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 .
Specification
The disclosure is objected to because of the following informalities:
On paragraph [0038], “As further shown in FIG. 5, the data collector 210 can collected data according to an automated scheduled collection procedure 520” should read “As further shown in FIG. 5, the data collector 210 can collect data according to an automated scheduled collection procedure 520.”
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a performance modeler that predicts, using a machine learning model and based on benchmark data associated with past performance metrics for a workload as performed by a computing system configured according to a first configuration, future performance metrics for the workload for respective candidate configurations” in claim 1.
“an upgrade recommendation engine that, based on the future performance metrics predicted by the performance modeler, generates a recommendation associated with changing the first configuration of the computing system to a second configuration of the candidate configurations” in claim 1.
“a data collector that facilitates collection of time series data at the computing system” in claim 2.
“a data synthesizer that facilitates providing the time series data to the machine learning model” in claim 3.
“wherein the data synthesizer further provides, to the machine learning model, system configuration data relating to the candidate configurations of the computing system” in claim 4.
“wherein the performance modeler constructs respective ones of the candidate configurations using respective groups of the second hardware” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claims are directed towards an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a system and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites:
a performance modeler that predicts…and based on benchmark data associated with past performance metrics for a workload as performed by a computing system configured according to a first configuration, future performance metrics for the workload for respective candidate configurations, comprising the first configuration, of the computing system (This limitation is a mental process as it encompasses a human mentally predicting performance metrics and is thus an evaluation.)
and an upgrade recommendation engine that, based on the future performance metrics predicted by the performance modeler, generates a recommendation associated with changing the first configuration of the computing system to a second configuration of the candidate configurations (This limitation is a mental process as it encompasses a human mentally generating recommendations and is thus an evaluation.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of:
A system, comprising: at least one memory that stores executable components; and at least one processor that executes the executable components stored in the at least one memory, wherein the executable components comprise (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
using a machine learning model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
A system, comprising: at least one memory that stores executable components; and at least one processor that executes the executable components stored in the at least one memory, wherein the executable components comprise uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
using a machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites the same abstract idea as claim 1. Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of:
wherein the executable components further comprise: a data collector that facilitates collection of time series data at the computing system, the time series data relating to performance of the computing system and comprising the benchmark data (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
wherein the executable components further comprise: a data collector that facilitates collection of time series data at the computing system, the time series data relating to performance of the computing system and comprising the benchmark data uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites:
and wherein the performance modeler predicts the future performance metrics in response to the time series data being determined to have been successfully provided to the machine learning model (This limitation is a mental process as it encompasses a human mentally predicting performance metrics and is thus an evaluation.)
Therefore, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites additional elements of:
wherein the executable components further comprise: a data synthesizer that facilitates providing the time series data to the machine learning model (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 3 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
wherein the executable components further comprise: a data synthesizer that facilitates providing the time series data to the machine learning model is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites the same abstract idea as claim 3. Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional elements of:
wherein the data synthesizer further provides, to the machine learning model, system configuration data relating to the candidate configurations of the computing system (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 4 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
wherein the data synthesizer further provides, to the machine learning model, system configuration data relating to the candidate configurations of the computing system is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites:
wherein the benchmark data is associated with a hardware device of the computing system (This limitation is a mental process as it is further modifying the benchmark data defined in claim 1.)
Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 does not further recite any additional elements. Therefore, claim 5 is not
integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 5 does not provide significantly more than
the abstract idea itself, taken alone and in combination. Therefore, claim 5 is subject-matter
ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites:
wherein the hardware device is a first hardware device (This limitation is a mental process as it is further modifying the hardware device defined in claim 5.)
and wherein the recommendation generated by the upgrade recommendation engine relates to an action selected from a group of actions comprising (1) replacing the first hardware device with a second hardware device that is not the first hardware device and (2) adding a third hardware device to the computing system that is not the first hardware device or the second hardware device (This limitation is a mental process as it is further modifying the recommendation defined in claim 1.)
Therefore, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 does not further recite any additional elements. Therefore, claim 6 is not
integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 6 does not provide significantly more than
the abstract idea itself, taken alone and in combination. Therefore, claim 6 is subject-matter
ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites:
wherein the benchmark data relates to performance of the hardware device while configured according to a first configuration property (This limitation is a mental process as it is further modifying the benchmark data defined in claim 1.)
and wherein the recommendation generated by the upgrade recommendation engine relates to changing the first configuration property of the hardware device to a second configuration property that is not the first configuration property (This limitation is a mental process as it is further modifying the recommendation defined in claim 1.)
