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. This office action is in response to the original application filed on 09/15/2023. Claims 1-20 have been examined. Information Disclosure Statement The information disclosure statement (IDS) submitted on 09/15/2023 is being considered by the examiner. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim (s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kothari et al, US 2022/0091947 (hereinafter Kothari) in view of Cella et al, US 2022/0366494 (hereinafter Cella ). Claim 1: Kothari teaches a computer-implemented method comprising: training a machine learning model using training data indicating historical technical recovery exercises and downtimes associated with the historical technical recovery exercises, wherein the machine learning model is implemented using an artificial neural network, and wherein the training the machine learning model comprises weighting one or more nodes of the artificial neural network based on the training data; ( para. [0026], perform extreme technical recovery exercise in a cloud environment para. [0036] –[ 0044], [0051]–[0056]) , t he risk assessment module 174 is configured to make an execution recommendation for performance of a technology recovery exercise and failover assessment module 178 is configured to track, record, and assess aspects of a recovery exercise and/or failover event. The control module 180 is configured to implement various instructions such as a failover event, permissions, and/or other aspects of a recovery exercise , para. [0051] –[ 0055] , the risk scoring model may be predetermined, e.g., may include one or more predetermined weights or scores for each risk factor that may be used to determine the resiliency and risk score of an application. .. the scores are determined as a weighted average of scores for the risk factors included in the model ) . receiving, by a computing device, technical recovery exercise data that indicates, for each of a plurality of application failover waves, one or more different technical recovery exercises; for a first application failover wave of the plurality of application failover waves: ( para. [0026], perform extreme technical recovery exercise include moving all production applications in one cloud region to another cloud region in the production environment in a systematic, organized, risk-averse manner. In some embodiments, cross-region application dependencies for each application are identified and mitigated to reduce a potential negative impact to any applications' stability. Para [0044], the recovery exercise system 160 may output, e.g., via the GUI module 176 and/or information associated with the enriched data ) executing, by the computing device, one or more applications associated with the first application failover wave; (para. [0046]–[0050], [0066]–[0072]) takes one or more raw data and combines and enriches them to enable and simplify analysis, and/or provide direct and programmatic access to the enriched data .. . a GUI configured to allow one or more users to view one or more predefined dashboards that may include at least a portion of the enriched data related to cross-region application dependencies and cross-region traffic flows for any of the applications in the cloud infrastructure 110. , [ 0066] During a failover, an application participating in the technical recovery exercise may failover completely from one region 130 in the cloud infrastructure 110 to another region using one or more of manual steps, automated scripts, etc. A complete failover of an application demonstrates that the application is geo-resilient without depending on hardware or software resources in the previous cloud regio n) . generating, by the computing device and by monitoring one or more events associated with the one or more applications associated with the first application failover wave during one or more first technical recovery exercises of the first application failover wave, event data; (para. [0036] –[ 0044], [0070]–[0076], teaches monitoring application operation during exercises, collecting telemetry/status and exercise telemetry for assessment … a failover of a particular application may satisfy an automated trigger condition resulting in performance of one or more acts by the control module 180. Such acts may include, but are not limited to: identifying a start time of the technical recovery exercise, e.g., by polling a technical recovery exercise API, triggering a failover of the application to an alternate cloud region at the identified start time; identifying an end time of the exercise, e.g., by polling the technical recovery exercise API; triggering a failback of the application to the cloud region 130 at the identified end time, etc. The technical recovery exercise API may be configured to return one or more variables in response to being polled such as, but not limited to: a True/False technical recovery exercise start flag; the technical recovery exercise start time, the technical recovery exercise end time, etc. , issues, incidents, and/or customer impacts may be weighted based on, for example severity, impact, magnitude, or the like. In response to the identified issues, incidents, and/or customer impacts exceeding the one or more predetermined thresholds, the failover assessment module 178 may be configured to generate a recommendation to pause and/or stop the failover and/or exercise. In some embodiments, the failover assessment module 178 may be configured to automatically revert a failover, and/or un-isolate the cloud region 130 ) . providing, as input to the trained machine learning model, the event data; ( para. [0051] –[ 0056], [0067]–[0069], feeding exercise/operational data to ML/risk models and producing resiliency/risk outputs (including a recovery time objective (RTO) and recovery