DETAILED ACTION
Claims 1-20 are presented for examination.
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.
Claims 1-4 and 7-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
As per claim 1, in step 1 of the 101 analysis, the examiner has determined that the claim is directed to a method. Therefore, the claim is directed to one of the four statutory categories of invention.
In step 2A prong 1 of the 101 analysis, the examiner has determined that the claim recites a judicial exception. Specifically, the limitations “determining, based on the first metric data, that an allocation of a computing resource for the software application is to be reduced from a first level of allocation; performing a mitigative check to determine a performance degradation likelihood score; and reducing allocation of the computing resource to a third level that is lower than the first level based on a determination that the performance degradation likelihood score does not meet a threshold score” recite mental processes. The limitations encompass a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1 Step 2A.
In step 2A prong 2 of the 101 analysis, the examiner has determined that the additional elements, alone or in combination do not integrate the judicial exceptions into a practical application for the following rationale:
The limitations “receiving first metric data associated with the software application executing on a distributed computing system” represent insignificant, extra-solution activities. The term "extra-solution activity" can be understood as "activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim" (MPEP 2106.05(g)). The examiner has determined that the limitations “receiving first metric data associated with the software application executing on a distributed computing system” are directed to mere data gathering activities which is a category of insignificant extra-solution activities (MPEP 2106.05(g)).
In step 2B of the 101 analysis, the examiner has determined that the additional elements, alone or in combination do not recite significantly more than the abstract ideas identified above for the following rationale:
The limitations “receiving first metric data associated with the software application executing on a distributed computing system” represent insignificant, extra-solution activities and are well-understood, routine, or conventional because they are directed to "receiving or transmitting data" (MPEP 2106.05(d)). These are additional elements that the courts have recognized as well understood, routine, or conventional (MPEP 2106.05(d)). The citation of court cases in the MPEP meets the Berkheimer evidentiary burden since citation of a court case in the MPEP is one of the 4 types of evidentiary support that can be used to prove that the additional elements are well-understood, routine, or conventional (see 125 USPQ2d 1649 Berkheimer v. HP, Inc.). Thus, the limitations do not amount to significantly more than the abstract idea.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
As per claim 12, it is a media/product type claim of claim 1, so it is rejected for the same reasons as claim 1. Additionally, claim 12 recites “a non-transitory computer-readable medium comprising instructions that, responsive to execution by a processing device” which are generic computing components that do not integrate the judicial exceptions into a practical application and do not provide significantly more.
As per claim 2, it recites “wherein performing the mitigative check comprises: providing feature attributes of the software application to a trained machine learning (ML) model; identifying, by the trained ML model based on the provided feature attributes, one or more congruent software applications that are similar to the software application; comparing the first level of allocation of the computing resource for the software application to a level of allocation of the computing resource for the one or more congruent software applications; and determining the performance degradation likelihood score based on the comparison”, which encompasses a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
As per claim 3, it recites “wherein performing the mitigative check comprises: identifying a second software application that is associated with the software application, wherein the second software application is one of: a dependent application an adjacent application, and an invoking application of the software application obtaining baseline metric data associated with the second software application executing on the distributed computing system; adjusting a level of allocation of the computing resource of the second software application, wherein the adjustment comprises one or more of: an increase and a decrease in the level of allocation of the computing resource; obtaining post adjustment metric data associated with the second software application executing on the distributed computing system subsequent to the adjustment of the level of allocation of the computing resource of the second software application; and determining the performance degradation likelihood score based on the comparison of the post adjustment metric data and the baseline metric data”, which encompasses a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
As per claim 4, it recites “wherein determining that the allocation of a computing resource for the software application is to be reduced from the first level of allocation comprises determining a second level of allocation, and wherein performing the mitigative check comprises: reducing a level of allocation of the computing resource to a fourth level of allocation greater than the second level of allocation; obtaining post reduction metric data associated with the software application executing on the distributed computing system subsequent to the reduction of the level of allocation of the computing resource of the software application to the fourth level; and determining the performance degradation likelihood score based on the post reduction metric data”, which encompasses a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
As per claim 7, it recites “obtaining historical metric data associated with the software application; programmatically analyzing the obtained historical metric data and the first metric data; and determining that the allocation of the computing resource is to be reduced from a first level of allocation based on the programmatic analysis”, which encompasses a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
As per claim 8 (and similarly for claim 13), it recites “wherein the computing resource is memory allocated to the software application on the distributed computing system” which further describes the abstract idea.
As per claim 9 (and similarly for claim 14), it recites “wherein the distributed computing system is a serverless computing system, and wherein the software application is a function or package configured to be executable on the serverless computing system” which further describes the abstract idea.