Therefore, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 does not further recite any additional elements. Therefore, claim 7 is not
integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 7 does not provide significantly more than
the abstract idea itself, taken alone and in combination. Therefore, claim 7 is subject-matter
ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites the same abstract idea as claim 1. Therefore, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 further recites additional elements of:
wherein the machine learning model is trained using first data associated with first hardware of the computing system and second data associated with second hardware, comprising the first hardware and at least one other hardware other than the first hardware (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of training a model (see MPEP 2106.05(g)).)
Therefore, claim 8 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 8 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
wherein the machine learning model is trained using first data associated with first hardware of the computing system and second data associated with second hardware, comprising the first hardware and at least one other hardware other than the first hardware is the well understood, routine, and conventional activity of iteratively training a model (US 2021/0125108 A1, Metzler et al. page 11, paragraph 0052, “a classifier is trained using a conventional iterative machine learning training process that determines weights for each result list position”).
Therefore, claim 8 is subject-matter ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites:
wherein the performance modeler constructs respective ones of the candidate configurations using respective groups of the second hardware (This limitation is a mental process as it encompasses a human mentally constructing configurations and is thus an evaluation.)
Therefore, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 does not further recite any additional elements. Therefore, claim 9 is not
integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 9 does not provide significantly more than
the abstract idea itself, taken alone and in combination. Therefore, claim 9 is subject-matter
ineligible.
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 10 recites:
wherein the recommendation generated by the upgrade recommendation engine comprises an explanation of a reason for the recommendation (This limitation is a mental process as it is further modifying the recommendation defined in claim 1.)
Therefore, claim 10 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 10 does not further recite any additional elements. Therefore, claim 10 is not
integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 10 does not provide significantly more than
the abstract idea itself, taken alone and in combination. Therefore, claim 10 is subject-matter
ineligible.
Regarding Claim 11:
Subject Matter Eligibility Analysis Step 1:
Claim 11 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 11 recites:
A method, comprising: determining…predicted performance metrics for the workload as performed by the second system at a second time that is after the first time for respective candidate configurations, comprising the recorded configuration, of the second system at the second time (This limitation is a mental process as it encompasses a human mentally determining performance metrics and is thus an evaluation.)
and generating, by the first system and based on the predicted performance metrics, a recommendation associated with changing the recorded configuration of the second system to a candidate configuration of the candidate configurations (This limitation is a mental process as it encompasses a human mentally generating recommendations and is thus an evaluation.)
Therefore, claim 11 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 11 further recites additional elements of:
by a first system comprising at least one processor (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
and using a machine learning model applied to recorded performance metrics for a workload performed at a first time by a second system configured according to a recorded configuration (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 11 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 11 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
by a first system comprising at least one processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and using a machine learning model applied to recorded performance metrics for a workload performed at a first time by a second system configured according to a recorded configuration uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 11 is subject-matter ineligible.
Regarding Claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 12 recites the same abstract idea as claim 11. Therefore, claim 12 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 12 further recites additional elements of:
facilitating, by the first system, collection of time series data at the second system, the time series data comprising the recorded performance metrics for the workload (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 12 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 12 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
facilitating, by the first system, collection of time series data at the second system, the time series data comprising the recorded performance metrics for the workload uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 12 is subject-matter ineligible.
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 13 recites:
wherein the determining of the predicted performance metrics is in response to the time series data being determined to have been successfully transferred to the machine learning model (This limitation is a mental process as it is further modifying the determining of the predicted performance metrics defined in claim 11.)
Therefore, claim 13 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 13 further recites additional elements of:
facilitating, by the first system, a transfer of the time series data from the second system to the machine learning model (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
Therefore, claim 13 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 13 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
facilitating, by the first system, a transfer of the time series data from the second system to the machine learning model is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 13 is subject-matter ineligible.
Regarding Claim 14:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 14 recites the same abstract idea as claim 3. Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 14 further recites additional elements of:
wherein the recorded performance metrics relate to a hardware component of the second system (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 14 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 14 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
wherein the recorded performance metrics relate to a hardware component of the second system uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 14 is subject-matter ineligible.
Regarding Claim 15:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 15 recites:
and wherein the recommendation relates to an action selected from a group of actions comprising: replacing the first hardware component with a second hardware component that is not the first hardware component and adding a third hardware component to the second system (This limitation is a mental process as it is further modifying the recommendation defined in claim 11.)
Therefore, claim 15 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 15 further recites additional elements of:
wherein the hardware component is a first hardware component (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 15 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 15 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
wherein the hardware component is a first hardware component uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 15 is subject-matter ineligible.
Regarding Claim 16:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 16 recites:
and wherein the recommendation relates to changing the first configuration property of the hardware component to a second configuration property that is not the first configuration property (This limitation is a mental process as it is further modifying the recommendation defined in claim 11.)
