IP Library › Granted Patent US 12,450,503
Granted Patent B2
US 12,450,503 · App. 18/542,282 · Granted Oct 21, 2025

Automatically-generated labels for time series data and numerical lists to use in analytic and machine learning systems

Inventors: Amr Fawzy Fahmy (Foxboro, MA); Sreeji Krishnan Das (Fremont, CA); Adrienne Wong (Redwood City, CA); Jae Young Yoon (San Mateo, CA); Dhileeban Kumaresan (Foster City, CA); Eric L. Sutton (Naperville, IL)
Assignee: Oracle International Corporation
G06N5/04G06F16/24G06N20/00
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Quick Facts
Patent No.
US 12,450,503
App. No.
18/542,282
Granted
Oct 21, 2025
Kind
B2
Abstract

Techniques for performing analytics using automatically generated labels for time series data and numerical lists are disclosed. In some embodiments, a system loads a set of one or more time series datasets. A respective time series dataset may include a set of data points based on varying values of a metric of one or more computing resources over a window of time. The system assigns labels to a subset of the data points in the time series datasets. The label assigned to a given data point may be descriptive of a pattern reflected by the data point relative to other data points in the time series. The system further identifies a pattern of automatically assigned labels that is indicative of an event affecting the one or more computing resources. Responsive to identifying the pattern of labels, the system may trigger a responsive action.

Claims (34)

1. A method comprising:

receiving a query that includes a composite label associated with an event, wherein the composite label is associated with a particular pattern of different labels that include at least a first label and a second label;

responsive to receiving the query, executing at least one of a vertical or horizontal search for the particular pattern of different labels in a set of labels automatically assigned to data points in at least one time series;

based on at least one of the vertical or horizontal search, identifying the particular pattern of labels associated with the composite label, wherein the particular pattern is identified based at least in part on at least one occurrence of a first label assigned to a first data point and a second label assigned to a second data point within a threshold number of labels from each other in the at least one time series; and

responsive to identifying the particular pattern of labels associated with the composite label, triggering a responsive action.

2. The method of claim 1 , wherein the composite label is a custom-defined label for an event affecting at least one computing resource.

3. The method of claim 1 , wherein the query is executed periodically on newly received time series data to monitor for the event; wherein the query causes one or more labels automatically assigned to the newly received time series to be compared to one or more labels in a baseline.

4. The method of claim 3 , wherein the query is executed on historical time series data to search for previous occurrences of the event.

5. The method of claim 1 , wherein the at least one time series includes a first time series and a second time series, wherein the identifying the particular pattern includes determining that the first label occurs in the first time series within a threshold number of labels from the second label in the second time series.

6. The method of claim 1 , wherein identifying the particular pattern further includes: determining that the first label occurs within a threshold amount of time with respect to the second label.

7. The method of claim 1 , wherein identifying the particular pattern includes detecting an ordered sequence of labels that occur within at the at least one time series.

8. The method of claim 1 , wherein the instructions further cause: receiving a set of user labels identifying a plurality of occurrences of the event; responsive to receiving the user labels, automatically learning the particular pattern of labels based on which labels occur at or nearest to occurrences of the events on a statistically significant basis.

9. The method of claim 8 , wherein the instructions further cause: automatically generating the query based on the particular pattern of labels; and refining the query based on newly received user labels.

10. The method of claim 1 , wherein the first label identifies at least one of a peak, valley, starting point for an upward trend, starting point for a downward trend, ending point for an upward trend, ending point for a downward trend, maximum, or minimum within a particular window of time.

11. The method of claim 1 , wherein the instructions further cause: receiving a request to expand or contract the particular window of time; responsive to the request, automatically assigning at least one new label to a data point within the particular window of time that was not previously assigned to the data point.

12. The method of claim 1 , wherein the responsive action includes generating an alert to notify a user of the event.

13. The method of claim 1 , wherein the responsive action includes configuring a computing resource based on the event.

14. The method of claim 1 , wherein the instructions further cause: training a machine learning model to learn patterns in automatically-assigned labels associated with the event, wherein the query is generated using the trained machine learning model.

15. The method of claim 14 , wherein training the machine learning model includes using time series data within a sliding window of time series data to iteratively train a baseline model using labels assigned to data points within a threshold age.

16. The method of claim 14 , wherein training the machine learning model includes generating feature vectors that indicate a presence or absence of different labels across various time series.

17. The method of claim 14 , wherein the machine learning model is a neural network.

18. The method of claim 1 wherein the data points in the at least one time series are extracted from at least one log record.

19. A system comprising:

one or more hardware processors;

one or more non-transitory computer-readable media storing instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising:

receiving a query that includes a composite label associated with an event, wherein the composite label is associated with a particular pattern of different labels that include at least a first label and a second label;

responsive to receiving the query, executing at least one of a vertical or horizontal search for the particular pattern of different labels in a set of labels automatically assigned to data points in at least one time series;

based on at least one of the vertical or horizontal search, identifying the particular pattern of labels associated with the composite label, wherein the particular pattern is identified based at least in part on at least one occurrence of a first label assigned to a first data point and a second label assigned to a second data point within a threshold number of labels from each other in the at least one time series; and

responsive to identifying the particular pattern of labels associated with the composite label, triggering a responsive action.

20. One or more non-transitory computer-readable media storing instructions which, when executed by one or more hardware processors, cause performance of operations comprising:

receiving a query that includes a composite label associated with an event, wherein the composite label is associated with a particular pattern of different labels that include at least a first label and a second label;

responsive to receiving the query, executing at least one of a vertical or horizontal search for the particular pattern of different labels in a set of labels automatically assigned to data points in at least one time series;

based on at least one of the vertical or horizontal search, identifying the particular pattern of labels associated with the composite label, wherein the particular pattern is identified based at least in part on at least one occurrence of a first label assigned to a first data point and a second label assigned to a second data point within a threshold number of labels from each other in the at least one time series; and

responsive to identifying the particular pattern of labels associated with the composite label, triggering a responsive action.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 22, 2023
From: FAHMY, AMR FAWZY; DAS, SREEJI KRISHNAN; WONG, ADRIENNE; YOON, JAE YOUNG; KUMARESAN, DHILEEBAN; SUTTON, ERIC L.
To: ORACLE INTERNATIONAL CORPORATION
Reel/Frame 065946/0758 →
Continuity (3)
Continuation 16856790 · Apr 23, 2020
Provisional Application 62900418 · Sep 13, 2019
Related Publication 20240119319A1 · Apr 11, 2024
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