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 .
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 21-26,29-34,37-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes – concepts performed in the human mind.
Regarding claim 21, with the exception of the recitation of the limitation ‘A computer program product comprising instructions stored on a non-transitory computer-readable storage medium…’, the claim recites mental processes that can be performed in the human mind.
The limitation ‘detect a temporal pattern of component states of the plurality of components, based on the component states; and predict a future application state of the at least one application, based on the temporal pattern of the component states; identify at least one component of the plurality of components and at least one corresponding anomaly for which resolution of the at least one corresponding anomaly would prevent the future application state from occurring’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional element ‘A computer program product comprising instructions stored on a non-transitory computer-readable storage medium…; train at least one unsupervised machine learning algorithm to construct a corresponding anomaly detection model for each component of the plurality of components; process the performance metrics for each component, using its corresponding anomaly detection model, including processing, at each component of the plurality of components, an output of a preceding anomaly detection model of a preceding component within the service model, to thereby determine components states of the plurality of components’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘receive a data stream of performance metrics characterizing a technology landscape, the technology landscape including at least one application provided by a plurality of components, the at least one application being associated with a service model that provides causal relationships between the plurality of components and the at least one application; modify the at least one component of the plurality of components to resolve the at least one corresponding anomaly and thereby prevent the future application state from occurring’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), in this case mere data gathering.
Regarding claim 22, the limitation ‘receive the data stream of performance metrics including a set of Known Performance Indicators (KPIs) characterizing the plurality of components and the at least one application’ is directed to adding insignificant solution activity to the judicial exception (MPEP 2106.05(g)), in this case data gathering; ‘reduce the set of KPIs by applying a Principal Component Analysis (PCA) to the set of KPIs and thereby obtain a reduced set of KPIs’ is directed to mathematical concepts; and ‘determine the component states and an application state of the at least one application at corresponding timestamps, based on the reduced set of KPIs’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment.
Regarding claim 23, the limitation ‘receive the data stream of performance metrics including reports of user interactions with the at least one application’ is directed to adding insignificant solution activity to the judicial exception (MPEP 2106.05(g)), in this case data gathering; and ‘determine, based on the reports, the component states and an application state of the at least one application at a corresponding timestamp of each report of the reports’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment.
Regarding claim 24, the limitation ‘determine the component states and application states of the at least one application at corresponding timestamps, using each corresponding anomaly detection model’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 25, the limitation ‘apply each component state of the component states, and each application state of the application states, as a label of each corresponding timestamp, to obtain labelled training data’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment; ‘train a supervised machine learning algorithm using the labelled training data, to obtain at least one state prediction model; and predict the future application state, using the at least one state prediction model to classify the temporal pattern of the component states’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 26, the limitation ‘ classify the component states and application states with respect to anomaly thresholds, using each corresponding anomaly detection model’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)); and ‘generate an alert when one or more of the component states or the application states crosses one of the anomaly thresholds’ is directed to adding insignificant solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 29, the limitation ‘predict the future application state of the at least one application, including predicting a bounded time window during which the future application state will occur’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment.
Regarding claim 30, the limitation ‘detect a temporal pattern of application states of the at least one application, based on the performance metrics; and predict the future application state of the at least one application, based on the temporal pattern of application states’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment.
Regarding claim 31, the claim recites mental processes that can be performed in the human mind.
The limitations ‘detecting a temporal pattern of component states of the plurality of components, based on the component states; and predicting a future application state of the at least one application, based on the temporal pattern of the component states; identifying at least one component of the plurality of components and at least one corresponding anomaly for which resolution of the at least one corresponding anomaly would prevent the future application state from occurring’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional element ‘A computer program product comprising instructions stored on a non-transitory computer-readable storage medium…; train at least one unsupervised machine learning algorithm to construct a corresponding anomaly detection model for each component of the plurality of components; process the performance metrics for each component, using its corresponding anomaly detection model, including processing, at each component of the plurality of components, an output of a preceding anomaly detection model of a preceding component within the service model, to thereby determine components states of the plurality of components’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘receiving a data stream of performance metrics characterizing a technology landscape, the technology landscape including at least one application provided by a plurality of components, the at least one application being associated with a service model that provides causal relationships between the plurality of components and the at least one application; modifying at least one component of the plurality of components to resolve the at least one corresponding anomaly and thereby prevent the future application state from occurring’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), in this case mere data gathering.
