DETAILED ACTION
This action is in response to the application filed on 09/01/2022. Claims 1-10 are pending in the application and have been examined.
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 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:
configuration comparison unit configured to extract in claim 1.
model generation unit configured to generate in claim 1.
configuration comparison unit restricts in claim 4.
model generation unit generates in claim 4.
model generation unit generates in claim 5.
model management unit configured to manage in claim 6.
model update unit configured to … generate in claim 6.
model update unit sets in claim 7
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1, 4, 5, 6 and 7,
Claim limitations “configuration comparison unit configured to extract” in claim 1, “model generation unit configured to generate” in claim 1, “configuration comparison unit restricts” in claim 4, “model generation unit generates” in claim 4, “model generation unit generates” in claim 5, “model management unit configured to manage” in claim 6, “model update unit configured to … generate” in claim 6, “model update unit sets” in claim 7 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The recited units in the specification are described by their function; however, the specification fails to provide sufficient structure for the recited functions. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Dependent claims 2-3, 8-10 are rejected for inheriting the indefiniteness of their parent claims.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Regarding Claims 1, 4, 5, 6 and 7,
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed functions of extracting, generating, restricting, managing, or updating accomplished by units. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Dependent claims 2-3, 8-10 are rejected for inheriting the indefiniteness of their parent claims
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.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter
Regarding Claim 10,
Claims 10 recites “a storage medium”. The specification does not describe a storage medium, thus the broadest reasonable interpretation of “a storage medium” includes signals per se. As such, the claim does not fall within at least one of the four statutory categories of patent eligible matter
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
(Step 1): Claim 1 recites A model management system managing a prediction model, thus a machine, one of the four statutory categories of patentable subject matter.
(Step 2A Prong 1): However, Claim 1 further recites managing a prediction model which constitutes the evaluation of the model to determine its managed properties, thus corresponding to a mental process which can be done mentally or by pen and paper,
predicting processing performance of software configuring an application in a deployment destination which constitutes the evaluation of software configuring an application in a deployment destination to determine a processing performance, thus corresponding to a mental process which can be done mentally or by pen and paper
extract a difference between second configuration information representing a configuration of a deployment destination of target software, which is a prediction target, and the first configuration information which constitutes the evaluation of first and second configuration information to determine a difference between the configurations, thus corresponding to a mental process which can be done mentally or by pen and paper
generate a prediction model through learning using configuration information … by adding the difference to the first configuration information which constitutes the evaluation of configuration information to determine a prediction model, thus corresponding to a mental process which can be done mentally or by pen and paper
Thus, Claim 1 recites an abstract idea.
(Step 2A Prong 2): The claim does not recite any additional elements which integrate the abstract idea into a practical application because the additional elements consist of:
a prediction model for predicting, which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
a model management table configured to store a first common model which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
a first common model that is a prediction model able to be commonly used for prediction of processing performance of software of a same type which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
a data management table configured to store first configuration information which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
first configuration information representing a configuration of a deployment destination of software used for learning when the first common model is generated which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
a configuration comparison unit configured to extract, which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
a model generation unit configured to generate, which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
and set the prediction model as a second common model that is a new common model which is insignificant extra-solution activity of data outputting (MPEP 2106.05(g))
and thus, the claim is directed to the abstract idea of managing a prediction model to predict processing performance of a prediction model by calculating a difference in model configurations.
(Step 2B) The additional elements, taken alone or in combination, cannot provide significantly
more than the abstract idea itself because element a), b), d), f), and g) ((via MPEP 2106.05(f), “apply it on a computer”) cannot provide an inventive concept, elements c) and e) (via MPEP 2106.05(h)) cannot integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself, and element h) is further well-understood, routine, and conventional activity of “storing and retrieving information in memory,” by MPEP 2106.05(d), which cannot provide significantly more than the abstract idea itself. Thus, Claim 1 is subject-matter ineligible.
Claim 2, dependent upon Claim 1 recites the additional elements:
a) wherein, when the first common model is generated, a deployment destination of software, of which processing performance is predicted using the common model, is a computer of on-premises which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
b) wherein a deployment destination of the target software is a computer of a public cloud which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
The additional elements, taken alone or in combination, cannot provide significantly
more than the abstract idea itself because elements a) and b) (via MPEP 2106.05(h)) cannot integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, Claim 2 is subject-matter ineligible.
