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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter.
Claims 1-6, and 13-14 are rejected under 35 U.S.C. 101.
As per claim 1, the claim recites a method, therefore is a process.
“ . . . counting … determining … from each number of the plurality of counted numbers that are obtained, whether the job is active or inactive … “ These limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. Thus, the claim recites a mental process.
The elements of “obtaining a set of samples for said each metric … ” amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g); this limitation is also a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g). The claim is ineligible.
As discussed above, “obtaining … ” amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g); this limitation is also a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. “emitting a termination command to terminate the job and release each resource” is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See Cheng. The claim is ineligible.
As per claim 2, see rejection on claim 1. “wherein each machine learning model of said at least one machine learning model is a one class classification machine learning algorithm … ” is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See Ucci. The claim is ineligible.
As per claim 3, see rejection on claim 1. “wherein the set of samples corresponds to a predefined duration of activity“ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See Kumar. The claim is ineligible.
As per claim 4, see rejection on claim 1. “… determine if the job is active or inactive from said each number of the plurality of counted numbers that is obtained “ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See Holler. The claim is ineligible.
As per claim 5, see rejection on claim 1. “determine if the at least one metric associated therewith is active or inactive … the determining whether the job is active or inactive comprises for said each node comprising said at least one resource allocated to the job … determining whether said each metric is active or inactive by feeding the at least one machine learning model that corresponds therewith with the plurality of counted numbers associated . . . determining whether the each node is active or inactive based on a number of metrics determined as inactive … determining whether the job is active or inactive based on a number of nodes determined as inactive “ These limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. The claim is ineligible.
As per claim 6, see rejection on claim 5. “… determined as inactive when the number of metrics that is determined as inactive exceeds a metric threshold, and wherein the job is determined as inactive when the number of nodes that is determined as inactive exceeds a node threshold “These limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. The claim is ineligible.
As claim 13, see rejection on claim 1.
As per claim 14, see rejection on claim 13. The elements of “a non-transitory computer program product . . . non-transitory high-performance computer” are recited at a high-level of generality(i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
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.
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 1 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ruan et al (Ruan, Shaolun, et al. "BatchLens: A visualization approach for analyzing batch jobs in cloud systems." 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2022) (hereinafter Ruan) in view of Cheng et al (US 2003/0005130) (hereinafter Cheng).
As per claim 1, Ruan teaches:
A method for releasing resources in a high-performance computer , the high-performance computer comprising at least one node, each node comprising at least one resource and being associated with at least one metric, each metric taking values within a range of values divided into a plurality of sub-ranges of values, said at least one resource being allocated to a job, the method comprising:
for said each node comprising said at least one resource allocated to the job, for said each metric associated with the node, obtaining a set of samples for said metric (Ruan, III. VISUAL DESIGN, B. Line Charts—under BRI, a set of samples can be samples that form a valley or spike);
counting, for each sub-range of values of the plurality of sub-ranges of values associated with said each metric, a number of samples of the set of samples whose values are comprised within said plurality of sub-range of values, in order to obtain a plurality of counted numbers for said each metric, each number of the plurality of counted numbers being related to one of the plurality of sub-ranges of values (Ruan, III. VISUAL DESIGN, B. Line Charts—under BRI, counting a number of samples of the set of samples whose values are comprised within values . . . each number of the plurality of counted numbers being related to one of the plurality of sub-ranges of values can be considering samples whose values are comprised within said plurality of sub-range of values [0-infinity]);
determining, using at least one model, from each number of the plurality of counted numbers that are obtained, whether the job is active or inactive (Ruan, IV. CASE STUDY—under BRI, an inactive job can be jobs associated with thrashing);
Ruan does not expressly teach:
wherein the model is a machine learning model;
if the job is determined as inactive, emitting a termination command to terminate the job and release each resource of said at least one resource that is allocated to the job.
However, Ruan discloses (in another embodiment):
wherein the model is a machine learning model (Ruan, I. INTRODUCTION);
Both Ruan and Ruan pertain to the art of cloud computing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Ruan’s method to use a deep learning model because it is well-known in the art that deep learning models provide state-of-the-art accuracy for complex tasks.
