DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/19/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
101 Rejection Arguments
Applicant asserts:
Applicant argues, on page 12, that the claimed invention is an improvement upon conventional functioning of a computer, or upon conventional technology or technological processes.
Examiner response:
Examiner respectfully disagrees. Examiner notes that applicant’s arguments to improvement is set forth in a conclusory manner. This is not sufficient in determining that the claim improves the technology. The additional limitations are considered post extra solution activities. The remedial action is not defined within the claims and spec and therefore interpreted broadly.
103 Rejection Arguments
Applicant asserts:
Applicant argues, on page 14, that the prior art does not teach “determining whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed; and executing the remedial action based on determining that the remedial action is capable of being automatically executed.”
Examiner response:
Examiner respectfully disagrees. Examiner notes that the prior art does teach the amended limitation. Examiner points to Ahad Column 35 Line 57 to show that determining if the remedial action is capable to execute if the parent component contains the policy to initiate/execute it. Refer to 103 mapping shown below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“processing, by the one or more ML computer models, input data to generate a forecast output, wherein the forecast output specifies at least one forecasted IT event or at least one KPI impact of the plurality of KPI impacts;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could evaluate the input data to generate a forecast.
“correlating the forecast output with at least one of the one or more IT computing resources or one or more of the organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output to generate a list comprising at least one of affected computing resources, or at least of affected organizational processes;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could correlate the forecast output to a correlation graph to generate a list of affected computing resources or organizational processes.
“and generating a remedial action recommendation based on the forecast output and correlation output.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could generate a remedial action recommendation based on evaluation of forecast and correlation output.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A method, comprising: executing machine learning training of one or more machine learning (ML) computer models based on historical data representing logged information technology (IT) events and key performance indicators (KPIs) of organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“generating at least one correlation graph data structure that maps at least one of the IT events to one or more IT computing resources, or a plurality of KPI impacts to the organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“determining whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“and executing the remedial action based on determining that the remedial action is capable of being automatically executed.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A method, comprising: executing machine learning training of one or more machine learning (ML) computer models based on historical data representing logged information technology (IT) events and key performance indicators (KPIs) of organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“generating at least one correlation graph data structure that maps at least one of the IT events to one or more IT computing resources, or a plurality of KPI impacts to the organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“determining whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed;” (well-understood, routine, conventional MPEP 2106.05(d))
“and executing the remedial action based on determining that the remedial action is capable of being automatically executed.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 2:
Claim 2 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 3:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 2, wherein applying the at least one correlation graph data structure to the forecast output comprises at least one of: identifying, in the OP correlation graph data structure, at least one OP operation affected by the at least one KPI impact; or identifying, in the IT correlation graph data structure, at least one IT topology component correlated with the at least one forecasted IT event.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could identify at least one OP operation affected by the forecasted KPI impact in the OP correlation graph data structure.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 4:
Claim 4 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 5:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The method of claim 1, further comprising: simulating second input data for remedial action corresponding to the remedial action recommendation; which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could simulate a second input data for a remedial action.
“and processing the second input data by the one or more ML computer models to generate a predicted impact outcome of the remedial action on at least one of the KPIs or the IT events.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could process the second input data and generate a predicted impact outcome of the remedial action.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 6:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 5, wherein generating the remedial action recommendation comprises identifying a plurality of candidate remedial actions based on the forecast output,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could identify a plurality of candidate remedial actions based on the forecasted output.
“and executing the simulation of the second input data” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could simulate the second input data.
“and processing of the second input data by the one or more ML computer models for each candidate remedial action in the plurality of candidate remedial actions.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could process the second input data for each candidate remedial action in the plurality of candidate remedial actions.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 7:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 6, further comprising: ranking candidate remedial actions in the plurality of candidate remedial actions relative to one another based on predicted impact outcomes for each of the candidate remedial actions;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could rank the candidate remedial actions in the plurality of candidate remedial actions relative to one another based on the predicted outcomes.
“and selecting a candidate remedial action to be a recommended remedial action based on the relative ranking, wherein the remedial action recommendation specifies the selected candidate remedial action.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select a candidate remedial action to be a recommended remedial action based on the relative ranking.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 8:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 1, further comprising executing a computer simulation employing a counterfactual analysis that simulates different counterfactual conditions not present in the input data” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could simulate different counterfactual conditions not present in the input data.
“and generates corresponding predicted outcomes based on execution of the one or more ML computer models on the different counterfactual conditions.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person generate corresponding predicted outcomes based on the simulations of the counterfactual conditions.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 9:
Claim 9 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 10:
Claim 10 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 11:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“process, by the one or more ML computer models, input data to generate a forecast output, wherein the forecast output specifies at least one forecasted IT event or at least one KPI impact of the plurality of KPI impacts;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could evaluate the input data to generate a forecast.
“correlate the forecast output with at least one of the one or more IT computing resources or one or more of the organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output to generate a list comprising at least one of affected computing resources, or at least of affected organizational processes;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could correlate the forecast output to a correlation graph to generate a list of affected computing resources or organizational processes.
“and generate a remedial action recommendation based on the forecast output and correlation output.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could generate a remedial action recommendation based on evaluation of forecast and correlation output.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed in a data processing system, causes the data processing system to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“execute machine learning training of one or more machine learning (ML) computer models based on historical data representing logged information technology (IT) events and key performance indicators (KPIs) of organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“generate at least one correlation graph data structure that maps at least one of the IT events to one or more IT computing resources, or a plurality of KPI impacts to the organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“determine whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“and execute the remedial action based on determining that the remedial action is capable of being automatically executed.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed in a data processing system, causes the data processing system to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“execute machine learning training of one or more machine learning (ML) computer models based on historical data representing logged information technology (IT) events and key performance indicators (KPIs) of organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“generate at least one correlation graph data structure that maps at least one of the IT events to one or more IT computing resources, or a plurality of KPI impacts to the organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“determine whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed;” (well-understood, routine, conventional MPEP 2106.05(d))
“and execute the remedial action based on determining that the remedial action is capable of being automatically executed.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 12:
Claim 12 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 13:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer program product of claim 11, wherein applying the at least one correlation graph data structure to the forecast output comprises at least one of: identifying, in the OP correlation graph data structure, at least one OP operation affected by the at least one KPI impact; or identifying, in the IT correlation graph data structure, at least one IT topology component correlated with the at least one forecasted IT event.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could identify at least one OP operation affected by the forecasted KPI impact in the OP correlation graph data structure.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 14:
Claim 14 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 15:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer program product of claim 11, wherein the computer readable program further causes the data processing system to: simulate second input data for remedial action corresponding to the remedial action recommendation;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could simulate a second input data for a remedial action.
“and process the second input data by the one or more ML computer models to generate a predicted impact outcome of the remedial action on at least one of the KPIs or the IT events.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could process the second input data and generate a predicted impact outcome of the remedial action.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 16:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer program product of claim 15, wherein generating the remedial action recommendation comprises identifying a plurality of candidate remedial actions based on the forecast output,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could identify a plurality of candidate remedial actions based on the forecasted output.
“and executing the simulation of the second input data” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could simulate the second input data.
“and processing of the second input data by the one or more ML computer models for each candidate remedial action in the plurality of candidate remedial actions.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could process the second input data for each candidate remedial action in the plurality of candidate remedial actions.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 17:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer program product of claim 16, wherein the computer readable program further causes the data processing system to: rank candidate remedial actions in the plurality of candidate remedial actions relative to one another based on predicted impact outcomes for each of the candidate remedial actions;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could rank the candidate remedial actions in the plurality of candidate remedial actions relative to one another based on the predicted outcomes.
