Belief propagation in a hierarchical temporal memory based...

Details Belief propagation in a hierarchical temporal memory based... Belief propagation in a hierarchical temporal memory based...

2011-03-01

2011-03-01

Holmes, Michael B. (Department: 2129)

Data processing: artificial intelligence

Knowledge processing system

Knowledge representation and reasoning technique

C706S046000, C706S020000

Reexamination Certificate

active

07899775

ABSTRACT:
A hierarchy of computing modules is configured to (i) learn a cause of input data sensed over space and time, and (ii) determine a cause of novel sensed input data dependent on the learned cause. The hierarchy has a first level of computing modules and a second level of at least one computing module, wherein a computing module in the first level is configured to output to the computing module in the second level a first set of values representing probabilities of possible causes of input data received by the system.

REFERENCES:
patent: 4766534 (1988-08-01), DeBenedictis
patent: 4845744 (1989-07-01), DeBenedictis
patent: 5255348 (1993-10-01), Nenov
patent: 5712953 (1998-01-01), Langs
patent: 6122014 (2000-09-01), Panusopone et al.
patent: 6195622 (2001-02-01), Altschuler et al.
patent: 6400996 (2002-06-01), Hoffberg et al.
patent: 6468069 (2002-10-01), Lemelson et al.
patent: 6567814 (2003-05-01), Banker et al.
patent: 6625585 (2003-09-01), MacCuish et al.
patent: 6714941 (2004-03-01), Lerman et al.
patent: 6751343 (2004-06-01), Ferrell et al.
patent: 7251637 (2007-07-01), Caid et al.
patent: 7739208 (2010-06-01), George et al.
patent: 2002/0150044 (2002-10-01), Wu et al.
patent: 2003/0069002 (2003-04-01), Hunter et al.
patent: 2003/0123732 (2003-07-01), Miyazaki et al.
patent: 2003/0167111 (2003-09-01), Kipersztok et al.
patent: 2004/0002838 (2004-01-01), Oliver et al.
patent: 2004/0148520 (2004-07-01), Talpade et al.
patent: 2004/0267395 (2004-12-01), Discenzo et al.
patent: 2005/0063565 (2005-03-01), Nagaoka et al.
patent: 2005/0190990 (2005-09-01), Burt et al.
patent: 2005/0222811 (2005-10-01), Jakobson et al.
patent: 2006/0184462 (2006-08-01), Hawkins
patent: 2006/0212444 (2006-09-01), Handman et al.
patent: 2006/0235320 (2006-10-01), Tan et al.
patent: 2006/0248026 (2006-11-01), Aoyama et al.
patent: 2006/0248073 (2006-11-01), Jones et al.
patent: 2006/0253491 (2006-11-01), Gokturk et al.
patent: 2006/0259163 (2006-11-01), Hsiung et al.
patent: 2007/0005531 (2007-01-01), George et al.
patent: 2007/0192264 (2007-08-01), Hawkins et al.
patent: 2007/0192267 (2007-08-01), Hawkins et al.
patent: 2007/0192268 (2007-08-01), Hawkins et al.
patent: 2007/0192269 (2007-08-01), Saphir et al.
patent: 2007/0192270 (2007-08-01), Hawkins et al.
patent: 2007/0228703 (2007-10-01), Breed
patent: 2007/0276774 (2007-11-01), Ahmad et al.
patent: 2008/0059389 (2008-03-01), Jaros et al.
patent: 2009/0006289 (2009-01-01), Jaros et al.
patent: 2010/0207754 (2010-08-01), Shostak et al.
patent: 1557990 (2005-07-01), None
patent: WO 2006/063291 (2006-06-01), None
patent: WO 2008/067326 (2008-06-01), None
patent: WO 2009/006231 (2009-01-01), None
Dimitrova et al., N., “Motion Recovery for Video Content Classification”, 1995.
George et al., D., “Invariant Pattern Recognition using Bayesian Inference on Hierarchical Sequences”, 2005.
Wu et al., G., “Multi-camera Spatio-temporal Fusion and Biased Sequence-data Learning for Security Surveillance”, 2003.
Park et al., S., “Recognition of Two-person Interactions Using a Hierarchical Bayesian Network”, 2003.
Dolin et al., R., “Scalable Collection Summarization and Selection”, 1999.
Agrawal, R. et al., “Mining Sequential Patterns,” IEEE, 1995, pp. 3-14.
Fine, S. et al., “The Hierarchical Hidden Markov Model: Analysis and Applications,” Machine Learning, Jul. 1998, pp. 41-62, vol. 32.
Foldiak, P., “Learning Invariance from Transformation Sequences,” Neural Computation, 1991, pp. 194-200, vol. 3, No. 2.
Fukushima, K., “Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position,” Biol. Cybernetics, 1980, pp. 193-202, vol. 36.
George, D. et al., “A Hierarchical Bayesian Model of Invariant Pattern Recognition in the Visual Cortex,” Mar. 2005.