Claim 3, dependent upon Claim 2 recites the additional element:
a) wherein the prediction model is a regression equation that calculates an objective variable representing processing performance of an arithmetic operation process on the basis of a descriptive variable relating to an amount of resources provided for the arithmetic operation process which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
The additional elements, taken alone or in combination, cannot provide significantly
more than the abstract idea itself because element a) (via MPEP 2106.05(h)) cannot integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, Claim 3 is subject-matter ineligible.
Claim 4, dependent upon Claim 3 recites additional steps of the abstract idea (Claim 4: restricts the first configuration information to the range of the amount of resources included in the second configuration information, which constitutes the evaluation of the first and second configuration information resources to determine an imposed restriction for the first configuration information, thus corresponding to a mental process which can be done mentally or by pen and paper; extracts a difference between the second configuration information and the restricted first configuration information, which constitutes the evaluation of the second configuration information and the restricted first configuration information to determine a difference, thus corresponding to a mental process which can be done mentally or by pen and paper; generates the second common model on the basis of the restricted first configuration information and the difference extracted by restricting the first configuration information, which constitutes the evaluation of the restricted first configuration information and its difference to determine the second common model, thus corresponding to a mental process which can be done mentally or by pen and paper). The claim does not recite any additional elements which integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself because the additional elements consist of:
wherein, in a case in which a range of an amount of resources included in the first configuration information is wider than a range that can be taken by the amount of resources included in the second configuration information which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
the configuration comparison unit restricts which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
the model generation unit generates which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
The additional elements, taken alone or in combination, cannot provide significantly more than the abstract idea itself, because element a) (via MPEP 2106.05(h)) cannot integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself and elements b), c) ((via MPEP 2106.05(f), “apply it on a computer”) cannot provide an inventive concept. Thus, Claim 4 is subject-matter ineligible.
Claim 5, dependent upon Claim 2 recites additional steps of the abstract idea (Claim 5: generates a second common model). The claim does not recite any additional elements which integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself because the additional elements consist of:
wherein, in a case in which the computer of the public cloud provides a unique service that is a unique computing service not provided by the computer of the on-premises, and the target software is deployed in the public cloud using the unique service which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
the model generation unit generates which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
a second common model that can be used for predicting processing performance of the target software using the unique service in the computer of the public cloud on the basis of the first common model that can be used for predicting processing performance of software of the same type as that of the target software in the computer of the on-premises which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
The additional elements, taken alone or in combination, cannot provide significantly more than the abstract idea itself, because elements a) and c) (via MPEP 2106.05(h)) cannot integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself and element b) ((via MPEP 2106.05(f), “apply it on a computer”) cannot provide an inventive concept. Thus, Claim 5 is subject-matter ineligible.
Claim 6, dependent upon Claim 1 recites additional steps of the abstract idea (Claim 6: manage prediction models; generate a new prediction model by relearning data at the time of generation of the plurality of prediction models which constitutes the evaluation of the data at the time of generation to determine a prediction model, thus corresponding to a mental process which can be done mentally or by pen and paper). The claim does not recite any additional elements which integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself because the additional elements consist of:
a model management unit configured to manage which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
prediction models including the common model generated by the model generation unit and software, of which processing performance is predicted using the prediction model, in association with each other which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
a model update unit configured to … generate which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
when different prediction models are used for predicting processing performance of a plurality of pieces of software of the same type during management using the model management unit which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
when the processing performance of the plurality of pieces of software are predicted correctly using the new prediction model which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
set the new prediction model as a common model for the plurality of pieces of software which is insignificant extra-solution activity of data outputting (MPEP 2106.05(g))
The additional elements, taken alone or in combination, cannot provide significantly more than the abstract idea itself because elements a) and c) ((via MPEP 2106.05(f), “apply it on a computer”) cannot provide an inventive concept, elements b), d), and e) (via MPEP 2106.05(h)) cannot integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself, and element f) is further well-understood, routine, and conventional activity of “storing and retrieving information in memory,” by MPEP 2106.05(d), which cannot provide significantly more than the abstract idea itself. Thus, Claim 6 is subject-matter ineligible.
