Time modulated generative probabilistic models for automated...

Details Time modulated generative probabilistic models for automated... Time modulated generative probabilistic models for automated...

2011-06-07

2011-06-07

Vincent, David R (Department: 2129)

Data processing: artificial intelligence

Neural network

Learning task

Reexamination Certificate

active

07958069

ABSTRACT:
Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model.

REFERENCES:
patent: 6115462 (2000-09-01), Servi et al.
patent: 6578043 (2003-06-01), Nye
patent: 7020802 (2006-03-01), Gross et al.
patent: 7131037 (2006-10-01), LeFaive et al.
patent: 7257744 (2007-08-01), Sabet et al.
patent: 7529192 (2009-05-01), Labovitz
patent: 7633939 (2009-12-01), Curran-Gray et al.
patent: 7821947 (2010-10-01), MacCormick et al.
patent: 2002/0019870 (2002-02-01), Chirashnya et al.
patent: 2002/0116154 (2002-08-01), Nowak et al.
patent: 2003/0051026 (2003-03-01), Carter et al.
patent: 2004/0064261 (2004-04-01), Gulati
patent: 2005/0089131 (2005-04-01), Howell et al.
patent: 2006/0123278 (2006-06-01), Dini et al.
patent: 2006/0164997 (2006-07-01), Graepel et al.
patent: 2007/0074076 (2007-03-01), Imai et al.
patent: 2007/0088981 (2007-04-01), Noble et al.
patent: 2007/0157315 (2007-07-01), Moran
patent: 2007/0185586 (2007-08-01), Al-Attar et al.
patent: 2008/0015871 (2008-01-01), Eder
patent: 2008/0259799 (2008-10-01), van Beek
Bahl, et al., “Discovering Dependencies for Network Management”, Microsoft Research, Princeton.
Kant, et al., “Fault Localization and Self-Healing Mechanisms for FCS Networks”, Date: Dec. 2002, University of Delaware, Dept. of Computer and Information Sciences, Newark, USA.
Steinder, et al., “End-to-end Service Failure Diagnosis Using Belief Networks”, University of Delaware.

Affiliated with

Also associated with

Rating

Time modulated generative probabilistic models for automated... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Time modulated generative probabilistic models for automated..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Time modulated generative probabilistic models for automated... will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-2729211