As per claim 10 (and similarly for claim 15), it recites “obtaining metric data for a second software application at a plurality of allocation setpoints for the computing resource, and wherein determining that the allocation of the computing resource is to be reduced from the first level of allocation is based on a comparison of the first metric data to the obtained metric data for the second software application at the plurality of allocation setpoints; and determining an optimal allocation setpoint for the computing resource based on the comparison”, which encompasses a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
As per claim 11, it recites “wherein determining, based on the first metric data, that an allocation of a computing resource for the software application is to be reduced from a first level of allocation comprises: providing the first metric data to a second trained machine learning model; and receiving, from the second trained machine learning model, a second level of allocation for the computing resource, wherein the second level of allocation is lower than the first level of allocation” which is represent insignificant, extra-solution activities and do not provide significantly more.
As per claim 16, in step 1 of the 101 analysis, the examiner has determined that the claim is directed to a method. Therefore, the claim is directed to one of the four statutory categories of invention.
In step 2A prong 1 of the 101 analysis, the examiner has determined that the claim recites a judicial exception. Specifically, the limitations “determining, based on the first metric data, that an allocation of a computing resource for the software application is to be reduced from a first level of allocation; determining, that an allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application; and based on a determination that the allocation of the computing resource to the third level of allocation for the software application is likely to not cause the performance degradation, reducing allocation of the computing resource to the third level that is lower than the first level of allocation” recite mental processes. The limitations encompass a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1 Step 2A.
In step 2A prong 2 of the 101 analysis, the examiner has determined that the additional elements, alone or in combination do not integrate the judicial exceptions into a practical application for the following rationale:
The limitations “receiving first metric data associated with the software application executing on a distributed computing system” represent insignificant, extra-solution activities. The term "extra-solution activity" can be understood as "activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim" (MPEP 2106.05(g)). The examiner has determined that the limitations “receiving first metric data associated with the software application executing on a distributed computing system” are directed to mere data gathering activities which is a category of insignificant extra-solution activities (MPEP 2106.05(g)).
In step 2B of the 101 analysis, the examiner has determined that the additional elements, alone or in combination do not recite significantly more than the abstract ideas identified above for the following rationale:
The limitations “receiving first metric data associated with the software application executing on a distributed computing system” represent insignificant, extra-solution activities and are well-understood, routine, or conventional because they are directed to "receiving or transmitting data" (MPEP 2106.05(d)). These are additional elements that the courts have recognized as well understood, routine, or conventional (MPEP 2106.05(d)). The citation of court cases in the MPEP meets the Berkheimer evidentiary burden since citation of a court case in the MPEP is one of the 4 types of evidentiary support that can be used to prove that the additional elements are well-understood, routine, or conventional (see 125 USPQ2d 1649 Berkheimer v. HP, Inc.). Thus, the limitations do not amount to significantly more than the abstract idea.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
As per claim 17, it recites “wherein determining that the allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application comprises: providing feature attributes of the software application to a trained machine learning (ML) model; identifying, by the trained ML model based on the provided feature attributes, one or more congruent software applications that are similar to the software application; comparing the first level of allocation of the computing resource for the software application to a level of allocation of the computing resource for the one or more congruent software applications; and determining that the allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application based on the comparison”, which encompasses a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
As per claim 18, it recites “wherein determining, that the allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application comprises: identifying a second software application that is associated with the software application, wherein the second software application is one of: a dependent application, an adjacent application, and an invoking application of the software application; obtaining baseline metric data associated with the second software application executing on the distributed computing system; adjusting a level of allocation of the computing resource of the second software application, wherein the adjustment comprises one or more of: an increase and a decrease in the level of allocation of the computing resource; obtaining post adjustment metric data associated with the second software application executing on the distributed computing system subsequent to the adjustment of the level of allocation of the computing resource of the second software application; and determining that the allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application based on the comparison of the post adjustment metric data and the baseline metric data”, which encompasses a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
As per claim 19, it recites “wherein determining that the allocation of a computing resource for the software application is to be reduced from the first level of allocation comprises determining a second level of allocation, and wherein determining, that the allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application comprises: reducing a level of allocation of the computing resource to a fourth level of allocation greater than the second level of allocation; obtaining post reduction metric data associated with the software application executing on the distributed computing system subsequent to reduction of the level of allocation of the computing resource of the software application to the fourth level; and determining that the allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application based on the post reduction metric data”, which encompasses a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
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) 1-2 and 7-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suarez et al. (US Pat No. 10,782,990) further in view of Jothiprakash (US Pat No. 12,158,828).
Regarding claim 1, Suarez teaches a method to manage a computing resource allocation for a software application implemented on a distributed computing system, comprising:
receiving first metric data associated with the software application executing on a distributed computing system (col 3 lines 46-57);
determining, based on the first metric data, that an allocation of a computing resource for the software application is to be reduced from a first level of allocation (col 3 lines 57-60, wherein container instances are to be removed based on the metric data);
reducing allocation of the computing resource to a third level that is lower than the first level
Suarez does not teach performing a mitigative check to determine a performance degradation likelihood score.
Jothiprakash using a machine learning model to predict an impact of subsequent external events on application performance and scale computing resources used to host the application based in part on the predicted impact of the external events on the performance of the application (col 3 lines 26-32). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to performing a mitigative check a performance degradation likelihood score A person having ordinary skill in the art would have understood that a change in resource allocation (scaling down) constitutes an “external” event and that predicting its impact on application performance is a logical step to avoid detrimental consequences.
