IP Library › Granted Patent US 12,282,386
Granted Patent B2
US 12,282,386 · App. 18/519,822 · Granted Apr 22, 2025

Recommendations for remedial actions

Inventors: Sai Eswar Garapati (San Jose, CA); Erhan Giral (Danville, CA)
Assignee: BMC Helix, Inc.
G06F11/0793G06F11/0709G06F11/0769G06F11/079G06F16/9024G06N3/08G06N5/022H04L41/0636H04L41/065H04L41/12H04L41/145
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Quick Facts
Patent No.
US 12,282,386
App. No.
18/519,822
Granted
Apr 22, 2025
Kind
B2
Abstract

Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Claims (35)

1. A computer-implemented method for recommending remedial actions, the method comprising:

receiving a plurality of source alarms and a plurality of target remedial actions;

extracting features from event graphs representing the plurality of source alarms and the plurality of target remedial actions;

processing the features using a remedial action recommendation (RAR) model, wherein the RAR model is continuously trained based on previous features from a plurality of previous source alarms, features from a plurality of previous target remedial actions, implicit feedback, and explicit feedback;

outputting ranked recommended remedial actions to a user interface that enables a selection of at least one of the recommended remedial actions to initiate performance of the selection; and

in response to the selection, performing an automation associated with the selected one of the recommended remedial actions to respond to the plurality of source alarms and providing implicit feedback to the RAR model based on the selection.

2. The computer-implemented method as in claim 1 , wherein the ranked recommended remedial actions include a confidence value.

3. The computer-implemented method as in claim 1 , wherein the implicit feedback includes positive reinforcement implicit feedback when a target remedial action from the plurality of target remedial actions closes a corresponding source alarm from the plurality of source alarms.

4. The computer-implemented method as in claim 1 , wherein the implicit feedback includes negative reinforcement implicit feedback.

5. The computer-implemented method as in claim 1 , wherein the implicit feedback includes feedback received without manual intervention.

6. The computer-implemented method as in claim 1 , wherein the explicit feedback includes a selected response from a user.

7. A computer program product for recommending remedial actions, the computer program product being tangibly embodied on a non-transitory computer-readable medium and including executable code that, when executed, causes a computing device to:

receive a plurality of source alarms and a plurality of target remedial actions;

extract features from event graphs representing the plurality of source alarms and the plurality of target remedial actions;

process the features using a remedial action recommendation (RAR) model, wherein the RAR model is continuously trained based on previous features from a plurality of previous source alarms, features from a plurality of previous target remedial actions, implicit feedback, and explicit feedback;

output ranked recommended remedial actions to a user interface that enables a selection of at least one of the recommended remedial actions to initiate performance of the selection; and

in response to the selection, perform an automation associated with the selected one of the recommended remedial actions to respond to the plurality of source alarms and provide implicit feedback to the RAR model based on the selection.

8. The computer program product of claim 7 , wherein the ranked recommended remedial actions include a confidence value.

9. The computer program product of claim 7 , wherein the implicit feedback includes positive reinforcement implicit feedback when a target remedial action from the plurality of target remedial actions closes a corresponding source alarm from the plurality of source alarms.

10. The computer program product of claim 7 , wherein the implicit feedback includes negative reinforcement implicit feedback.

11. The computer program product of claim 7 , wherein the implicit feedback includes feedback received without manual intervention.

12. The computer program product of claim 7 , wherein the explicit feedback includes a selected response from a user.

13. A system for recommending remedial actions, comprising:

at least one processor; and

a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to implement a remediation generator that is configured to:

receive a plurality of source alarms and a plurality of target remedial actions;

extract features from event graphs representing the plurality of source alarms and the plurality of target remedial actions;

process the features using a remedial action recommendation (RAR) model, wherein the RAR model is continuously trained based on previous features from a plurality of previous source alarms, features from a plurality of previous target remedial actions, implicit feedback, and explicit feedback;

output ranked recommended remedial actions to a user interface that enables a selection of at least one of the recommended remedial actions to initiate performance of the selection; and

in response to the selection, perform an automation associated with the selected one of the recommended remedial actions to respond to the plurality of source alarms and provide implicit feedback to the RAR model based on the selection.

14. The system of claim 13 , wherein the ranked recommended remedial actions include a confidence value.

15. The system of claim 13 , wherein the implicit feedback includes positive reinforcement implicit feedback when a target remedial action from the plurality of target remedial actions closes a corresponding source alarm from the plurality of source alarms.

16. The system of claim 13 , wherein the implicit feedback includes negative reinforcement implicit feedback.

17. The system of claim 13 , wherein the implicit feedback includes feedback received without manual intervention.

18. The system of claim 13 , wherein the explicit feedback includes a selected response from a user.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 7, 2025
From: BMC SOFTWARE, INC.
To: BMC HELIX, INC.
Reel/Frame 070442/0197 →
GRANT OF SECOND LIEN SECURITY INTEREST IN PATENT RIGHTS Recorded Nov 13, 2024
From: BMC SOFTWARE, INC.; BLADELOGIC, INC.
To: GOLDMAN SACHS BANK USA, AS COLLATERAL AGENT
Reel/Frame 069352/0568 →
GRANT OF FIRST LIEN SECURITY INTEREST IN PATENT RIGHTS Recorded Nov 13, 2024
From: BMC SOFTWARE, INC.; BLADELOGIC, INC.
To: GOLDMAN SACHS BANK USA, AS COLLATERAL AGENT
Reel/Frame 069352/0628 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 6, 2023
From: GARAPATI, SAI ESWAR; GIRAL, ERHAN
To: BMC SOFTWARE, INC.
Reel/Frame 065773/0462 →
Continuity (8)
Continuation 17657626 · Mar 31, 2022
Provisional Application 63269807 · Mar 23, 2022
Provisional Application 63262997 · Oct 25, 2021
Provisional Application 63262994 · Oct 25, 2021
Provisional Application 63262995 · Oct 25, 2021
Provisional Application 63261627 · Sep 24, 2021
Provisional Application 63261629 · Sep 24, 2021
Related Publication 20240095117A1 · Mar 21, 2024
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