DETAILED ACTION
Notice of Pre-AIA or AIA Status
This Final Office Action is in response to amendments and remarks filed on July 14, 2026, for the application with serial number 18/189,988.
Claims 1, 8, 9, 12, 14, 18, and 19 are amended.
Claim 10 is canceled.
Claims 1-9 and 11-20 are pending.
Interview
The Examiner acknowledges the interview conducted on June 30, 20266, in which the subject matter eligibility and prior art rejections were discussed with respect to proposed amendments.
Response to Arguments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the present claims recite steps that improve the functioning of a computer system. See Remarks pp. 7-8. In response, the Examiner submits that predicting an outage risk and suggesting mitigating actions to reduce outage risk is not an improvement to the functioning of a computer system. The claims do not provide a particular solution to a particular problem. Instead, the claims broadly recite the idea of suggesting mitigating actions to reduce risk in a cloud environment. Performing a cost/benefit analysis of mitigating actions to determine which action to perform is not an improvement to a technology or technical field. The rejection, below, does not conclude that the steps are merely a mental process that could be performed in the human mind. Instead, the rejection concludes that the claims recite steps that could be rules or instructions for a human being to follow to reduce risk. However, a general purpose computer employing machine learning is recited for implementation.
The Applicant further contends that the claims provide a practical application by implementing one recommendation from a plurality of recommendations. See Remarks p. 9. In response, the Examiner submits that this step merely amount to a determination as to which recommendation is best; based on a cost/benefit analysis. Determining a preferred recommendation or best mitigating action, recited at such a high level of generality, does not provide a practical application. The claims do not provide a particular technical solution to a particular technical problem. Instead, the claims are a drafting effort intended to overcome the rejection for lack of subject matter eligibility.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-9 and 11-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under Step 1 of the subject matter eligibility analysis, claims(s) 1-9 and 11-20 are all directed to one of the four statutory categories of invention. However, under step 2A, prong one, the claims recite a judicial exception: predicting an outage risk and suggesting mitigations to prevent outages (as evidenced by exemplary independent claim 1; “predicting . . . an outage risk;” and “implementing . . . a recommendation from the plurality of recommendations”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “receiving . . . at least one change request (CR);” “predicting . . . an outage risk for the at least one CR;” “identifying . . . a plurality of recommendations to mitigate the outage risk for the at least one CR;” “measuring . . . a risk reduction and cost associated with each of the plurality of recommendations;” “ranking . . . the plurality of recommendations based on the measured risk reduction and cost;” and “implementing . . . a recommendation . . . based on the ranking.” The steps are all steps for managing personal behavior related to the abstract idea of predicting an outage risk and suggesting mitigations to prevent outages that, when considered alone and in combination, are part of the abstract idea of predicting an outage risk and suggesting mitigations to prevent outages. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of predicting an outage risk and suggesting mitigations to prevent outages. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes predicting service disruptions and mitigating the service disruptions based on the predictions..
Under step 2A, prong two, of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a processor set in independent claim 1; a computer readable medium in independent claim 12; and a system with a processor set and computer readable media in independent claim 18). See MPEP $2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP $2106.05(a). The claims do recite the use of machine learning, but the abstract idea of predicting an outage risk for change requests and suggesting mitigations to prevent the outages is generally linked to a machine learning environment for implementation. Therefore, the machine learning merely amount to a technological environment that does not provide a practical application or significantly more in the claims. Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). Under step 2B of the subject matter eligibility analysis, the claims do not integrate the abstract idea into a judicial exception. Referring to the additional elements provided in the analysis in step one, above, the generic computer hardware does not provide significantly more than the recited abstract idea. See MPEP §2106.05(f).
For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
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) 1-3, 5, 7-9, 12, 13, 15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190108465 A1 to Zhou et al. (hereinafter ‘ZHOU’) in view of US 20100191952 A1 to Keinan (hereinafter ‘KEINAN’) and US 11503061 B1 to Lin et al. (hereinafter ‘LIN’).