Claim 7, dependent upon Claim 6 recites the additional elements:
a) wherein, when different common models are used for predicting processing performance of a plurality of pieces of software of the same type of which configurations of the deployment destinations are the same during management using the model management unit which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
b) the model update unit sets which is implementing an abstract idea on generic computer components (MPEP 2106.05(f))
c) sets any one of the common models as a common model for the plurality of pieces of software which is insignificant extra-solution activity of data outputting (MPEP 2106.05(g))
The additional elements, taken alone or in combination, cannot provide significantly
more than the abstract idea itself because element a) (via MPEP 2106.05(h)) cannot integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself, element b) ((via MPEP 2106.05(f), “apply it on a computer”) cannot provide an inventive concept, and element c) is further well-understood, routine, and conventional activity of “storing and retrieving information in memory,” by MPEP 2106.05(d), which cannot provide significantly more than the abstract idea itself. Thus, Claim 7 is subject-matter ineligible.
Claim 8, dependent upon Claim 1 recites the additional elements:
a) wherein, when the common model is generated, a deployment destination of software of which processing performance is predicted using the common model is a computer of a public cloud which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
b) wherein the deployment destination of the target software is a computer of on-premises which merely recites the particular technological environment or field of use in which the abstract idea is to be performed (MPEP 2106.05(h))
The additional elements, taken alone or in combination, cannot provide significantly
more than the abstract idea itself because elements a), b) (via MPEP 2106.05(h)) cannot integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, Claim 8 is subject-matter ineligible.
Claim 9 recites the method performed by the system of Claim 1. As performance of an abstract idea on generic computing components cannot integrate an abstract idea into a practical application nor provide significantly more than the abstract idea itself (see MPEP 2106.05(f)), Claim 9 is rejected for reasons set forth in the rejection of Claim 1.
Claim 10 recites a storage medium storing a program to perform the same process as the system of Claim 1. As performance of an abstract idea on generic computing components cannot integrate an abstract idea into a practical application nor provide significantly more than the abstract idea itself (see MPEP 2106.05(f)), Claim 10 is rejected for reasons set forth in the rejection of Claim 1.
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.
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.
Claims 1, 6-7; 9; and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Horikawa et al. (JP2004220453A, hereinafter “Horikawa”) in view of Tanimoto et al. (US20180082185A1, hereinafter “Tanimoto”) further in view of Kaniwa et al. (US20170351972A1, hereinafter “Kaniwa”) further in view of Zheng et al. (CN112288100A, hereinafter “Zheng”).
Regarding Claim 1,
Horikawa discloses a prediction model for predicting processing performance of software configuring an application in a deployment destination (Horikawa [0001]; “a system performance prediction method based on performance measurement of software components, and more particularly to a system performance prediction method based on performance measurement of software components in a system constructed by combining a plurality of software components”
Horikawa [0045]; “FIG. 6 is a block diagram showing the configuration of the component performance measuring means 20 that measures the CPU usage time as the performance for each software component. A software component (hereinafter referred to as a measurement target component) 22 to be measured by the component performance measuring unit 20 constitutes a part of a measurement target system (hereinafter referred to as a measurement target system) 21, and is measured. The target system 21 is a test in which an operating system 23 in which a kernel probe 24 for detecting an event occurring in the measurement target system 21 is inserted and an application probe 26 for detecting an event in the measurement target component 22 is inserted. An event recording means 27 for recording events detected by the driver 25 and the application probe 26 in time series as an event trace 28, and when the measurement target component 22 processes the request by analyzing the event trace 28; And a performance analysis means 29 for determining the CPU usage time” wherein the system performance encompasses configuring an application in a deployment)
Horikawa fails to disclose but Tanimoto discloses a configuration comparison unit configured to extract a difference between second configuration information representing a configuration of a deployment destination of target software, which is a prediction target, and the first configuration information (Tanimoto [0031]; “ The predictive model evaluation unit 13 can use various indexes for the prediction result. For example, the outcome of statistical processing (e.g. the sum of the squares of difference, variance calculation, etc.) on the difference between the predicted value by the predictive model before update and the predicted value by the predictive model after update may be defined as the closeness in prediction result of the predictive model. A smaller change in prediction result for the same object indicates a smaller change in predictive model.” wherein the evaluation unit calculating a difference between a pre-update and post-update deployment configuration is performed)
