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:
Dec. 10, 2024

Filed:

Mar. 27, 2023
Applicant:

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

Inventors:

Ram Sriharsha, Oakland, CA (US);

Kristal Lyn Curtis, San Francisco, CA (US);

Iryna Vogler-Ivashchanka, Los Altos, CA (US);

Clark Eugene Mullen, San Mateo, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/23 (2019.01); G06F 9/38 (2018.01); G06F 9/54 (2006.01); G06F 16/14 (2019.01); G06F 16/16 (2019.01); G06F 16/22 (2019.01); G06F 16/242 (2019.01); G06F 16/2453 (2019.01); G06F 16/2455 (2019.01); G06F 16/2458 (2019.01); G06F 16/28 (2019.01); G06F 16/901 (2019.01); G06F 17/16 (2006.01); G06F 17/18 (2006.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01);
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 16/24534 (2019.01); G06F 16/24568 (2019.01); G06F 16/2465 (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 in an asynchronous manner as the data is being ingested to detect potential anomalies. For example, one or more streaming data processors can convert data as the data is ingested into a comparable data structure, determine whether the comparable data structure should be assigned to an existing data pattern or a new data pattern, and optionally update a characteristic of the data pattern to which the comparable data structure is assigned. The streaming data processor(s) can perform these operations automatically in real-time or in periodic batches. Once one or more comparable data structures have been assigned to one or more data patterns, the streaming data processor(s) can analyze the comparable data structures assigned to a particular data pattern to determine whether any of the comparable data structures appear to be anomalous.


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