Identification of progress towards complete message system integration using automation degree of implementation metrics

• US 10,153,992 B2
• Filed: 04/28/2015
• Issued: 12/11/2018
• 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:
  US14/697,749
• Publication Number:
  US 20160323208A1
• Time in Patent Office: 1,323 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 executed by a processor, a plurality of messages relating to events occurring on a host system;

classifying, by the message system, each of the plurality of messages into one of three message groups comprising a critical automated messages group, a critical non-automated messages group, and a non-critical messages group, wherein the three message groups classified are stored in a first data storage;

determining, by the message system, progress towards complete message system automation by analyzing one or more of the three message groups, wherein the plurality of messages within the three message groups 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, analyzed results of the one or more of the three message groups are stored in a consolidated data storage independent from the first data storage, such that the message system has the capability to directly refer back to the consolidated data storage to obtain analyzed results without accessing the first data storage; and

automating, by the message system, non-automated messages, based, at least in part, on the classified messages.

2. The method of claim 1, further comprising counting, by the message system, the number of times messages classified into the critical non-automated messages group are received per day.

3. The method of claim 1, wherein determining progress towards complete message system automation comprises determining an automation degree of implementation metric indicative of the degree of automation implemented in the message system.

4. The method of claim 3, wherein the automation degree of implementation metric comprises a percentage or an absolute number of messages automated in the message system.

5. The method of claim 4, further comprising displaying at least one of the progress towards complete message system automation and the automation degree of implementation metric.

6. The method of claim 5, wherein displaying comprises displaying daily.

7. 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;

classifying each of the plurality of messages into one of three message groups comprising a critical automated messages group, a critical non-automated messages group, and a non-critical messages group, wherein the three message groups classified are stored in a first data storage;

determining progress towards complete message system automation by analyzing one or more of the three message groups, wherein the plurality of messages within the three message groups 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, analyzed results of the one or more of the three message groups are stored in a consolidated data storage independent from the first data storage, such that the apparatus has the capability to directly refer back to the consolidated data storage to obtain analyzed results without accessing the first data storage; and

automating non-automated messages based, at least in part, on the classified messages.

8. The apparatus of claim 7, wherein the processor is further configured to perform a step of counting the number of times messages classified into the critical non-automated messages group are received per day.

9. The apparatus of claim 7, wherein determining progress towards complete message system automation comprises determining an automation degree of implementation metric indicative of the degree of automation implemented in the message system.

10. The apparatus of claim 9, wherein the automation degree of implementation metric comprises a percentage or an absolute number of messages automated in the message system.

11. The apparatus of claim 10, wherein the processor is further configured to perform a step of displaying at least one of the progress towards complete message system automation and the automation degree of implementation metric.

12. The apparatus of claim 11, wherein the step of displaying comprises displaying daily.

13. 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;

classify each of the plurality of messages into one of three message groups comprising a critical automated messages group, a critical non-automated messages group, and a non-critical messages group, wherein the three message groups classified are stored in a first data storage;

determine progress towards complete message system automation by analyzing one or more of the three message groups, wherein the plurality of messages within the three message groups 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, analyzed results of the one or more of the three message groups are stored in a consolidated data storage independent from the first data storage, such that the computer program product has the capability to directly refer back to the consolidated data storage to obtain analyzed results without accessing the first data storage; and

automate non-automated messages based, at least in part, on the classified messages.

14. The computer program product of claim 13, 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 times messages classified into the critical non-automated messages group are received per day.

15. The computer program product of claim 13, wherein determining progress towards complete message system automation comprises determining an automation degree of implementation metric indicative of the degree of automation implemented in the message system.

16. The computer program product of claim 15, wherein the automation degree of implementation metric comprises a percentage or an absolute number of messages automated in the message system.

17. The computer program product of claim 16, wherein the non-transitory computer readable medium further comprises instructions which, when executed by the processor of the computing system, cause the processor to display at least one of the progress towards complete message system automation and the automation degree of implementation metric.