Detailed Action
This action is in response to the application filed on 01/21/2026. Claims 1-20 are pending and have been fully examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 are rejected under 35 U.S.C. 101
Claims 1-19 are rejected under 35 U.S.C. 103
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
Regarding Claim 1,
Step 2A Prong 1 Analysis: The Limitations:
determining a plurality of errors affecting application availability of the application at or above a threshold reduction rate during the time period; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
generating a first causal statement for a set of errors from the plurality of errors MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
wherein the first causal statement is generated with a hypothesis usable to test if the set of errors cause the application availability to be affected at or above the threshold reduction rate using at least an anomaly detection operation, MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
determining data for the availability data of the application that is associated with the set of errors, wherein the data comprises a set of values associated with the application availability for the set of errors; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
detecting abnormal fluctuations in the data using the anomaly detection operation; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
transforming the data for a feature space associated with the application and the set of errors based on the detected abnormal fluctuations, wherein the transformed data magnifies an effect associated with the detected abnormal fluctuations in the feature space of each value in the set of values on the application availability; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
smoothen the data in the feature space for the detected abnormal fluctuations… that allows a deviation between two or more values in the set of values that are associated with the detected abnormal fluctuations; MPEP 2106.04(a)(2); This limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation recites a mathematical concept.
analyzing the set of errors in the feature space using the hypothesis and based on the transformed and smoothened data, wherein the analyzing includes determining whether the set of errors combine to cause the application availability to be affected at or above the threshold reduction rate; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
determining, with the first causal statement, a result of the analyzing, wherein the result indicates that the set of errors of the first causal statement meet or exceed an error rate threshold; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
Verifying the first causal statement meets or exceeds the error threshold based on a set of error rates for the set of errors and a reduction in the application availability caused by the set of errors; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
utilizing the first causal statement with an error detection system, wherein the first causal statement causes the error detection system to detect the set of errors and alert to an error resolution endpoint when detected. MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Step 2A Prong Two Analysis:
Claim 1 additionally recites,
tracking, over a time period, errors in an application and availability data of the application based on error logs for the application and a first performance parameter of the application; MPEP 2106.05(g); This limitation recites additional elements that amount to no more than insignificant extra solution activity
using a causal machine learning (ML) model, MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
wherein the causal ML model is trained to identify an importance of other errors on a selected error affecting the application availability; MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
…using a baseline anomaly detection model… MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity” and “mere instructions to apply”. Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. (See MPEP 2106.05(g)). The claim is not patent eligible.
Regarding Claim 2,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 1
Step 2A Prong 2 Analysis:
Claim 2 additionally recites,
wherein the result comprises at least one direct error and at least one indirect error of the set of errors causing the application availability to be affected at or above the threshold reduction rate, MPEP 2106.05(e); This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
wherein each of the at least one direct error and the at least one indirect error have a corresponding reduction rate of the application availability when occurring in the error logs, MPEP 2106.05(e); This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
wherein the result further comprises a confidence value of the application availability being affected due to the first causal statement. MPEP 2106.05(e); This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 3,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 1
Step 2A Prong 2 Analysis:
Claim 3 additionally recites,
wherein the set of errors for the first causal statement reduces the application availability from a production level availability during a runtime of the application in a production computing environment. MPEP 2106.05(e); This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 4,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 1
Step 2A Prong 2 Analysis:
Claim 4 additionally recites,
providing one or more of the error logs and the determined availability data for the set of errors with the result. MPEP 2106.05(g); This limitation recites additional elements that amount to no more than insignificant extra solution activity
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity”. Mere insignificant extra-solution activity cannot provide an inventive concept. (See MPEP 2106.05(g)). The claim is not patent eligible.
Regarding Claim 5,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 4
Step 2A Prong 2 Analysis:
Claim 5 additionally recites,
wherein the providing includes notifying an error resolution endpoint of the first causal statement with the one or more of the error logs and the determined availability data. MPEP 2106.05(g); This limitation recites additional elements that amount to no more than insignificant extra solution activity
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity”. Mere insignificant extra-solution activity cannot provide an inventive concept. (See MPEP 2106.05(g)). The claim is not patent eligible.
Regarding Claim 6,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 1
Step 2A Prong 2 Analysis:
Claim 6 additionally recites,
wherein the result further comprises a pattern analysis of the set of errors affecting the application availability based on the analyzing, and wherein the pattern analysis indicates a causation of the set of errors from the error logs. MPEP 2106.05(e); This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 7,
Step 2A Prong 1 Analysis: The limitations:
wherein the determining data of the availability data comprises transforming the availability data to identify one or more fluctuations in the application availability caused by the set of errors using a computation associated with a service level agreement (SLO) threshold or a business rule threshold. MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
Regarding Claim 8,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 1
Step 2A Prong 2 Analysis:
Claim 8 additionally recites,
wherein the causal ML model is trained based on features associated with inputs from the error logs, application success request logs, and application total requests logs. MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Step 2A Prong 1 Analysis: The Limitations:
generate a causal statement for a plurality of errors linked to a reduction in an application performance of an application MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
wherein the causal statement identifies, for testing at least one direct error and at least one indirect error from the plurality of errors that combine to cause the reduction; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
determine performance data of the application and comprising measurements of the application performance at points in time corresponding to the plurality of errors, wherein the performance data comprises a set of values associated with the application performance for the plurality of errors; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
detecting abnormal fluctuations in the data using an anomaly detection operation; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
transforming the data for a feature space associated with the application and the set of errors based on the detected abnormal fluctuations, wherein the transformed performance data magnifies an effect associated with the detected abnormal fluctuations in the feature space of each value in the set of values on the application performance; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
smoothen the performance data in the feature space for the detected abnormal fluctuations… that allows a deviation between two or more values in the set of values that are associated with the detected abnormal fluctuations; MPEP 2106.04(a)(2); This limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation recites a mathematical concept.