Claim 1 (Currently Amended)
ZHOU discloses a computer-implemented method, comprising: receiving, by a processor set (see ¶[0017] and Fig. 2; the exemplary computing device includes a processor), at least one change request (CR) for a modification in a cloud environment (see ¶[0015]; the service provider is subject to one or more changes by way of one or more corresponding change requests. See also ¶[0004], [0016], and [0022]; and Figs. 1 and 2; an application service relying on server assets);
predicting, by the processor set, an outage risk for the at least one CR in the cloud environment (see ¶[0024]; a variety of predictive risk models may be generated to predict one or more service outages in response to a change) using a predictive machine learning model (see ¶[0011]-[0012]; the inventors have developed a machine learning approach), wherein the predicting the outage risk is based on historical data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change an including a text description, and various factors associated with the change), historical features (see again abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change including a text description, and various factors associated with the change).
ZHOU does not specifically disclose, but KEINAN discloses, and impact of one or more prior change requests on the at least one CR (see ¶[0030]-[0031] and Table 4; severity indicates whether collision is expected and its severity);
identifying, by the processor set, a plurality of recommendations to mitigate the outage risk for the at least one CR in the cloud environment (see abstract and ¶[0011], [0027], & [0030]; implement change requests and mitigate risk by suggesting alternative factor values to lower a level of risk. Mitigate service disruptions and failures during change execution); and
measuring, by the processor set, risk reduction and cost associated with each of the plurality of recommendations (see ¶[0033]; a user can weigh the reduction of risk against the time/cost of performing the testing to determine whether to implement the suggestion).
The combination of ZHOU and KEINAN does not specifically disclose, but LIN discloses, ranking, by the processor set, the plurality of recommendations based on the measured risk reduction and cost associated with each of the plurality of recommendations, wherein the ranking includes generating an impact per cost value for each of the plurality of recommendations and ordering the recommendations based on the impact per cost values (see col 4, ln 62-col 5, ln 11 ; cost factors may be combined with the risk score reduction of individual remediation action plans to calculate a ranking metric for the plan (e.g. a security gain per unit of cost).; and
implementing, by the processor set, a recommendation from the plurality of recommendations based on the ranking of the plurality of recommendations (see again col 4, ln 62-col 5, ln 11; the remediation planning system may then use the ranking metric to rank candidate remediation plans or recommend selected plans to the user. In this manner, the remediation planning system is able to use the ER model to programmatically determine remediation plans with the optimal balance of security gains and associated costs).
ZHOU does not specifically disclose, but KEINAN discloses, wherein the implemented recommendation mitigates the outage risk for the at least one CR in the cloud environment (see abstract and ¶[0011], [0027], & [0030]; implement change requests and mitigate risk by suggesting alternative factor values to lower a level of risk. Mitigate service disruptions and failures during change execution).
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. KEINAN discloses risk mitigation of computer system change requests that includes alternative factor values to lower a level of risk of disruptions and failures during change execution. It would have been obvious for one of ordinary skill in the art at the time of invention to include the risk mitigation as taught by KEINAN in the system executing the method of ZHOU with the motivation to prevent service disruptions.
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. KEINAN discloses risk mitigation of computer system change requests that includes alternative factor values to lower a level of risk of disruptions and failures during change execution. LIN discloses remediation plans using risk modeling that includes ranking plans according to security gain per unit cost. It would have been obvious for one of ordinary skill in the art at the time of invention to include the ranking as taught by LIN in the system executing the method of ZHOU and KEINAN with the motivation to mitigate predicted risk in services.
Claim 2 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the method as set forth in claim 1.
ZHOU additionally discloses further comprising predicting a plurality of environmental variables at a time of scheduled deployment for the at least one CR in the cloud environment (see ¶[0023] and [0033] and Table 1; variable 3 includes the environment. The variable receives a coefficient of importance).
Claim 3 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the method as set forth in claim 1.
ZHOU further discloses wherein the outage risk for the at least one CR is predicted using the predictive machine learning model (see ¶[0011]-[0012]; the inventors have developed a machine learning approach) which predicts based on historical CR data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change and include a text description, and various factors associated with the change). and historical environmental features (see ¶[0023] and [0033] and Table 1; variable 3 includes the environment. The variable receives a coefficient of importance).
Claim 5 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the method as set forth in claim 3.