Therefore, claim 16 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 16 further recites additional elements of:
wherein the recorded performance metrics further relate to a first configuration property of the hardware component (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 16 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 16 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
wherein the recorded performance metrics further relate to a first configuration property of the hardware component uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 16 is subject-matter ineligible.
Regarding Claim 17:
Subject Matter Eligibility Analysis Step 1:
Claim 17 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 17 recites:
predicting…based on performance data associated with first performance metrics for a workload as performed by a computing system while configured according to a first configuration, second performance metrics for the workload as performed by the computing system while configured according to respective candidate configurations comprising the first configuration (This limitation is a mental process as it encompasses a human mentally predicting performance metrics and is thus an evaluation.)
and based on the second performance metrics, generating a recommendation associated with changing the first configuration of the computing system to a second configuration of the candidate configurations (This limitation is a mental process as it encompasses a human mentally generating recommendations and is thus an evaluation.)
Therefore, claim 17 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 17 further recites additional elements of:
A non-transitory machine-readable medium comprising computer executable instructions that, when executed by at least one processor, facilitate performance of operations, the operations comprising (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
using a machine learning model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 17 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 17 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
A non-transitory machine-readable medium comprising computer executable instructions that, when executed by at least one processor, facilitate performance of operations, the operations comprising uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
using a machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 17 is subject-matter ineligible.
Regarding Claim 18:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 18 recites:
wherein the predicting of the second performance metrics is in response to the time series data being determined to have been successfully transferred to the machine learning model (This limitation is a mental process as it is further modifying the predicting of the second performance metrics defined in claim 17.)
Therefore, claim 18 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 18 further recites additional elements of:
wherein the operations further comprise: collecting the performance data from the computing system as time series data (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
and transferring the time series data to the machine learning model (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
Therefore, claim 18 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 18 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
wherein the operations further comprise: collecting the performance data from the computing system as time series data uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and transferring the time series data to the machine learning model is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 18 is subject-matter ineligible.
Regarding Claim 19:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 19 recites:
wherein the performance data is associated with a hardware device of the computing system (This limitation is a mental process as it is further modifying the performance data defined in claim 17.)
Therefore, claim 19 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 19 does not further recite any additional elements. Therefore, claim 19 is not
integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 19 does not provide significantly more than
the abstract idea itself, taken alone and in combination. Therefore, claim 19 is subject-matter
ineligible.
Regarding Claim 20:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 20 recites:
wherein the hardware device is a first hardware device (This limitation is a mental process as it is further modifying the hardware device defined in claim 19.)
and wherein the recommendation relates to an action selected from a group of actions comprising: replacing the first hardware device with a second hardware device that is not the first hardware device, adding a third hardware device to the computing system, and changing a configuration property of the first hardware device (This limitation is a mental process as it is further modifying the recommendation defined in claim 17.)
Therefore, claim 20 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 20 does not further recite any additional elements. Therefore, claim 20 is not
integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 20 does not provide significantly more than
the abstract idea itself, taken alone and in combination. Therefore, claim 20 is subject-matter
ineligible.
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)(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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-9, 11-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nagpal et al. (US20200034745).
Regarding claim 1, Nagpal teaches
A system, comprising: at least one memory that stores executable components; and at least one processor that executes the executable components stored in the at least one memory, wherein the executable components comprise (Nagpal, paragraph 0210, “A module as used herein can be implemented using any mix of any portions of the system memory and any extent of hard-wired circuitry including hard-wired circuitry embodied as a data processor.”)
a performance modeler that predicts, using a machine learning model and based on benchmark data associated with past performance metrics for a workload as performed by a computing system configured according to a first configuration, future performance metrics for the workload for respective candidate configurations, comprising the first configuration, of the computing system (Nagpal, paragraph 0072, “Usage data 121 comprises data that represents the usage of the resources of the system or systems to be evaluated, and usage data may be stored jointly or separately on one or more databases or on a single database such as the database 120, as illustrated.” Nagpal, paragraph 0074, “Furthermore, usage data and system/configuration data may be store separately as illustrated, or may be stored jointly. As is discussed more fully below, the analytics system uses some or all of the usage data and system and configuration data to generate, train, predict, recommend, and otherwise output results” Nagpal, paragraph 0106, “The process continues with at step 306, where the analytics system generates predictions and determines runways. As discussed previously, generating predictions and determining runways includes iterating through one or more prediction models such that a series of values are determined for one or more characteristics for some period of time, when some condition is met, or any combination thereof. A user interface facilitates a user to evaluate one or any number of characteristics to determined predictions for system usage at some future time period.” Nagpal, paragraph 0099, “Furthermore, the user can schedule daily evaluations of same without retraining the prediction models every day but Sunday. Such data can then be used to collect data for generating reports, and for a closed loop system monitoring process such that trend information over time can be used to compare the results from previous prediction models and the actual data collected, and ultimately feed that information back into the trainer 112 such that an adjustment can be made for prediction models that have been consistently over forecasted or under forecasted” Nagpal, paragraph 0085, “The prediction models can be any of a variety of known mathematical models (e.g., ARIMA, ETS, STL, THETA, TBATS, etc.), neural networks, random walk models, seasonal naïve, mean and/or linear regression models, etc.” Examiner notes the performance metrics are the usage data, the prediction models are machine learning models, and the models use previous usage data and configurations to generate future predictions of usage data.)
