DETAILED ACTION
This office action is in response to applicant’s remarks filed on July 16, 2025 in application 18/461,543.
Claims 1-20 are presented for examination. Claims 1, 8, and 15 are amended.
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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wollny et al. (US 2023/0161662) in further view of Mandal et al. (US 2023/0105304) in further view of Cropper et al. (US 2017/0230306).
In regard to claim 1, Wollny et al. teach a method comprising:
prior to generating fault predictions (machine learning model may be trained to identify predictive indicators of error conditions, para. 30, it is noted that the machine learning model is created/train before errors conditions can be predicted or identify, hence the modelling is done prior to the generating fault predictions), modelling faults (machine learning model, para. 33) using source data from a computing system (collect operating data from a plurality of computing devices, para. 31) comprising server computers running instances of a software program (proactive support method implemented at one or more servers, para. 29) and remote data storage system that is used by the computing system to store data associated with the software program (added to a knowledge base for further use, para. 30),; and
generating fault predictions based on current data from the computing system and the remote data storage system and the modelled faults (one or more predictive indicators associated with one or more conditions are identified, para. 34).
Wollny et al. teach of collecting operation data from a plurality of computing devices (abstract) but does not explicitly teach the source data consisting of prior SaaS computing system performance, remote data storage system performance, and source data that is not a measure of performance consisting of SaaS computing system workload, data storage system workload, SaaS computing system configuration data and remote data storage system configuration data the current data; the current data consistning of current SaaS computing system performance, remote data storage system performance, and current data that is not a measure of performance comprising SaaS computing system workload, data storage system workload, SaaS computing system configuration data, and remote data storage system configuration data
Mandal et al. teach of monitoring a computing environment by monitoring each component associated set of key performance indicators (KPIs) (para. 24). Data types can span a variety of data, for example, performance metrics, transaction metrics, logs, traces, topology, etc. (para. 48-50).
It would have been obvious to modify the method of Wollny et al. by adding Mandal et al. proactive avoidance of performance issues in computing environment. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in proactively detecting performance issues (para. 24).
Wollny et al. and Mandal et al. does not explicitly teach prior to generating storage capacity exhaustion fault predictions, modelling storage capacity exhaustion faults occurring in a remote storage system using source data from a software as a service (SaaS), the SaaS computing system generating a storage capacity exhaustion fault prediction based on current data from the SaaS computing and the remote data storge system; and responsive to the storage capacity exhaustion fault prediction, the SaaS computing system signaling a reclaimable storage remediation message to the remote data storage system, thereby causing the remote data storage system to reclaim storage space as indicated in the reclaimable storage remediation message to avoid the predicted storage capacity exhaustion fault.
Cropper et al. teach of a cloud computing model for on-demand access to a shared pool of configurable computing resources (para. 19). The service models could be a Software as a Service (SaaS) (para. 26-29). Asset management of configurable computing resources use a set of asset weight values to determine an impact on a resource utilization value or a set of asset priority values to spread/balance assets (para. 51-62, fig. 4).
It would have been obvious to modify the method of Wollny et al. and Mandal et al. by adding Cropper et al. asset management. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would provide a method to configured or reconfigured asset to spread/balance in regard to a threshold (para. 51).
In regard to claim 2, Wollny et al. teach the method of claim 1 further comprising generating rules for selecting fault avoidance recommendations based on the source data (one or more corresponding conditional corrective scripts are selected for execution by the computing device to avoid, correct or recover from, or otherwise remediate one or more error conditions associated with each detected trigger condition, para. 26).
In regard to claim 3, Wollny et al. teach the method of claim 2 further comprising using the rules with the current data to select fault avoidance recommendations corresponding to predicted faults (corrective scripts may be stored in the knowledge base associated with one or more error conditions or predictive indicators to facilitate later selection and use… one or more corrective scripts may be added to the knowledge base as a known solution to the error condition, para. 36, the corrective scripts may be selected for the one or more error conditions from corrective scripts previously generated, or the corrective scrips may be generated based upon the error conditions associated with the predictive indicators, para. 38).