ZHOU further discloses wherein the predictive machine learning model is trained using the historical CR data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change and include a text description, and various factors associated with the change) and the historical environmental features (see ¶[0023] and [0033] and Table 1; variable 3 includes the environment. The variable receives a coefficient of importance) to predict the outage risk for the at least one CR (see ¶[0024]; a variety of predictive risk models may be generated to predict one or more service outages in response to a change).
Claim 7 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the method as set forth in claim 3.
ZHOU further discloses wherein the predictive machine learning model is further configured to estimate a magnitude of the outage risk for the at least one CR (see ¶[0040]; quantify the probability of a problem or incident).
Claim 8 (Currently Amended)
The combination of ZHOU, KEINAN, and LIN discloses the method as set forth in claim 1.
ZHOU does not specifically disclose, but KEINAN discloses, wherein the plurality of recommendations to mitigate the outage risk for the at least one CR comprise an alternative action for the at least one CR (see abstract and ¶[0011]; implement change requests and mitigate risk by suggesting alternative factor values to lower a level of risk. Mitigate service disruptions and failures during change execution).
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. KEINAN discloses risk mitigation of computer system change requests that includes alternative factor values to lower a level of risk of disruptions and failures during change execution. It would have been obvious for one of ordinary skill in the art at the time of invention to include the risk mitigation as taught by KEINAN in the system executing the method of ZHOU with the motivation to prevent service disruptions.
Claim 9 (Currently Amended)
The combination of ZHOU, KEINAN, and LIN discloses the method as set forth in claim 1.
ZHOU does not specifically disclose, but KEINAN discloses, wherein the implemented recommendation comprises a modification of an action for the at least one CR (see abstract and ¶[0011], [0018]-[0019], and [0028]; probability of Failure is calculated based on risk factors that are defined by a user during a configuration process. implement change requests and mitigate risk by suggesting alternative factor values to lower a level of risk. Notify the user on possible actions to reduce the risk of the change request).
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. KEINAN discloses risk mitigation of computer system change requests that includes alternative factor values to lower a level of risk of disruptions and failures during change execution. It would have been obvious for one of ordinary skill in the art at the time of invention to include the risk mitigation as taught by KEINAN in the system executing the method of ZHOU with the motivation to prevent service disruptions.
Claim 12 (Currently Amended)
ZHOU discloses a computer program product (see ¶[0051]; embodiments include software) comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media (see ¶[0049]; computer readable media), the program instructions executable to:
receive at least one change request (CR) for a modification in a cloud environment (see ¶[0015]; the service provider is subject to one or more changes by way of one or more corresponding change requests. See also ¶[0004], [0016], and [0022]; and Figs. 1 and 2; an application service relying on server assets);
predict an outage risk for the at least one CR in the cloud environment (see ¶[0024]; a variety of predictive risk models may be generated to predict one or more service outages in response to a change) using a predictive machine learning model (see ¶[0011]-[0012]; the inventors have developed a machine learning approach), wherein the predicting the outage risk is based on historical data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change an including a text description, and various factors associated with the change), historical features (see again abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change including a text description, and various factors associated with the change).
ZHOU does not specifically disclose, but KEINAN discloses, and impact of one or more prior change requests on the at least one CR (see ¶[0030]-[0031] and Table 4; severity indicates whether collision is expected and its severity);
identify a plurality of recommendations to mitigate the outage risk for the at least one CR in the cloud environment (see abstract and ¶[0011], [0027], & [0030]; implement change requests and mitigate risk by suggesting alternative factor values to lower a level of risk. Mitigate service disruptions and failures during change execution);
measure risk reduction and cost associated with each of the plurality of recommendations (see ¶[0033]; a user can weigh the reduction of risk against the time/cost of performing the testing to determine whether to implement the suggestion).
The combination of ZHOU and KEINAN does not specifically disclose, but LIN discloses, rank the plurality of recommendations based on the measured risk reduction and cost associated with each of the plurality of recommendations, wherein the ranking includes generating an impact per cost value for each of the plurality of recommendations and ordering the recommendations based on the impact per cost values (see col 4, ln 62-col 5, ln 11 ; cost factors may be combined with the risk score reduction of individual remediation action plans to calculate a ranking metric for the plan (e.g. a security gain per unit of cost).; and
implement a recommendation from the plurality of recommendations based on the ranking of the plurality of recommendations (see again col 4, ln 62-col 5, ln 11; the remediation planning system may then use the ranking metric to rank candidate remediation plans or recommend selected plans to the user. In this manner, the remediation planning system is able to use the ER model to programmatically determine remediation plans with the optimal balance of security gains and associated costs).
