DETAILED ACTION
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 is a Non-Final Office Action Correspondence in response to U.S. Application No. 19/094,261 filed on 03/28/2025.
Claims 1-20 are pending. Claims 1, 8 and 16 are independent claims.
Examiner’s Comments 35 U.S.C. 101
The Examiner interprets the “computer-readable storage medium” recited in claims 16-20 to include only non-transitory type of media in view of Applicant’s specification at paragraph 0130. Therefore, claims 16-20 are statutory under 35 U.S.C. 101.
Claim Objections
Claim 20 is objected to because of the following informalities:
Claim 20 recites “The met computer-readable storage medium of claim 16,…”. The word “met” appears to be a typo and thus it should be removed.
Appropriate correction is required.
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, 8, 12-13, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Narayan et al. (U.S. PG Pub. No. 2019/0065261 A1, hereinafter “Narayan”) in view of Sharma (U.S. PG Pub. No. 2021/0149999 A1).
Regarding claim 1, Narayan teaches a system comprising:
a processor (Narayan ¶0025); and
a memory device that stores program code structured (Narayan ¶0025) to cause the processor to:
receive production telemetry data representative of a production system (Narayan ¶0112),
generate a training dataset based at least on the production telemetry data (Narayan ¶0116).
Narayan fails to explicitly teach utilize the training dataset to train a database population-change events model to mimic behavior of the production system. However, in the same field of endeavor, Sharma teaches utilize the training dataset to train a database population-change events model to mimic behavior of the production system (Sharma ¶0020, i.e., a workload simulation for a plurality of nodes). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Narayan by incorporating the teachings of Sharma. The motivation would be to provide a workload simulator that could be used to test cluster (Sharma Abstract).
Narayan and Sharma also teaches cause an orchestration framework to use an output of the database population-change events model to test resource management in a database system (Narayan ¶0109).
As to claim 2, Narayan as modified by Sharma also teaches the system of claim 1, wherein to cause the orchestration framework to use the output, the program code is further structured to cause the processor to:
cause the orchestration framework to use the output to test a model-defined initial population of databases in a database ring of the database system (Narayan ¶0101, i.e., test initializing workloads on managed nodes of a data center (which the Examiner interprets as a database ring of a database system see also ¶0077)).
As to claim 3, Narayan as modified by Sharma also teaches the system of claim 1, wherein the program code is further structured to cause the processor to:
execute the database population-change events model in a simulated environment (Sharma ¶0022);
determine a similarity between a result of the execution and a production curve of the simulated environment (Sharma ¶0025); and
validate the database population-change events model based on the determined similarity (Sharma ¶0029, i.e., “the cluster tuner may provide adjustments 146 to implement to the configuration of the test cluster 126 (e.g., changes to how a simulated workload 124 is distributed amongst resources 130a-b) and use the results 148 of the adjustment 146 (e.g., changes in resource consumption data 134 resulting from adjustments 146) to update the configuration recommender 142”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Narayan by incorporating the teachings of Sharma. The motivation would be to implement functions of the configuration recommender for cluster optimization (Sharma ¶0029).
As to claim 4, Narayan as modified by Sharma also teaches the system of claim 1, wherein the training dataset specifies a number of database create events exhibited for the production system in a first predetermined time period and to utilize the training dataset to train the database population-change events model (Narayan ¶0076, i.e., “identifying phases of execution (e.g., time periods in which different operations, each having different resource utilizations characteristics, are performed) of the workload (e.g., the application 1532) and pre-emptively identifying available resources in the data center 100 and allocating them to the managed node 1570 (e.g., within a predefined time period of the associated phase beginning”), the program code is further structured to cause the processor to:
utilize the training dataset to train a create database model to estimate a probability of a database creation event in a second period of time (Narayan ¶0077, i.e., “The orchestrator server 1520 may determine the differences based on the telemetry data stored in the hierarchical model and factor the differences into a prediction of future resource utilization of a workload if the workload is reassigned from one managed node to another managed node, to accurately balance resource utilization in the data center 100”).