and an upgrade recommendation engine that, based on the future performance metrics predicted by the performance modeler, generates a recommendation associated with changing the first configuration of the computing system to a second configuration of the candidate configurations (Nagpal, paragraph 0107, “The process continues with at step 308, where the analytics system generates recommendations. As discuss previously, generating recommendations includes generating recommendations for configuration changes, hardware changes, software changes or some combination thereof.” Nagpal, paragraph 0097, “The recommendation engine may use any appropriate sizing unit 162 such that the recommendation engine may provide as inputs the predicted future values and/or requirements to the sizing unit to size the future system and/or to provide recommendations on how to modify the present system.” Nagpal, paragraph 0079, “Recommendations might pertain to the predicted performance of the measured system.” Examiner notes the recommendations are generated using predicted future performance values, and the recommendations include configurations of the system.)
Regarding claim 2, Nagpal teaches
The system of claim 1, wherein the executable components further comprise: a data collector that facilitates collection of time series data at the computing system, the time series data relating to performance of the computing system and comprising the benchmark data (Nagpal, paragraph 0005, “Time series analysis refers to the process of collecting and analyzing time series data so as to extract meaningful statistics and characteristics about the collected data.” Nagpal, paragraph 0049, “The embodiment shown in FIG. 1A1 depicts a measured system environment (left side) that includes a management console (top left) for identifying system parameters of interest and a system of interest (bottom left) such that system measurements pertaining to the parameters of interest (e.g., CPU usage, IO latency, etc.) can be collected over time. An analytics environment is also shown (right side). Measurements collected within the measured system environment (e.g., at step 103) can be forwarded to the analytics environment. The analytics environment may include a database of predictive models, often many of which pertain to the parameters of interest.” Examiner notes the benchmark data is the usage data, which are the usage parameters of interest collected.)
Regarding claim 3, Nagpal teaches
The system of claim 2, wherein the executable components further comprise: a data synthesizer that facilitates providing the time series data to the machine learning model (Nagpal, paragraph 0049, “Measurements collected within the measured system environment (e.g., at step 103) can be forwarded to the analytics environment. The analytics environment may include a database of predictive models, often many of which pertain to the parameters of interest.”)
and wherein the performance modeler predicts the future performance metrics in response to the time series data being determined to have been successfully provided to the machine learning model (Nagpal, paragraph 0106, “The process continues with at step 306, where the analytics system generates predictions and determines runways. As discussed previously, generating predictions and determining runways includes iterating through one or more prediction models such that a series of values are determined for one or more characteristics for some period of time, when some condition is met, or any combination thereof. A user interface facilitates a user to evaluate one or any number of characteristics to determined predictions for system usage at some future time period.”)
Regarding claim 4, Nagpal teaches
The system of claim 3, wherein the data synthesizer further provides, to the machine learning model, system configuration data relating to the candidate configurations of the computing system (Nagpal, paragraph 0058, “Such prediction models can be trained using selected measurements and/or usage data. In some cases, a prediction engine relies on configuration data 124 to determine the nature of predictions” Nagpal, FIG. 1A2, Database 120, Usage Data 121, System and Configuration Data 124.)
Regarding claim 5, Nagpal teaches
The system of claim 1, wherein the benchmark data is associated with a hardware device of the computing system (Nagpal, paragraph 0084, “As shown, the system 1E00 receives a plurality of measurements that have been or can be organized into a time-ordered series of measurements (e.g., selected measurement series 1091, selected measurement series 109N, etc.). The received measurements are used to form a time series data set pertaining to a particular parameter (e.g., a system parameter such as memory usage, CPU usage, IO usage, IO latency, etc.).” Examiner notes the benchmark data is the usage data, which can be associated with a hardware device of the system such as a memory or CPU.)