ZHOU does not specifically disclose, but KEINAN discloses, wherein the implemented recommendation mitigates the outage risk for the at least one CR in the cloud environment (see abstract and ¶[0011], [0027], & [0030]; implement change requests and mitigate risk by suggesting alternative factor values to lower a level of risk. Mitigate service disruptions and failures during change execution).
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. KEINAN discloses risk mitigation of computer system change requests that includes alternative factor values to lower a level of risk of disruptions and failures during change execution. It would have been obvious for one of ordinary skill in the art at the time of invention to include the risk mitigation as taught by KEINAN in the system executing the method of ZHOU with the motivation to prevent service disruptions.
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. KEINAN discloses risk mitigation of computer system change requests that includes alternative factor values to lower a level of risk of disruptions and failures during change execution. LIN discloses remediation plans using risk modeling that includes ranking plans according to security gain per unit cost. It would have been obvious for one of ordinary skill in the art at the time of invention to include the ranking as taught by LIN in the system executing the method of ZHOU and KEINAN with the motivation to mitigate predicted risk in services.
Claim 13 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the computer program product as set forth in claim 12.
ZHOU further discloses wherein the outage risk for the at least one CR is predicted using the predictive machine learning model (see ¶[0011]-[0012]; the inventors have developed a machine learning approach) which predicts based on historical CR data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change and include a text description, and various factors associated with the change). and historical environmental features (see ¶[0023] and [0033] and Table 1; variable 3 includes the environment. The variable receives a coefficient of importance).
Claim 15 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the computer program product as set forth in claim 13.
ZHOU further discloses wherein the predictive machine learning model is trained using the historical CR data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change and include a text description, and various factors associated with the change) and the historical environmental features (see ¶[0023] and [0033] and Table 1; variable 3 includes the environment. The variable receives a coefficient of importance) to predict the outage risk for the at least one CR (see ¶[0024]; a variety of predictive risk models may be generated to predict one or more service outages in response to a change).
Claim 17 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the computer program product as set forth in claim 13.
ZHOU further discloses wherein the predictive machine learning model is further configured to estimate a magnitude of the outage risk for the at least one CR (see ¶[0040]; quantify the probability of a problem or incident).
Claim 18 (Currently Amended)
ZHOU discloses a system comprising: a processor set (see ¶[0017] and Fig. 2; the exemplary computing device includes a processor), one or more computer readable storage media (see ¶[0049]; computer readable media), and program instructions collectively stored on the one or more computer readable storage media (see ¶[0051]; embodiments include software), the program instructions executable to:
receive at least one change request (CR) for a modification in a cloud environment (see ¶[0015]; the service provider is subject to one or more changes by way of one or more corresponding change requests. See also ¶[0004], [0016], and [0022]; and Figs. 1 and 2; an application service relying on server assets);
predict an outage risk for the at least one CR in the cloud environment (see ¶[0024]; a variety of predictive risk models may be generated to predict one or more service outages in response to a change) using a predictive machine learning model (see ¶[0011]-[0012]; the inventors have developed a machine learning approach), wherein the predicting the outage risk is based on historical data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change an including a text description, and various factors associated with the change), historical features (see again abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change including a text description, and various factors associated with the change).
ZHOU does not specifically disclose, but KEINAN discloses, and impact of one or more prior change requests on the at least one CR (see ¶[0030]-[0031] and Table 4; severity indicates whether collision is expected and its severity);
identify a plurality of recommendations to mitigate the outage risk for the at least one CR in the cloud environment (see abstract and ¶[0011], [0027], & [0030]; implement change requests and mitigate risk by suggesting alternative factor values to lower a level of risk. Mitigate service disruptions and failures during change execution);
measure risk reduction and cost associated with each of the plurality of recommendations (see ¶[0033]; a user can weigh the reduction of risk against the time/cost of performing the testing to determine whether to implement the suggestion).