analyze the causal statement in the feature space using the hypothesis and based on the transformed and smoothened performance data; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
determine a confidence value in the causal statement causing the reduction in the application performance based on analyzing the causal statement, wherein the confidence value indicates that the at least one direct error and the at least one indirect error of the causal statement meet or exceed an error rate threshold; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
verify the causal statement meets or exceeds the error threshold based on a plurality of error rates for the plurality of errors and a reduction in the application performance caused by the at least one direct error and the at least one indirect error; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept., this limitation recites a mental process.
utilize the causal statement with an error detection system, wherein the causal statement causes the error detection system to detect the at least one direct error and the at least one indirect error and alert to an error resolution endpoint when detected. MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Step 2A Prong Two Analysis:
Claim 9 additionally recites,
using a causal machine learning (ML) model, MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
…using a baseline anomaly detection model… MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
and notify an error resolution endpoint of the causal statement having the plurality of errors and the confidence value. MPEP 2106.05(g); This limitation recites additional elements that amount to no more than insignificant extra solution activity
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity” and “mere instructions to apply”. Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. (See MPEP 2106.05(g)). The claim is not patent eligible.
Regarding Claim 10,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 9
Step 2A Prong 2 Analysis:
Claim 10 additionally recites,
wherein the application performance is associated with one of at least one key performance indicator (KPI) for the application, an application availability for the application, or an application health indicator for the application. MPEP 2106.05(e); This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 11,
Step 2A Prong 1 Analysis: The limitations:
determine, prior to generating the causal statement, a feature importance of indirect errors on a direct error using the causal ML model; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
select the plurality of errors for the causal statement based on the feature importance, the causal ML model, and feature importance threshold. MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Regarding Claim 12,
Step 2A Prong 1 Analysis: The limitations:
wherein generating the causal statement comprises generating a hypothesis of the causal statement for testing using the anomaly detection operation and the performance data. MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Regarding Claim 13,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 9
Step 2A Prong 2 Analysis:
Claim 13 additionally recites,
wherein notifying the error resolution endpoint comprises providing a report of one or more error logs associated with the plurality of errors to the error resolution endpoint. MPEP 2106.05(g); This limitation recites additional elements that amount to no more than insignificant extra solution activity
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity”. Mere insignificant extra-solution activity cannot provide an inventive concept. (See MPEP 2106.05(g)). The claim is not patent eligible.
Regarding Claim 14,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 13
Step 2A Prong 2 Analysis:
Claim 14 additionally recites,
wherein the report further includes a pattern analysis of the reduction in the application performance from each indirect error in the plurality of errors that affects a direct error in the plurality of errors. MPEP 2106.05(e); This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 15,
Step 2A Prong 1 Analysis: The limitations:
wherein determining the performance data comprises transforming the performance data to identify one or more fluctuations in the application performance caused by the plurality of errors using a computation associated with a service level agreement (SLO) threshold or a business rule threshold. MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
Regarding Claim 16,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 9
Step 2A Prong 2 Analysis:
Claim 16 additionally recites,
wherein the causal ML model is trained based on features associated with inputs from error logs associated with the plurality of errors, application success request logs, and application total requests logs. MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 17,
Step 2A Prong 1 Analysis: The Limitations:
identifying a set of errors from the plurality of errors… wherein the set of errors are identified with a hypothesis usable to test if the set of errors cause a fluctuation in the performance indicator; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
determining performance data of the application in association with the set of errors based on the error logs, wherein the performance data comprises a set of value associated with the performance indicator for the set of errors; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
detecting abnormal fluctuations in the data using the anomaly detection operation; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
transforming the performance data for a feature space associated with the application and the set of errors based on the detected abnormal fluctuations, wherein the transformed data magnifies an effect associated with the detected abnormal fluctuations in the feature space of each value in the set of values on the performance indicator; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
smoothen the performance data in the feature space for the detected abnormal fluctuations… that allows a deviation between two or more values in the set of values that are associated with the detected abnormal fluctuations; MPEP 2106.04(a)(2); This limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation recites a mathematical concept.
analyzing the set of errors in the feature space using the hypothesis and based on the transformed and smoothened performance data; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
determining that the set of errors cause the fluctuation in the performance indicator to meet or exceed a threshold change; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
Verifying the set of errors meets or exceeds the error threshold based on a set of error rates for the set of errors and a reduction in the performance indicator caused by the set of errors; MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
directing an error resolution process to one or more causes associated with the set of errors, wherein the directing includes providing, in the error resolution process, a causal statement of the one or more errors and a confidence value that the set of errors cause the fluctuation, and wherein the causal statement causes the error resolution process to detect the set of errors and alert to an error resolution endpoint when detected. MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
Step 2A Prong Two Analysis:
Claim 17 additionally recites,
receiving error logs for an application that record a plurality of errors affecting a performance indicator of the application based on a first performance parameter; MPEP 2106.05(g); This limitation recites additional elements that amount to no more than insignificant extra solution activity
using a causal machine learning (ML) model, MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
…using a baseline anomaly detection model… MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity” and “mere instructions to apply”. Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. (See MPEP 2106.05(g)). The claim is not patent eligible.
Regarding Claim 18,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 17
Step 2A Prong 2 Analysis:
Claim 18 additionally recites,
wherein the performance indicator comprises a percentage of application availability that is reduced when each of the plurality of errors occurs. MPEP 2106.05(e); This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 19,
Step 2A Prong 1 Analysis: The limitations:
wherein the determining the performance data comprises transforming the performance data to identify one or more fluctuations in the performance indicator caused by each error in the set of errors using a computation associated with a service level agreement (SLO) threshold or a business rule threshold. MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
Regarding Claim 20,
Step 2A Prong 1 Analysis: The limitations:
generating, prior to the detecting, the causal statement based on the hypothesis of the causal statement, MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
wherein the hypothesis is associated with the set of errors and impacts of each error on other errors in the set of errors, and wherein the hypothesis is tested during the analyzing the set of errors. MPEP 2106.04(a)(2); This limitation is a step that covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process. Additionally, or alternatively, this limitation covers mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. Therefore, this limitation additionally or alternately recites a mathematical concept.