Regarding claim 6, Nagpal teaches
The system of claim 5, wherein the hardware device is a first hardware device, and wherein the recommendation generated by the upgrade recommendation engine relates to an action selected from a group of actions comprising (1) replacing the first hardware device with a second hardware device that is not the first hardware device and (2) adding a third hardware device to the computing system that is not the first hardware device or the second hardware device (Nagpal, paragraph 0084, “As shown, the system 1E00 receives a plurality of measurements that have been or can be organized into a time-ordered series of measurements (e.g., selected measurement series 1091, selected measurement series 109N, etc.). The received measurements are used to form a time series data set pertaining to a particular parameter (e.g., a system parameter such as memory usage, CPU usage, IO usage, IO latency, etc.).” Nagpal, paragraph 0079, “Recommendations might pertain to the predicted performance of the measured system. For example, recommendations might include adding new nodes to the system and/or adding additional solid state storage.” Examiner notes the first hardware device is a solid state storage device, wherein the associated benchmark data is the IO usage/latency, and the generated recommendation is adding a third hardware device (additional solid state storage).)
Regarding claim 7, Nagpal teaches
The system of claim 5, wherein the benchmark data relates to performance of the hardware device while configured according to a first configuration property (Nagpal, paragraph 0071, “The analytics system may interface with a one or more databases such as database 120 that contain the location(s) for storing and/or retrieving relevant inputs and/or outputs. The database may comprise any combination of physical and/or logical structures as is ordinarily used for database systems such as hard disk drives (HDDs), solid state drives (SSDs), logical partitions, and the like. Here, database 120 is illustrated as a single database containing codifications (e.g., data items, table entries, etc.) of usage data 121 and system and configuration data 124.” Examiner notes the benchmark data is the usage data, the first configuration property is included in the configuration data, and the usage data is associated with the system configured with associated configuration data.)
and wherein the recommendation generated by the upgrade recommendation engine relates to changing the first configuration property of the hardware device to a second configuration property that is not the first configuration property (Nagpal, paragraph 0061, “The recommendation engine uses the results of the prediction and runway engine to determine one or more recommendations for configuration, hardware, software and/or other changes to the system.”)
Regarding claim 8, Nagpal teaches
The system of claim 1, wherein the machine learning model is trained using first data associated with first hardware of the computing system and second data associated with second hardware, comprising the first hardware and at least one other hardware other than the first hardware (Nagpal, paragraph 0099, “a user may schedule a complete system analysis including generating predictions and runways for major components such as CPU and non-volatile storage, and recommendations for maintaining a 20% excess capacity in CPU availability and a 30% excess capacity in non-volatile storage. Furthermore, the user can schedule daily evaluations of same without retraining the prediction models every day but Sunday. Such data can then be used to collect data for generating reports, and for a closed loop system monitoring process such that trend information over time can be used to compare the results from previous prediction models and the actual data collected, and ultimately feed that information back into the trainer 112 such that an adjustment can be made for prediction models that have been consistently over forecasted or under forecasted.” Nagpal, paragraph 0100, “Specifically, the trainer uses a distributed tournament selection process across multiple systems and one or more map reduce functions to enable the system to train multiple predictions models and evaluate those models over one or more sets of data.” Examiner notes the prediction models are trained by the trainer, which uses data from previous predictions which comprise usage data from a first hardware (CPU) and a second hardware (non-volatile storage).)
Regarding claim 9, Nagpal teaches
The system of claim 8, wherein the performance modeler constructs respective ones of the candidate configurations using respective groups of the second hardware (Nagpal, paragraph 0099, “a user may schedule a complete system analysis including generating predictions and runways for major components such as CPU and non-volatile storage, and recommendations for maintaining a 20% excess capacity in CPU availability and a 30% excess capacity in non-volatile storage.” Nagpal, paragraph 0047, “Recommendations are emitted based on the predictions and determined runways. Such recommendations may include recommended configuration/settings changes, installation changes, and/or may include upgrading certain hardware or components.” Examiner notes the second hardware is the non-volatile storage, and the system generates recommendations for using and maintaining the non-volatile storage. Examiner further notes recommendations include recommended configuration changes.)
Regarding claim 11, Nagpal teaches
A method, comprising: determining, by a first system comprising at least one processor (Nagpal, paragraph 0210, “A module as used herein can be implemented using any mix of any portions of the system memory and any extent of hard-wired circuitry including hard-wired circuitry embodied as a data processor.”)
