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:
Nov. 13, 2018

Filed:

Sep. 22, 2016
Applicant:

Adobe Systems Incorporated, San Jose, CA (US);

Inventors:

Suraj Satishkumar Sheth, Karnataka, IN;

Shagun Sodhani, Delhi, IN;

Rohit Bajaj, Uttar Pradesh, IN;

Nitin Goel, Uttar Pradesh, IN;

Manoj Awasthi, Jakarta Selatan, ID;

Kapil Malik, Haryana, IN;

Harsh Rathi, Haryana, IN;

Balaji Krishnamurthy, Uttar Pradesh, IN;

Assignee:

Adobe Systems Incorporated, San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 29/06 (2006.01); G06F 17/30 (2006.01); H04L 12/26 (2006.01);
U.S. Cl.
CPC ...
H04L 63/1416 (2013.01); G06F 17/3089 (2013.01); G06F 17/30958 (2013.01); H04L 43/00 (2013.01);
Abstract

In some embodiments, a processor accesses a metrics dataset, which includes metrics whose values indicate data network activity. The metrics dataset has segments. Each segment is a respective subset of the data items having a common feature. The processor identifies anomalous segments in the metrics dataset. Each anomalous segment has a segment trend that is different from a trend associated with the larger metrics dataset. The processor generates a data graph that includes nodes, which represent anomalous segments, and edges connecting the nodes. The processor applies weights to the edges. Each weight indicates (i) a similarity between a pair of anomalous segments represented by the nodes connected by the weighted edge and (ii) a relationship between the anomalous segments and the metrics dataset. The processor ranks the anomalous segments based on the applied weights and selects one or more segments with sufficiently high ranks.


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