point objective (RPO) and downtime ‑ related assessments) used to evaluate failover) receiving, as output from the trained machine learning model, output data, based on the event data, associated with application downtime; (para. [0051] –[ 0056], [0067]–[0069] Using a set of training data and a set of ground truths associated with the set of training data, is configured to output a result when provided with a given input. In an exemplary embodiment, the machine learning model may be trained to determine one or more weights for one or more risk factors … the scores are determined as a weighted average of scores for the risk factors included in the model. In some embodiments, the risk-scoring module may employ an algorithm or the like to determine the scores ). Kothari does not explicitly disclose, however, in analogous art Cella discloses based on determining that the output data satisfies one or more criteria, executing one or more second applications associated with a second application failover wave of the plurality of application failover waves. ( para . [0031] –[ 0036], [0032], [0033]–[0035]).and teaches intelligent agents, role ‑ based digital twins and automated orchestration/automatic adjustment of system parameters and execution of tasks in response to ML outputs (examples of autonomous agent actions and automatic parameter adjustment). I t would have been obvious to one ordinary skill in the art at the time the invention was made to modify the system disclose by Khotari with the teaching from Cella in order to use agents to act on model outputs reduces human latency and improve adherence to a recovery time objective and recovery point objective objectives disclosed by Khotari . A person ordinary skill in the art would be motivated by operational efficiency and risk reduction to implement automation that acts on machine learning predictions to improve increased efficiency, speed, reliability (see para. [0022] Cella ). Claim 2: Kothari in view of Cella teaches the method of claim 1. Kothari further di scloses wherein the output data comprises a system health score, wherein the system health score is positively correlated with the one or more applications associated with the first application failover wave operating within one or more target key performance indicator (KPI) ranges, and wherein the determining that the output data satisfies the one or more criteria comprises determining that the system health score exceeds a threshold system health score ( Kothari, para. [0053], applications may be assigned a level or tier of risk based on the score for that application ). Claim 3: Kothari in view of Cella teaches the method of claim 1. Kothari further discloses wherein the one or more criteria comprise a threshold rate of logins to the one or more applications associated with the first application failover wave, and wherein the determining that the output data satisfies the one or more criteria comprises determining that a rate of logins to the one or more applications is less than the threshold rate of logins ( Kothari, para. [0046], a ‘Cross environment client dependencies dashboard’, which shows if an application is receiving traffic across different environments, and may also display information indicative of anomalies in communications or traffic ) . Claim 4: Kothari in view of Cella teaches the method of claim 1. Kothari further discloses determining, based on the output data, an order of performing the plurality of application failover waves that is associated with satisfying the one or more criteria ( Kothari, paras. [0053] a Potential risk dashboard, which shows a list of applications ranked based on the tiers and/or scores ). Claim 5: Kothari in view of Cella teaches the method of claim 1. Kothari further discloses receiving user feedback indicating an efficacy associated with the determining that the output data satisfies the one or more criteria; and further training, based on the user feedback, the trained machine learning model ( Kothari, para. ]0032], the intelligent agent is at least one of trained and configured via feedback from at least one expert in the defined role regarding a set of outputs of the intelligent agent ). Claim s 6 and 20 : Kothari in view of Cella teaches the method of claim 1. Kothari further discloses wherein the one or more second applications associated with the second application failover wave are different from the one or more applications associated with the first application failover wave ( Kothari, para. [0026], such an exercise may include moving all production applications in one cloud region to another cloud region in the production environment in a systematic, organized, risk ‑ averse manner ) . Claim 7: Kothari in view of Cella teaches the method of claim 1. Kothari further discloses wherein the one or more different technical recovery exercises comprise one or more of: a chaos experiment, regional isolation of the one or more applications, and automated traffic switching of the one or more applications ( Kothari, para. [0080], The one or more scripts, processes, etc., may include, for example: a script configured to disable peering between the cloud region to be isolated and other cloud regions such that any connectivity between VPCs is severed; a script configured to disable cloud proxy services in the cloud region such that connectivity for any application or service using the proxy services to communicate with a service or application on the cloud region is severed; a script configured to shut down one or more ports between various switches and/or services in one or more cloud resources ). Claim s 8 , 14 and 18 : Kothari in view of Cella teaches the method of claim 1. Kothari further discloses wherein the one or more criteria comprise a recovery point objective (RPO), and wherein the determining that