DETAILED ACTION
This application has been examined. Claims 1-4,6-10,12-16,18 are pending. Claims 5,11,17 are cancelled.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/15/2026 has been entered.
Response to Arguments
Applicant's arguments filed 1/15/2026 have been fully considered but they are moot in view of the new grounds for rejection.
Baral-Gardner disclosed (re. Claim 1) wherein the unsupervised learning includes clustering data to include risk indicator features for generating the risk prediction model. (Baral-Paragraph 62, if the current rollout has already deployed to 100 clusters so far, the AIR impact to those earlier 100 clusters can be used as features to predict the potential AIR impact to later clusters remaining as targets in the current rollout by analyzing the causes of faults and similarities between the earlier clusters and the later clusters)
While Baral-Gardner substantially disclosed the claimed invention Baral-Gardner does not disclose (re. Claim 1) wherein the risk indicator features include text clusters derived from textual descriptions of the change
Kushmerick Paragraph 86 disclosed wherein event-message-clustering system includes an event-processing-and-distribution component 1410 and multiple clusters of event records 1412-1422.
Kushmerick Figure 14A-14C Paragraph 88 disclosed wherein subsequently received event messages are similarly processed. Either a subsequently received event message is assigned to an existing cluster, when a metric computed for the subsequently received event message is sufficiently close to a metric for an existing cluster, or a new cluster is created and the subsequently received event message becomes the first event message assigned to the new cluster. Thus, clusters are created dynamically as event messages are received and processed.
Kushmerick disclosed (re. Claim 1) wherein the risk indicator features include text clusters derived from textual descriptions of the change (Kushmerick Figure 14A-14C, Figure 27B Paragraph 88, clusters are created dynamically as event messages are received and processed,Paragraph 104, each cluster is associated with a parsing function that allows the event-message-clustering system to extract parameter values from the event message… event record may include a header with an indication of the event type 2506, a list of parameter values and associated parameter types 2508,)
Baral and Kushmerick are analogous art because they present concepts and practices regarding scheduling of software updates. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Gardner into Baral. The motivation for the said combination would have been to analyze and compute various significance metrics for various combinations of the elements of the event-message-type defining vector used to compute the significance of an event message (Kushmerick-Paragraph 151)
Priority
The effective date of the claims described in this application is March 31, 2023.
Information Disclosure Statement
The Applicant is respectfully reminded that each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability as defined in 37 CFR 1.56.
There were no information disclosure statements filed with this application.
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) 1-4,6-10,12-16,18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baral (USPGPUB 2023/0205509) further in view of Gardner (USPGPUB 2021/0067607) further in view of Kushmerick (USPGPUB 2016/0373293)
Regarding Claim 1
Baral Paragraph 20 disclosed wherein certain packages may be deployed to the cloud computing fleet wherein packages include new applications, operating system updates, software feature updates, security fixes, and/or configuration changes.
Baral Paragraph 21 disclosed safe deployment practice (SDP) framework 100 may involve phased rollout of the package to incrementally larger number of targets while validating the deployment of the package at various phases.
Baral Paragraph 27 disclosed wherein SDP framework reduces deployment risks by detecting and correcting errors early, and preventing widespread problems associated with deploying packages to the cloud computing fleet.
Baral disclosed (re. Claim 1) method for implementing a change to a plurality of devices in a computing infrastructure, the method comprising: generating a risk prediction model, (Baral-Paragraph 39, a risk prediction model may be trained to predict deployment risk associated with a payload and a cluster) the risk prediction model trained using a combination of supervised learning (Baral-Paragraph 69,risk prediction model 308 may include a customizer module that may enable an administrator to onboard risk scores, risk-related features, risk-related formulas) and unsupervised learning; (Baral-Paragraph 44, the prediction models 304 may include two machine-learning models: a time prediction model 306 and a risk prediction model 308 for predicting speed and risk, respectively, based on historical data)
identifying, using the risk prediction model, a first set of devices from the plurality of devices having a low risk of failure due to implementing the change and a second set of devices from the plurality of devices having a high risk of failure due to implementing the change; (Baral-Paragraph 70, one cluster may be working on an intensive process and experiencing high utilization, such that time and risk associated with deploying to this cluster would be high. Thus, it may be advantageous to avoid deploying to this cluster for now, if possible, and instead deploy to a different cluster that is relatively idle and thus currently has high speed and low risk associated with it for deployment,Paragraph 84, deployment may start with earlier phases that contain sets of fewer clusters and/or lower-risk clusters, and then progressively advance to later phases that contain sets of many clusters and/or higher-risk clusters.)