Regarding claim 32, the limitation ‘determining the component states and application states of the at least one application at corresponding timestamps, using each corresponding anomaly detection model’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 33, the limitation ‘applying each component state of the component states, and each application state of the application states, as a label of each corresponding timestamp, to obtain labelled training data’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment; ‘training a supervised machine learning algorithm using the labelled training data, to obtain at least one state prediction model; and predicting the future application state, using the at least one state prediction model to classify the temporal pattern of the component states’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 34, the limitation ‘ classifying the component states and application states with respect to anomaly thresholds, using each corresponding anomaly detection model’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)); and ‘generating an alert when one or more of the component states or the application states crosses one of the anomaly thresholds’ is directed to adding insignificant solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 37, the limitation ‘predicting the future application state of the at least one application, including predicting a bounded time window during which the future application state will occur’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment.
Regarding claim 38, with the exception of the recitation of the limitation ‘at least one memory including instructions; and at least one processor that is operation coupled to the at least one memory…’, the claim recites mental processes that can be performed in the human mind.
The limitation ‘detect a temporal pattern of component states of the plurality of components, based on the component states; and predict a future application state of the at least one application, based on the temporal pattern of the component states; identify at least one component of the plurality of components and at least one corresponding anomaly for which resolution of the at least one corresponding anomaly would prevent the future application state from occurring’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional element ‘at least one memory including instructions; and at least one processor that is operation coupled to the at least one memory; train at least one unsupervised machine learning algorithm to construct a corresponding anomaly detection model for each component of the plurality of components; process the performance metrics for each component, using its corresponding anomaly detection model, including processing, at each component of the plurality of components, an output of a preceding anomaly detection model of a preceding component within the service model, to thereby determine components states of the plurality of components’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘receive a data stream of performance metrics characterizing a technology landscape, the technology landscape including at least one application provided by a plurality of components, the at least one application being associated with a service model that provides causal relationships between the plurality of components and the at least one application; modify at least one component of the plurality of components to resolve the at least one corresponding anomaly and thereby prevent the future application state from occurring’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), in this case mere data gathering.
Regarding claim 39, ‘determine the component states and application states of the at least one application at corresponding timestamps, using each corresponding anomaly detection model’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 40, the limitation the limitation ‘apply each component state of the component states, and each application state of the application states, as a label of each corresponding timestamp, to obtain labelled training data’ is a mental process that can be performed by a human based on observation, evaluation, and/or judgment; ‘train a supervised machine learning algorithm using the labelled training data, to obtain at least one state prediction model; and predict the future application state, using the at least one state prediction model to classify the temporal pattern of the component states’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Response to Arguments
Applicant’s arguments and amendments have been fully considered. Applicant argues on page 9, “As also discussed in Applicant's Reply of March 31, 2025, the pending claims are eligible under 35 U.S.C. 101, because the claims do not constitute a mental process under Step 2A, Prong 1, or, alternatively, because the claims incorporate any alleged mental process into a practical application under Step 2A, Prong 2.” The Examiner would like to point out that certain limitations are mental processes and the limitations are not integrated into a practical application. The arguments directed to using a computer as a tool is directed having a computer implement an abstract idea. Therefore, the limitations are not directed to limitations being performed in the human mind.
Concerning Applicant’s arguments on pages 9-10 of Ex parte Bostic et al., the limitations of Bostic pertained to determining various information pertaining to webpages. The limitations of the present application deemed able to be performed in the human mind are based on detecting, predicting, and identifying, which is still viewed to be able to be performed by in the human mind.
Concerning Applicant’s arguments on the limitations improves technology or a technical field, the disclosed limitations are directed to using generic computer components to implement an abstract idea. There is no evidence given in the specification that the disclosed limitations are an improvement, despite Applicant disclosing perceived improvements. The limitations are described in a very generic manner. For instance, there is specificity as to how the ‘training’ is performed. The ‘modifying’ is insignificant extra-solution activity and disclosed in a very generic manner.
Concerning Applicant’s arguments on pages 11-12 of Ex parte Desjardins, the ‘training’ aspects are described in detail as opposed to the ‘training’ aspects of the present limitations. There are no additional elements to overcome the 101 abstract idea rejection. Those limitations identified as additional elements are rejected as indicated in the above rejection.
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/Yolanda L Wilson/Primary Examiner, Art Unit 2113