“and select a candidate remedial action to be a recommended remedial action based on the relative ranking, wherein the remedial action recommendation specifies the selected candidate remedial action.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select a candidate remedial action to be a recommended remedial action based on the relative ranking.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 18:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer program product of claim 11, wherein the computer readable program further causes the data processing system to execute a computer simulation employing a counterfactual analysis that simulates different counterfactual conditions not present in the input data” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could simulate different counterfactual conditions not present in the input data.
“and generates corresponding predicted outcomes based on execution of the one or more ML computer models on the different counterfactual conditions.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person generate corresponding predicted outcomes based on the simulations of the counterfactual conditions.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 19:
Claim 19 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 20:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“process, by the one or more ML computer models, input data to generate a forecast output, wherein the forecast output specifies at least one forecasted IT event or at least on KPI impact of the plurality of KPI impacts;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could evaluate the input data to generate a forecast.
“correlate the forecast output with at least one of the one or more IT computing resources or one or more of the organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output to generate a list comprising at least one of affected computing resources, or at least of affected organizational processes;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could correlate the forecast output to a correlation graph to generate a list of affected computing resources or organizational processes.
“and generate a remedial action recommendation based on the correlation output.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could generate a remedial action recommendation based on evaluation of forecast and correlation output.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“An apparatus comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to: execute machine learning training of one or more machine learning (ML) computer models based on historical data representing logged information technology (IT) events and key performance indicators (KPIs) of organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“generate at least one correlation graph data structure that maps at least one of the IT events to one or more IT computing resources, or a plurality of KPI impacts to the organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“correlate the forecasted output with at least one of one or more IT computing resources or one or more organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“determine whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“and execute the remedial action based on determining that the remedial action is capable of being automatically executed.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A method, comprising: executing machine learning training of one or more machine learning (ML) computer models based on historical data representing logged information technology (IT) events and key performance indicators (KPIs) of organizational processes, wherein the one or more ML computer models are trained to forecast at least one of IT events given KPIs in input data, or KPI impact given IT events in the input data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“generating at least one correlation graph data structure that maps at least one of IT events to IT computing resources, or KPI impacts to organizational processes;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“correlating the forecasted output with at least one of one or more IT computing resources or one or more organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“determine whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed;” (well-understood, routine, conventional MPEP 2106.05(d))
“and execute the remedial action based on determining that the remedial action is capable of being automatically executed.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 – 4, 11 – 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over JEONG; Jaeseong; US 20220394531 A1 (hereinafter “Jaeseong”) in view of Chris Bailey et al, "Observability, insights, and automation" (hereinafter “Chris”) in further view of Ahad; Rafiul; US-10853161-B2 (hereinafter “Ahad”).
Regarding claim 1, Jaeseong teaches A method, comprising: executing machine learning training of one or more machine learning (ML) computer models based on historical data representing logged information technology (IT) events and key performance indicators (KPIs) of organizational processes (Jaeseong Paragraph 0004; "The baseline dataset includes a plurality of key performance indicators, KPIs, that each have a continuous value, and a plurality of historical changes made to the configurable parameter. The computer system can perform training of a policy model (e.g., 520, 690) while offline the telecommunications network using the baseline dataset and Inverse Propensity Score on the plurality of KPIs as inputs to output from the policy model a probability of actions for controlling the configurable parameter." Examiner notes that the machine learning computer model/policy model is executing training based on historical data/baseline dataset representing logged IT events/changes made to configurable parameters and KPI of organizational processes/KPIs)
processing, by the one or more ML computer models, input data to generate a forecast output, wherein the forecast output specifies at least one forecasted IT event or at least one KPI impact of the plurality of KPI impacts; (Jaeseong Paragraph 0004; "The computer system can perform training of a policy model (e.g., 520, 690) while offline the telecommunications network using the baseline dataset and Inverse Propensity Score on the plurality of KPIs as inputs to output from the policy model a probability of actions for controlling the configurable parameter." Examiner notes that the policy model processes input data to generate a forecast output/actions for controlling the configurable parameter, wherein the output specifies forecasted IT event/actions for controlling the configurable parameter.)
Jaeseong does not teach generating at least one correlation graph data structure that maps at least one of the IT events to one or more IT computing resources, or a plurality of KPI impacts to the organizational processes;
correlating the forecast output with at least one of the one or more IT computing resources or one or more of the organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output to generate a list comprising: at least one of affected computing resources, or at least one of affected organizational processes;
However, Chris does teach generating at least one correlation graph data structure that maps at least one of the IT events to one or more IT computing resources, or a plurality of KPI impacts to the organizational processes; (Chris Section "The Observability Pyramid" Paragraph 6; "Observability platforms like Instana and Application Resource Management (ARM) tools like Turbonomic provide the required context by creating and maintaining a dynamic graph… This dynamic graph maintains a constant understanding of the vertical relationship between a business application or process, the replicas or instances of an application on which it executes, where those replicas are deployed, and the underlying infrastructure on which it executes. The dynamic graph also understands and maintains the horizontal relationships between any given replica or instance of an application and its upstream and downstream dependencies, such as the other IT systems or components that make requests or calls of it or that it makes requests to." Examiner notes that the dynamic graph/correlation graph structure is generated and maps IT events/requests or calls to IT computing resources/IT systems or components and maps KPI impacts/vertical relationships to organizational processes/business application or process)
correlating the forecast output with at least one of the one or more IT computing resources or one or more of the organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output to generate a list comprising: at least one of affected computing resources, or at least one of affected organizational processes; (Chris Section "The Observability Pyramid" Paragraph 7; "This contextualization becomes even more valuable when we move from observing a system to analyzing it, deriving insights from it, and being able to understand the root cause of such issues as errors, poor performance, or availability." Chris Section “Event storms, event grouping, and root-cause analysis” Paragraph 3; “In the scenario where there is the failure and restart of an underlying infrastructure node, conditions are likely to be triggered not just for the underlying node but also for every process running on it and every instance of every service that those processes provide…Instana can group related events together and carry out root-cause analysis using its dynamic graph to include related events and build a timeline to understand the underlying root cause.” Examiner notes that the forecasted output/action for configurable parameters is correlated by applying to at least one correlation graph to generate a correlation output/issues to generate a list comprising: at least one of affected computing resources (Instana groups/creates a list of services that is related to the event))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong and Chris. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. One of ordinary skill would have motivation to combine Jaeseong and Chris to understand underlying state of systems, detect faults, and rapidly carry out repair “Being able to vastly reduce the elapsed time of the IT service management incident process – and, therefore, client impact -- requires comprehensive observability. With comprehensive observability, operations teams can: Understand the underlying state of the systems, Perform analyses and discover insights to detect faults and isolate them to root causes, Provide automation to rapidly carry out diagnoses and repair” (Chris Section “Conclusion” Paragraph 1).
Jaeseong in view of Chris does not teach generating a remedial action recommendation based on the correlation output.