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.
Claims 1-9 and 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (U.S. Publication No. 2023/0040564 A1), hereinafter referred to as Wang, in view of Balla et al. (U.S. Publication No. 2024/0370328 A1), hereinafter referred to as Balla, in further view of Behl et al. (U.S. Publication No. 2024/0364724 A1).
With regards to Claim 1, Wang teaches:
A method comprising:
tracking, over a time period, errors in an application and availability data of the application based on error logs for the application and a first performance parameter of the application; ([0026]; regarding, “the production environment monitors error log data…”; [0028]; regarding “…to leverage the learned causal relationships in real-time together with application error log data to identify and localize an application error source as directed to one or more application micro-services.”; [0035]; regarding, “an error injection may be in the form of blocking a specific micro-service, slowing down operability of the micro-service…”);
determining a plurality of errors affecting application availability of the application at or above a threshold reduction rate during the time period; ([0052]; regarding, “To address the log files, and in an embodiment an abundant quantity of log data, the log files and corresponding log data collected at step (304) are subject to processing or pre-processing to filter out, e.g. remove, log data that is irrelevant to the injected error(s) (306).”);
generating a first causal statement for a set of errors from the plurality of errors using a causal machine learning (ML) model, ([0052]; regarding, “Following step (306), causal learning through intervention patterns is employed to identify directional connections between micro-services that are the subject to the log data that survived the pre-processing (308).”; [0026]; regarding, “As shown and described below, the production environment monitors error log data and leverages the learned causal model…”);
wherein the first causal statement is generated with a hypothesis usable to test if the set of errors cause the application availability to be affected at or above the threshold reduction rate using at least an anomaly detection operation, ([0073]; regarding, “Exemplary embodiments further involve… a causal knowledge graph, and… predictive analysis.” [0052]; regarding, “the causal learning includes learning a correlation score between micro-services based on an intervention pattern and a corresponding intervention matrix, and representation of a learned causal graph using transitive reduction… As shown and described in FIG. 4, the correlation score is assessed with respect to a configurable threshold.”; [0030]; regarding, “leverage the learned causal graph in the production environment to localize a detected application fault.”);
wherein the causal ML model is trained to identify an importance of other errors on a selected error affecting the application availability; ([0035]; regarding, “In an exemplary embodiment, the causal graph is an AI model, also referred to herein as a trained AI model.”; [0036]; regarding, “The causal learning effectively calculates a correspondence between a micro-service subject to an injected fault and each related micro-service to ascertain which micro-services are or were effected by the fault injection.”);
determining data for the availability data of the application that is associated with the set of errors, wherein the data comprises a set of values associated with the application availability for the set of errors; ([0035]; regarding, “For example, an error injection may be in the form of blocking a specific micro-service, slowing down operability of the micro-service, or otherwise making the micro-service unavailable to the application.”; [0042]; regarding, “The production manager (154) leverages the collected second error log data to calculate a correspondence between the micro-service that is the subject of the fault and other application micro-services”; [0053]; regarding, “The vector shown herein is directed at the fault injected micro-service, s′, across time bins, t, and documents a reaction of application micro-services to the fault injection. A plurality of the vectors is utilized to generate a corresponding intervention matrix, C.”);
transforming the data for a feature space associated with the application and the set of errors based on the detected abnormal fluctuations, wherein the transformed data magnifies an effect associated with the detected abnormal fluctuations in the feature space of each value in the set of values on the application availability; ([0053]; regarding, “The vector v(s′).sub.t represents how other micro-services in the application are effected by the blocked micro-service, s′, at time bin t. As shown in this example, entries in the vector are in bit form, 0's and 1's.”; [0058]; regarding, “The estimated ancestral edges from various fault injections are combined into a succinct representation by performing transitive reduction (314) to ensure that only a subset of true causal edges that preserve ancestry are in the representation.”; [0041]; regarding, “the error log data associated with the production manager (154) is referred to herein as second error log data… the production manager (154) responds to application errors detected during application processing and execution.”; [0049]; regarding, “API.sub.1 (222) provides functional support to an on-line task for collecting all micro-service error log data, also referred to herein as second error log data, corresponding to an application error and building an ancestral matrix based on the learned causal graph”);
determining, with the first causal statement, a result of the analyzing, wherein the result indicates that the set of errors of the first causal statement meet or exceed an error rate threshold; ([0052]; regarding, “As shown and described in FIG. 4, the correlation score is assessed with respect to a configurable threshold. The assessment from step (308) generates output in the form of a DAG comprised of a set of edges that surpassed the correlation score assessment, with each edge representing the micro-service that is the subject of the fault and an effected micro-service (310). In an exemplary embodiment, and as shown herein, the graph generated at step (310) is subject to a transitive reduction to selectively remove one or more edges and generate a casual graph (312).”)
verifying the first causal statement meets or exceeds the error threshold based on a set of error rates for the set of errors and a reduction in the application availability caused by the set of errors; ([0074]; regarding, “Aspects of identifying and verifying causal pairs are shown and described with the tools and APIs shown in FIGS. 1 and 2”; [0057]; regarding, “As shown herein, the correlation score between micro-services s′ and s is learned and assessed against a threshold value for a correlation score τ, which in an embodiment is a tunable threshold.”; [0053]; regarding, “an entry of 0 in the vector indicates that the micro-service is unaffected by the blocked micro-service, and an entry of 1 in the vector indicates that the micro-service is effected, e.g. experiencing an error.”)
