DETAILED ACTION
This action is in response to the application filed 12/16/2022. Claims 1-9 are pending 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 .
Information Disclosure Statement
The information disclosure statement filed 12/16/2025 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the foreign references listed were not provided and Wei, “Meta-Learning Hyperparameter Performance Prediction with Neural Processes” is illegible. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Specification
The disclosure is objected to because of the following informalities:
In paragraph 0039, “By the above processing, new mechanism enabling the specification of an appropriate distributed instance number or a hyperparameter with respect to a prescribed data set may be provided” should read “By the above processing, a new mechanism enabling the specification of an appropriate distributed instance number or a hyperparameter with respect to a prescribed data set may be provided”
Appropriate correction is required.
Claim Objections
Claims 2 and 4 objected to because of the following informalities:
Regarding claim 2 the Examiner respectfully notes that claim 2 is a method claim and the limitation of “when the prescribed data set is input to the prediction model and machine learning of the prescribed learning model is performed” is a contingent limitation and therefore under the broadest reasonable interpretation these limitation may not be performed (“The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” MPEP 2111.04(II)). Accordingly the Examiner recommends the Applicant positively recite these limitations to state “in response to the prescribed data set being input …” to avoid a contingent interpretation of these limitations.
Regarding claim 4 the Examiner respectfully notes that claim 4 is a method claim and the limitation of “when the prescribed data set is input to the prediction model and machine learning of the prescribed learning model is performed” is a contingent limitation and therefore under the broadest reasonable interpretation these limitation may not be performed (“The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” MPEP 2111.04(II)). Accordingly the Examiner recommends the Applicant positively recite these limitations to state “in response to the prescribed data set being input …” to avoid a contingent interpretation of these limitations.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claim 1-9 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 claim 1, claim 1 recites the limitation "learning performance" in lines 15-16. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to whether this learning performance is the same learning performance as recited in line 13 or if this is a different learning performance. For purposes of examination, Examiner has interpreted this learning performance to be the same as the learning performance recited in line 13.
Claim 1 recites the limitation "an instance number" in line 16. There is insufficient antecedent basis for this limitation in the claim. It is unclear as whether this instance number is the same as the instance number as recited in line 7. For purposes of examination, Examiner has interpreted this instance number to the same as the instance number in line 7.
Claim 1 recites the limitation "a hyperparameter" in line 16. There is insufficient antecedent basis for this limitation in the claim. It is unclear as whether this hyperparameter is the same as the hyperparameter as recited in line 7. For purposes of examination, Examiner has interpreted this hyperparameter to the same as the hyperparameter in claim 7.
Claims 2-7 are rejected for at least the same reasons as claim 1 since claims 2-7 depend on claim 1.
Regarding claim 2, claim 2 recites the limitation "learning performance" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to whether this learning performance is the same learning performance as recited in line 13 of claim 1 or if this is a different learning performance. For purposes of examination, Examiner has interpreted this learning performance to be the same as the learning performance recited in line 13 of claim 1.
Claim 2 recites the limitation "the prescribed data set" in line 3. There is insufficient antecedent basis for this limitation in the claim. This prescribed data set is not recited prior and therefore, it is unclear as to what prescribed data set this is referring to. For purposes of examination, Examiner has interpreted this prescribed data set to be the first reference of prescribed data set.
Regarding claim 3, claim 3 recites the limitation "learning performance" in line 6. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to whether this learning performance is the same learning performance as recited in line 13 of claim 1 or if this is a different learning performance. For purposes of examination, Examiner has interpreted this learning performance to be the same as the learning performance recited in line 13 of claim 1.
Regarding claim 3, claim 3 recites the limitation "learning performance" in line 9. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to whether this learning performance is the same learning performance as recited in line 13 of claim 1 or if this is a different learning performance. For purposes of examination, Examiner has interpreted this learning performance to be the same as the learning performance recited in line 13 of claim 1.
Regarding claim 3, claim 3 recites the limitation "a learning time" in line 13. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to whether this learning time is the same learning time as recited in line 3 or if this is a different learning time. For purposes of examination, Examiner has interpreted this learning time to be the same as the learning performance recited in line 3.
Regarding claim 4, claim 4 recites the limitation "learning performance" in line 2. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to whether this learning performance is the same learning performance as recited in line 2 of claim 3 or if this is a different learning performance. For purposes of examination, Examiner has interpreted this learning performance to be the same as the learning performance recited in line 2 of claim 3.
Regarding claim 4, claim 4 recites the limitation "a learning time" in line 3. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to whether this learning time is the same learning time as recited in line 3 of claim 3, or if this is a different learning time. For purposes of examination, Examiner has interpreted this learning time to be the same as the learning performance recited in line 3 of claim 3.
Claim 6 recites the limitation "an instance number" in line 4. There is insufficient antecedent basis for this limitation in the claim. It is unclear as whether this instance number is the same as the instance number as recited in claim 5. For purposes of examination, Examiner has interpreted this instance number to the same as the instance number in claim 5.
Claim 6 recites the limitation "a hyperparameter" in line 4. There is insufficient antecedent basis for this limitation in the claim. It is unclear as whether this hyperparameter is the same as the hyperparameter as recited in claim 5. For purposes of examination, Examiner has interpreted this hyperparameter to the same as the hyperparameter in claim 5.
Regarding claim 8, claim 8 recites the limitation "learning performance" in line 15. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to whether this learning performance is the same learning performance as recited in line 13 or if this is a different learning performance. For purposes of examination, Examiner has interpreted this learning performance to be the same as the learning performance recited in line 13.