Ruan does not expressly teach:
if the job is determined as inactive, emitting a termination command to terminate the job and release each resource of said at least one resource that is allocated to the job.
However, Cheng discloses:
if the job is determined as inactive, emitting a termination command to terminate the job and release each resource of said at least one resource that is allocated to the job (Cheng, [0050]—under BRI, a termination command can be the FAILED message).
Both Cheng and Ruan pertain to the art of cloud computing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Cheng’s method to terminate a job and return resources because it is well-known in the art that when a job is finished, the resources are no longer needed, thus free for other task to utilize.
As per claim 13, see rejection on claim 1.
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ruan/Cheng as applied above, and further in view of Ucci et al (US 2023/0216746) (hereinafter Ucci) .
As per claim 2, Ruan/Cheng teaches:
The method according to claim 1 (see rejection on claim 1).
Ruan/Cheng does not expressly teach:
wherein each machine learning model of said at least one machine learning model is a one class classification machine learning algorithm.
However, Ucci discloses:
wherein each machine learning model of said at least one machine learning model is a one class classification machine learning algorithm (Ucci, [0050]—under BRI, a one class classification machine learning algorithm can be one-class SVM).
Both Ucci and Ruan/Cheng pertain to the art of cloud computing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Ucci’s method to use a one class classification machine learning algorithm because it is well-known in the art that one-class classification (OCC) simplifies building machine learning models by training exclusively on data representing the target or "normal" class. Its primary benefit is enabling accurate anomaly and novelty detection when obtaining labeled data for rare events or failures is too difficult, expensive, or impossible.
As per claim 14, Ruan/Cheng teaches:
The non-transitory high-performance computer according to claim 13 (see rejection on claim 13).
Ruan/Cheng does not expressly teach:
further comprising a non-transitory computer program product comprising instructions configured to be executed by said non-transitory high-performance computer .
However, Ucci discloses:
further comprising a non-transitory computer program product comprising instructions configured to be executed by said non-transitory high-performance computer (Ucci, claim 12).
Both Ucci and Ruan/Cheng pertain to the art of cloud computing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Ucci’s method to use a non-transitory computer program product comprising instructions configured to be executed by a non-transitory high-performance computer because using a non-transitory computer program product with computer-executable instructions provides permanent, tangible storage on physical media like hard drives or DVDs. This ensures software reliability, offline availability, secure distribution, and clear intellectual property ownership compared to temporary data streams.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Ruan/Cheng as applied to claim 1above, and further in view of Kumar et al (US 2020/0183703) (hereinafter Kumar).
As per claim 3, Ruan/Cheng teaches:
The method according to claim 1 (see rejection on claim 1).
Ruan/Cheng does not expressly teach:
wherein the set of samples corresponds to a predefined duration of activity.
However, Kumar discloses:
wherein the set of samples corresponds to a predefined duration of activity( Kumar, [0039]).
Both Kumar and Ruan/Cheng pertain to the art of cloud computing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Kumar’s method to use sampling periods because it is well-known in the art that using a specific sampling period offers core benefits like drastically reduced resource costs, minimized processing time, and lower data storage requirements.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ruan/Cheng as applied to claim 1 above, and further in view of Holler et al (US 2015/0058843) (hereinafter Holler).
As per claim 4, Ruan/Cheng teaches:
The method according to claim 1 (see rejection on claim 1), wherein a single model is a machine learning model (Ruan, I. INTRODUCTION).
Ruan/Cheng does not expressly teach:
wherein the determining whether the job is active or inactive uses the single model able to directly determine if the job is active or inactive from said each number of the plurality of counted numbers that is obtained.
However, Holler discloses:
wherein the determining whether the job is active or inactive uses a single model able to directly determine if the job is active or inactive from said each number of the plurality of counted numbers that is obtained (Holler, [0041]).
Both Holler and Ruan/Cheng pertain to the art of cloud computing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Holler’s method to determine job status because it is well-known in the art that knowing job status would help system make critical decisions such as reclaim resources for other jobs.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2013/0074081 teaches a method of terminating jobs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLIE SUN whose telephone number is (571)270-5100. The examiner can normally be reached 9AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached at (571) 272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLIE SUN/Primary Examiner, Art Unit 2198
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