Identification of automation candidates using automation degree of implementation metrics

• US 9,667,573 B2
• Filed: 04/28/2015
• Issued: 05/30/2017
• Est. Priority Date: 04/28/2015
• Status: Active Grant

• Current Assignee: Unisys Corporation
• Original Assignee: Unisys Corporation
• Inventors: Malnati, James R, Troini, John, Jamieson, Robert
• Primary Examiner(s): HIGA, BRENDAN Y
• Application Number:
  US14/697,738
• Publication Number:
  US 20160323207A1
• Time in Patent Office: 763 Days
• CPC Class Codes: H04L 41/06 Management of faults, events, alarms or notifications
• CPC Class Codes: H04L 41/0627 by acting on the notification or alarm source
• CPC Class Codes: H04L 41/0681 Configuration of triggering conditions
• CPC Class Codes: H04L 41/0876 Aspects of the degree of configuration automation
• CPC Class Codes: H04L 41/0886 Fully automatic configuration
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 51/02 using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• CPC Class Codes: H04L 51/214 using selective forwarding

Abstract

Non-automated read-and-reply console messages may be automated. These messages may be classified into impact groups in which the messages may be removed from the database or sent to an automation analyzer for analysis. As more messages become automated, a debugging mode may be enabled to allow an operator to respond to a message with a proposed action. If the proposed action is aligned with an action predetermined in response to the automation analysis, the operator may be allowed to respond to future actions.

Claims

1. A method, comprising:

receiving, by a message system, a plurality of messages relating to events occurring on a host system;

determining, by the message system for each message of the plurality of messages, whether each message evokes an automated response;

determining, by the message system for the messages determined to not evoke an automated response, duplication within the non-automated messages;

transmitting, by the message system, the duplicated messages to an automation analyzer, wherein the automation analyzer is configured to analyze a potential of automating the duplicated messages; and

classifying, by the message system, the messages determined to not evoke an automated response into one or more impact groups, where at least one of the one or more impact groups includes critical non-automated messages.

2. The method of claim 1, further comprising counting, by the message system, the number of duplicate messages that are received per day.

3. The method of claim 1, further comprising generating, by the message system, categorization rules for the duplicate messages in response to the determined duplication.

4. The method of claim 1, wherein the plurality of messages are analyzed using at least one of a Correlation Analysis, a Monte-Carlo simulation, a Factor Analysis, a Mean Square Weighted Deviation (MSWD), a Regression Analysis, and a Time Series Analysis.

5. The method of claim 1, further comprising:

logging the critical non-automated messages and logging the number of times each of the critical non-automated messages is received; and

classifying one or more of the critical non-automated messages as candidates for automation, wherein classifying a message as a candidate for automation identifies the message as a message capable of improving an automation degree of implementation metric if automated.

6. An apparatus, comprising:

a processor; and

a memory coupled to the processor, where the processor is configured to perform the steps of;

receiving a plurality of messages relating to events occurring on a host system;

determining, for each message of the plurality of messages, whether each message evokes an automated response;

determining, for the messages determined to not evoke an automated response, duplication within the non-automated messages;

transmitting the duplicated messages to an automation analyzer, wherein the automation analyzer is configured to analyze a potential of automating the duplicated messages; and

wherein the processor is further configured to perform a step of classifying the messages determined to not evoke an automated response into one or more impact groups, where at least one of the one or more impact groups includes critical non-automated messages.

7. The apparatus of claim 6, wherein the processor is further configured to perform a step of counting the number of duplicate messages that are received per day.

8. The apparatus of claim 6, wherein the processor is further configured to perform a step of generating categorization rules for the duplicate messages in response to the determined duplication.

9. The apparatus of claim 6, wherein the plurality of messages are analyzed using at least one of a Correlation Analysis, a Monte-Carlo simulation, a Factor Analysis, a Mean Square Weighted Deviation (MSWD), a Regression Analysis, and a Time Series Analysis.

10. The apparatus of claim 6, wherein the processor is further configured to perform steps of:

logging the critical non-automated messages and logging the number of times each of the critical non-automated messages is received; and

classifying one or more of the critical non-automated messages as candidates for automation, wherein classifying a message as a candidate for automation identifies the message as a message capable of improving an automation degree of implementation metric if automated.

11. A computer program product, comprising:

a non-transitory computer readable medium comprising instructions which, when executed by a processor of a computing system, cause the processor to;

receive a plurality of messages relating to events occurring on a host system;

determine, for each message of the plurality of messages, whether each message evokes an automated response;

determine, for the messages determined to not evoke an automated response, duplication within the non-automated messages;

transmit the duplicated messages to an automation analyzer, wherein the automation analyzer is configured to analyze a potential of automating the duplicated messages; and

to classify the messages determined to not evoke an automated response into one or more impact groups, where at least one of the one or more impact groups includes critical non-automated messages.

12. The computer program product of claim 11, wherein the non-transitory computer readable medium further comprises instructions which, when executed by the processor of the computing system, cause the processor to count the number of duplicate messages that are received per day.

13. The computer program product of claim 11, wherein the non-transitory computer readable medium further comprises instructions which, when executed by the processor of the computing system, cause the processor to generate categorization rules for the duplicate messages in response to the determined duplication.

14. The computer program product of claim 11, wherein the plurality of messages are analyzed using at least one of a Correlation Analysis, a Monte-Carlo simulation, a Factor Analysis, a Mean Square Weighted Deviation (MSWD), a Regression Analysis, and a Time Series Analysis.

15. The computer program product of claim 14, wherein the non-transitory computer readable medium further comprises instructions which, when executed by the processor of the computing system, cause the processor to:

log the critical non-automated messages and log the number of times each of the critical non-automated messages is received; and

classify one or more of the critical non-automated messages as candidates for automation, wherein classifying a message as a candidate for automation identifies the message as a message capable of improving an automation degree of implementation metric if automated.