However, Ahad does teach generating a remedial action recommendation based on the correlation output. (Ahad Column 34 Paragraph 6; "At step 1308, a policy for resolving the anomaly is identified. The policy may be identified by the ADRC in the component. A policy store may be searched to identify one or more policies having one or more rule(s) that are satisfied by the anomaly. At step 1310, a determination may be made that a rule in the policy is satisfied by the anomaly. The determination may be made by the ADRC in the component. A policy may indicate one or more corrective actions for resolving the anomaly in the component in which the anomaly event is detected." Examiner notes that the remedial action recommendation is based on the anomaly/correlation output)
determining whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed; (Ahad Column 35 Line 57; “the ADRC of the parent component may identify a policy for it to resolve the anomaly in the parent component. At step 1416, the ADRC of the parent component may initiate a corrective action identified in the policy for resolving the anomaly in the parent component… the ADRC of the parent component may not have a policy for resolving the anomaly in the parent component. The parent component may propagate data about the anomaly event to higher level components, such as a parent component of the parent component… The ADRC of the higher level parent component may initiate corrective action to resolve the anomaly provided that the ADRC can identify a policy for resolving the anomaly in the higher level parent component.” Examiner notes that ADRC determines whether a remedial action (policy for resolving the anomaly), associated with the at least one of affected computing resources (parent component), is capable of being automatically executed (determine whether parent component has policy to initiate corrective action))
and executing the remedial action based on determining that the remedial action is capable of being automatically executed. (Examiner refers to previous mapping to show that the remedial action (policy to resolve the anomaly) is executed based on determining that the remedial action is capable of being automatically executed (if parent component contains policy to initiate corrective action))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, and Ahad. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. One of ordinary skill would have motivation to combine Jaeseong, Chris, and Ahad to reduce human involvement in anomaly resolution “Techniques disclosed herein can reduce, if not eliminate, human involvement in addressing the size and complexity of large computing systems (e.g., cloud systems), and thus, lead to autonomic computing systems.” (Ahad Column 2 Paragraph 4 “).
Regarding claim 2, Jaeseong does not teaches The method of claim 1, wherein the at least one correlation graph data structure comprises an organizational process (OP) correlation graph data structure that correlates different types of OP operations with corresponding KPIs,
and an IT correlation graph data structure that correlates an IT topology with corresponding IT events.
However, Chris does teach The method of claim 1, wherein the at least one correlation graph data structure comprises an organizational process (OP) correlation graph data structure that correlates different types of OP operations with corresponding KPIs, (Chris Section "The Observability Pyramid" Paragraph 6; "Observability platforms like Instana and Application Resource Management (ARM) tools like Turbonomic provide the required context by creating and maintaining a dynamic graph… This dynamic graph maintains a constant understanding of the vertical relationship between a business application or process, the replicas or instances of an application on which it executes, where those replicas are deployed, and the underlying infrastructure on which it executes. The dynamic graph also understands and maintains the horizontal relationships between any given replica or instance of an application and its upstream and downstream dependencies, such as the other IT systems or components that make requests or calls of it or that it makes requests to." Examiner notes that the dynamic graph/correlation graph structure correlates KPI impacts/vertical relationships with business process/OP operations)
and an IT correlation graph data structure that correlates an IT topology with corresponding IT events. (Chris Section "The Observability Pyramid" Paragraph 6; "Observability platforms like Instana and Application Resource Management (ARM) tools like Turbonomic provide the required context by creating and maintaining a dynamic graph… This dynamic graph maintains a constant understanding of the vertical relationship between a business application or process, the replicas or instances of an application on which it executes, where those replicas are deployed, and the underlying infrastructure on which it executes. The dynamic graph also understands and maintains the horizontal relationships between any given replica or instance of an application and its upstream and downstream dependencies, such as the other IT systems or components that make requests or calls of it or that it makes requests to." Examiner notes that the dynamic graph/correlation graph structure correlates IT events/requests or calls to IT topology/IT systems or components)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong and Chris. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. One of ordinary skill would have motivation to combine Jaeseong and Chris to understand underlying state of systems, detect faults, and rapidly carry out repair “Being able to vastly reduce the elapsed time of the IT service management incident process – and, therefore, client impact -- requires comprehensive observability. With comprehensive observability, operations teams can: Understand the underlying state of the systems, Perform analyses and discover insights to detect faults and isolate them to root causes, Provide automation to rapidly carry out diagnoses and repair” (Chris Section “Conclusion” Paragraph 1).
Regarding claim 3, Jaeseong does not teach The method of claim 2, wherein applying the at least one correlation graph data structure to the forecast output comprises at least one of: identifying, in the OP correlation graph data structure, at least one OP operation affected by the at least one KPI impact; or identifying, in the IT correlation graph data structure, at least one IT topology component correlated with the at least one forecasted IT event.
However, Chris does teach The method of claim 2, wherein applying the at least one correlation graph data structure to the forecast output comprises at least one of: identifying, in the OP correlation graph data structure, at least one OP operation affected by the at least one KPI impact; or identifying, in the IT correlation graph data structure, at least one IT topology component correlated with the at least one forecasted IT event. (Chris Section "The four golden signals" Paragraph 1; "The four golden signals are the most valuable metrics for any service that exposes an interface, whether that interface is used by other services or by end users. The four golden signals are: • Latency -- How long it takes to handle or service a request against the interface" Examiner notes that the looking for one of the 4 golden signals is identifying at least one IT topology component/interface correlated with the forecasted IT event/request)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong and Chris. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. One of ordinary skill would have motivation to combine Jaeseong and Chris to understand underlying state of systems, detect faults, and rapidly carry out repair “Being able to vastly reduce the elapsed time of the IT service management incident process – and, therefore, client impact -- requires comprehensive observability. With comprehensive observability, operations teams can: Understand the underlying state of the systems, Perform analyses and discover insights to detect faults and isolate them to root causes, Provide automation to rapidly carry out diagnoses and repair” (Chris Section “Conclusion” Paragraph 1).
Regarding claim 4, Jaeseong does not teach The method of claim 1, wherein generating the remedial action recommendation comprises performing a lookup operation in a site reliability engineering database of remediation actions corresponding to at least one of the one or more IT computing resources or the one or more organizational processes.
However, Ahad does teach The method of claim 1, wherein generating the remedial action recommendation comprises performing a lookup operation in a site reliability engineering database of remediation actions corresponding to at least one of the one or more IT computing resources or the one or more organizational processes. (Ahad Column 34 Paragraph 6; "At step 1308, a policy for resolving the anomaly is identified. The policy may be identified by the ADRC in the component. A policy store may be searched to identify one or more policies having one or more rule(s) that are satisfied by the anomaly. At step 1310, a determination may be made that a rule in the policy is satisfied by the anomaly. The determination may be made by the ADRC in the component. A policy may indicate one or more corrective actions for resolving the anomaly in the component in which the anomaly event is detected." Examiner notes that the remedial action recommendation is generated by performing a lookup operation in a site reliability engineering database of remediation actions/search policy store corresponding to at least one of the one or more IT computing resources or one or more organizational processes/policy)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, and Ahad. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. One of ordinary skill would have motivation to combine Jaeseong, Chris, and Ahad to reduce human involvement in anomaly resolution “Techniques disclosed herein can reduce, if not eliminate, human involvement in addressing the size and complexity of large computing systems (e.g., cloud systems), and thus, lead to autonomic computing systems.” (Ahad Column 2 Paragraph 4 “).
Regarding claim 11, Jaeseong teaches A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed in a data processing system, causes the data processing system to: (Jaeseong Paragraph 0011; “According to some embodiments, a computer program product can be provided that includes a non-transitory computer readable medium storing instructions that, when executed on at least one processor, cause the at least one processor to carry out methods performed by the network node.”)
execute machine learning training of one or more machine learning (ML) computer models based on historical data representing logged information technology (IT) events and key performance indicators (KPIs) of organizational processes, (Jaeseong Paragraph 0004; "The baseline dataset includes a plurality of key performance indicators, KPIs, that each have a continuous value, and a plurality of historical changes made to the configurable parameter. The computer system can perform training of a policy model (e.g., 520, 690) while offline the telecommunications network using the baseline dataset and Inverse Propensity Score on the plurality of KPIs as inputs to output from the policy model a probability of actions for controlling the configurable parameter." Examiner notes that the machine learning computer model/policy model is executing training based on historical data/baseline dataset representing logged IT events/changes made to configurable parameters and KPI of organizational processes/KPIs)
process, by the one or more ML computer models, input data to generate a forecast output, wherein the forecast output specifies at least one forecasted IT event or at least one KPI impact of the plurality of KPI impacts; (Jaeseong Paragraph 0004; "The computer system can perform training of a policy model (e.g., 520, 690) while offline the telecommunications network using the baseline dataset and Inverse Propensity Score on the plurality of KPIs as inputs to output from the policy model a probability of actions for controlling the configurable parameter." Examiner notes that the policy model processes input data to generate a forecast output/actions for controlling the configurable parameter, wherein the output specifies forecasted IT event/actions for controlling the configurable parameter.)