Wang does not explicitly disclose while Balla teaches:
detecting abnormal fluctuations in the data using the anomaly detection operation; ([0060]; regarding, “various embodiments enable optimized methods and systems for identification of at least one anomaly in data logs and automatically triggering the alerts for the at least one anomaly to at least one entity”);
analyzing the set of errors in the feature space using the hypothesis and based on the transformed and smoothened data, wherein the analyzing includes determining whether the set of errors combine to cause the application availability to be affected at or above the threshold reduction rate; ([0087]; regarding, “identifying… a set of new events associated with the at least one anomaly based on the comparison of the set of data logs with each of the plurality of previously stored set of data logs.” [0092]; regarding, “The results…to identify deviations in the data logs and to identify a pattern associated with at least one anomaly… is displayed to a user… depict a trend associated with the selected error…“In the event the selected error exceeds a tolerable limit or threshold, thereby reducing the availability of the application to unsatisfactory levels, the alert is triggered to the user…”; [0089]; regarding, “the pattern of occurrence of the at least one anomaly in data logs is identified and displayed to the at least one entity in a form of a visual representation. In another example, the visual representation may be in the form of a tabular or graphical representation of the occurrence of the at least one anomaly.”);
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Wang with the teachings of Balla. Doing so aids in the prompt identification of data anomalies and the triggering of alerts to a production support team (Balla, [0042]).
utilizing the first causal statement with an error detection system, wherein the first causal statement causes the error detection system to detect the set of errors and alert to an error resolution endpoint when detected. ([0039]; regarding, “… the system analyzes the set of new events to identify a pattern associated with the at least one anomaly. In an example, the analysis of the set of new events to identify the pattern associated with the at least one anomaly is performed using a trained model… the system displays the identified pattern associated with the at least one anomaly to at least one entity… the at least one entity may correspond to a user, production team, support team, developer, error resolution platform and the like.” [0065]; regarding, “ADA devices that efficiently implement a method for the identification of the at least one anomaly in data logs and automatically triggering the alerts for the at least one anomaly to at least one entity for resolution of the identified at least one anomaly in the data logs.”);
Wang in view of Balla fails to explicitly disclose but Behl teaches:
smoothen the data in the feature space for the detected abnormal fluctuations using a baseline anomaly detection model that allows a deviation between two or more values in the set of values that are associated with the detected abnormal fluctuations; ([0082]; regarding, “The multivariate Gaussian (Normal) distribution model may be used as one of the models 455 of the ensemble 450. The multivariate Gaussian distribution model can take into account how data of the metrics 460 change with other data of the metrics 460. The model can include a co-variance matrix based on the metrics 460. This model can account for the covariance between all the data of the metrics 460 by utilizing the power of the covariance matrix. This model can form a normal distribution graph of the metrics 460.”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Wang and Balla with the teaching of Behl. Doing so can reduce computational resources spent on malfunctioning microservices by more quickly targeting and resolving failures (Behl, [0109]).
With regards to Claim 2, Wang in view of Balla in further view of Behl teaches the method of Claim 1 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein the result comprises at least one direct error and at least one indirect error of the set of errors causing the application availability to be affected at or above the threshold reduction rate, (Wang, [0051]; regarding, “With respect to an application and its embedded micro-services, and more specifically with respect to the micro-service(s) error injection, the log data identifies a direct or indirect effect of the injected error on other application micro-services…”);
wherein each of the at least one direct error and the at least one indirect error have a corresponding reduction rate of the application availability when occurring in the error logs, (Wang, [0051]; regarding, “the log data identifies a direct or indirect effect of the injected error on other application micro-services… the log data is a log file… associated with the functionality of… micro-services…”);
and wherein the result further comprises a confidence value of the application availability being affected due to the first causal statement. (Wang, [0052]; regarding, “The correlation score assessment at step (308) identifies the strength of a correspondence between the micro-service that is the subject of the fault, s′, and a micro-service(s) identified from the subset of the log data… The assessment from step (308) generates output in the form of a DAG comprised of a set of edges that surpassed the correlation score assessment, with each edge representing the micro-service that is the subject of the fault and an effected micro-service (310).”).
With regards to Claim 3, Wang in view of Balla in further view of Behl teaches the method of Claim 1 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein the set of errors for the first causal statement reduces the application availability from a production level availability during a runtime of the application in a production computing environment. (Wang, [0026]; regarding, “As shown and described below, the production environment monitors error log data and leverages the learned causal model…”; [0055]; regarding, “As shown and described in FIG. 1, the correlation assessment occurs in the production environment and is managed by the production manager (154).”).
With regards to Claim 4, Wang in view of Balla in further view of Behl teaches the method of Claim 1 as referenced above. Wang in view of Balla in further view of Behl further teaches:
providing one or more of the error logs and the determined availability data for the set of errors with the result. (Wang, [0026]; regarding, “the production environment monitors error log data…”; [0028]; regarding “…to leverage the learned causal relationships in real-time together with application error log data to identify and localize an application error source as directed to one or more application micro-services.”; [0035]; regarding, “an error injection may be in the form of blocking a specific micro-service, slowing down operability of the micro-service…”);
With regards to Claim 5, Wang in view of Balla in further view of Behl teaches the method of Claim 4 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein the providing includes notifying an error resolution endpoint of the first causal statement with the one or more of the error logs and the determined availability data. (Wang, [0044]; “The production manager (154), which is in communication with the knowledge base (160), uses the learned causal graph”).
With regards to Claim 6, Wang in view of Balla in further view of Behl teaches the method of Claim 1 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein the result further comprises a pattern analysis of the set of errors affecting the application availability based on the analyzing, and wherein the pattern analysis indicates a causation of the set of errors from the error logs. (Wang, [0052]; regarding, “At step (308), the causal learning includes learning a correlation score between micro-services based on an intervention pattern and a corresponding intervention matrix, and representation of a learned causal graph using transitive reduction.”).
