Data processing: artificial intelligence – Neural network – Structure
Reexamination Certificate
2005-03-31
2009-02-17
Vincent, David R (Department: 2129)
Data processing: artificial intelligence
Neural network
Structure
C706S003000, C706S016000
Reexamination Certificate
active
07493295
ABSTRACT:
A system, method and computer program for developing artificial intelligence through the generational evolution of one or more genomes. Each genome includes a set of functions. The method includes creating one or more cortices, operating the one or more cortices to perform one or more specified tasks, calculating a fitness score for each cortex based on its ability to perform the specified tasks, and selecting one or more of the cortices based on the respective fitness scores. Each cortex includes a plurality of cortical units. Each cortical unit includes a set of functions. Each cortical unit is created from the one or more genomes.
REFERENCES:
patent: 4979126 (1990-12-01), Pao et al.
patent: 4994982 (1991-02-01), Duranton et al.
patent: 5093899 (1992-03-01), Hiraiwa
patent: 5136686 (1992-08-01), Koza
patent: 5140530 (1992-08-01), Guha et al.
patent: 5214746 (1993-05-01), Fogel et al.
patent: 5245696 (1993-09-01), Stork et al.
patent: 5249259 (1993-09-01), Harvey
patent: 5283855 (1994-02-01), Motomura et al.
patent: 5349646 (1994-09-01), Furuta et al.
patent: 5375250 (1994-12-01), Van den Heuvel
patent: 5398302 (1995-03-01), Thrift
patent: 5400436 (1995-03-01), Nara et al.
patent: 5402522 (1995-03-01), Alkon et al.
patent: 5428710 (1995-06-01), Toomarian et al.
patent: 5452402 (1995-09-01), Sakiyama et al.
patent: 5455891 (1995-10-01), Hirotsu et al.
patent: 5459817 (1995-10-01), Shima
patent: 5485601 (1996-01-01), Ching
patent: 5515477 (1996-05-01), Sutherland
patent: 5566273 (1996-10-01), Huang et al.
patent: 5568590 (1996-10-01), Tolson
patent: 5590218 (1996-12-01), Ornstein
patent: 5617483 (1997-04-01), Osawa
patent: 5633988 (1997-05-01), Takatori et al.
patent: 5640494 (1997-06-01), Jabri et al.
patent: 5704016 (1997-12-01), Shigematsu et al.
patent: 5717832 (1998-02-01), Steimle et al.
patent: 5720002 (1998-02-01), Wang
patent: 5729662 (1998-03-01), Rozmus
patent: 5761381 (1998-06-01), Arci et al.
patent: 5812992 (1998-09-01), de Vries
patent: 5832466 (1998-11-01), Feldgajer
patent: 5835901 (1998-11-01), Duvoisin, III et al.
patent: 5841949 (1998-11-01), Nakaya et al.
patent: 5864832 (1999-01-01), Meyers
patent: 5870728 (1999-02-01), Yatsuzuka et al.
patent: 5870729 (1999-02-01), Yoda
patent: 5920851 (1999-07-01), Muller
patent: 5930781 (1999-07-01), Toomarian et al.
patent: 5937432 (1999-08-01), Yamaguchi et al.
patent: 5974404 (1999-10-01), Moed et al.
patent: 6038338 (2000-03-01), Nguyen
patent: 6151592 (2000-11-01), Inazumi
patent: 6205556 (2001-03-01), Watanabe et al.
patent: 6269351 (2001-07-01), Black
patent: 6349293 (2002-02-01), Yamaguchi
patent: 6366897 (2002-04-01), Means et al.
patent: 6415272 (2002-07-01), Ulyanov
patent: 6424961 (2002-07-01), Ayala
patent: 6449603 (2002-09-01), Hunter
patent: 6493686 (2002-12-01), Francone et al.
patent: 6516309 (2003-02-01), Eberhart et al.
patent: 6523016 (2003-02-01), Michalski
patent: 6553357 (2003-04-01), Mathias et al.
patent: 6601053 (2003-07-01), Schaffer et al.
patent: 6658645 (2003-12-01), Akuta et al.
patent: 7139740 (2006-11-01), Ayala
patent: 2002/0059154 (2002-05-01), Rodvold
patent: 2002/0069036 (2002-06-01), Mizokawa
patent: 2002/0082065 (2002-06-01), Fogel et al.
patent: 2002/0138246 (2002-09-01), Czora
patent: 2003/0004904 (2003-01-01), Krishenbaum
patent: 2003/0014378 (2003-01-01), Goodnight et al.
patent: 2003/0018598 (2003-01-01), Cawse et al.
patent: WO 90/11568 (1990-10-01), None
patent: WO 01/16881 (2001-03-01), None
patent: WO 01/16881 (2001-03-01), None
Kobayashi et al., “A New Indirect Encoding Method with Variable Length Gene Code to Optimize Neural Network Structures”, IEEE, IJCNN, Jul. 1999.
Harter, D., et al., 2001; “Models of Ontogenetic Development for Autonomous Adaptive Systems”; Proceedings of the 23rd Annual Conference of the Cognitive Science Society, pp. 405-405.