Jaeseong does not teach generate at least one correlation graph data structure that maps at least one of the IT events to one or more IT computing resources, or a plurality of KPI impacts to the organizational processes;
correlate the forecast output with at least one of the one or more IT computing resources or one or more of the organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output to generate a list comprising: at least one of affected computing resources, or at least one of affected organizational processes;
However, Chris does teach generate at least one correlation graph data structure that maps at least one of the IT events to one or more IT computing resources, or a plurality of KPI impacts to the organizational processes; (Chris Section "The Observability Pyramid" Paragraph 6; "Observability platforms like Instana and Application Resource Management (ARM) tools like Turbonomic provide the required context by creating and maintaining a dynamic graph… This dynamic graph maintains a constant understanding of the vertical relationship between a business application or process, the replicas or instances of an application on which it executes, where those replicas are deployed, and the underlying infrastructure on which it executes. The dynamic graph also understands and maintains the horizontal relationships between any given replica or instance of an application and its upstream and downstream dependencies, such as the other IT systems or components that make requests or calls of it or that it makes requests to." Examiner notes that the dynamic graph/correlation graph structure is generated and maps IT events/requests or calls to IT computing resources/IT systems or components and maps KPI impacts/vertical relationships to organizational processes/business application or process)
correlate the forecast output with at least one of the one or more IT computing resources or one or more of the organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output to generate a list comprising: at least one of affected computing resources, or at least one of affected organizational processes; (Chris Section "The Observability Pyramid" Paragraph 7; "This contextualization becomes even more valuable when we move from observing a system to analyzing it, deriving insights from it, and being able to understand the root cause of such issues as errors, poor performance, or availability." Chris Section “Event storms, event grouping, and root-cause analysis” Paragraph 3; “In the scenario where there is the failure and restart of an underlying infrastructure node, conditions are likely to be triggered not just for the underlying node but also for every process running on it and every instance of every service that those processes provide…Instana can group related events together and carry out root-cause analysis using its dynamic graph to include related events and build a timeline to understand the underlying root cause.” Examiner notes that the forecasted output/action for configurable parameters is correlated by applying to at least one correlation graph to generate a correlation output/issues to generate a list comprising: at least one of affected computing resources (Instana groups/creates a list of services that is related to the event))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong and Chris. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. One of ordinary skill would have motivation to combine Jaeseong and Chris to understand underlying state of systems, detect faults, and rapidly carry out repair “Being able to vastly reduce the elapsed time of the IT service management incident process – and, therefore, client impact -- requires comprehensive observability. With comprehensive observability, operations teams can: Understand the underlying state of the systems, Perform analyses and discover insights to detect faults and isolate them to root causes, Provide automation to rapidly carry out diagnoses and repair” (Chris Section “Conclusion” Paragraph 1).
Jaeseong in view of Chris does not teach generate a remedial action recommendation based on the correlation output.
determine whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed;
and execute the remedial action based on determining that the remedial action is capable of being automatically executed.
However, Ahad does teach and generate a remedial action recommendation based on the forecast output and correlation output. (Ahad Column 34 Paragraph 6; "At step 1308, a policy for resolving the anomaly is identified. The policy may be identified by the ADRC in the component. A policy store may be searched to identify one or more policies having one or more rule(s) that are satisfied by the anomaly. At step 1310, a determination may be made that a rule in the policy is satisfied by the anomaly. The determination may be made by the ADRC in the component. A policy may indicate one or more corrective actions for resolving the anomaly in the component in which the anomaly event is detected." Examiner notes that the remedial action recommendation is based on the anomaly/forecast output and correlation output)
determine whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed; (Ahad Column 35 Line 57; “the ADRC of the parent component may identify a policy for it to resolve the anomaly in the parent component. At step 1416, the ADRC of the parent component may initiate a corrective action identified in the policy for resolving the anomaly in the parent component… the ADRC of the parent component may not have a policy for resolving the anomaly in the parent component. The parent component may propagate data about the anomaly event to higher level components, such as a parent component of the parent component… The ADRC of the higher level parent component may initiate corrective action to resolve the anomaly provided that the ADRC can identify a policy for resolving the anomaly in the higher level parent component.” Examiner notes that ADRC determines whether a remedial action (policy for resolving the anomaly), associated with the at least one of affected computing resources (parent component), is capable of being automatically executed (determine whether parent component has policy to initiate corrective action))
and execute the remedial action based on determining that the remedial action is capable of being automatically executed. (Examiner refers to previous mapping to show that the remedial action (policy to resolve the anomaly) is executed based on determining that the remedial action is capable of being automatically executed (if parent component contains policy to initiate corrective action))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, and Ahad. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. One of ordinary skill would have motivation to combine Jaeseong, Chris, and Ahad to reduce human involvement in anomaly resolution “Techniques disclosed herein can reduce, if not eliminate, human involvement in addressing the size and complexity of large computing systems (e.g., cloud systems), and thus, lead to autonomic computing systems.” (Ahad Column 2 Paragraph 4 “).
Regarding claim 12, Jaeseong does not teaches The computer program product of claim 11, wherein the at least one correlation graph data structure comprises an organizational process (OP) correlation graph data structure that correlates different types of OP operations with corresponding KPIs,
and an IT correlation graph data structure that correlates an IT topology with corresponding IT events.
However, Chris does teach The computer program product of claim 11, wherein the at least one correlation graph data structure comprises an organizational process (OP) correlation graph data structure that correlates different types of OP operations with corresponding KPIs, (Chris Section "The Observability Pyramid" Paragraph 6; "Observability platforms like Instana and Application Resource Management (ARM) tools like Turbonomic provide the required context by creating and maintaining a dynamic graph… This dynamic graph maintains a constant understanding of the vertical relationship between a business application or process, the replicas or instances of an application on which it executes, where those replicas are deployed, and the underlying infrastructure on which it executes. The dynamic graph also understands and maintains the horizontal relationships between any given replica or instance of an application and its upstream and downstream dependencies, such as the other IT systems or components that make requests or calls of it or that it makes requests to." Examiner notes that the dynamic graph/correlation graph structure correlates KPI impacts/vertical relationships with business process/OP operations)
and an IT correlation graph data structure that correlates an IT topology with corresponding IT events. (Chris Section "The Observability Pyramid" Paragraph 6; "Observability platforms like Instana and Application Resource Management (ARM) tools like Turbonomic provide the required context by creating and maintaining a dynamic graph… This dynamic graph maintains a constant understanding of the vertical relationship between a business application or process, the replicas or instances of an application on which it executes, where those replicas are deployed, and the underlying infrastructure on which it executes. The dynamic graph also understands and maintains the horizontal relationships between any given replica or instance of an application and its upstream and downstream dependencies, such as the other IT systems or components that make requests or calls of it or that it makes requests to." Examiner notes that the dynamic graph/correlation graph structure correlates IT events/requests or calls to IT topology/IT systems or components)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong and Chris. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. One of ordinary skill would have motivation to combine Jaeseong and Chris to understand underlying state of systems, detect faults, and rapidly carry out repair “Being able to vastly reduce the elapsed time of the IT service management incident process – and, therefore, client impact -- requires comprehensive observability. With comprehensive observability, operations teams can: Understand the underlying state of the systems, Perform analyses and discover insights to detect faults and isolate them to root causes, Provide automation to rapidly carry out diagnoses and repair” (Chris Section “Conclusion” Paragraph 1).