updating the risk prediction model using data obtained from implementing the change to the portion of the first set of devices; (Baral-Paragraph 62, if the current rollout has already deployed to 100 clusters so far, the AIR impact to those earlier 100 clusters can be used as features to predict the potential AIR impact to later clusters remaining as targets in the current rollout by analyzing the causes of faults and similarities between the earlier clusters and the later clusters) and
iteratively performing the identifying, the generating, the implementing, and the updating. (Baral-Paragraph 62, if the current rollout has already deployed to 100 clusters so far, the AIR impact to those earlier 100 clusters can be used as features to predict the potential AIR impact to later clusters remaining as targets in the current rollout by analyzing the causes of faults and similarities between the earlier clusters and the later clusters,Paragraph 71, Any suitable period may be chosen for retraining the prediction models 304, such as every hour, every day, etc. Alternatively, retraining may be performed on demand and/or as needed as a certain amount of new input data becomes available to be processed by the feature engineering module 302.)
Baral Paragraph disclosed an optimal sequence of clusters and/or optimal deployment parameters. The output sequence of clusters may include the recommended deployment order of clusters for each AZ, each region, and each SDP phase that are included in the overall target for the deployment.
While Baral substantially disclosed the claimed invention Baral does not disclose (re. Claim 1) generating a schedule for automatically implementing the change to the first set of devices; implementing the change to a portion of the first set of devices according to the schedule.
Gardner Paragraph 84 disclosed installing the modified workflows on the server 120, on one or more particular servers part of the server 120, or one or more particular server environments within or part of the server 120. Implementing the modified workflows 128 in the server 120 may involve running (e.g., processing) the modified workflows 128, scheduling one or more times to run each of the modified workflows 128, or setting one or more other conditions (e.g., triggering events) for each of the modified workflows 128 that when satisfied result in running the modified workflows 128.
Gardner disclosed (re. Claim 1) generating a schedule for automatically implementing the change to the first set of devices; (Gardner-Paragraph 58, the workflow publishing server 110 may have scheduled to send the server 120 a workflow listing every two days. The schedule for the server 130 may be different than the schedule of the server 120.)
implementing the change to a portion of the first set of devices according to the schedule.(Gardner-Paragraph 84, Implementing the modified workflows 128 in the server 120 may involve running (e.g., processing) the modified workflows 128, scheduling one or more times to run each of the modified workflows 128, or setting one or more other conditions (e.g., triggering events) for each of the modified workflows 128 that when satisfied result in running the modified workflows 128.)
Baral and Gardner are analogous art because they present concepts and practices regarding scheduling of software updates. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Gardner into Baral. The motivation for the said combination would have been to enable analysis and/or leveraging one or more machine learning to determine which workflows are likely work based on what workflows are associated with conditions similar to the observed conditions, what workflows have a high rate of success. (Gardner-Paragraph 66)
Baral-Gardner disclosed (re. Claim 1) wherein the unsupervised learning includes clustering data to include risk indicator features for generating the risk prediction model. (Baral-Paragraph 62, if the current rollout has already deployed to 100 clusters so far, the AIR impact to those earlier 100 clusters can be used as features to predict the potential AIR impact to later clusters remaining as targets in the current rollout by analyzing the causes of faults and similarities between the earlier clusters and the later clusters)
While Baral-Gardner substantially disclosed the claimed invention Baral-Gardner does not disclose (re. Claim 1) wherein the risk indicator features include text clusters derived from textual descriptions of the change
Kushmerick Paragraph 86 disclosed wherein event-message-clustering system includes an event-processing-and-distribution component 1410 and multiple clusters of event records 1412-1422.
Kushmerick Figure 14A-14C Paragraph 88 disclosed wherein subsequently received event messages are similarly processed. Either a subsequently received event message is assigned to an existing cluster, when a metric computed for the subsequently received event message is sufficiently close to a metric for an existing cluster, or a new cluster is created and the subsequently received event message becomes the first event message assigned to the new cluster. Thus, clusters are created dynamically as event messages are received and processed.