With regards to Claim 7, Wang in view of Balla in further view of Behl teaches the method of Claim 1 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein the determining data of the availability data comprises transforming the availability data to identify one or more fluctuations in the application availability caused by the set of errors using a computation associated with a service level agreement (SLO) threshold or a business rule threshold. (Wang, [0035]; “an error injection may be in the form of blocking a specific micro-service, slowing down operability of the micro-service, or otherwise making the micro-service unavailable to the application… subjecting the log data to pre-processing to identify the error logs corresponding to or associated with the injected error(s).”; [102]; regarding, “Service Layer Agreement (SLA) planning and fulfillment provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.”).
With regards to Claim 8, Wang in view of Balla in further view of Behl teaches the method of Claim 1 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein the causal ML model is trained based on features associated with inputs from the error logs, application success request logs, and application total requests logs.
(Wang, [0035]; regarding, “In an exemplary embodiment, the causal graph is an AI model, also referred to herein as a trained AI model.”; [0052]; regarding, “For example, log data may include a message, e.g. error message, that a particular micro-service may not be able to process a request in response to a fault injected into a different application micro-service.”).
With regards to Claim 9, Wang in view of Balla in further view of Behl teaches:
A system comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to execute instructions to cause the system to:
generate a causal statement for a plurality of errors linked to a reduction in an application performance of an application using a causal machine learning (ML) model, (Wang [0035]; regarding, “Error injection is directed at creating a problem associated with functionality of application micro-services.”; [0052]; regarding, “In an embodiment, causal learning is a form of machine learning that employs causal reasoning.”; [0036]; regarding, “Accordingly, the staging manager generates a causal graph of application micro-services from error log data.”).
wherein the causal statement identifies, for testing, at least one direct error and at least one indirect error from the plurality of errors that combine to cause the reduction; (Wang, [0051]; regarding, “With respect to an application and its embedded micro-services, and more specifically with respect to the micro-service(s) error injection, the log data identifies a direct or indirect effect of the injected error on other application micro-services…”; [0026]; regarding, “interventional causal learning is applied to one or more cloud applications in a pre-deployment environment, also referred to herein as a staging environment that is commonly used for software testing… the production environment monitors error log data and leverages the learned causal model… ”);
determine performance data of the application and comprising measurements of the application performance at points in time corresponding to the plurality of errors, (Balla, [0039]; regarding, “The pattern identification is used to identify trend of occurrence of the at least one anomaly in the data that may be associated with known set of errors provided by application developers, sudden change in a data traffic on cloud-based servers, sudden increase in error messages received from the end-users, and the like.”);
wherein the performance data comprises a set of values associated with the application performance for the plurality of errors; (Balla, [0039]; regarding, “The pattern identification is used to identify trend of occurrence of the at least one anomaly in the data that may be associated with known set of errors… the system displays the identified pattern associated with the at least one anomaly to at least one entity.”; [0092]; regarding, “In the event the selected error exceeds a tolerable limit or threshold, thereby reducing the availability of the application to unsatisfactory levels, the alert is triggered to the user by the ADA device 608.”);
detecting abnormal fluctuations in the data using the anomaly detection operation; (Balla, [0060]; regarding, “various embodiments enable optimized methods and systems for identification of at least one anomaly in data logs and automatically triggering the alerts for the at least one anomaly to at least one entity”);
transform the performance data for a feature space associated with the application and the plurality of errors based on the detected abnormal fluctuations, wherein the transformed performance data magnifies an effect associated with the detected abnormal fluctuations in the feature space of each value in the set of values on the application performance; (Wang, [0053]; regarding, “The vector v(s′).sub.t represents how other micro-services in the application are effected by the blocked micro-service, s′, at time bin t. As shown in this example, entries in the vector are in bit form, 0's and 1's.”; [0058]; regarding, “The estimated ancestral edges from various fault injections are combined into a succinct representation by performing transitive reduction (314) to ensure that only a subset of true causal edges that preserve ancestry are in the representation.”; [0041]; regarding, “the error log data associated with the production manager (154) is referred to herein as second error log data… the production manager (154) responds to application errors detected during application processing and execution.”; [0049]; regarding, “API.sub.1 (222) provides functional support to an on-line task for collecting all micro-service error log data, also referred to herein as second error log data, corresponding to an application error and building an ancestral matrix based on the learned causal graph”);
smoothen the performance data in the feature space for the detected abnormal fluctuations using a baseline anomaly detection model that allows a deviation between two or more values in the set of values that are associated with the detected abnormal fluctuations; (Behl, [0082]; regarding, “The multivariate Gaussian (Normal) distribution model may be used as one of the models 455 of the ensemble 450. The multivariate Gaussian distribution model can take into account how data of the metrics 460 change with other data of the metrics 460. The model can include a co-variance matrix based on the metrics 460. This model can account for the covariance between all the data of the metrics 460 by utilizing the power of the covariance matrix. This model can form a normal distribution graph of the metrics 460.”);
analyze the causal statement in the feature space using the hypothesis and based on the transformed and smoothened performance data; (Balla, [0041]; “The present disclosure uses a trained model to automatically adjust alerting thresholds based on various parameters such as traffic patterns at the cloud-based servers associated with the organization. The identified occurrence of the at least one anomaly is associated with the identified set of errors provided by the application developer, an unusual change in data traffic at the cloud-based servers, an unusual increase in errors reported by end users, and the like.”; [0092]; regarding, “The results…to identify deviations in the data logs and to identify a pattern associated with at least one anomaly… is displayed to a user… depict a trend associated with the selected error…“In the event the selected error exceeds a tolerable limit or threshold, thereby reducing the availability of the application to unsatisfactory levels, the alert is triggered to the user…”; [0089]; regarding, “the pattern of occurrence of the at least one anomaly in data logs is identified and displayed to the at least one entity in a form of a visual representation. In another example, the visual representation may be in the form of a tabular or graphical representation of the occurrence of the at least one anomaly.”);