Regarding claim 13, Jaeseong does not teach The computer program product of claim 12, wherein applying the at least one correlation graph data structure to the forecast output comprises at least one of: Identifying, in the OP correlation graph data structure, at least one OP operation affected by the at least one KPI impact; or identifying, in the IT correlation graph data structure, at least one IT topology component correlated with the at least one forecasted IT event.
However, Chris does teach The computer program product of claim 12, wherein applying the at least one correlation graph data structure to the forecast output comprises at least one of: Identifying, in the OP correlation graph data structure, at least one OP operation affected by the at least one KPI impact; or identifying, in the IT correlation graph data structure, at least one IT topology component correlated with the at least one forecasted IT event. (Chris Section "The four golden signals" Paragraph 1; "The four golden signals are the most valuable metrics for any service that exposes an interface, whether that interface is used by other services or by end users. The four golden signals are: • Latency -- How long it takes to handle or service a request against the interface" Examiner notes that the looking for one of the 4 golden signals is identifying at least one IT topology component/interface correlated with the forecasted IT event/request)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong and Chris. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. One of ordinary skill would have motivation to combine Jaeseong and Chris to understand underlying state of systems, detect faults, and rapidly carry out repair “Being able to vastly reduce the elapsed time of the IT service management incident process – and, therefore, client impact -- requires comprehensive observability. With comprehensive observability, operations teams can: Understand the underlying state of the systems, Perform analyses and discover insights to detect faults and isolate them to root causes, Provide automation to rapidly carry out diagnoses and repair” (Chris Section “Conclusion” Paragraph 1).
Regarding claim 14, Jaeseong does not teach The computer program product of claim 11, wherein generating the remedial action recommendation comprises performing a lookup operation in a site reliability engineering database of remediation actions corresponding to at least one of the one or more IT computing resources or the one or more organizational processes.
However, Ahad does teach The computer program product of claim 11, wherein generating the remedial action recommendation comprises performing a lookup operation in a site reliability engineering database of remediation actions corresponding to at least one of the one or more IT computing resources or the one or more organizational processes. (Ahad Column 34 Paragraph 6; "At step 1308, a policy for resolving the anomaly is identified. The policy may be identified by the ADRC in the component. A policy store may be searched to identify one or more policies having one or more rule(s) that are satisfied by the anomaly. At step 1310, a determination may be made that a rule in the policy is satisfied by the anomaly. The determination may be made by the ADRC in the component. A policy may indicate one or more corrective actions for resolving the anomaly in the component in which the anomaly event is detected." Examiner notes that the remedial action recommendation is generated by performing a lookup operation in a site reliability engineering database of remediation actions/search policy store corresponding to at least one of the one or more IT computing resources or one or more organizational processes/policy)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, and Ahad. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. One of ordinary skill would have motivation to combine Jaeseong, Chris, and Ahad to reduce human involvement in anomaly resolution “Techniques disclosed herein can reduce, if not eliminate, human involvement in addressing the size and complexity of large computing systems (e.g., cloud systems), and thus, lead to autonomic computing systems.” (Ahad Column 2 Paragraph 4 “).
Regarding claim 20, Jaeseong teaches An apparatus comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to: (Jaeseong Paragraph 0011; “According to some embodiments, a computer program product can be provided that includes a non-transitory computer readable medium storing instructions that, when executed on at least one processor, cause the at least one processor to carry out methods performed by the network node.”)
execute machine learning training of one or more machine learning (ML) computer models based on historical data representing logged information technology (IT) events and key performance indicators (KPIs) of organizational processes, (Jaeseong Paragraph 0004; "The baseline dataset includes a plurality of key performance indicators, KPIs, that each have a continuous value, and a plurality of historical changes made to the configurable parameter. The computer system can perform training of a policy model (e.g., 520, 690) while offline the telecommunications network using the baseline dataset and Inverse Propensity Score on the plurality of KPIs as inputs to output from the policy model a probability of actions for controlling the configurable parameter." Examiner notes that the machine learning computer model/policy model is executing training based on historical data/baseline dataset representing logged IT events/changes made to configurable parameters and KPI of organizational processes/KPIs)
process, by the one or more ML computer models, input data to generate a forecast output, wherein the forecast output specifies at least one forecasted IT event or at least one KPI impact of the plurality of KPI impacts; (Jaeseong Paragraph 0004; "The computer system can perform training of a policy model (e.g., 520, 690) while offline the telecommunications network using the baseline dataset and Inverse Propensity Score on the plurality of KPIs as inputs to output from the policy model a probability of actions for controlling the configurable parameter." Examiner notes that the policy model processes input data to generate a forecast output/actions for controlling the configurable parameter, wherein the output specifies forecasted IT event/actions for controlling the configurable parameter.)
Jaeseong does not teach generate at least one correlation graph data structure that maps at least one of IT events to one or more IT computing resources, or a plurality KPI impacts to the organizational processes;
correlate the forecast output with at least one of the one or more IT computing resources or one or more of the organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output to generate a list comprising: at least one or affected computing resources, or at least one of affected organizational processes;
However, Chris does teach generate at least one correlation graph data structure that maps at least one of IT events to one or more IT computing resources, or a plurality KPI impacts to the organizational processes; (Chris Section "The Observability Pyramid" Paragraph 6; "Observability platforms like Instana and Application Resource Management (ARM) tools like Turbonomic provide the required context by creating and maintaining a dynamic graph… This dynamic graph maintains a constant understanding of the vertical relationship between a business application or process, the replicas or instances of an application on which it executes, where those replicas are deployed, and the underlying infrastructure on which it executes. The dynamic graph also understands and maintains the horizontal relationships between any given replica or instance of an application and its upstream and downstream dependencies, such as the other IT systems or components that make requests or calls of it or that it makes requests to." Examiner notes that the dynamic graph/correlation graph structure is generated and maps IT events/requests or calls to IT computing resources/IT systems or components and maps KPI impacts/vertical relationships to organizational processes/business application or process)
correlate the forecast output with at least one of the one or more IT computing resources or one or more of the organizational processes, at least by applying the at least one correlation graph data structure to the forecast output to generate a correlation output to generate a list comprising: at least one or affected computing resources, or at least one of affected organizational processes; (Chris Section "The Observability Pyramid" Paragraph 7; "This contextualization becomes even more valuable when we move from observing a system to analyzing it, deriving insights from it, and being able to understand the root cause of such issues as errors, poor performance, or availability." Chris Section “Event storms, event grouping, and root-cause analysis” Paragraph 3; “In the scenario where there is the failure and restart of an underlying infrastructure node, conditions are likely to be triggered not just for the underlying node but also for every process running on it and every instance of every service that those processes provide…Instana can group related events together and carry out root-cause analysis using its dynamic graph to include related events and build a timeline to understand the underlying root cause.” Examiner notes that the forecasted output/action for configurable parameters is correlated by applying to at least one correlation graph to generate a correlation output/issues to generate a list comprising: at least one of affected computing resources (Instana groups/creates a list of services that is related to the event))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong and Chris. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. One of ordinary skill would have motivation to combine Jaeseong and Chris to understand underlying state of systems, detect faults, and rapidly carry out repair “Being able to vastly reduce the elapsed time of the IT service management incident process – and, therefore, client impact -- requires comprehensive observability. With comprehensive observability, operations teams can: Understand the underlying state of the systems, Perform analyses and discover insights to detect faults and isolate them to root causes, Provide automation to rapidly carry out diagnoses and repair” (Chris Section “Conclusion” Paragraph 1).
