Data processing: structural design – modeling – simulation – and em – Simulating electronic device or electrical system – Computer or peripheral device
Reexamination Certificate
2007-10-02
2007-10-02
Rodriguez, Paul (Department: 2123)
Data processing: structural design, modeling, simulation, and em
Simulating electronic device or electrical system
Computer or peripheral device
C709S224000
Reexamination Certificate
active
10195905
ABSTRACT:
A moving window of data is used to determine a local baseline as a moving average of the data weighted by the number of measurements in each time interval. A next measurement associated with a next time interval is compared to a value associated with the baseline to determine an outlier. In some cases, for example where the time series of the data shows small variability around a local mean, the next measurement is compared to a multiple of the weighted moving average to determine an outlier. In other cases, for example where the time series of the data shows significant variability around the local mean, the next measurement is compared to the sum of the weighted moving average and a multiple of a moving root mean square deviation value weighted by the number of measurements in each time interval and in some cases, a damping factor.
REFERENCES:
patent: 6363933 (2002-04-01), Berthon-Jones
patent: 6529811 (2003-03-01), Watson et al.
patent: 6708137 (2004-03-01), Carley
patent: 6724834 (2004-04-01), Garrett et al.
patent: 2003/0039212 (2003-02-01), Lloyd et al.
patent: 2003/0191837 (2003-10-01), Chen
patent: 2004/0092809 (2004-05-01), Decharms
patent: 2004/0172228 (2004-09-01), Aragones
Dokas et al. (2002 Paper) teaches a data mining for network intrusion detection p. 21-30.
Floyd Bullard, “A Brief Introduction to Bayesian Statistics”, pp. 1-14, NCTM 2001.
Tianhang Hou, Lloyd C. Huff, and Larry Mayer, “Automatic Detection of Outliers in Multibeam Echo Sounding Data”, University of New Hampshire, pp. 1-12.
Igor V. Cadez and P.S. Bradley, “Model BAsed Population Tracking and Automatic Detection of Distribution Changes” pp. 1-8.
Edwin M. Knorr and Raymond T. Ng, “A Unified Approach for Mining Outliers”, Universityof British Columbia, pp. 1-13.
Mark Last and Abraham Kandel, Automated Detection of Outliers in Real-World Data, pp. 1-10.
Dantong Yu, Gholam Sheikholeslami and Aidong Zhang, “Find Out: Finding Outliers in Very Large Datasets”, University of New York at Buffalo, pp. 1-19.
Hiroyuki Ohsaki, Mitsushige Morita and Masayuki Murata, “Measurment-Based Modeling of Internet Round-Trip Time Dynamics using System Identification”, pp. 1-20.
Polly Huang, Anja Feldmann and Walter Willinger, “A non-intrusive, wavelet-based approach to detecting network performance problems”, pp. 1-15.
Matthew Mathis, Jeffrey Semke and Jamshid Mahdavi, “The Macroscopic Behavior of the TCP Congestion Avoidance Algorithm”, ACM SIGCOMM, vol. 27, No. 3, (Jul. 1997), pp. 1-16.
Nevil Brownlee and Chris Loosley, “Fundamentals of Internet Measurement: A Tutorial” Keynote, (May 1, 2001) pp. 1-14.
M. Mathis and M. Allman, RFC 3148 “A Framework for Defining Empirical Bulk Transfer Capcity Metrics”, Internet Society (Jul. 2001), pp. 1-16.
G. Almes, S. Kalidindi and M. Zekauskas, RFC 2681 “A Round-Trip Delay Metric for IPPM”, Internet Society (Sep. 1999), pp. 1-20.
G. Almes, S. Kalidindi and M. Zekauskas, RFC 2680 “A One-Way Packet Loss Metric for IPPM”, Internet Society (Sep. 1999), pp. 1-15.
G. Almes, S. Kalidindi and M. Zekauskas, RFC 2679 “A One-Way Delay Metric for IPPM”, Internet Society (Sep. 1999), pp. 1-20.
J. Mahdavi and V. Paxson, RFC 2498 “IPPM Metrics for Measuring Connectivity”, Internet Society, (Sep. 1999), pp. 1-10.
V. Paxson et al., RFC 2330 “Framework for IP Performace Metrics” Internet Society, (May 1998), pp. 1-40.
Vern Paxson, “End-to-End Routing Behavior in the Internet”, University of California, (May 23, 1996), pp. 1-23.
“Handbook for Statistical Analysis of Environment Background Data”, Naval Facilities Engineering Command, (Jul. 1999), pp. 1-83.
Surendra P. Verma, “Sixteen Statistical Tests for Outlier Detection and Rejection in Evaluation of International Geochemical Reference Materials: Example of Microgabbro PM-S”, Geostandards Newsletter, vol. 21, No. 1, (Jun. 1997), pp. 59-75.