the output data satisfies the one or more criteria comprises determining that an amount of data lost by the one or more applications associated with the first application failover wave does not exceed a threshold amount of data based on the RPO ( Kothari, para. [0067] -[ 0068], the control module 180 may be configured to determine at least a portion of the information associated with the exercise such as, but not limited to: actual RTO and/or RPO values for each application participating in the exercise, whereby actual RTO is a duration of the exercise… and actual RPO is a time difference between a last database or data backup and a time at which an incident… started … the control module 180 may be configured to determine a failover result based on a comparison between the actual RTO and a predefined RTO, and/or between the actual RPO and a predetermined RPO ) Claim s 9 , 15 and 19 : Kothari in view of Cella teaches the method of claim 1. Kothari further discloses wherein the one or more criteria comprise a recovery time objective (RTO), and wherein the determining that the output data satisfies the one or more criteria comprises determining that an amount of time used to perform a failover of the one or more applications associated with the first application failover wave does not exceed a threshold amount of time based on the RTO ( Kothari, para. [0067] -[ 0068], the control module 180 may be configured to determine at least a portion of the information associated with the exercise such as, but not limited to: actual RTO and/or RPO values for each application participating in the exercise, whereby actual RTO is a duration of the exercise… and actual RPO is a time difference between a last database or data backup and a time at which an incident… started … in response to determining that the actual RPO and actual RTO are less than or equal to the respective predetermined values, the control module 180 may be configured to determine that the failover exercise was successful ). Claim s 10 and 16 : Kothari in view of Cella teaches the method of claim 1. Kothari further discloses wherein the output data comprises a risk associated with executing the one or more second applications associated with the second application failover wave, wherein the determining that the output data satisfies the one or more criteria comprises determining that the risk does not exceed a threshold risk, and wherein the risk is positively correlated with a probability that the one or more second applications lose data or do not complete a failove r ( Kothari, [0051]-[0059], Risk factors that may be included in the model include, but are not limited to: application’s cross ‑ region dependencies, current resiliency abilities and deficiencies, resiliency tier showing application’s importance, customer impact potential, previous history of severity incidents caused by an application… In an exemplary use case, the machine learning model may determine that performing a failover exercise on the cloud region 130 presents a high risk of a high severity incident during an extreme technical recovery exercise, and in response may generate a ‘No ‑ go’ recommendation ) Claim 11: Kothari in view of Cella teaches the method of claim 1. Kothari further discloses wherein the output data comprises a number of the one or more second applications associated with the second application failover wave ( Kothari, para. [0065], the GUI may include a ‘Failover Exercise’ dashboard, which may show one or more of all participating applications’ failover status, failover region location of each application after completion of failover, or other associated failover details such as failover start time, failover end time, failover result, database replication frequency (if applicable), failover notes, business validation result status, etc . ) Claim 12: Kothari in view of Cella teaches the method of claim 1. Kothari further discloses wherein the output data comprises one or more indications of one or more types of the one or more second applications associated with the second application failover wave ( Kothari, para. [0047], an ‘Application dependencies by resiliency tier dashboard’, which displays the resiliency tier of applications that an application is dependent upon, whereby in some embodiments, resiliency tier is indicative of an application criticality within the entity as defined internally within the entity; an ‘Application resiliency view—Traffic and Resources distribution dashboard’, which displays information indicative of a traffic distribution across the cloud regions, and/or information usable to perform active analysis or determine a count of each resource type by cloud region for an application such as total number of EC2 instances in a cloud region, etc . ) Claim s 13 and 17 : Claim 13 recites substantially similar scope to claim 1 above, therefore, is also rejected under the same rationale set forth for claim 1 above. In addition, Kothari discloses a computing device /a non-transitory machine-readable medium , comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the computing device to perform steps ( Kothari, Fig 1, processor and memory ). Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ganesan et al. ( US 11978058 ) directed to s ystems and methods provide customers with a need-based warranty using a deep learning neural network. Naeini (US 20230038164 ) directed to a monitoring and alerting system backed by a machine learning engine for anomaly detection and prediction of time series data indicative of health of an application, a system, an environment, or a person . SANKARANARAYANAN et al. (US 20230153223 ) directed to monitor for faults in a cloud based network for a plurality of features comprising an application and dependent features. 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