Jaeseong in view of Chris does not teach and generate a remedial action recommendation based on the forecast output and correlation output.
determine whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed;
and execute the remedial action based on determining that the remedial action is capable of being automatically executed.
However, Ahad does teach and generate a remedial action recommendation based on the forecast output and correlation output. (Ahad Column 34 Paragraph 6; "At step 1308, a policy for resolving the anomaly is identified. The policy may be identified by the ADRC in the component. A policy store may be searched to identify one or more policies having one or more rule(s) that are satisfied by the anomaly. At step 1310, a determination may be made that a rule in the policy is satisfied by the anomaly. The determination may be made by the ADRC in the component. A policy may indicate one or more corrective actions for resolving the anomaly in the component in which the anomaly event is detected." Examiner notes that the remedial action recommendation is based on the anomaly/forecast output and correlation output)
determine whether a remedial action, associated with the at least one of affected computing resources, or the at least one of affected organizational processes, is capable of being automatically executed; (Ahad Column 35 Line 57; “the ADRC of the parent component may identify a policy for it to resolve the anomaly in the parent component. At step 1416, the ADRC of the parent component may initiate a corrective action identified in the policy for resolving the anomaly in the parent component… the ADRC of the parent component may not have a policy for resolving the anomaly in the parent component. The parent component may propagate data about the anomaly event to higher level components, such as a parent component of the parent component… The ADRC of the higher level parent component may initiate corrective action to resolve the anomaly provided that the ADRC can identify a policy for resolving the anomaly in the higher level parent component.” Examiner notes that ADRC determines whether a remedial action (policy for resolving the anomaly), associated with the at least one of affected computing resources (parent component), is capable of being automatically executed (determine whether parent component has policy to initiate corrective action))
and execute the remedial action based on determining that the remedial action is capable of being automatically executed. (Examiner refers to previous mapping to show that the remedial action (policy to resolve the anomaly) is executed based on determining that the remedial action is capable of being automatically executed (if parent component contains policy to initiate corrective action))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, and Ahad. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. One of ordinary skill would have motivation to combine Jaeseong, Chris, and Ahad to reduce human involvement in anomaly resolution “Techniques disclosed herein can reduce, if not eliminate, human involvement in addressing the size and complexity of large computing systems (e.g., cloud systems), and thus, lead to autonomic computing systems.” (Ahad Column 2 Paragraph 4 “).
Claim(s) 5 – 7 and 15 – 17 are rejected under 35 U.S.C. 103 as being unpatentable over JEONG; Jaeseong; US 20220394531 A1 (hereinafter “Jaeseong”) in view of Chris Bailey et al, "Observability, insights, and automation" (hereinafter “Chris”) in further view of Ahad; Rafiul; US-10853161-B2 (hereinafter “Ahad”) in further view of Mauro Tortonesi, "Business-impact analysis and simulation of critical incidents in IT service management" (hereinafter “Mauro”).
Regarding claim 5, Jaeseong does not teach The method of claim 1, further comprising: simulating second input data for the remedial action corresponding to the remedial action recommendation;
and processing the second input data by the one or more ML computer models to generate a predicted impact outcome of the remedial action on at least one of the KPIs or the IT events.
However, Mauro does teach The method of claim 1, further comprising: simulating second input data for the remedial action corresponding to the remedial action recommendation; (Mauro Section IV Paragraph 3; "SYMIAN implements an accurate model of IT support organizations which allows, via discrete event simulation" Examiner notes that the discrete event simulation is simulating second input data for a remedial action corresponding to the remedial action recommendation)
and processing the second input data by the one or more ML computer models to generate a predicted impact outcome of the remedial action on at least one of the KPIs or the IT events. (Mauro Section IV Paragraph 3; "SYMIAN implements an accurate model of IT support organizations which allows, via discrete event simulation, to reproduce their behavior and to evaluate their KPls in the context of each candidate strategy for critical incident management." Examiner notes that the model of IT support organizations/trained ML computer models processes the second input data/discrete event to generate a predicted outcome of the remedial action on KPI/reproduce their behavior and to evaluate their KPls)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, and Mauro. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Mauro teaches simulating IT strategies and evaluating the KPIs for each strategy. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, and Mauro to analyze each strategy that best aligns with business objectives “helps a user to select the one with the best alignment with business objectives.” (Mauro Section I Paragraph 6).
Regarding claim 6, Jaeseong does not teach The method of claim 5, wherein generating the remedial action recommendation comprises identifying a plurality of candidate remedial actions based on the forecast output,
and executing the simulation of the second input data and processing of the second input data by the one or more ML computer models for each candidate remedial action in the plurality of candidate remedial actions.
However, Ahad does teach The method of claim 5, wherein generating the remedial action recommendation comprises identifying a plurality of candidate remedial actions based on the forecast output, (Ahad Column 34 Paragraph 6; "At step 1308, a policy for resolving the anomaly is identified. The policy may be identified by the ADRC in the component. A policy store may be searched to identify one or more policies having one or more rule(s) that are satisfied by the anomaly. At step 1310, a determination may be made that a rule in the policy is satisfied by the anomaly. The determination may be made by the ADRC in the component. A policy may indicate one or more corrective actions for resolving the anomaly in the component in which the anomaly event is detected." Examiner notes that the remedial action recommendation comprises identifying a plurality of candidate remedial actions/one or more polices based on the forecast output)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, and Ahad. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. One of ordinary skill would have motivation to combine Jaeseong, Chris, and Ahad to reduce human involvement in anomaly resolution “Techniques disclosed herein can reduce, if not eliminate, human involvement in addressing the size and complexity of large computing systems (e.g., cloud systems), and thus, lead to autonomic computing systems.” (Ahad Column 2 Paragraph 4 “).
Jaeseong in view of Ahad does not teach and executing the simulation of the second input data and processing of the second input data by the one or more ML computer models for each candidate remedial action in the plurality of candidate remedial actions.
However, Mauro does teach and executing the simulation of the second input data and processing of the second input data by the one or more ML computer models for each candidate remedial action in the plurality of candidate remedial actions. (Mauro Section IV Paragraph 3; "SYMIAN implements an accurate model of IT support organizations which allows, via discrete event simulation, to reproduce their behavior and to evaluate their KPls in the context of each candidate strategy for critical incident management." Examiner notes that the SYMIAN is executing the simulation of the second input/discrete event and processing the second input by the trained model/model of IT support organizations for each candidate remedial action/each candidate strategy for critical incident management in the plurality of candidate remedial actions)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, and Mauro. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Mauro teaches simulating IT strategies and evaluating the KPIs for each strategy. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, and Mauro to analyze each strategy that best aligns with business objectives “helps a user to select the one with the best alignment with business objectives.” (Mauro Section I Paragraph 6).
Regarding claim 7, Jaeseong does not teach The method of claim 6, further comprising: ranking candidate remedial actions in the plurality of candidate remedial actions relative to one another based on predicted impact outcomes for each of the candidate remedial actions;
and selecting a candidate remedial action to be a recommended remedial action based on the relative ranking, wherein the remedial action recommendation specifies the selected candidate remedial action.