determining…and using a machine learning model applied to recorded performance metrics for a workload performed at a first time by a second system configured according to a recorded configuration, predicted performance metrics for the workload as performed by the second system at a second time that is after the first time for respective candidate configurations, comprising the recorded configuration, of the second system at the second time (Nagpal, paragraph 0072, “Usage data 121 comprises data that represents the usage of the resources of the system or systems to be evaluated, and usage data may be stored jointly or separately on one or more databases or on a single database such as the database 120, as illustrated.” Nagpal, paragraph 0074, “Furthermore, usage data and system/configuration data may be store separately as illustrated, or may be stored jointly. As is discussed more fully below, the analytics system uses some or all of the usage data and system and configuration data to generate, train, predict, recommend, and otherwise output results” Nagpal, paragraph 0106, “The process continues with at step 306, where the analytics system generates predictions and determines runways. As discussed previously, generating predictions and determining runways includes iterating through one or more prediction models such that a series of values are determined for one or more characteristics for some period of time, when some condition is met, or any combination thereof. A user interface facilitates a user to evaluate one or any number of characteristics to determined predictions for system usage at some future time period.” Nagpal, paragraph 0099, “Furthermore, the user can schedule daily evaluations of same without retraining the prediction models every day but Sunday. Such data can then be used to collect data for generating reports, and for a closed loop system monitoring process such that trend information over time can be used to compare the results from previous prediction models and the actual data collected, and ultimately feed that information back into the trainer 112 such that an adjustment can be made for prediction models that have been consistently over forecasted or under forecasted” Nagpal, paragraph 0085, “The prediction models can be any of a variety of known mathematical models (e.g., ARIMA, ETS, STL, THETA, TBATS, etc.), neural networks, random walk models, seasonal naïve, mean and/or linear regression models, etc.” Examiner notes the performance metrics are the usage data, the prediction models are machine learning models, and the models use previous usage data and configurations to generate future predictions of usage data.)
and generating, by the first system and based on the predicted performance metrics, a recommendation associated with changing the recorded configuration of the second system to a candidate configuration of the candidate configurations (Nagpal, paragraph 0107, “The process continues with at step 308, where the analytics system generates recommendations. As discuss previously, generating recommendations includes generating recommendations for configuration changes, hardware changes, software changes or some combination thereof.” Nagpal, paragraph 0097, “The recommendation engine may use any appropriate sizing unit 162 such that the recommendation engine may provide as inputs the predicted future values and/or requirements to the sizing unit to size the future system and/or to provide recommendations on how to modify the present system.” Nagpal, paragraph 0079, “Recommendations might pertain to the predicted performance of the measured system.” Examiner notes the recommendations are generated using predicted future performance values, and the recommendations include configurations of the system.)
Regarding claim 12, Nagpal teaches
The method of claim 11, further comprising: facilitating, by the first system, collection of time series data at the second system, the time series data comprising the recorded performance metrics for the workload (Nagpal, paragraph 0005, “Time series analysis refers to the process of collecting and analyzing time series data so as to extract meaningful statistics and characteristics about the collected data.” Nagpal, paragraph 0049, “The embodiment shown in FIG. 1A1 depicts a measured system environment (left side) that includes a management console (top left) for identifying system parameters of interest and a system of interest (bottom left) such that system measurements pertaining to the parameters of interest (e.g., CPU usage, IO latency, etc.) can be collected over time. An analytics environment is also shown (right side). Measurements collected within the measured system environment (e.g., at step 103) can be forwarded to the analytics environment. The analytics environment may include a database of predictive models, often many of which pertain to the parameters of interest.” Examiner notes the benchmark data is the usage data, which are the usage parameters of interest collected.)
Regarding claim 13, Nagpal teaches
The method of claim 12, further comprising: facilitating, by the first system, a transfer of the time series data from the second system to the machine learning model (Nagpal, paragraph 0049, “Measurements collected within the measured system environment (e.g., at step 103) can be forwarded to the analytics environment. The analytics environment may include a database of predictive models, often many of which pertain to the parameters of interest.”)
wherein the determining of the predicted performance metrics is in response to the time series data being determined to have been successfully transferred to the machine learning model (Nagpal, paragraph 0106, “The process continues with at step 306, where the analytics system generates predictions and determines runways. As discussed previously, generating predictions and determining runways includes iterating through one or more prediction models such that a series of values are determined for one or more characteristics for some period of time, when some condition is met, or any combination thereof. A user interface facilitates a user to evaluate one or any number of characteristics to determined predictions for system usage at some future time period.”)
Regarding claim 14, Nagpal teaches
The method of claim 11, wherein the recorded performance metrics relate to a hardware component of the second system (Nagpal, paragraph 0084, “As shown, the system 1E00 receives a plurality of measurements that have been or can be organized into a time-ordered series of measurements (e.g., selected measurement series 1091, selected measurement series 109N, etc.). The received measurements are used to form a time series data set pertaining to a particular parameter (e.g., a system parameter such as memory usage, CPU usage, IO usage, IO latency, etc.).” Examiner notes the benchmark data is the usage data, which can be associated with a hardware device of the second system such as a memory or CPU.)