determine a confidence value in the causal statement causing the reduction in the application performance based on analyzing the causal statement, wherein the confidence value indicates that the at least one direct error and the at least one indirect error of the causal statement meet or exceed an error rate threshold; (Wang, [0052]; “The correlation score assessment at step (308) identifies the strength of a correspondence between the micro-service that is the subject of the fault, s′, and a micro-service(s) identified from the subset of the log data… The assessment from step (308) generates output in the form of a DAG comprised of a set of edges that surpassed the correlation score assessment”; [0053]; regarding, “a set of micro-services, S, related to the fault injected micro-service, s′, that are the subject of the processed error logs and emitted one or more errors, are evaluated based on a correspondence assessment to selectively populate and form the generated causal graph.”; [0057]; regarding, “The intervention matrix is a compilation of intervention pattern vectors v(s′). As shown herein, the correlation score between micro-services s′ and s is learned and assessed against a threshold value for a correlation score τ, which in an embodiment is a tunable threshold.”);
verify the causal statement meets or exceeds the error threshold based on a plurality of error rates for the plurality of errors and a reduction in the application performance caused by the at least one direct error and the at least one indirect error; (Wang, [0074]; regarding, “Aspects of identifying and verifying causal pairs are shown and described with the tools and APIs shown in FIGS. 1 and 2”; [0057]; regarding, “As shown herein, the correlation score between micro-services s′ and s is learned and assessed against a threshold value for a correlation score τ, which in an embodiment is a tunable threshold.”; [0053]; regarding, “an entry of 0 in the vector indicates that the micro-service is unaffected by the blocked micro-service, and an entry of 1 in the vector indicates that the micro-service is effected, e.g. experiencing an error… a set of micro-services, S, related to the fault injected micro-service, s′, that are the subject of the processed error logs and emitted one or more errors, are evaluated based on a correspondence assessment to selectively populate and form the generated causal graph.”);
and notify an error resolution endpoint of the causal statement having the plurality of errors and the confidence value. (Wang, [0044]; “The production manager (154), which is in communication with the knowledge base (160), uses the learned causal graph”).
utilize a causal statement with an error detection system, wherein the causal statement causes the error detection system to detect the at least one direct error and the at least one indirect error and alert to an error resolution endpoint when detected. (Balla, [0041]; regarding, “the system is configured to identify events associated with data anomaly and automatically trigger the alerts to the production support team.”; [0092]; regarding, “As illustrated in FIG. 6, Comparator 1, Comparator 2, and Comparator 3 correspond to at least one processor configured to compare the set of data logs for identification of at least one from among at least one data anomaly associated with a possible set of errors”; [0039]; regarding, “… the system analyzes the set of new events to identify a pattern associated with the at least one anomaly. In an example, the analysis of the set of new events to identify the pattern associated with the at least one anomaly is performed using a trained model… the system displays the identified pattern associated with the at least one anomaly to at least one entity… the at least one entity may correspond to a user, production team, support team, developer, error resolution platform and the like.” [0065]; regarding, “ADA devices that efficiently implement a method for the identification of the at least one anomaly in data logs and automatically triggering the alerts for the at least one anomaly to at least one entity for resolution of the identified at least one anomaly in the data logs.”)
With regards to Claim 11, Wang in view of Balla in further view of Behl teaches the system of Claim 9 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein executing the instructions further cause the system to:
determine, prior to generating the causal statement, a feature importance of indirect errors on a direct error using the causal ML model; (Wang, [0051]; regarding, “With respect to an application and its embedded micro-services, and more specifically with respect to the micro-service(s) error injection, the log data identifies a direct or indirect effect of the injected error on other application micro-services…”);
and select the plurality of errors for the causal statement based on the feature importance, the causal ML model, and feature importance threshold. (Wang, [0052]; regarding, “As shown and described in FIG. 4, the correlation score is assessed with respect to a configurable threshold. The assessment from step (308) generates output in the form of a DAG comprised of a set of edges that surpassed the correlation score assessment, with each edge representing the micro-service that is the subject of the fault and an effected micro-service (310)… Transitive reduction is an edge-removing operation on directed graphs that preserves some important properties and structure of the graph. The transitive reduction is used to preserve important structural properties of the learned causal graph and build the ancestry of learned causal graph for localizing faulty services.”).
With regards to Claim 12, Wang in view of Balla in further view of Behl teaches the system of Claim 11 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein generating the causal statement comprises generating a hypothesis of the causal statement for testing using the anomaly detection operation and the performance data. (Behl, [0035]; regarding, “In operation 115, the server receives a set of metrics from the one or more microservices. The server can receive the set of metrics by retrieving them from a database 110, from the microservices individually or collectively, or from a network. Likewise, the database or microservices can transmit the metrics to the server. The metrics can include information pertaining to the operation and functionality of a microservice.”; [0038]; regarding, “Using the metrics, an ensemble 130 of detection algorithms may be created. The detection ensemble can include a multitude of different models (e.g., models 135A-C). The ensemble 130 can enable an evaluation of the metrics by each model to be amalgamated to derive a final estimate, and a prediction, among others… The models can include models such as DBSCAN, Isolation Forest, or multivariate Gaussian distribution and can perform one or more functionalities on the set of metrics to prepare them for classification… Each model can evaluate the metrics to determine if a microservice is suffering from an anomaly.”)
With regards to Claim 13, Wang in view of Balla in further view of Behl teaches the system of Claim 9 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein notifying the error resolution endpoint comprises providing a report of one or more error logs associated with the plurality of errors to the error resolution endpoint. (Wang, [0044]; “The production manager (154), which is in communication with the knowledge base (160), uses the learned causal graph”).
With regards to Claim 14, Wang in view of Balla in further view of Behl teaches the system of Claim 13 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein the report further includes a pattern analysis of the reduction in the application performance from each indirect error in the plurality of errors that affects a direct error in the plurality of errors. (Wang, [0052]; regarding, “At step (308), the causal learning includes learning a correlation score between micro-services based on an intervention pattern and a corresponding intervention matrix, and representation of a learned causal graph using transitive reduction.”).
