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 . Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/16/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 5-13, 17 & 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning” by Zhang et al. (published in 2019, hereinafter “Zhang”).
Zhang teaches:
1. A method, comprising:
collecting, by a processor set, a plurality of metrics for running service instances within a cloud-based system [Zhang, page 418, § 2.2.2, Metrics Collector];
classifying, by the processor set, the running service instances into a database classification using a machine learning algorithm based on the collected plurality of metrics for the running service instances [Zhang, page 419, § 3.2, State and Policy paragraphs]; and
performing, by the processor set, at least one operational decision corresponding to the classified running service instances [Zhang, page 419, § 3.2, Action paragraph].
2. The method of claim 1, wherein the plurality of metrics is selected from the group consisting of network utilization, memory utilization, CPU usage, and network connection count [Zhang, page 417, § 2.1, Training Data paragraph].
5. The method of claim 1, wherein the running service instances within the cloud-based system comprise a database service instance [Zhang, page 415, Abstract].
6. The method of claim 1, wherein performing the at least one operational decision comprises deploying a change across the cloud-based system via a phased rollout based on a classification of the running service instances [Zhang, page 419, § 3.2, Action and RL Working Process paragraphs].
7. The method of claim 6, wherein deploying the change across the cloud-based system via the phased rollout comprises deploying the change across a representative subset of the running service instances in an initial rollout phase to detect issues with the deployed change as soon as possible [Zhang, page 419, § 3.2, Action and RL Working Process paragraphs].
8. The method of claim 7, wherein deploying the change across the cloud-based system via the phased rollout further comprises deploying the change across remaining subsets of the running service instances in a final rollout phase in response to no detected issues with the deployed change across the representative subset, the remaining subsets of the running service instances including the running service instances without the representative subset of the running service instances [Zhang, page 419, § 3.2, Action and RL Working Process paragraphs].
9. The method of claim 1, wherein performing the at least one operational decision comprises adjusting alert thresholds to reflect a state and a classification of the running service instances [Zhang, page 419, § 3.2, Action and RL Working Process paragraphs].
10. The method of claim 1, wherein performing the at least one operational decision comprises applying targeted actions to the classified running service instances based on a classification of the classified running service instances [Zhang, page 419, § 3.2, Action and RL Working Process paragraphs].
11. The method of claim 1, wherein the collected metrics comprise time-series data [Zhang, page 417, § 2.1, Training Data paragraph].
12. A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
collect a plurality of metrics as time-series data for running service instances within a system [Zhang, page 418, § 2.2.2, Metrics Collector, and page 417, § 2.1, Training Data paragraph];
classify the running service instances into a color classification using a machine learning algorithm based on the collected plurality of metrics for the running service instances [Zhang, page 419, § 3.2, State and Policy paragraphs]; and
deploy a change across the system via a phased rollout based on a classification of the running service instances [Zhang, page 419, § 3.2, Action and RL Working Process paragraphs].
13. The computer program product of claim 12, wherein the system comprises a cloud-based system [Zhang, page 415, Abstract].
17. The computer program product of claim 12, wherein the deploying the change across the system via the phased rollout comprises deploying the change across a representative subset of the running service instances in an initial rollout phase to detect issues with the deployed change as soon as possible [Zhang, page 419, § 3.2, Action and RL Working Process paragraphs].
18. The computer program product of claim 17, wherein the deploying the change across the cloud-based system via the phased rollout further comprises deploying the change across remaining subsets of the running service instances in a final rollout phase in response to no detected issues with the deployed change across the representative subset, the remaining subsets of the running service instances including the running service instances without the representative subset of the running service instances [Zhang, page 419, § 3.2, Action and RL Working Process paragraphs].
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 (i.e., changing from AIA to pre-AIA ) 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 3, 4, 14-16, 19 & 20 are rejected under 35 U.S.C. 103 as being unpatentable over:
(i) Zhang in view of
(ii) “Analysis of Unsupervised Machine Learning Techniques for an Efficient Customer Segmentation using Clustering Ensemble and Spectral Clustering” by Hicham & Karim (published in 2022, hereinafter “Hicham”).
Zhang does not explicitly teach, but Hicham teaches:
3. The method of claim 1, wherein the machine learning algorithm comprises a k-means clustering algorithm [Hicham, page 122, right column, third paragraph].
Zhang and Hicham are analogous art because they are in the same field of endeavor, machine learning algorithm applications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Zhang with the various machine learning algorithms discussed in Hicham to provide the skilled artisan with alternative algorithms for clustering and optimization use cases in various applications.
The combination of Zhang and Hicham teaches:
4. The method of claim 1, wherein the machine learning algorithm comprises a dbscan algorithm [Hicham, page 122, right column, third paragraph].
14. The computer program product of claim 12, wherein the machine learning algorithm comprises a k-means clustering algorithm [Hicham, page 122, right column, third paragraph].
15. The computer program product of claim 12, wherein the machine learning algorithm comprises a dbscan algorithm [Hicham, page 122, right column, third paragraph].
16. The computer program product of claim 12, wherein the machine learning algorithm comprises a spectral clustering algorithm [Hicham, page 122, right column, third paragraph].
19. A system comprising:
a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
collect a plurality of metrics for running service instances within a cloud-based system [Zhang, page 418, § 2.2.2, Metrics Collector];
classify the running service instances into a database classification using a k-means clustering algorithm [Hicham, page 122, right column, third paragraph] based on the collected plurality of metrics for the running service instances [Zhang, page 419, § 3.2, State and Policy paragraphs]; and
perform at least one operational decision corresponding to the classified running service instances [Zhang, page 419, § 3.2, Action paragraph].
20. The system of claim 19, wherein the plurality of metrics is selected from the group consisting of a network utilization, memory utilization, CPU usage, and network connection count [Zhang, page 417, § 2.1, Training Data paragraph].
Conclusion
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/Scott A. Waldron/Primary Examiner, Art Unit 2156
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