Regarding claim 15, Nagpal teaches
The method of claim 14, wherein the hardware component is a first hardware component, and wherein the recommendation relates to an action selected from a group of actions comprising: replacing the first hardware component with a second hardware component that is not the first hardware component, and adding a third hardware component to the second system (Nagpal, paragraph 0084, “As shown, the system 1E00 receives a plurality of measurements that have been or can be organized into a time-ordered series of measurements (e.g., selected measurement series 1091, selected measurement series 109N, etc.). The received measurements are used to form a time series data set pertaining to a particular parameter (e.g., a system parameter such as memory usage, CPU usage, IO usage, IO latency, etc.).” Nagpal, paragraph 0079, “Recommendations might pertain to the predicted performance of the measured system. For example, recommendations might include adding new nodes to the system and/or adding additional solid state storage.” Examiner notes the first hardware component is a solid state storage device, wherein the associated benchmark data is the IO usage/latency, and the generated recommendation is adding a third hardware device (additional solid state storage).)
Regarding claim 16, Nagpal teaches
The method of claim 14, wherein the recorded performance metrics further relate to a first configuration property of the hardware component (Nagpal, paragraph 0071, “The analytics system may interface with a one or more databases such as database 120 that contain the location(s) for storing and/or retrieving relevant inputs and/or outputs. The database may comprise any combination of physical and/or logical structures as is ordinarily used for database systems such as hard disk drives (HDDs), solid state drives (SSDs), logical partitions, and the like. Here, database 120 is illustrated as a single database containing codifications (e.g., data items, table entries, etc.) of usage data 121 and system and configuration data 124.” Examiner notes the recorded performance metrics are the usage data, the first configuration property is included in the configuration data, and the usage data is associated with the system configured with associated configuration data.)
and wherein the recommendation relates to changing the first configuration property of the hardware component to a second configuration property that is not the first configuration property (Nagpal, paragraph 0061, “The recommendation engine uses the results of the prediction and runway engine to determine one or more recommendations for configuration, hardware, software and/or other changes to the system.”)
Regarding claim 17, Nagpal teaches
A non-transitory machine-readable medium comprising computer executable instructions that, when executed by at least one processor, facilitate performance of operations, the operations comprising (Nagpal, paragraph 0210, “A module as used herein can be implemented using any mix of any portions of the system memory and any extent of hard-wired circuitry including hard-wired circuitry embodied as a data processor.”)
predicting, using a machine learning model and based on performance data associated with first performance metrics for a workload as performed by a computing system while configured according to a first configuration, second performance metrics for the workload as performed by the computing system while configured according to respective candidate configurations comprising the first configuration (Nagpal, paragraph 0072, “Usage data 121 comprises data that represents the usage of the resources of the system or systems to be evaluated, and usage data may be stored jointly or separately on one or more databases or on a single database such as the database 120, as illustrated.” Nagpal, paragraph 0074, “Furthermore, usage data and system/configuration data may be store separately as illustrated, or may be stored jointly. As is discussed more fully below, the analytics system uses some or all of the usage data and system and configuration data to generate, train, predict, recommend, and otherwise output results” Nagpal, paragraph 0106, “The process continues with at step 306, where the analytics system generates predictions and determines runways. As discussed previously, generating predictions and determining runways includes iterating through one or more prediction models such that a series of values are determined for one or more characteristics for some period of time, when some condition is met, or any combination thereof. A user interface facilitates a user to evaluate one or any number of characteristics to determined predictions for system usage at some future time period.” Nagpal, paragraph 0099, “Furthermore, the user can schedule daily evaluations of same without retraining the prediction models every day but Sunday. Such data can then be used to collect data for generating reports, and for a closed loop system monitoring process such that trend information over time can be used to compare the results from previous prediction models and the actual data collected, and ultimately feed that information back into the trainer 112 such that an adjustment can be made for prediction models that have been consistently over forecasted or under forecasted” Nagpal, paragraph 0085, “The prediction models can be any of a variety of known mathematical models (e.g., ARIMA, ETS, STL, THETA, TBATS, etc.), neural networks, random walk models, seasonal naïve, mean and/or linear regression models, etc.” Examiner notes the performance metrics are the usage data, the prediction models are machine learning models, and the models use previous usage data and configurations to generate future predictions of usage data.)
and based on the second performance metrics, generating a recommendation associated with changing the first configuration of the computing system to a second configuration of the candidate configurations (Nagpal, paragraph 0107, “The process continues with at step 308, where the analytics system generates recommendations. As discuss previously, generating recommendations includes generating recommendations for configuration changes, hardware changes, software changes or some combination thereof.” Nagpal, paragraph 0097, “The recommendation engine may use any appropriate sizing unit 162 such that the recommendation engine may provide as inputs the predicted future values and/or requirements to the sizing unit to size the future system and/or to provide recommendations on how to modify the present system.” Nagpal, paragraph 0079, “Recommendations might pertain to the predicted performance of the measured system.” Examiner notes the recommendations are generated using predicted future performance values, and the recommendations include configurations of the system.)