With regards to Claim 15, Wang in view of Balla in further view of Behl teaches the system of Claim 9 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein determining the performance data comprises transforming the performance data to identify one or more fluctuations in the application performance caused by the plurality of errors using a computation associated with a service level agreement (SLO) threshold or a business rule threshold. (Wang, [0035]; “an error injection may be in the form of blocking a specific micro-service, slowing down operability of the micro-service, or otherwise making the micro-service unavailable to the application… subjecting the log data to pre-processing to identify the error logs corresponding to or associated with the injected error(s).”; [102]; regarding, “Service Layer Agreement (SLA) planning and fulfillment provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.”).
With regards to Claim 16, Wang in view of Balla in further view of Behl teaches the system of Claim 9 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein the causal ML model is trained based on features associated with inputs from error logs associated with the plurality of errors, application success request logs, and application total requests logs. (Wang, [0035]; regarding, “In an exemplary embodiment, the causal graph is an AI model, also referred to herein as a trained AI model.”; [0052]; regarding, “For example, log data may include a message, e.g. error message, that a particular micro-service may not be able to process a request in response to a fault injected into a different application micro-service.”).
With regards to Claim 17, Wang in view of Balla in further view of Behl teaches:
A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:
receiving error logs for an application that record a plurality of errors affecting a performance indicator of the application based on a first performance parameter; (Wang, [0026]; regarding, “the production environment monitors error log data…”; [0028]; regarding “…to leverage the learned causal relationships in real-time together with application error log data to identify and localize an application error source as directed to one or more application micro-services.”; [0035]; regarding, “an error injection may be in the form of blocking a specific micro-service, slowing down operability of the micro-service…”);
identifying a set of errors from the plurality of errors using a causal machine learning (ML) model, (Wang, [0052]; regarding, “In an embodiment, causal learning is a form of machine learning that employs causal reasoning.”; [0036]; regarding, “Accordingly, the staging manager generates a causal graph of application micro-services from error log data.”);
wherein the set of errors are identified with a hypothesis usable to test if the set of errors cause a fluctuation in the performance indicator; (Wang, [0073]; regarding, “Exemplary embodiments further involve… a causal knowledge graph, and… predictive analysis.”; [0035]; regarding, “Error injection is directed at creating a problem associated with functionality of application micro-services. For example, an error injection may be in the form of blocking a specific micro-service, slowing down operability of the micro-service, or otherwise making the micro-service unavailable to the application.”)
determining performance data of the application in association with the set of errors based on the error logs, wherein the performance data comprises a set of value associated with the performance indicator for the set of errors; (Balla, [0039]; regarding, “The pattern identification is used to identify trend of occurrence of the at least one anomaly in the data that may be associated with known set of errors provided by application developers, sudden change in a data traffic on cloud-based servers, sudden increase in error messages received from the end-users, and the like.”; [0087]; regarding, “identifying, by the at least one processor 104, a set of new events associated with the at least one anomaly based on the comparison of the set of data logs…”; [0092]; regarding, “In the event the selected error exceeds a tolerable limit or threshold, thereby reducing the availability of the application to unsatisfactory levels, the alert is triggered to the user by the ADA device 608.”);
detecting abnormal fluctuations in the data using the anomaly detection operation; (Balla, [0060]; regarding, “various embodiments enable optimized methods and systems for identification of at least one anomaly in data logs and automatically triggering the alerts for the at least one anomaly to at least one entity”);
transform the performance data for a feature space associated with the application and the set of errors based on the detected abnormal fluctuations, wherein the transformed performance data magnifies an effect associated with the detected abnormal fluctuations in the feature space of each value in the set of values on the performance indicator; (Wang, [0053]; regarding, “The vector v(s′).sub.t represents how other micro-services in the application are effected by the blocked micro-service, s′, at time bin t. As shown in this example, entries in the vector are in bit form, 0's and 1's.”; [0058]; regarding, “The estimated ancestral edges from various fault injections are combined into a succinct representation by performing transitive reduction (314) to ensure that only a subset of true causal edges that preserve ancestry are in the representation.”; [0041]; regarding, “the error log data associated with the production manager (154) is referred to herein as second error log data… the production manager (154) responds to application errors detected during application processing and execution.”; [0049]; regarding, “API.sub.1 (222) provides functional support to an on-line task for collecting all micro-service error log data, also referred to herein as second error log data, corresponding to an application error and building an ancestral matrix based on the learned causal graph”);
smoothen the performance data in the feature space for the detected abnormal fluctuations using a baseline anomaly detection model that allows a deviation between two or more values in the set of values that are associated with the detected abnormal fluctuations; (Behl, [0082]; regarding, “The multivariate Gaussian (Normal) distribution model may be used as one of the models 455 of the ensemble 450. The multivariate Gaussian distribution model can take into account how data of the metrics 460 change with other data of the metrics 460. The model can include a co-variance matrix based on the metrics 460. This model can account for the covariance between all the data of the metrics 460 by utilizing the power of the covariance matrix. This model can form a normal distribution graph of the metrics 460.”).;
analyzing the set of errors in the feature space using the hypothesis and based on the transformed and smoothened performance data, (Balla, [0087]; regarding, “identifying… a set of new events associated with the at least one anomaly based on the comparison of the set of data logs with each of the plurality of previously stored set of data logs.” [0092]; regarding, “The results…to identify deviations in the data logs and to identify a pattern associated with at least one anomaly… is displayed to a user… depict a trend associated with the selected error…”; [0089]; regarding, “the pattern of occurrence of the at least one anomaly in data logs is identified and displayed to the at least one entity in a form of a visual representation. In another example, the visual representation may be in the form of a tabular or graphical representation of the occurrence of the at least one anomaly.”);
determining that the set of errors cause the fluctuation in the performance indicator to meet or exceed a threshold change; (Balla, [0092]; regarding, “compare the set of data logs for identification of at least one from among at least one data anomaly associated with a possible set of errors provided by an application developer, sudden change in the data traffic on the cloud-based servers, sudden increase in error messages received from the end-users, and the like.”; “In the event the selected error exceeds a tolerable limit or threshold, thereby reducing the availability of the application to unsatisfactory levels, the alert is triggered to the user by the ADA device 608.”);
verifying the set of errors meets or exceeds the error threshold based on a set of error rates for the set of errors and a reduction in the performance indicator caused by the set of errors; (Wang, [0074]; regarding, “Aspects of identifying and verifying causal pairs are shown and described with the tools and APIs shown in FIGS. 1 and 2”; [0057]; regarding, “As shown herein, the correlation score between micro-services s′ and s is learned and assessed against a threshold value for a correlation score τ, which in an embodiment is a tunable threshold.”; [0053]; regarding, “an entry of 0 in the vector indicates that the micro-service is unaffected by the blocked micro-service, and an entry of 1 in the vector indicates that the micro-service is effected, e.g. experiencing an error.”)