The combination of ZHOU and KEINAN does not specifically disclose, but LIN discloses, rank the plurality of recommendations based on the measured risk reduction and cost associated with each of the plurality of recommendations, wherein the ranking includes generating an impact per cost value for each of the plurality of recommendations and ordering the recommendations based on the impact per cost values (see col 4, ln 62-col 5, ln 11 ; cost factors may be combined with the risk score reduction of individual remediation action plans to calculate a ranking metric for the plan (e.g. a security gain per unit of cost).; and
implement a recommendation from the plurality of recommendations based on the ranking of the plurality of recommendations (see again col 4, ln 62-col 5, ln 11; the remediation planning system may then use the ranking metric to rank candidate remediation plans or recommend selected plans to the user. In this manner, the remediation planning system is able to use the ER model to programmatically determine remediation plans with the optimal balance of security gains and associated costs).
ZHOU does not specifically disclose, but KEINAN discloses, wherein the implemented recommendation mitigates the outage risk for the at least one CR in the cloud environment (see abstract and ¶[0011], [0027], & [0030]; implement change requests and mitigate risk by suggesting alternative factor values to lower a level of risk. Mitigate service disruptions and failures during change execution).
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. KEINAN discloses risk mitigation of computer system change requests that includes alternative factor values to lower a level of risk of disruptions and failures during change execution. It would have been obvious for one of ordinary skill in the art at the time of invention to include the risk mitigation as taught by KEINAN in the system executing the method of ZHOU with the motivation to prevent service disruptions.
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. KEINAN discloses risk mitigation of computer system change requests that includes alternative factor values to lower a level of risk of disruptions and failures during change execution. LIN discloses remediation plans using risk modeling that includes ranking plans according to security gain per unit cost. It would have been obvious for one of ordinary skill in the art at the time of invention to include the ranking as taught by LIN in the system executing the method of ZHOU and KEINAN with the motivation to mitigate predicted risk in services.
Claim 19 (Currently Amended)
The combination of ZHOU, KEINAN, and LIN discloses the system as set forth in claim 18.
ZHOU further discloses wherein the outage risk for the at least one CR is predicted using the predictive machine learning model (see ¶[0011]-[0012]; the inventors have developed a machine learning approach) which predicts based on historical CR data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change and include a text description, and various factors associated with the change). and historical environmental features (see ¶[0023] and [0033] and Table 1; variable 3 includes the environment. The variable receives a coefficient of importance).
Claim 20 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the system as set forth in claim 19.
ZHOU further discloses wherein the predictive machine learning model is trained using the historical CR data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change and include a text description, and various factors associated with the change) and the historical environmental features (see ¶[0023] and [0033] and Table 1; variable 3 includes the environment. The variable receives a coefficient of importance) to predict the outage risk for the at least one CR (see ¶[0024]; a variety of predictive risk models may be generated to predict one or more service outages in response to a change).
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190108465 A1 to ZHOU et al. in view of US 20100191952 A1 to KEINAN and US 11503061 B1 to LIN et al. as applied to claims 1 and 3 above, and further in view of US 9075788 B1 to Roth et al. (hereinafter ‘ROTH’).
Claim 4 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the method as set forth in claim 3.
ZHOU further discloses wherein the historical CR data comprises at least one feature of the historical CR data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change and include a text description, and various factors associated with the change)
The combination of ZHOU, KEINAN, and LIN does not specifically disclose, but ROTH discloses, and the historical environment features comprises at least one feature of a cloud environment at a time of a schedule deployment of a historical CR (see abstract; simulate services in a cloud computing environment. Receive an account state change simulation request and respond with an indication of an expected failure of an operation. See also col 26, ln 25-65; save histories of previous causal analysis).
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. ROTH discloses account state simulation in a cloud computing environment that includes a simulation with a response that includes an indication of an expected failure. It would have been obvious for one of ordinary skill in the art at the time of invention to include the simulation as taught by ROTH in the system executing the method of ZHOU with the motivation to predict problems at service providers implementing change requests.
Claim 14 (Currently Amended)
The combination of ZHOU, KEINAN, and LIN discloses the computer program product as set forth in claim 13.