However, Mauro does teach The method of claim 6, further comprising: ranking candidate remedial actions in the plurality of candidate remedial actions relative to one another based on predicted impact outcomes for each of the candidate remedial actions; (Mauro Section IV Paragraph 5; "The Alignment Estimator component calculates the alignment of strategies with business objectives, and compares them to find out which one has the minimum impact on business." Examiner notes that the alignment estimator ranks the candidate remedial actions in the plurality of candidate remedial actions relative to one another based on the predicted impact for each candidate action by calculating the alignment of strategies with business objectives and compares them to find which one has the minimum impact on business.)
and selecting a candidate remedial action to be a recommended remedial action based on the relative ranking, wherein the remedial action recommendation specifies the selected candidate remedial action. (Mauro Section V.C Paragraph 1; "Tile objective of the business impact driven optimization process is the selection of the strategy for critical incident management scoring the highest level of alignment with the given business objectives." Examiner notes that the candidate remedial action/strategy for critical incident management is selected to be a recommended remedial action based on the relative ranking, wherein the remedial action recommendation specifies the selected candidate action.)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, and Mauro. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Mauro teaches simulating IT strategies and evaluating the KPIs for each strategy. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, and Mauro to analyze each strategy that best aligns with business objectives “helps a user to select the one with the best alignment with business objectives.” (Mauro Section I Paragraph 6).
Regarding claim 15, Jaeseong does not teach The computer program product of claim 11, wherein the computer readable program further causes the data processing system to: simulate second input data for the remedial action corresponding to the remedial action recommendation;
and process the second input data by the one or more ML computer models to generate a predicted impact outcome of the remedial action on at least one of the KPIs or the IT events.
However, Mauro does teach The computer program product of claim 11, wherein the computer readable program further causes the data processing system to: simulate second input data for the remedial action corresponding to the remedial action recommendation; (Mauro Section IV Paragraph 3; "SYMIAN implements an accurate model of IT support organizations which allows, via discrete event simulation" Examiner notes that the discrete event simulation is simulating second input data for a remedial action corresponding to the remedial action recommendation)
and process the second input data by the one or more ML computer models to generate a predicted impact outcome of the remedial action on at least one of the KPIs or the IT events. (Mauro Section IV Paragraph 3; "SYMIAN implements an accurate model of IT support organizations which allows, via discrete event simulation, to reproduce their behavior and to evaluate their KPls in the context of each candidate strategy for critical incident management." Examiner notes that the model of IT support organizations/trained ML computer models processes the second input data/discrete event to generate a predicted outcome of the remedial action on KPI/reproduce their behavior and to evaluate their KPls)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, and Mauro. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Mauro teaches simulating IT strategies and evaluating the KPIs for each strategy. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, and Mauro to analyze each strategy that best aligns with business objectives “helps a user to select the one with the best alignment with business objectives.” (Mauro Section I Paragraph 6).
Regarding claim 16, Jaeseong does not teach The computer program product of claim 15, wherein generating the remedial action recommendation comprises identifying a plurality of candidate remedial actions based on the forecast output,
and executing the simulation of the second input data and processing of the second input data by the one or more ML computer models for each candidate remedial action in the plurality of candidate remedial actions.
However, Ahad does teach The computer program product of claim 15, wherein generating the remedial action recommendation comprises identifying a plurality of candidate remedial actions based on the forecast output, (Ahad Column 34 Paragraph 6; "At step 1308, a policy for resolving the anomaly is identified. The policy may be identified by the ADRC in the component. A policy store may be searched to identify one or more policies having one or more rule(s) that are satisfied by the anomaly. At step 1310, a determination may be made that a rule in the policy is satisfied by the anomaly. The determination may be made by the ADRC in the component. A policy may indicate one or more corrective actions for resolving the anomaly in the component in which the anomaly event is detected." Examiner notes that the remedial action recommendation comprises identifying a plurality of candidate remedial actions/one or more polices based on the forecast output)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, and Ahad. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. One of ordinary skill would have motivation to combine Jaeseong, Chris, and Ahad to reduce human involvement in anomaly resolution “Techniques disclosed herein can reduce, if not eliminate, human involvement in addressing the size and complexity of large computing systems (e.g., cloud systems), and thus, lead to autonomic computing systems.” (Ahad Column 2 Paragraph 4 “).
Jaeseong in view of Ahad does not teach and executing the simulation of the second input data and processing of the second input data by the one or more ML computer models for each candidate remedial action in the plurality of candidate remedial actions.
However, Mauro does teach and executing the simulation of the second input data and processing of the second input data by the one or more ML computer models for each candidate remedial action in the plurality of candidate remedial actions. (Mauro Section IV Paragraph 3; "SYMIAN implements an accurate model of IT support organizations which allows, via discrete event simulation, to reproduce their behavior and to evaluate their KPls in the context of each candidate strategy for critical incident management." Examiner notes that the SYMIAN is executing the simulation of the second input/discrete event and processing the second input by the trained model/model of IT support organizations for each candidate remedial action/each candidate strategy for critical incident management in the plurality of candidate remedial actions)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, and Mauro. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Mauro teaches simulating IT strategies and evaluating the KPIs for each strategy. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, and Mauro to analyze each strategy that best aligns with business objectives “helps a user to select the one with the best alignment with business objectives.” (Mauro Section I Paragraph 6).
Regarding claim 17, Jaeseong does not teach The computer program product of claim 16, wherein the computer readable program further causes the data processing system to: rank candidate remedial actions in the plurality of candidate remedial actions relative to one another based on predicted impact outcomes for each of the candidate remedial actions;
and select a candidate remedial action to be a recommended remedial action based on the relative ranking, wherein the remedial action recommendation specifies the selected candidate remedial action.
However, Mauro does teach The computer program product of claim 16, wherein the computer readable program further causes the data processing system to: rank candidate remedial actions in the plurality of candidate remedial actions relative to one another based on predicted impact outcomes for each of the candidate remedial actions; (Mauro Section IV Paragraph 5; "The Alignment Estimator component calculates the alignment of strategies with business objectives, and compares them to find out which one has the minimum impact on business." Examiner notes that the alignment estimator ranks the candidate remedial actions in the plurality of candidate remedial actions relative to one another based on the predicted impact for each candidate action by calculating the alignment of strategies with business objectives and compares them to find which one has the minimum impact on business.)
and select a candidate remedial action to be a recommended remedial action based on the relative ranking, wherein the remedial action recommendation specifies the selected candidate remedial action. (Mauro Section V.C Paragraph 1; "Tile objective of the business impact driven optimization process is the selection of the strategy for critical incident management scoring the highest level of alignment with the given business objectives." Examiner notes that the candidate remedial action/strategy for critical incident management is selected to be a recommended remedial action based on the relative ranking, wherein the remedial action recommendation specifies the selected candidate action.)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, and Mauro. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Mauro teaches simulating IT strategies and evaluating the KPIs for each strategy. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, and Mauro to analyze each strategy that best aligns with business objectives “helps a user to select the one with the best alignment with business objectives.” (Mauro Section I Paragraph 6).
Claim(s) 8, 9, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over JEONG; Jaeseong; US 20220394531 A1 (hereinafter “Jaeseong”) in view of Chris Bailey et al, "Observability, insights, and automation" (hereinafter “Chris”) in further view of Ahad; Rafiul; US-10853161-B2 (hereinafter “Ahad”) in further view of Susanne Dandl, "Counterfactual Explanations" (hereinafter “Susanne”).
Regarding claim 8, Jaeseong does not teach The method of claim 1, further comprising executing a computer simulation employing a counterfactual analysis that simulates different counterfactual conditions not present in the input data and generates corresponding predicted outcomes based on execution of the one or more ML computer models on the different counterfactual conditions.