Regarding claim 18, Nagpal teaches
The non-transitory machine-readable medium of claim 17, wherein the operations further comprise: collecting the performance data from the computing system as time series data (Nagpal, paragraph 0005, “Time series analysis refers to the process of collecting and analyzing time series data so as to extract meaningful statistics and characteristics about the collected data.” Nagpal, paragraph 0049, “The embodiment shown in FIG. 1A1 depicts a measured system environment (left side) that includes a management console (top left) for identifying system parameters of interest and a system of interest (bottom left) such that system measurements pertaining to the parameters of interest (e.g., CPU usage, IO latency, etc.) can be collected over time. An analytics environment is also shown (right side). Measurements collected within the measured system environment (e.g., at step 103) can be forwarded to the analytics environment. The analytics environment may include a database of predictive models, often many of which pertain to the parameters of interest.” Examiner notes the performance data is the usage data, which are the usage parameters of interest collected.)
and transferring the time series data to the machine learning model (Nagpal, paragraph 0049, “Measurements collected within the measured system environment (e.g., at step 103) can be forwarded to the analytics environment. The analytics environment may include a database of predictive models, often many of which pertain to the parameters of interest.”)
wherein the predicting of the second performance metrics is in response to the time series data being determined to have been successfully transferred to the machine learning model (Nagpal, paragraph 0106, “The process continues with at step 306, where the analytics system generates predictions and determines runways. As discussed previously, generating predictions and determining runways includes iterating through one or more prediction models such that a series of values are determined for one or more characteristics for some period of time, when some condition is met, or any combination thereof. A user interface facilitates a user to evaluate one or any number of characteristics to determined predictions for system usage at some future time period.”)
Regarding claim 19, Nagpal teaches
The non-transitory machine-readable medium of claim 17, wherein the performance data is associated with a hardware device of the computing system (Nagpal, paragraph 0084, “As shown, the system 1E00 receives a plurality of measurements that have been or can be organized into a time-ordered series of measurements (e.g., selected measurement series 1091, selected measurement series 109N, etc.). The received measurements are used to form a time series data set pertaining to a particular parameter (e.g., a system parameter such as memory usage, CPU usage, IO usage, IO latency, etc.).” Examiner notes the performance data is the usage data, which can be associated with a hardware device of the system such as a memory or CPU.)
Regarding claim 20, Nagpal teaches
The non-transitory machine-readable medium of claim 19, wherein the hardware device is a first hardware device, and wherein the recommendation relates to an action selected from a group of actions comprising: replacing the first hardware device with a second hardware device that is not the first hardware device, adding a third hardware device to the computing system, and changing a configuration property of the first hardware device (Nagpal, paragraph 0084, “As shown, the system 1E00 receives a plurality of measurements that have been or can be organized into a time-ordered series of measurements (e.g., selected measurement series 1091, selected measurement series 109N, etc.). The received measurements are used to form a time series data set pertaining to a particular parameter (e.g., a system parameter such as memory usage, CPU usage, IO usage, IO latency, etc.).” Nagpal, paragraph 0079, “Recommendations might pertain to the predicted performance of the measured system. For example, recommendations might include adding new nodes to the system and/or adding additional solid state storage.” Examiner notes the first hardware device is a solid state storage device, wherein the associated benchmark data is the IO usage/latency, and the generated recommendation is adding a third hardware device (additional solid state storage) to the system.)
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries 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.
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.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being anticipated by Nagpal in view of Daly et al. (US20200219004).
Regarding claim 10, Nagpal teaches The system of claim 1. Nagpal does not, but Daly teaches
wherein the recommendation generated by the upgrade recommendation engine comprises an explanation of a reason for the recommendation (Daly, paragraph 0060, “A recommendation engine 414 (e.g., a recommendation component) may infer one or more recommended actions (“recommendations”) if the recommended actions may be executed internally within a computing system (e.g., where the necessary information and factors can be in control of a cognitive system such as, for example, a recommended action of lowering a price).” Daly, paragraph 0072, “Table 625 illustrates one or more columns that include features needing improvement, a recommended action, a recommendation engine name, and an explanation. For example, the features needing improvement of “role experience” may be linked to a recommended action such as, for example, add a team member. The recommendation engine name may be recommended via “team recommender” service by the recommendation engine name. The explanation provided may be a recommendation to add a team member with experience in the role so as to improve the score in the role dimension.”)
Nagpal and Daly both utilize machine learning models to generate recommendations and thus are analogous to the claimed invention. It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the application to have modified Nagpal to generate explanations of a reason for recommendations like Daly. Doing so would have been advantageous because “These explanations help a user understand both the predicted outcome and also those factors that may be influencing the prediction and to what extent” (Daly, paragraph 0014).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Zared O. Cohen whose telephone number is (571)270-0531. The examiner can normally be reached M-Th, 8am to 5pm ET.
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/Z.O.C./ Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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