Kushmerick disclosed (re. Claim 1) wherein the risk indicator features include text clusters derived from textual descriptions of the change (Kushmerick Figure 14A-14C, Figure 27B Paragraph 88, clusters are created dynamically as event messages are received and processed,Paragraph 104, each cluster is associated with a parsing function that allows the event-message-clustering system to extract parameter values from the event message… event record may include a header with an indication of the event type 2506, a list of parameter values and associated parameter types 2508,)
Baral and Kushmerick are analogous art because they present concepts and practices regarding scheduling of software updates. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Gardner into Baral. The motivation for the said combination would have been to analyze and compute various significance metrics for various combinations of the elements of the event-message-type defining vector used to compute the significance of an event message (Kushmerick-Paragraph 151)
Regarding Claim 7
Claim 7 (re. computer program) recites substantially similar limitations as Claim 1. Claim 17 is rejected on the same basis as Claim 1.
Regarding Claim 13
Claim 13 (re. system) recites substantially similar limitations as Claim 1. Claim 13 is rejected on the same basis as Claim 1.
Regarding Claim 2,8,14
Baral-Gardner-Kushmerick disclosed (re. Claim 2,8,14) wherein the change to the plurality of devices includes a software patch to a plurality of computing devices.(Baral-Paragraph 20,packages include new applications, operating system updates, software feature updates, security fixes, and/or configuration changes.)
Regarding Claim 3,9,15
Baral-Gardner-Kushmerick disclosed (re. Claim 3,9,15) collecting historic data on previous changes implemented on the plurality of devices; (Baral-Paragraph 62, if the current rollout has already deployed to 100 clusters so far, the AIR impact to those earlier 100 clusters can be used as features to predict the potential AIR impact to later clusters remaining as targets in the current rollout by analyzing the causes of faults and similarities between the earlier clusters and the later clusters, Paragraph 44, the prediction models 304 may include two machine-learning models: a time prediction model 306 and a risk prediction model 308 for predicting speed and risk, respectively, based on historical data ) implementing the change to a test group of the plurality of devices; identifying failed devices from the test group where implementing the change failed to be implemented; and inputting data from the failed devices to the risk prediction model. (Baral-Paragraph 62, if the current rollout has already deployed to 100 clusters so far, the AIR impact to those earlier 100 clusters can be used as features to predict the potential AIR impact to later clusters remaining as targets in the current rollout by analyzing the causes of faults and similarities between the earlier clusters and the later clusters)
Regarding Claim 4,10,16
Baral-Gardner-Kushmerick disclosed (re. Claim 4,10,16) monitoring metrics for the test group of the plurality of devices where implementing the change failed;(Gardner-Paragraph 36, workflow can specify data to be acquired (e.g., to determine current settings, current performance metrics, etc.)) correlating deviations in the metrics with configuration data for the test group of the plurality of devices where implementing the change failed; (Baral-Paragraph 27,SDP framework reduces deployment risks by detecting and correcting errors early, and preventing widespread problems associated with deploying packages to the cloud computing fleet, Paragraph 70-71, one cluster may be working on an intensive process and experiencing high utilization, such that time and risk associated with deploying to this cluster would be high.)
and determining a causal relationship between the change and the metrics.(Baral-Paragraph 58-59, the risk prediction model 308 may learn that a payload of a certain size and of a certain type carries a certain level of risk to deploy on a certain cluster of a specific number of computers of a certain machine type having a certain usage status based on historical data of similar deployments in the past.)
Regarding Claim 6,12,18
Baral-Gardner-Kushmerick disclosed (re. Claim 6,12,18) wherein the supervised learning includes historic data and data from implementing the change to the test group. (Baral-Paragraph 62, if the current rollout has already deployed to 100 clusters so far, the AIR impact to those earlier 100 clusters can be used as features to predict the potential AIR impact to later clusters remaining as targets in the current rollout by analyzing the causes of faults and similarities between the earlier clusters and the later clusters, Paragraph 44, the prediction models 304 may include two machine-learning models: a time prediction model 306 and a risk prediction model 308 for predicting speed and risk, respectively, based on historical data )
Conclusion
Examiner’s Note: In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREG C BENGZON whose telephone number is (571)272-3944. The examiner can normally be reached on Monday - Friday 8 AM - 4:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Follansbee can be reached on (571) 272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GREG C BENGZON/Primary Examiner, Art Unit 2444