However, Susanne does teach The method of claim 1, further comprising executing a computer simulation employing a counterfactual analysis that simulates different counterfactual conditions not present in the input data and generates corresponding predicted outcomes based on execution of the one or more ML computer models on the different counterfactual conditions. (Susanne Section "Example" Paragraph 2; "The goal is to find counterfactual explanations for a customer with feature values in Table 15.1… The SVM predicts that the probability that the person has a good credit risk is 24.2%. The counterfactuals should answer how the input features need to be changed to get a predicted probability larger than 50%. Table 15.2…and the last column displays the predicted probability. " Examiner notes that under Section Example, a computer simulation is being executed employing counterfactual analysis that simulates different counterfactual condition/counterfactuals shows in table 15.2 not present in the input data/customer with feature values in table 15.1; and generates corresponding predicted outcomes based on execution of the one or more trained ml computer models on the counterfactual conditions/the last column displays predicted probability)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, and Susanne. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Susanne teaches generating counterfactuals. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, and Susanne to utilize counterfactuals to provide truthful and simple justification about a model “Counterfactuals are useful for the goal of justification, especially recourse, since they are truthful and simple.” (Susanne Section “Strengths” Paragraph 6).
Regarding claim 9, Jaeseong does teach and using the one or more ML computer models to forecast corresponding KPIs. (Jaeseong Paragraph 0004; "The computer system can perform training of a policy model (e.g., 520, 690) while offline the telecommunications network using the baseline dataset and Inverse Propensity Score on the plurality of KPIs as inputs to output from the policy model a probability of actions for controlling the configurable parameter." Examiner notes that the policy model/one or more trained ML computer model to forecast corresponding KPIs.)
Jaeseong does not teach The method of claim 8, wherein executing the computer simulation employing the counterfactual analysis comprises modifying the input data to the one or more ML computer models to represent a return to a normalcy condition for one or more IT metrics
However, Susanne does teach The method of claim 8, wherein executing the computer simulation employing the counterfactual analysis comprises modifying the input data to the one or more ML computer models to represent a return to a normalcy condition for one or more IT metrics (Susanne Paragraph 12; "The last requirement is that a counterfactual instance should have feature values that are likely." Examiner notes that the counterfactual is modified input data and focuses on normalcy condition/values that are likely for one or more IT metrics)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, and Susanne. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Susanne teaches generating counterfactuals. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, and Susanne to utilize counterfactuals to provide truthful and simple justification about a model “Counterfactuals are useful for the goal of justification, especially recourse, since they are truthful and simple.” (Susanne Section “Strengths” Paragraph 6).
Regarding claim 18, Jaeseong does not teach The computer program product of claim 11, wherein the computer readable program further causes the data processing system to execute a computer simulation employing a counterfactual analysis that simulates different counterfactual conditions not present in the input data and generates corresponding predicted outcomes based on execution of the one or more ML computer models on the different counterfactual conditions.
However, Susanne does teach The computer program product of claim 11, wherein the computer readable program further causes the data processing system to execute a computer simulation employing a counterfactual analysis that simulates different counterfactual conditions not present in the input data and generates corresponding predicted outcomes based on execution of the one or more ML computer models on the different counterfactual conditions. (Susanne Section "Example" Paragraph 2; "The goal is to find counterfactual explanations for a customer with feature values in Table 15.1… The SVM predicts that the probability that the person has a good credit risk is 24.2%. The counterfactuals should answer how the input features need to be changed to get a predicted probability larger than 50%. Table 15.2…and the last column displays the predicted probability. " Examiner notes that under Section Example, a computer simulation is being executed employing counterfactual analysis that simulates different counterfactual condition/counterfactuals shows in table 15.2 not present in the input data/customer with feature values in table 15.1; and generates corresponding predicted outcomes based on execution of the one or more trained ml computer models on the counterfactual conditions/the last column displays predicted probability)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, and Susanne. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Susanne teaches generating counterfactuals. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, and Susanne to utilize counterfactuals to provide truthful and simple justification about a model “Counterfactuals are useful for the goal of justification, especially recourse, since they are truthful and simple.” (Susanne Section “Strengths” Paragraph 6).
Regarding claim 19, Jaeseong does teach and using the one or more ML computer models to forecast corresponding KPIs. (Jaeseong Paragraph 0004; "The computer system can perform training of a policy model (e.g., 520, 690) while offline the telecommunications network using the baseline dataset and Inverse Propensity Score on the plurality of KPIs as inputs to output from the policy model a probability of actions for controlling the configurable parameter." Examiner notes that the policy model/one or more trained ML computer model to forecast corresponding KPIs.)
Jaeseong does not teach The computer program product of claim 18, wherein executing the computer simulation employing the counterfactual analysis comprises modifying the input data to the one or more ML computer models to represent a return to a normalcy condition for one or more IT metrics
However, Susanne does teach The computer program product of claim 18, wherein executing the computer simulation employing the counterfactual analysis comprises modifying the input data to the one or more ML computer models to represent a return to a normalcy condition for one or more IT metrics (Susanne Paragraph 12; "The last requirement is that a counterfactual instance should have feature values that are likely." Examiner notes that the counterfactual is modified input data and focuses on normalcy condition/values that are likely for one or more IT metrics)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, and Susanne. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Susanne teaches generating counterfactuals. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, and Susanne to utilize counterfactuals to provide truthful and simple justification about a model “Counterfactuals are useful for the goal of justification, especially recourse, since they are truthful and simple.” (Susanne Section “Strengths” Paragraph 6).
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over JEONG; Jaeseong; US 20220394531 A1 (hereinafter “Jaeseong”) in view of Chris Bailey et al, "Observability, insights, and automation" (hereinafter “Chris”) in further view of Ahad; Rafiul; US-10853161-B2 (hereinafter “Ahad”) in further view of Susanne Dandl, "Counterfactual Explanations" (hereinafter “Susanne”) in further view of Vik Paruchuri, "Using Linear Regression for Predictive Modeling in R" (hereinafter “Vik”)
Regarding claim 10, Jaeseong does not teach The method of claim 8, wherein executing the computer simulation employing the counterfactual analysis comprises performing a linear regression on the forecast output to simulate no remediation of forecasted conditions and project the forecasted conditions into future time points.
However, Vik does teach The method of claim 8, wherein executing the computer simulation employing the counterfactual analysis comprises performing a linear regression on the forecast output to simulate no remediation of forecasted conditions and project the forecasted conditions into future time points. (Vik Section "Building blocks of a linear regression model" Paragraph 1; "Linear regression describes the relationship between a response variable (or dependent variable) of interest and one or more predictor (or independent) variables." Vik Section "Using out simple linear model to make predictions" Paragraph 1; "Our model is suitable for making predictions!" Examiner notes that section "building blocks of a linear regression model shows performing a linear regression on the forecast output to simulate no remediation of forecasted conditions; the section "Using our simple linear model to make predictions" shows using the model to project the forecasted conditions into future time points)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Jaeseong, Chris, Ahad, Susanne and Vik. Jaeseong teaches training a machine learning model to generate actions for controlling configurable parameters given KPI inputs. Chris teaches ways to observe and gain insight on the IT infrastructure with regards to business processes. Ahad teaches an automatic anomaly detection and resolution system. Susanne teaches generating counterfactuals. Vik teaches using linear regression to predict. One of ordinary skill would have motivation to combine Jaeseong, Chris, Ahad, Susanne, and Vik to utilize linear regression because it is the most simple and common ML model “Linear regression is one of the simplest and most common supervised machine learning algorithms that data scientists use for predictive modeling.” (Vik Paragraph 3).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.D.T./Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147