In regard to claim 4, Wollny et al. teach the method of claim 3 further comprising proactively implementing at least some of the fault avoidance recommendations (one or more corrective scripts may be selected in order to avoid or remediate one or more error conditions, para. 39).
In regard to claim 5, Wollny et al. teach the method of claim 4 further comprising generating messages indicating that specific fault avoidance recommendations have been implemented (may be configured to provide the dashboards, generate reports, and provide a website that may be accessed by users to obtain additional information regarding known error conditions or solutions, para. 44).
In regard to claim 6, Wollny et al. teach the method of claim 5 further comprising using the modelled faults and the current data from the computing system and the associated remote data storage system to calculate efficacy of implementation of the fault avoidance recommendations to avoid predicted faults (operating data may be included as separate variables in the operating data message for data efficiency, para. 23).
In regard to claim 7, Wollny et al. teach the method of claim 6 further comprising including performance, workload, and configuration data in the source data and the current data (monitor real-time system performance which may be analyze to perform aspects of the proactive support techniques, para. 43).
In regard to claim 8, Wollny et al. teach an apparatus comprising:
a computing system comprising server computers that run instances of a computer software program (proactive support method implemented at one or more servers, para. 29);
a remote data storage system that maintains data used by the instances of the computer software program (data warehouse, fig. 2, 214, para. 43); and
a fault prediction system configured to: generate a model of faults (machine learning model, para. 33) using source data from the computing system (collect operating data from a plurality of computing devices, para. 31) and the remote data storage system (added to a knowledge base for further use, para. 30) prior to generating fault predictions (machine learning model may be trained to identify predictive indicators of error conditions, para. 30, it is noted that the machine learning model is created/train before errors conditions can be predicted or identify, hence the modelling is done prior to the generating fault predictions); and generate fault predictions using current data from the computing system and the remote data storage system and the model (one or more predictive indicators associated with one or more conditions are identified, para. 34).
Wollny et al. teach of collecting operation data from a plurality of computing devices (abstract) but does not explicitly teach the source data comprising prior computing system performance, remote data storage system performance, computing system workload, data storage system workload, computing system configuration data and remote data storage system configuration data the current data comprising current computing system performance, remote data storage system performance, computing system workload, data storage system workload, computing system configuration data, and remote data storage system configuration data
Mandal et al. teach of monitoring a computing environment by monitoring each component associated set of key performance indicators (KPIs) (para. 24). Data types ca span a variety of data, for example, performance metrics, transaction metrics, logs, traces, topology, etc. (para. 48-50).
Refer to claim 1 for motivational statement.
Wollny et al. and Mandal et al. does not explicitly teach prior to generating storage capacity exhaustion fault predictions, modelling storage capacity exhaustion faults occurring in a remote storage system using source data from a software as a service (SaaS), the SaaS computing system generating a storage capacity exhaustion fault prediction based on current data from the SaaS computing and the remote data storge system; and responsive to the storage capacity exhaustion fault prediction, the SaaS computing system signaling a reclaimable storage remediation message to the remote data storage system, thereby causing the remote data storage system to reclaim storage space as indicated in the reclaimable storage remediation message to avoid the predicted storage capacity exhaustion fault.
Cropper et al. teach of a cloud computing model for on-demand access to a shared pool of configurable computing resources (para. 19). The service models could be a Software as a Service (SaaS) (para. 26-29). Asset management of configurable computing resources use a set of asset weight values to determine an impact on a resource utilization value or a set of asset priority values to spread/balance assets (para. 51-62, fig. 4).
Refer to claim 1 for motivational statement.
In regard to claim 9, Wollny et al. teach the apparatus of claim 8 further comprising the fault prediction system being configured to generate rules for selecting fault avoidance recommendations based on the source data (one or more corresponding conditional corrective scripts are selected for execution by the computing device to avoid, correct or recover from, or otherwise remediate one or more error conditions associated with each detected trigger condition, para. 26).
