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. 07, 2023

Filed:

Jan. 31, 2023
Applicant:

Splunk Inc., San Francisco, CA (US);

Inventor:

Ram Sriharsha, Oakland, CA (US);

Assignee:

Splunk Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/901 (2019.01); G06F 16/2458 (2019.01); G06F 16/28 (2019.01); G06F 16/23 (2019.01); G06N 20/20 (2019.01); G06F 9/38 (2018.01); G06F 9/54 (2006.01); G06F 16/2455 (2019.01); G06F 16/14 (2019.01); G06F 16/22 (2019.01); G06F 16/2453 (2019.01); G06N 20/00 (2019.01); G06F 16/16 (2019.01); G06F 17/16 (2006.01); G06F 17/18 (2006.01); G06F 16/242 (2019.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01);
U.S. Cl.
CPC ...
G06F 16/901 (2019.01); G06F 9/3885 (2013.01); G06F 9/544 (2013.01); G06F 16/144 (2019.01); G06F 16/156 (2019.01); G06F 16/168 (2019.01); G06F 16/2246 (2019.01); G06F 16/23 (2019.01); G06F 16/2379 (2019.01); G06F 16/242 (2019.01); G06F 16/2465 (2019.01); G06F 16/24534 (2019.01); G06F 16/24568 (2019.01); G06F 16/285 (2019.01); G06F 17/16 (2013.01); G06F 17/18 (2013.01); G06F 18/2148 (2023.01); G06F 18/2185 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06F 16/22 (2019.01); G06F 16/2264 (2019.01); G06F 16/2282 (2019.01);
Abstract

Systems and methods are described for processing ingested data using an online machine learning algorithm as the data is being ingested. For example, the online machine learning algorithm can be an adaptive thresholding algorithm used to identify outliers in a moving window of data. As another example, the online machine learning algorithm can be a sequential outlier detector that detects anomalous sequences of logs or events. As another example, the online machine learning algorithm can be a sentiment analyzer that determines whether text has a positive, negative, or neutral sentiment. As another example, the online machine learning algorithm can be a drift detector that detects whether ingested data marks the start of a change in the distribution of a time-series.


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