a model generation unit configured to generate a prediction model through learning using configuration information (Tanimoto [0069]; “The predictive model updating system may include: predictive model extraction means (e.g. the predictive model update determination unit 11) which extracts a predictive model meeting a condition prescribed by a rule (e.g. relearning rule) for determining whether or not to relearn the predictive model, from among a plurality of predictive models; and predictive model relearning means (e.g. the predictive model relearning unit 12) which relearns the extracted predictive model. The predictive model evaluation means 81 may evaluate the closeness in property between the relearned predictive model obtained by the predictive model relearning means and the pre-relearning predictive model. With such a structure, the relearning target predictive models can be narrowed, so that computational costs (e.g. machine resources) can be reduced. This is more effective in the case where there are a larger number of predictive models as targets. The pre-relearning predictive model and the relearned predictive model may be a predictive model (e.g. a tree structure predictive model, a predictive model generated by a heterogeneous mixture learning algorithm, etc.) whose component used for prediction of a sample of a prediction target is determined according to contents of the sample”)
It would have been obvious modify Horikawa’s prediction models that predict processing performance of software components at an application configuration deployment target destination to perform Tanimoto’s method of comparing the configuration information of prediction models and generating prediction models accordingly. One would have been motivated to do so because “With such a structure, the relearning target predictive models can be narrowed, so that computational costs (e.g. machine resources) can be reduced. This is more effective in the case where there are a larger number of predictive models as targets.” (Tanimoto [0070])
The combination of Horikawa/Tanimoto fails to disclose but Kaniwa discloses a model management table configured to store a first common model that is a prediction model able to be commonly used for prediction of processing performance of software of a same type (Kaniwa [Figure 21];
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Wherein the table storing the prediction model paths reads on a model management table)
a data management table configured to store first configuration information representing a configuration of a deployment destination of software used for learning when the first common model is generated (Kaniwa [Figure 21];
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Wherein the table storing the usage method information and usage purpose reads on a data management table configured to store first configuration information of the generated stored models)
It would have been obvious modify Horikawa/Tanimoto’s method of comparing the configuration information of software processing performance prediction models and generating prediction models accordingly to use Kaniwa’s tables to manage the models. One would have been motivated to do so because “prediction information that is predicted by the learning model, actual reflection data, and so forth are stored in the table for managing the learning model, with respect to each of the learning models” (Kaniwa [0103]), thus allowing visualization of associated information for each respective learning model.
The combination of Horikawa/Tanimoto/Kaniwa fails to explicitly disclose but Zheng discloses configuration information that is acquired by adding the difference to the first configuration information and set the prediction model as a second common model that is a new common model (Zheng [Page 15 Line 34]; “In some embodiments, the parameter adjustment increment obtaining module 450 calculates the parameter adjustment increment based on the following formula (14):”
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Among them, represents the parameter adjustment increment, N represents the total number of samples, s represents the element of the aggregation matrix of the second operation value, 𝑎 represents the element corresponding to 𝑠 in the aggregation matrix of the first operation, and ϵ is a non-zero constant”
Zheng [Abstract]; “calculating a first matrix of operation values based on at least the gradient matrix and a first hyperparameter; calculating a second matrix of operation values based on the gradient matrix; uploading the first operation value matrix and the second operation value matrix to the server so that the server can update the model parameters of the model to be trained at the server end; and obtaining the updated model parameters from the server to be used as a model to be trained for carrying out next iteration updating, or determining a final model based on the updated model parameters” wherein the determination of a final model based on the updated model parameters reads on generation of a prediction model using the acquired configuration information)
It would have been obvious modify Horikawa/Tanimoto/Kaniwa’s method of comparing the configuration information of software processing performance prediction models to perform Zheng’s method of computing the configuration information by calculating the difference in configuration information and adding said difference to the first configuration information. One would have been motivated to do so because “the parameter adjusting increment cannot generate large swing due to the influence of the gradient, and therefore the model training convergence speed is improved” (Zheng [Page 6 Line 17]).