In regard to claim 10, Wollny et al. teach the apparatus of claim 9 further comprising the fault prediction system being configured to use the rules with the current data to select fault avoidance recommendations corresponding to predicted faults (corrective scripts may be stored in the knowledge base associated with one or more error conditions or predictive indicators to facilitate later selection and use… one or more corrective scripts may be added to the knowledge base as a known solution to the error condition, para. 36, the corrective scripts may be selected for the one or more error conditions from corrective scripts previously generated, or the corrective scrips may be generated based upon the error conditions associated with the predictive indicators, para. 38).
In regard to claim 11, Wollny et al. teach the apparatus of claim 10 further comprising the fault prediction system being configured to proactively implement at least some of the fault avoidance recommendations (one or more corrective scripts may be selected in order to avoid or remediate one or more error conditions, para. 39).
In regard to claim 12, Wollny et al. teach the apparatus of claim 11 further comprising the fault prediction system being configured to receive messages indicating that specific fault avoidance recommendations have been implemented (may be configured to provide the dashboards, generate reports, and provide a website that may be accessed by users to obtain additional information regarding known error conditions or solutions, para. 44).
In regard to claim 13, Wollny et al. teach the apparatus of claim 12 further comprising the fault prediction system being configured to use the model and the current data from the computing system and the remote data storage system to calculate efficacy of implementation of the fault avoidance recommendations to avoid predicted faults (operating data may be included as separate variables in the operating data message for data efficiency, para. 23).
In regard to claim 14, Wollny et al. teach the apparatus of claim 13 in which the source data and the current data comprise performance, workload, and configuration data (monitor real-time system performance which may be analyze to perform aspects of the proactive support techniques, para. 43).
In regard to claim 15, Wollny et al. teach a non-transitory computer-readable storage medium with instructions that when executed by a computer perform a method comprising:
prior to generating fault predictions (machine learning model may be trained to identify predictive indicators of error conditions, para. 30, it is noted that the machine learning model is created/train before errors conditions can be predicted or identify, hence the modelling is done prior to the generating fault predictions), modelling faults (machine learning model, para. 33) using source data from a computing system (collect operating data from a plurality of computing devices, para. 31) comprising server computers running a software program (proactive support method implemented at one or more servers, para. 29) and an associated remote data storage system (data warehouse, fig. 2, 214, para. 43) that is used by the computing system to store data associated with the software program (added to a knowledge base for further use, para. 30);
generating fault predictions based on current data from the computing system and the remote data storage system and the modelled faults (one or more predictive indicators associated with one or more conditions are identified, para. 34).
Wollny et al. teach of collecting operation data from a plurality of computing devices (abstract) but does not explicitly teach the source data comprising prior computing system performance, remote data storage system performance, computing system workload, data storage system workload, computing system configuration data and remote data storage system configuration data the current data comprising current computing system performance, remote data storage system performance, computing system workload, data storage system workload, computing system configuration data, and remote data storage system configuration data
Mandal et al. teach of monitoring a computing environment by monitoring each component associated set of key performance indicators (KPIs) (para. 24). Data types ca span a variety of data, for example, performance metrics, transaction metrics, logs, traces, topology, etc. (para. 48-50).
Refer to claim 1 for motivational statement.
Wollny et al. and Mandal et al. does not explicitly teach prior to generating storage capacity exhaustion fault predictions, modelling storage capacity exhaustion faults occurring in a remote storage system using source data from a software as a service (SaaS), the SaaS computing system generating a storage capacity exhaustion fault prediction based on current data from the SaaS computing and the remote data storge system; and responsive to the storage capacity exhaustion fault prediction, the SaaS computing system signaling a reclaimable storage remediation message to the remote data storage system, thereby causing the remote data storage system to reclaim storage space as indicated in the reclaimable storage remediation message to avoid the predicted storage capacity exhaustion fault.