Suarez and Jothiprakash do not explicitly teach reducing allocation of the computing resource based on a determination that the performance degradation likelihood score does not meet a threshold score.
It is old and well known that machine learning models inherently produce probabilistic outputs or scores representing the confidence or likelihood of an event (in this case, performance degradation). A person having ordinary skill in the art would understand that these outputs require a threshold to translate predictions into actionable decisions. Without a threshold, the system would be constantly reacting to even minor predicted fluctuations in performance, leading to instability and unnecessary resource adjustments. Furthermore, the use of thresholds is a well-established practice in control systems and signal processing. Setting a threshold allow for filtering out noise, minimizing false positives, and ensuring that decisions are only made when the predicted impact exceeds a meaningful level.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate a “threshold score” to determine when to reduce resources based on the performance degradation likelihood score predicted by the machine learning model in Jothiprakash. One would have been motivated by the desire to obvious step to implement a more robust and reliable resource allocation system.
Regarding claim 2, Jothiprakash teaches wherein performing the mitigative check comprises: providing feature attributes of the software application to a trained machine learning (ML) model (col 16 lines 36-50); identifying, by the trained ML model based on the provided feature attributes, one or more congruent software applications that are similar to the software application(col 16 lines 36-50); comparing the first level of allocation of the computing resource for the software application to a level of allocation of the computing resource for the one or more congruent software applications (col 16 lines 50-56); and determining the performance degradation likelihood score based on the comparison (col 16 lines 50-56).
Regarding claim 7, Jothiprakash teaches obtaining historical metric data associated with the software application (col 16 lines 36-50); programmatically analyzing the obtained historical metric data and the first metric data (col 16 lines 50-56); and determining that the allocation of the computing resource is to be reduced from a first level of allocation based on the programmatic analysis (col 16 lines 57-67).
Regarding claim 8, Jothiprakash teaches wherein the computing resource is memory allocated to the software application on the distributed computing system (col 3 lines 61-64, wherein it is inherent that the computing resource can comprise memory).
Regarding claim 9, Suarez teaches wherein the distributed computing system is a serverless computing system, and wherein the software application is a function or package configured to be executable on the serverless computing system (col 1 lines 5-24).
Regarding claim 10, Jothiprakash teaches further comprising: obtaining metric data for a second software application at a plurality of allocation setpoints for the computing resource (col 16 lines 36-50), and wherein determining that the allocation of the computing resource is to be reduced from the first level of allocation is based on a comparison of the first metric data to the obtained metric data for the second software application at the plurality of allocation setpoints (col 16 lines 50-56); and determining an optimal allocation setpoint for the computing resource based on the comparison (col 16 lines 57-67; col 1 lines 16-32).
Regarding claim 11, Jothiprakash teaches wherein determining, based on the first metric data, that an allocation of a computing resource for the software application is to be reduced from a first level of allocation comprises: providing the first metric data to a second trained machine learning mode (col 16 lines 36-50); and receiving, from the second trained machine learning model, a second level of allocation for the computing resource, wherein the second level of allocation is lower than the first level of allocation (col 16 lines 50-67).
Regarding claims 12-15, it is the non-transitory computer-readable medium claims of claims 1 and 8-10 above. Therefore, they are rejected for the same reasons as claims 1 and 8-10 above.
Regarding claim 16, Suarez teaches a method to manage a computing resource allocation for a software application implemented on a distributed computing system, comprising:
receiving first metric data associated with the software application executing on a distributed computing system (col 3 lines 46-57);
determining, based on the first metric data, that an allocation of a computing resource for the software application is to be reduced from a first level of allocation (col 3 lines 57-60, wherein container instances are to be removed based on the metric data);
Suarez does not teach determining, that an allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application.
Jothiprakash using a machine learning model to predict an impact of subsequent external events on application performance and scale computing resources used to host the application based in part on the predicted impact of the external events on the performance of the application (col 3 lines 26-32). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to determine, that an allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application. A person having ordinary skill in the art would have understood that a change in resource allocation (scaling down) constitutes an “external” event and that predicting its impact on application performance is a logical step to avoid detrimental consequences.
Regarding claim 17, Jothiprakash teaches wherein determining that the allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application comprises: providing feature attributes of the software application to a trained machine learning (ML) model (col 16 lines 36-50); identifying, by the trained ML model based on the provided feature attributes, one or more congruent software applications that are similar to the software application (col 16 lines 36-50); comparing the first level of allocation of the computing resource for the software application to a level of allocation of the computing resource for the one or more congruent software applications (col 16 lines 50-56); and determining that the allocation of the computing resource to a third level of allocation for the software application that is lower than the first level is likely to not cause a performance degradation of the software application based on the comparison (col 16 lines 50-56).
Allowable Subject Matter
Claims 3-4 and 18-19 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claims 5-6 and 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC C WAI whose telephone number is (571)270-1012. The examiner can normally be reached Monday - Friday 9-5.
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/Eric C Wai/Primary Examiner, Art Unit 2195