ZHOU further discloses wherein the historical CR data comprises at least one feature of the historical CR data (see abstract and ¶[0012] & [0038]; access change records for historical change records to predict a probability of a problem. The historical records relate to a prior change and include a text description, and various factors associated with the change)
The combination of ZHOU, KEINAN, and LIN does not specifically disclose, but ROTH discloses, and the historical environment features comprises at least one feature of a cloud environment at a time of a schedule deployment of a historical CR (see abstract; simulate services in a cloud computing environment. Receive an account state change simulation request and respond with an indication of an expected failure of an operation. See also col 26, ln 25-65; save histories of previous causal analysis).
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. ROTH discloses account state simulation in a cloud computing environment that includes a simulation with a response that includes an indication of an expected failure. It would have been obvious for one of ordinary skill in the art at the time of invention to include the simulation as taught by ROTH in the system executing the method of ZHOU with the motivation to predict problems at service providers implementing change requests.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190108465 A1 to ZHOU et al. in view of US 20100191952 A1 to KEINAN and US 11503061 B1 to LIN et al. as applied to claims 1 and 3 above, and further in view of US 20070203871 A1 to Tesauro et al. (hereinafter ‘TESAURO’).
Claim 6 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the method as set forth in claim 3.
The combination of ZHOU, KEINAN, and LIN does not specifically disclose, but TESAURO discloses, wherein the predictive machine learning model predicts based on control features which are adjusted to mitigate a risk impact of the at least one CR (see ¶[0002]; performance tuning of system control parameters, dynamic configuration management, automatic repair or remediation of system faults and actions to mitigate or avoid observed or predicted malicious attacks or cascading system failures).
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. KEINAN discloses risk mitigation of computer system change requests that includes alternative factor values to lower a level of risk of disruptions and failures during change execution. TESAURO discloses reward-based learning of system management policies that includes performance tuning of control parameters to mitigate system failures. It would have been obvious for one of ordinary skill in the art at the time of invention to include the tuning of control parameters for risk mitigation as taught by TESAURO in the system executing the method of ZHOU and KEINAN with the motivation to mitigate risks of change requests that result in disruptions.
Claim 16 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the computer program product as set forth in claim 13.
The combination of ZHOU, KEINAN, and LIN does not specifically disclose, but TESAURO discloses, wherein the predictive machine learning model predicts based on control features which are adjusted to mitigate a risk impact of the at least one CR (see ¶[0002]; performance tuning of system control parameters, dynamic configuration management, automatic repair or remediation of system faults and actions to mitigate or avoid observed or predicted malicious attacks or cascading system failures).
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. KEINAN discloses risk mitigation of computer system change requests that includes alternative factor values to lower a level of risk of disruptions and failures during change execution. TESAURO discloses reward-based learning of system management policies that includes performance tuning of control parameters to mitigate system failures. It would have been obvious for one of ordinary skill in the art at the time of invention to include the tuning of control parameters for risk mitigation as taught by TESAURO in the system executing the method of ZHOU and KEINAN with the motivation to mitigate risks of change requests that result in disruptions.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190108465 A1 to ZHOU et al. in view of US 20100191952 A1 to KEINAN and US 11503061 B1 to LIN et al. as applied to claim 1 above, and further in view of US 11132249 B1 to Puri et al .(hereinafter ‘PURI’).
Claim 11 (Original)
The combination of ZHOU, KEINAN, and LIN discloses the method as set forth in claim 1.
The combination of ZHOU, KEINAN, and LIN does not specifically disclose, but PURI discloses, further comprising generating an island graph for the at least one CR using historical CR data and root cause analysis (RCA) data (see col 13, ln 1-46 and Fig. 2; a code reversal tool that traverses a graph to determine the change request that is the root cause of the malfunctioning component).
ZHOU discloses predicting probabilities of problems at service providers based on changes implemented at the service providers. PURI discloses software code change reversal that includes traversing a graph to find a change request that is a root cause of a malfunctioning component. It would have been obvious for one of ordinary skill in the art at the time of invention to include the root cause graph as taught by PURI in the system executing the method of ZHOU with the motivation to analyze and predict problems with change requests.
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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/RICHARD N SCHEUNEMANN/ Primary Examiner, Art Unit 3624