Cropper et al. teach of a cloud computing model for on-demand access to a shared pool of configurable computing resources (para. 19). The service models could be a Software as a Service (SaaS) (para. 26-29). Asset management of configurable computing resources use a set of asset weight values to determine an impact on a resource utilization value or a set of asset priority values to spread/balance assets (para. 51-62, fig. 4).
Refer to claim 1 for motivational statement.
In regard to claim 16, Wollny et al. teach the non-transitory computer-readable storage medium of claim 15 in which the method further comprises generating rules for selecting fault avoidance recommendations based on the source data (one or more corresponding conditional corrective scripts are selected for execution by the computing device to avoid, correct or recover from, or otherwise remediate one or more error conditions associated with each detected trigger condition, para. 26).
In regard to claim 17, Wollny et al. teach the non-transitory computer-readable storage medium of claim 16 in which the method further comprises using the rules with the current data to select fault avoidance recommendations corresponding to predicted faults (corrective scripts may be stored in the knowledge base associated with one or more error conditions or predictive indicators to facilitate later selection and use… one or more corrective scripts may be added to the knowledge base as a known solution to the error condition, para. 36, the corrective scripts may be selected for the one or more error conditions from corrective scripts previously generated, or the corrective scrips may be generated based upon the error conditions associated with the predictive indicators, para. 38).
In regard to claim 18, Wollny et al. teach the non-transitory computer-readable storage medium of claim 17 in which the method further comprises proactively implementing at least some of the fault avoidance recommendations (one or more corrective scripts may be selected in order to avoid or remediate one or more error conditions, para. 39).
In regard to claim 19, Wollny et al. teach the non-transitory computer-readable storage medium of claim 18 in which the method further comprises generating messages indicating that specific fault avoidance recommendations have been implemented (may be configured to provide the dashboards, generate reports, and provide a website that may be accessed by users to obtain additional information regarding known error conditions or solutions, para. 44).
In regard to claim 20, Wollny et al. teach the non-transitory computer-readable storage medium of claim 19 in which the method further comprises using the modelled faults and the current data from the computing system and the associated remote data storage system to calculate efficacy of implementation of the fault avoidance recommendations to avoid predicted faults (operating data may be included as separate variables in the operating data message for data efficiency, para. 23).
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Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892.
Chou et al. (US 2015/0254088) prediction for reclaimed data block
Chang et al. (US 9,460,147) reclaim space of deleted records
Cropper et al. (US 2017/0111289) reclaim resources, SaaS
Ari et al. (US 12,301,423) computing capacity on demand
Zhuravlev et al. (US 2022/0261164) storage configuration utilization patterns
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Chandana et al. (US 2024/0364712) ML model and predict
Sethi et al. (US 12228,999) Dell- logs, performance metrics
Faulhaber, Jr. et al. (US 2019/0156247) ML and model selector
Dayama et al. (US 2021/0326746) ML performance verification
Cui et al. (US 10,853,116) ML predicted to fail
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Marakala et al. (US 2024/0419561) predictive failure model
Swidan et al. (US 2025/0036537) various model such as SaaS service model
Karr (US 2023/0385154) recovery for object stores
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Darling et al. (US 2015/0019912) predictive model for error prediction
Hu et al. (US 12,051,008) reliability measure of predictive system
Kim et al. (US 11,810,003) learning model tree structure
Ishida (US 2019/0188598) learning error prediction model
Fujimura et al. (US 11,593,817 prediction apparatus
Kakuda et al. (US 2024/0232659) prediction device of learning device
Takada (US 2022/0076161) prediction model
Lin et al. (US 2012/0284212) predictive model
Mann et al. (US 2015/0170049) predictive analytic modeling
Breckenridge et al. (US 8,762,299) predictive analytical model training
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Loan L.T. Truong/Primary Examiner, Art Unit 2114 Loan.truong@uspto.gov