Data processing: artificial intelligence – Machine learning
Reexamination Certificate
2007-09-04
2007-09-04
Hirl, Joseph P (Department: 2129)
Data processing: artificial intelligence
Machine learning
C706S014000, C706S046000
Reexamination Certificate
active
10161058
ABSTRACT:
The present invention describes the use of autonomous devices, which can be arranged in networks, such as neural networks, to better identify, track, and acquire sources of signals present in an environment. The environment may be a physical environment, such as a battlefield, or a more abstract environment, such as a communication network. The devices may be mobile, in the form of vehicles with sensors, or may be information agents, and may also interact with one another, thus allowing for a great deal of flexibility in carrying out a task. In some cases, the devices may be in the form of autonomous vehicles which can collaboratively sense, identify, or classify a number of sources or targets concurrently. The autonomous devices may function as mobile agents or attractors in a network, such as a neural network. The devices may also be aggregated to form a network of networks and provide scalability to a system in which the autonomous devices are operating.
REFERENCES:
patent: 5736982 (1998-04-01), Suzuki et al.
patent: 6422061 (2002-07-01), Sunshine et al.
patent: 2003/0222819 (2003-12-01), Karr et al.
Ren C. Luo et al, Multiagent Based Multisensor Resource Management System, Oct. 1998, IEEE, Conference on Intelligent Robots and Systems, 1034-1039.
http://www.fas.org/man/dod-101/sys/ship/ddg-993.htm, DDG-993 KIDD-class, FAS printed Nov. 21, 2006 but of historic significance dating to 1978, 1-9.
Jeffrey P. Sutton, Report Documentation Page, OMB No. 0704-0188, Reconfigurable Network of Networks for Multi-Scale Computing, Aug, 27, 2002, Office of Naval Research, 6.
J.P. Sutton and I. Jamieson, “Reconfigurable Network of Neural Networks for Autonomous Sensing and Analysis”, Proceedings of the Fifth International Conference on Cognitive an Neural Systems, (2001), 64.
J.A. Anderson, M.T. Gately, P.A. Penz and D.R. Collins, “Radar Signal Categorization Using a Neural Network”, IEEE Proceedings, 78 (1990), pages 1646-1657.
J.P. Sutton, J.S. Beis and E.H. Trainor, “A Hierarchical Model of Neocortical Synaptic Organization”, Mathematical Computatinal Modelling vol. 11, 346-350, (1988).
J. Kennedy and R.C. Eberhart, “Swarm Intelligence”, Morgan Kaufmann. (ISBN 1-55860-595-9) pages 114 and 287 to 325 inclusive.
J.M. Epstein and R. Axtell, “Growing Artificial Societies: Social Science from the Bottom Up”, MIT Press, (1996).
C.W. Reynolds, “Flocks, Herds and Schools: A Distributed Behavioral Model”, Computer Graphics, 21, 25-34 (1987).
J. Toner and Y. Tu, “Flocks, Herds and Schools: A Quantitative Theory of Flocking”, Physical Review E., 58, 4828-4859, (1999).
B. Latane, “The Psychology of Social Impact”, American Psychologist, 36, pp. 343-356, (1981).
E. Hutchins, “Cognition in the Wild”, Bradford Books (ISBN 0-262-58149-9) pages 175 to 262 inclusive.
J. Sutton and I. Jamieson, Reconfigurable Networking for Coordinated Multi-Agent Sensing and Communications.
J. Sutton and I. Jamieson, “Coherent Behavior Among Weakly Interacting Search Vehicles”, (2002).
Jamieson Ian MacQueen Drummond
Sutton Jeffrey P.
Hirl Joseph P
The General Hospital Corporation
Wolf Greenfield & Sacks P.C.
LandOfFree
Reconfigurable autonomous device networks does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Reconfigurable autonomous device networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Reconfigurable autonomous device networks will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3780463