The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 11, 2010

Filed:

Feb. 28, 2007
Applicants:

Romain J. Thibaux, San Francisco, CA (US);

Emre M. Kiciman, Seattle, WA (US);

David A. Maltz, Bellevue, WA (US);

John C. Platt, Redmond, WA (US);

Inventors:

Romain J. Thibaux, San Francisco, CA (US);

Emre M. Kiciman, Seattle, WA (US);

David A. Maltz, Bellevue, WA (US);

John C. Platt, Redmond, WA (US);

Assignee:

Microsoft Corporation, Redmond, WA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 17/18 (2006.01);
U.S. Cl.
CPC ...
Abstract

A strategy is described for identifying anomalies in time-series data. The strategy involves dividing the time-series data into a plurality of collected data segments and then using a modeling technique to fit local models to the collected data segments. Large deviations of the time-series data from the local models are indicative of anomalies. In one approach, the modeling technique can use an absolute value (L) measure of error value for all of the collected data segments. In another approach, the modeling technique can use the Lmeasure for only those portions of the time-series data that are projected to be anomalous. The modeling technique can use a squared-term (L) measure of error value for normal portions of the time-series data. In another approach, the modeling technique can use an iterative expectation-maximization strategy in applying the Land Lmeasures.


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