J.R. Koza, et al., “Evoloving Inventions”; Scientific American; 2003; pp. 52-59.
C.O. Willke et al., “Evolution of Digital Organisms at High Mutation Rates Leads to Survival of the Flattest”; Nature, vol. 412, Jul. 19, 2001; 331-333.
R.E. Lenski et al., The Evolutionary Origin of Complex Features; Nature, vol. 423, May 8, 2003; pp. 139-144.
International Search Report for Co-Pending Related PCT Application No. PCT/US2006/010813, Mailing Date—Nov. 8, 2007.
Wasserman et al., “Neural Networks, Part 2:” IEEE Expert, 1988.
Scalero et al., “A Fast New Algorithm for Trainging Feedforward, Neural Networks”, IEEE Transactions on Signal Processing, Jan. 1992.
Dasgupta et al., “Designing Application-Specific Neural Networks using the Stuctured Genetic Algorithm”, Department of Computer Science, University of Strathclyde, IEEE Computer Society Press, Jun. 6, 1992.
Lee et al., “Evolutionary Ordered Neural Network with a Linked-list Encoding Scheme”, Department of Electrical Engineering, May 20-22, 1996.
Kbayashi et al., “A New Indirect Encoding Method with Variable Length Gene Code to Optimize Neural Network Structures”, IEEE, IJCNN, Jul. 1999.
Hallinan, J., et al., 1998 “Cooperative Evolution of a Neural Classifier and Feature Subset”; Proc. 2ndAsia-Pacific Conference on Simulated Evolution and Learing.
Harter, D., et al., 2001; “Models of Ontogenetic Development for Autonomous Adaptive Systems”; Proceedings of the 23rdAnnual Conference of the Cognitive Science Society, pp. 405-410.
Hochman, R., et al., 1996; “Using the Genetic Algorithm to Building Optimal Neural Networks for Fault-Prone Module Detection”; In Proc. the Seventh Int. Symposium on Software Reliablity Engineering, pp. 152-162; White Plains, N.Y.
Keane, M.A., et al., 2002; Automatic Using Genetic Programming of an Imporved General-Purpose Controller for Industrially Representative Plants; Evolvable Hardware, 2002; pp. 113-122.
Michel, O., 1995; An Artificial Life Approach for the Synthesis of Autonomous Agents; In J. Alliot, E. Lutton, E. Ronald, M. Schoenauer, and D. Snyers (Eds.), Proceedings of the European Conference on Artificial Evolution, Spring-Verlag.
Kaelbling, L.P., et al., Reinforcement Learning: A Survey, (1996), Journal of Artificial Intelligence Research 4: 237-285.
Touretzky, D.S., et al., (1997) Operant Conditioning in Skinnerbots. Adaptive Behavior 5:219-247.
Byoung-Tak Zhang et al., Evolutionary Induction of Sparse Neual Trees, Evolutionary Computation, 1997, vol. 5, No. 2, pp. 213-236.
Byoung-Tak Zhang et al., Evolutionary Neural Trees for Modeling and Predicting Complex Systems; Engng, Applic. Artif. Intell. vol. 10, No. 5, pp. 473-483, 1997.
Koza, John R., 1989, Hierarchical Genetic Algorithms Operating on Populations of Computer Programs, Reprinted: Computer-Science Department, Stanford University, Stanford, CA; pp. 578-584.
F. Pasemann; “Pole-Balancing with Different Evolved Neurocontrollers”; Max-Planck-Institute for Mathematics in the Sciences, D-04103 Leipzig, Germany; pp. 1-7.
J.R. Koza, et al., “Evolving Inventions”; Scientific American; 2003; pp. 52-59.
H. Lipson et al, “Automatic Design and Manufacture of Robotic Lifeforms”; Nature, vol. 406, Aug. 31, 2000; pp. 974-978.
C.O. Willke et al., “Evolution of Digital Organisms at High Mutation Rates Leads to Survival of the Flattest”; Nature, vol. 412, Jul. 19, 2001; pp. 331-333.
R.E. Lenski et al., “The Evolutionary Origin of Complex Features”; Nature, vol. 423, May 8, 2003; pp. 139-144.
Y. Shigematsu et al., “A dynamic Neural Cell Model Driven by Pulse Train”; Electotechnical Laboratory; Umezono Tsukuba Ibaraki Japan; pp. 130-133.
K.E. Kinnear, Jr., “Derivative Methods in Gentic Programming”; ‘Evolutionary Computation 1, Basic Algorithms and Operators’, Beck T. et al (eds) 2000; Institute of Physics Publishing, Bristol; pp. 102-113.
P.J. Angeline; “Parse Trees”; Evolutionary Computation 1, Basic Algorithms and Operators, Beck T. et al (eds) 2000; Institute of Physics Publishing, Bristol; pp. 155-159.
D
Ayala Francisco J.
Gardere Wynne & Sewell LLP
Kennedy Adrian L
Vincent David R
LandOfFree
Method, system and computer program for developing cortical... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method, system and computer program for developing cortical..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method, system and computer program for developing cortical... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4139239