Regarding Claim 6,
The combination of Horikawa/Tanimoto/Kaniwa/Zheng teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination already discloses a model management unit configured to manage prediction models including the common model generated by the model generation unit and software, of which processing performance is predicted using the prediction model, in association with each other (Tanimoto [0022]; “The predictive model update determination unit 11 determines a predictive model of an update candidate. In detail, the predictive model update determination unit 11 extracts a relearning target predictive model as an update candidate from a plurality of predictive models, based on a rule (hereafter referred to as “relearning rule”) for determining whether or not to relearn the predictive model. The relearning rule is a rule prescribing, based on a predetermined evaluation index, whether or not the predictive model needs to be relearned”
Tanimoto [0025]; “The predictive model relearning unit 12 relearns the predictive model extracted by the predictive model update determination unit 11. Any relearning method may be used. For example, the predictive model relearning unit 12 may select a data interval, and relearn the predictive model by random restart using parameters determined by a predetermined method. The predictive model relearning unit 12 may relearn the predictive model based on an algorithm defined in the relearning rule.” wherein the update determination unit managing the plurality of predictive models to designate a target predictive model as well as a relearned predictive model reads on a model management units)
a model update unit configured to, when different prediction models are used for predicting processing performance of a plurality of pieces of software of the same type during management using the model management unit, generate a new prediction model by relearning data at the time of generation of the plurality of prediction models (Tanimoto [0060]; “The following describes the operation of the predictive model updating system in this exemplary embodiment. FIG. 6 is a flowchart depicting an example of the operation of the predictive model updating system in this exemplary embodiment. First, the predictive model update determination unit 11 extracts a predictive model of an update candidate from the plurality of predictive models based on the relearning rule (step S11). The predictive model relearning unit 12 relearns the extracted predictive model”)
and, when the processing performance of the plurality of pieces of software are predicted correctly using the new prediction model, set the new prediction model as a common model for the plurality of pieces of software (Tanimoto [0043]; “ The above describes the case where the predictive model evaluation unit 13 performs evaluation by focusing on the change in property of the predictive model. The change of the predictive model to be focused is, however, not limited to the change in prediction result or the structural change of the predictive model. The predictive model evaluation unit 13 may, for example, evaluate the change in evaluation index such as the change in estimation accuracy or the change in the number of samples used in the predictive model, as the change in property of the predictive model.” wherein the evaluation unit performs predictions and evaluates the estimations
Tanimoto [0050]; “The predictive model updating unit 14 updates the pre-relearning predictive model with the relearned predictive model, in the case where the closeness in property between both predictive models evaluated by the predictive model evaluation unit 13 meets the condition prescribed by the update evaluation rule. The update evaluation rule prescribes the closeness that allows updating the predictive model, depending on the evaluation. The predictive model updating unit 14 may alert the user, instead of automatically updating the predictive model. Any alerting method may be used, such as display on a screen or notification by mail.
The result output unit 15 outputs the relearning result by the predictive model relearning unit 12 and/or the update result by the predictive model updating unit 14. The result output unit 15 may display the relearning result and/or the update result on a display device” wherein the result output unit comprising the replacement of the pre-relearning predictive model with the evaluated relearned predictive model reads on setting the new prediction model as a common model upon completion of prediction evaluation of the relearned model)
Regarding Claim 7,
The combination of Horikawa/Tanimoto/Kaniwa/Zheng teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). The combination already discloses wherein, when different common models are used for predicting processing performance of a plurality of pieces of software of the same type of which configurations of the deployment destinations are the same during management using the model management unit, the model update unit sets any one of the common models as a common model for the plurality of pieces of software (Tanimoto [0022]; “The relearning rule is a rule prescribing, based on a predetermined evaluation index, whether or not the predictive model needs to be relearned. The evaluation index used in the relearning rule may be any index. Examples of the evaluation index include the period from the previous learning of the predictive model, the period from the previous update of the predictive model, the amount of increase of learning data, the degree of accuracy degradation over time, the change of the number of samples, and the computational resources. The evaluation index is, however, not limited to such, and any index that can be used to determine whether or not to update the predictive model may be used. The evaluation index is also not limited to data calculated from the prediction result”
Tanimoto [0051]; “The result output unit 15 outputs the relearning result by the predictive model relearning unit 12 and/or the update result by the predictive model updating unit 14. The result output unit 15 may display the relearning result and/or the update result on a display device (not depicted). For example, the result output unit 15 may visualize the evaluation index of the predictive model conforming to the relearning rule in a manner distinguishable (e.g. highlighting) from other evaluation indexes. FIG. 3 is an explanatory diagram depicting an example of visualizing the predictive model accuracy index … In the example in FIG. 3, relearning is performed in the case where the predictive model meets the relearning rule “the maximum error absolute value is more than 5 for three consecutive months”” wherein the result output unit, in the case where the different plurality of prediction models are deemed to not satisfy the relearning rule due to not being distinguishable under evaluation indices, returns the predictive model relearning unit result model which is read as setting the common model as any one of the (indistinguishable) common models)
Claim 9 recites the method performed by the system of Claim 1. Thus, Claim 9 is rejected for reasons set forth in the rejection of Claim 1.
