within the captured data. The system may also include at least one critic and/or at least one recognition system. The at least one filter forwards said at least one state change to the critic and/or recognition system. Novel schemes are introduced to allow processors to interconnect themselves into brain-like structures that contemplate both the environment and the model thereof, unifying disparate data into discoveries. The significance of such discoveries is recognized either through neural activation patterns or the topologies of interconnecting neural modules.
US20230351166 - Electro-optical devices and methods for identifying and inducing topological states formed among interconnecting neural modules, 08/27/2024.
A system for monitoring an environment may include an input device for monitoring and capturing pattern-based states of a model of the environment. The system may also include a thalamobot embodied in at least a first processor in communication with the input device. The thalamobot may include at least one filter for monitoring captured data from the input device and for identifying at least one state change within the captured data. The system may also include at least one critic and/or at least one recognition system. The at least one filter forwards said at least one state change to the critic and/or recognition system. Novel schemes are introduced to allow processors to interconnect themselves into brain-like structures that contemplate both the environment and the model thereof, unifying disparate data into discoveries. The significance of such discoveries is recognized either through neural activation patterns or the topologies of interconnecting neural modules.