George, D. et al, “Invariant Pattern Recognition Using Bayesian Inference on Hierarchical Sequences”, Technical Report, Sep. 17, 2004, pp. 1-8, U.S.A.
Hawkins, J. et al., “On Intelligence,” Sep. 2004, Times Books, Henry Holt and Company, New York, NY 10011.
Hyvarinen, A. et al., “Bubbles: A Unifying Framework for Low-Level Statistical Properties of Natural Image Sequences,” J. Opt. Soc. Am. A., 2003, pp. 1237-1252, vol. 20, No. 7.
International Search Report & Written Opinion, PCT/US2005/044729, May 14, 2007, 14 pages.
Lee, T. et al, “Hierarchical Bayesian Inference in the Visual Cortex”, Journal of the Optical Society of America, Jul. 7, 2003, pp. 1434-1448, vol. 20, No. 7, U.S.A.
Murphy, K. et al., “Using the Forest to See the Trees: A Graphical Model Relating Features, Objects and Scenes,” Advances in Neural Processing System, 2004, vol. 16.
Murray, S.O. et al., “Shaper Perception Reduces Activity in Human Primary Visual Cortex,” Proceedings of the Nat. Acad. Of Sciences of the USA, Nov. 2002, pp. 15164-151169, vol. 99, No. 23.
Olshausen, B.A. et al., “A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing Information,” Jnl. Of Neuroscience, Nov. 1993.
Riesenhuber, M. et al, “Hierarchical Models of Object Recognition in Cortex”, Nature Neuroscience, Nov. 1999, pp. 1019-1025, vol. 2, No. 11, U.S.A.
Stringer, S.M. et al., “Invariant Object Recognition in the Visual System with Novel Views of 3D Objects,” Neural Computation, Nov. 2002, pp. 2585-2596, vol. 14, No. 11.
Thomson, A.M. et al., “Interlaminar Connections in the Neocortex,” Cerebral Cortex, 2003, pp. 5-14, vol. 13, No. 1.
Tsukada, M, “A Theoretical Model of the Hippocampal-Cortical Memory System Motivated by Physiological Functions in the Hippocampus”, Proceedings of the 1993 International Joint Conference on Neural Networks, Oct. 25, 1993, pp. 1120-1123, vol. 2, Japan.
Van Essen, D.C. et al., “Information Processing Strategies and Pathways in the Primate Visual System,” An Introduction to Neural and Electronic Networks, 1995, 2nded.
Vlajic, N. et al., “Vector Quantization of Images Using Modified Adaptive Resonance Algorithm for Hierarchical Clustering”, IEEE Transactions on Neural Networks, Sep. 2001, pp. 1147-1162, vol. 12, No. 5.
Wiskott, L. et al., “Slow Feature Analysis: Unsupervised Learning of Invariances,” Neural Computation, 2002, pp. 715-770, vol. 14, No. 4.
George, D. et al., “The HTM Learning Algorithm,” Mar. 1, 2007, [Online] [Retrieved on Jan. 1, 2009] Retrieved from the Internet<URL:http://www.numenta.com/for-developers/education/Numenta—HTM—Learning—Algos.pdf>.
PCT International Search Report and Written Opinion, PCT Application No. PCT/US2009/047250, Sep. 25, 2009, 13 pages.
Adelson, E.H. et al., “The Perception of Shading and Reflectance,” Perception as Bayesian Inference, Knill, D.C. et al., ed., 1996, pp. 409-423, Cambridge University Press, UK.
Demeris, Y. et al., “From Motor Babbling to Hierarchical Learning by Imitation: A Robot Developmental Pathway,” Proceedings of the Fifth International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems, 2005, pp. 31-37.
Felleman, D.J. et al., “Distributed Hierarchical Processing in the Primate Cerebral Cortex,” Cerebral Cortex, Jan./Feb. 1991, pp. 1-47, vol. 1.
Guo, C-E. et al., “Modeling Visual Patterns by Integrating Descriptive and Generative Methods,” International Journal of Computer Vision, May 29, 2003, 28 pages, vol. 53, No. 1.
Han, K. et al., “Automated Robot Behavior Recognition Applied to Robotic Soccer,” In Proceedings of the IJCAI-99 Workshop on Team Behaviors and Plan Recognition, 1999, 6 pages.
Hawkins, J. et al., “On Intelligence,” 2004, pp. 23-29, 106-174, 207-232, Times Books.
Hernandez-Gardiol, N. et al., “Hierarchical Memory-Based Reinforcement Learning,” Proceedings o

Affiliated with

Also associated with

Rating

Belief propagation in a hierarchical temporal memory based... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Belief propagation in a hierarchical temporal memory based..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Belief propagation in a hierarchical temporal memory based... will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-2766036