Kenneth C. Glossbrenner, Approved Text for new Recommendation I.380: “Internet Protocol Data Communication Service—IP Packet Transfer and Availability Performance Parameters”, (May 3, 1999), pp. 1-28.
V. Raisanen, G. Grotefeld & A. Morton, draft-ietf-ippm-npmps-07 “Network Performance measurement With Periodic Streams”, Internet Society, pp. 1-31.
Rajeev Koodli & R. Raukanth draft-ietf-ippm-loss-pattern-07, “One-Way Loss Pattern Sample Metrics”, Internet Society IPPM Working Group, (Mar. 28, 2002), pp. 1-23.
C. Demichelis & P. Chimento draft-ietf-ippm-ipdv-09, “IP Packet Delay Variation Metric for IPPM”, Internet Society Network Working Group, (Apr. 2002), pp. 1-31.
Vern Paxson and Sally Floyd, “Why We Don't Know How to Stimulate the Internet”, University of California, (Dec. 1997), pp. 1-8.
Edwin M. Knorr and Raymond T. Ng, “Algorithms for Mining Distance-Based Outliers in Large Datasets”, University of British Columbia, (1998), pp. 1-12.
Vern Paxson, Measurments and Analysis of End-to-End Internet Dynamics, University of California, (Apr. 1997), pp. 1-392.
Tianhang Hou, Lloyd C. Huff, and Larry Mayer, “Automatic Detection of Outliers in Multibeam Echo Sounding Data”, University of New Hampshire, pp. 1-12 (May 2001).
Igor V. Cadez and P.S. Bradley, “Model BAsed Population Tracking and Automatic Detection of Distribution Changes” pp. 1-8 (2001).
Edwin M. Knorr and Raymond T. Ng, “A Unified Approach for Mining Outliers”, Universityof British Columbia, pp. 1-13 (Sep. 1997.
Mark Last and Abraham Kandel, Automated Detection of Outliers in Real-World Data, pp. 1-10 (Aug. 2001.
Dantong Yu, Gholam Sheikholeslami and Aidong Zhang, “Find Out: Finding Outliers in Very Large Datasets”, University of New York at Buffalo, pp. 1-19 (Oct. 2002).
Hiroyuki Ohsaki, Mitsushige Morita and Masayuki Murata, “Measurment-Based Modeling of Internet Round-Trip Time Dynamics using System Identification”, pp. 1-20 (May 2002).
Polly Huang, Anja Feldmanm and Walter Willinger, “A non-intrusive, wavelet-based approach to detecting network performance problems”, pp. 1-15 (Sep. 2001).
V. Raisanen, G. Grotefeld & A. Morton, draft-ietf-ippm-npmps-07 “Network Performance measurement With Periodic Streams”, Internet Society, pp. 1-31 (Nov. 2002).
Vern Paxson and Sally Floyd, “Why We Don't Know How to Stimulate the Internet”, University of California, (Dec. 1997), pp. 1-8.
J. Mahdavi and V. Paxson, “IPPM Metrics for Measuring Connectivity,” RFC 2678, Internet Society, pp. 1-10 (Sep. 1999).
Floyd Bullard, “A Brief Introduction to Bayesian Statistics”, pp. 1-14, NCTM 2001.
Tianhang Hou, Lloyd C. Huff, and Larry Mayer, “Automatic Detection of Outliers in Multibeam Echo Sounding Data”, University of New Hampshire, pp. 1-12, May 22-May 24, 2001.
Igor V. Cadez and P.S. Bradley, “Model BAsed Population Tracking and Automatic Detection of Distribution Changes” pp. 1-8, 2001.
Edwin M. Knorr and Raymond T. Ng, “A Unified Approach for Mining Outliers”, Universityof British Columbia, pp. 1-13, 1997.
Mark Last and Abraham Kande, Automated Detection of Outliers in Real-World Data, pp. 1-10, 1999.
Dantong Yu, Gholam Sheikholeslami and Aidong Zhang, “Find Out: Finding Outliers in Very Large Datasets”, University of New York at Buffalo, pp. 1-19, 1999.
Hiroyuki Ohsaki, Mitsushige Morita and Masayuki Murata, “Measurement-Based Modeling of Internet Round-Trip Time Dynamics using System Identification”, pp. 1-20, 2002.
Polly Huang, Anja Feldmann and Walter Willinger, “A non-intrusive, wavelet-based approach to detecting network performance problems”, pp. 1-15, 2001.
Matthew Mathis, Jeffrey Semke and Jamshid Mahdavi, “The Macroscopic Behavior of the TCP Congestion Avoidance Algorithmȁ
Pavel Tomas J.
Wen Han C.
Blakely , Sokoloff, Taylor & Zafman LLP
Network Physics
Pierre-Louis Andre
Rodriguez Paul
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