Claim 10 recites a storage medium storing a program to perform the same process as the system of Claim 1. Thus, Claim 10 is rejected for reasons set forth in the rejection of Claim 1.
Claims 2, 5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Horikawa et al. (JP2004220453A, hereinafter “Horikawa”) in view of Tanimoto et al. (US20180082185A1, hereinafter “Tanimoto”) further in view of Kaniwa et al. (US20170351972A1, hereinafter “Kaniwa”) further in view of Zheng et al. (CN112288100A, hereinafter “Zheng”) and further in view of Zhang et al. (US20210166083, hereinafter “Zhang”).
Regarding Claim 2,
The combination of Horikawa/Tanimoto/Kaniwa/Zheng teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination fails to explicitly disclose but Zhang discloses wherein, when the first common model is generated, a deployment destination of software, of which processing performance is predicted using the common model, is a computer of on-premises (Zhang [0027]; “The cloud computing architecture 130 is remotely arranged to provide computation, software, data access, and storage services. The processing in the cloud computing architecture 130 may be referred to as “cloud computation.” In various implementations, the cloud computation provides services over a wide area network such as the Internet using an appropriate protocol. For example, providers of the cloud computing architecture 130 provision applications over the wide area network which can be accessed through a web browser or any other computing components. Software or components of the cloud computing architecture 130 and corresponding data may be stored on servers at a remote location. The computing resources in the cloud computing architecture 130 may be aggregated at a remote data center location or can be disaggregated. Cloud computing infrastructures can deliver services through a shared data center although they can act as individual access points for users. Thus, the components and functions described herein can be provided from a service provider at a remote location using the cloud computing architecture 130. Alternatively, they can be provided from conventional servers, or can be installed on client devices directly or in any of a variety of other manners. Although illustrated as a single device, it is to be appreciated that the computing device 140 may be any component that is in the cloud computing architecture 130 and has a computing capability. Accordingly, various portions of the computing device 140 may be distributed across the cloud computing architecture 130.”)
wherein a deployment destination of the target software is a computer of a public cloud (Zhang [0027]; “The cloud computing architecture 130 is remotely arranged to provide computation, software, data access, and storage services. The processing in the cloud computing architecture 130 may be referred to as “cloud computation.” In various implementations, the cloud computation provides services over a wide area network such as the Internet using an appropriate protocol. For example, providers of the cloud computing architecture 130 provision applications over the wide area network which can be accessed through a web browser or any other computing components. Software or components of the cloud computing architecture 130 and corresponding data may be stored on servers at a remote location. The computing resources in the cloud computing architecture 130 may be aggregated at a remote data center location or can be disaggregated. Cloud computing infrastructures can deliver services through a shared data center although they can act as individual access points for users. Thus, the components and functions described herein can be provided from a service provider at a remote location using the cloud computing architecture 130. Alternatively, they can be provided from conventional servers, or can be installed on client devices directly or in any of a variety of other manners. Although illustrated as a single device, it is to be appreciated that the computing device 140 may be any component that is in the cloud computing architecture 130 and has a computing capability. Accordingly, various portions of the computing device 140 may be distributed across the cloud computing architecture 130.”)
It would have been obvious to modify the software deployment destination of the configuration comparison-based