and directing an error resolution process to one or more causes associated with the set of errors, wherein the directing includes providing, in the error resolution process, a causal statement of the one or more errors and a confidence value that the set of errors cause the fluctuation, (Wang, [0052]; regarding, “The staging manager (152) is configured to selectively inject one or more errors into application micro-services, collect corresponding application log data, subject the error log data to a filter or filtering process to identify log data corresponding to the injected one or more errors, and leverage the error log data to generate a causal graph”; [0052]; regarding, “At step (308), the causal learning includes learning a correlation score between micro-services based on an intervention pattern and a corresponding intervention matrix, and representation of a learned causal graph using transitive reduction. The correlation score assessment at step (308) identifies the strength of a correspondence between the micro-service that is the subject of the fault, s′, and a micro-service(s) identified from the subset of the log data.”).
and wherein the causal statement causes the error resolution process to detect the set of errors and alert to an error resolution endpoint when detected. (Balla, [0041]; regarding, “the system is configured to identify events associated with data anomaly and automatically trigger the alerts to the production support team.”; [0092]; regarding, “As illustrated in FIG. 6, Comparator 1, Comparator 2, and Comparator 3 correspond to at least one processor configured to compare the set of data logs for identification of at least one from among at least one data anomaly associated with a possible set of errors”; [0039]; regarding, “… the system analyzes the set of new events to identify a pattern associated with the at least one anomaly. In an example, the analysis of the set of new events to identify the pattern associated with the at least one anomaly is performed using a trained model… the system displays the identified pattern associated with the at least one anomaly to at least one entity… the at least one entity may correspond to a user, production team, support team, developer, error resolution platform and the like.” [0065]; regarding, “ADA devices that efficiently implement a method for the identification of the at least one anomaly in data logs and automatically triggering the alerts for the at least one anomaly to at least one entity for resolution of the identified at least one anomaly in the data logs.”).
With regards to Claim 18, Wang in view of Balla in further view of Behl teaches the medium of Claim 17 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein the performance indicator comprises a percentage of application availability that is reduced when each of the plurality of errors occurs. (Balla, [0093]; regarding, “The present disclosure further aids the production support team by triggering the alerts in case the error affects the availability of application A. For instance, if the percentage availability of application A is targeted at 99%. The trained model enables the identification of events where the “error123” brings the availability of application A below the set target and then the trained model triggers an alert to the production support team.”);
With regards to Claim 19, Wang in view of Balla in further view of Behl teaches the medium of Claim 17 as referenced above. Wang in view of Balla in further view of Behl further teaches:
wherein the determining the performance data comprises transforming the performance data to identify one or more fluctuations in the performance indicator caused by each error in the set of errors using a computation associated with a service level agreement (SLO) threshold or a business rule threshold. (Wang, [0035]; “an error injection may be in the form of blocking a specific micro-service, slowing down operability of the micro-service, or otherwise making the micro-service unavailable to the application… subjecting the log data to pre-processing to identify the error logs corresponding to or associated with the injected error(s).”; [102]; regarding, “Service Layer Agreement (SLA) planning and fulfillment provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.”).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (U.S. Publication No. 2023/0040564 A1), hereinafter referred to as Wang, in view of Balla et al. (U.S. Publication No. 2024/0370328 A1), hereinafter referred to as Balla, in further view of Behl et al. (U.S. Publication No. 2024/0364724 A1), in further view of Nowak et al. (U.S. Publication No. US 2025/0094795 A1), hereinafter referred to as Nowak.
With regards to Claim 10, Wang in view of Balla in further view of Behl teaches the system of Claim 9 as referenced above. Wang in view of Balla in further view Behl fails to explicitly disclose but Nowak teaches:
wherein the application performance is associated with one of at least one key performance indicator (KPI) for the application, an application availability for the application, or an application health indicator for the application. ([0072]; the output data may comprise a system health score based on a combination of KPI indicators including application uptime, application downtime, an amount of time an application takes to process a transaction, network throughput, application error rates, application login approval rates, application login denial rates, and/or application resource usage (e.g., memory usage, network bandwidth usage, and/or processor usage).”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Wang, Balla, and Behl with the teaching of Nowak. Doing so may serve to enhance the security and integrity of applications (Nowak, [0033]).
Response to Arguments
Applicant’s arguments filed, 01/26/2026, have been fully considered but they are not persuasive.
With respect to the 101 rejections of claims 1-20 are addressed in the 35 U.S.C 101 rejections above. The amended claims do not integrate the judicial exception into a practical application or amount to an inventive concept. Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept.
Applicant’s arguments with respect to the previous rejection under 35 U.S.C. 103 on
independent Claim 1, and similarly Claims 9 and 17, have been considered and a new grounds
of rejection has been provided addressing the newly claimed matter.
Newly cited reference Bejl teaches smoothen the data in the feature space for the detected abnormal fluctuations using a baseline anomaly detection model that allows a deviation between two or more values in the set of values that are associated with the detected abnormal fluctuations.
Conclusion
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/M.D.G./Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113