Regarding claim 8, Narayan teaches a method for training a database population-change events model, the method comprising:
receiving production telemetry data representative of a production system (Narayan ¶0112);
generating a training dataset based at least on the production telemetry data (Narayan ¶0116).
Narayan fails to explicitly teach utilizing the training dataset to train a first database population-change events model to mimic behavior of the production system;
executing the first database population-change events model in a simulated environment;
determining a first similarity between a result of the execution and a production curve of the simulated environment; and
validating the first database population-change events model based on the determined similarity.
However, in the same field of endeavor, Sharma teaches utilizing the training dataset to train a first database population-change events model to mimic behavior of the production system (Sharma ¶0020, i.e. a workload simulation for a plurality of nodes);
executing the first database population-change events model in a simulated environment (Sharma ¶0022);
determining a first similarity between a result of the execution and a production curve of the simulated environment (Sharma ¶0025); and
validating the first database population-change events model based on the determined similarity (Sharma ¶0029, i.e., “the cluster tuner may provide adjustments 146 to implement to the configuration of the test cluster 126 (e.g., changes to how a simulated workload 124 is distributed amongst resources 130a-b) and use the results of the adjustment 146 (e.g., changes in resource consumption data 134 resulting from adjustments 146) to update the configuration recommender 142”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Narayan by incorporating the teachings of Sharma. The motivation would be to implement functions of the configuration recommender for cluster optimization (Sharma ¶0029).
As to claim 12, Narayan as modified by Sharma also teaches the method of claim 8, further comprising:
causing an orchestration framework to use an output of the first database population-change events model to test resource management in a database system (Narayan ¶0109).
As to claim 13, Narayan as modified by Sharma also teaches the method of claim 12, wherein said causing the orchestration framework to use the output comprises:
cause the orchestration framework to use the output to test a model-defined initial population of databases in a database ring of the database system (Narayan ¶0101, i.e., test initializing workloads on managed nodes of a data center (which the Examiner interprets as a database ring of a database system see also ¶0077)).
Claim 16 recites the limitations substantially similar to those of claim 1 and is similarly rejected.
Claim 17 recites the limitations substantially similar to those of claim 2 and is similarly rejected.
Claim 18 recites the limitations substantially similar to those of claim 3 and is similarly rejected.
Allowable Subject Matter
Claims 5-7, 9-11, 14-15, and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The features of limitations recited in claim 5 that includes “utilize the training dataset to train a drop database model to estimate a probability of a database drop event in a second period of time”; or in claim 6 that includes “for each of the plurality of different database population-change events models, utilize at least one probability distribution to fit the training dataset to the respective model, a result of the fitting being a plurality of trained models”; or in claim 9 that includes “for each of the plurality of different database population-change events models, utilizing at least one probability distribution to fit the training dataset to the respective model, a result of the fitting being a plurality of trained models”; or in claim 14 that includes “utilizing the training dataset to train a create database model to estimate a probability of a database creation event in a second period of time”; or in claim 15 that includes “utilizing the training dataset to train a drop database model to estimate a probability of a database drop event in a second period of time”; or in claim 19 that includes “determining a second similarity between a result of the execution of the second database population-change events model and the production curve of the simulated environment; and validating the first database population-change events model based on the first similarity being higher than the second similarity”; or as in claim 20 that includes “for each of the plurality of different database population-change events models, utilize at least one probability distribution to fit the training dataset to the respective model, a result of the fitting being a plurality of trained models” in combination with the other limitations recited in the context of their respective base claim(s) is allowable subject matter.
Claim 7 depends on claim 6 and claims 10-11 depend on claim 9, and thus would be allowable based on their dependency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Form PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER KHONG whose telephone number is (571)270-7127. The examiner can normally be reached Mon-Fri 8am-5pm EST.
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/ALEXANDER KHONG/Primary Examiner, Art Unit 2168