Regarding claim 9, claim 9 recites the limitation "learning performance" in line 14. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to whether this learning performance is the same learning performance as recited in line 12 or if this is a different learning performance. For purposes of examination, Examiner has interpreted this learning performance to be the same as the learning performance recited in line 12.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 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 1 recites
generating…a prediction model that predicts learning performance for each combination of an instance number and a hyperparameter (This limitation is a mental process as it encompasses a human mentally creating a model to predict learning performance.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
performed by an information processing apparatus having a storage device storing a prescribed learning model, and a processor, (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)).)
causing, by the processor, other respective information processing apparatuses to perform, on one or a plurality of data sets, machine learning by using the prescribed learning model according to respective combinations in which an instance number and a hyperparameter learned in parallel are arbitrarily changed (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)).)
acquiring, by the processor, learning performance, corresponding to the respective combinations, from the respective information processing apparatuses; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
performing, by the processor, supervised learning by using learning data including the respective combinations and the learning performance corresponding to the respective combinations; (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)).)
by the processor (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)).)
by the supervised learning (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
performed by an information processing apparatus having a storage device storing a prescribed learning model, and a processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
causing, by the processor, other respective information processing apparatuses to perform, on one or a plurality of data sets, machine learning by using the prescribed learning model according to respective combinations in which an instance number and a hyperparameter learned in parallel are arbitrarily changed uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
acquiring, by the processor, learning performance, corresponding to the respective combinations, from the respective information processing apparatuses 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)).
performing, by the processor, supervised learning by using learning data including the respective combinations and the learning performance corresponding to the respective combinations uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
by the processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
by the supervised learning 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
predicts, for each of the combinations, learning performance obtained when… machine learning of the prescribed learning model is performed. (This limitation is a mental process as it encompasses a human mentally predicting a learning performance.)
Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of
the processor (This element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(f)).)
the prescribed data set is input to the prediction model (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
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
the processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
the prescribed data set is input to the prediction 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 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites
the generation of the prediction model includes generating a prediction model that predicts learning performance and a learning time for each combination of an instance number and a hyperparameter (This limitation is a mental process as it encompasses a human mentally creating a prediction model to a predict learning performance and a learning time.)
Therefore, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites additional elements of
the acquisition of the learning performance includes acquiring a learning time together with the learning performance (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
the performing of the supervised learning includes performing supervised learning by using learning data including the respective combinations and learning performance and learning times corresponding to the respective combinations, (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)).)
by the supervised learning (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 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
the acquisition of the learning performance includes acquiring a learning time together with the learning performance 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)).
the performing of the supervised learning includes performing supervised learning by using learning data including the respective combinations and learning performance and learning times corresponding to the respective combinations uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
by the supervised learning uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
predicts, for each of the combinations, learning performance and a learning time obtained when …machine learning of the prescribed learning model is performed. (This limitation is a mental process as it encompasses a human mentally predicting a learning performance.)
Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional elements of
the processor (This element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(f)).)
the prescribed data set is input to the prediction model (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (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
the processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
the prescribed data set is input to the prediction 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 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
with the learning performance being a first variable and with the learning time being a second variable, generates relationship information in which the first and second variables and the instance number and hyperparameter are associated with each other. (This limitation is a mental process as it encompasses a human mentally generating relationship information.)
Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of
the processor (This element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(f)).)
Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
specifies an instance number and a hyperparameter corresponding to the first value and the second value on a basis of the relationship information. (This limitation is a mental process as it encompasses a human mentally specifying an instance number and a hyperparameter.)
Therefore, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 further recites additional elements of
the processor (This element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(f)).)
acquires a first value of the first variable and a second value of the second variable (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 6 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
acquires a first value of the first variable and a second value of the second variable 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 6 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites the same abstract ideas as claim 6. Therefore, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 further recites additional elements of
the processor performs control to display the specified instance number and the hyperparameter on a display device. (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 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the processor performs control to display the specified instance number and the hyperparameter on a display device. uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 7 is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 1:
Claim 8 recites an apparatus and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites
generates a prediction model that predicts learning performance for each combination of an instance number and a hyperparameter (This limitation is a mental process as it encompasses a human mentally creating a model to predict learning performance.)
Therefore, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 further recites additional elements of
An information processing apparatus comprising: a storage device; and a 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 ideas. (see MPEP 2106.05(f)).)
The storage device stores a prescribed learning model (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
The processor causes other respective information processing apparatuses to perform, on one or a plurality of data sets, machine learning by using the prescribed learning model according to respective combinations in which an instance number and a hyperparameter learned in parallel are arbitrarily changed (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)).)
acquires learning performance, corresponding to the respective combinations, from the respective information processing apparatuses; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
performs supervised learning by using learning data including the respective combinations and the learning performance corresponding to the respective combinations; (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)).)
by the supervised learning (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 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
An information processing apparatus comprising: a storage device; and a processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
The storage device stores a prescribed learning model is the well understood, routine, and conventional activity of “storing and retrieving information in memory” (See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
the processor causes other respective information processing apparatuses to perform, on one or a plurality of data sets, machine learning by using the prescribed learning model according to respective combinations in which an instance number and a hyperparameter learned in parallel are arbitrarily changed uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Acquires learning performance, corresponding to the respective combinations, from the respective information processing apparatuses 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)).
Performs supervised learning by using learning data including the respective combinations and the learning performance corresponding to the respective combinations uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
by the supervised learning uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 8 is subject-matter ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 1:
Claim 9 recites an apparatus and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites
generate a prediction model that predicts learning performance for each combination of an instance number and a hyperparameter (This limitation is a mental process as it encompasses a human mentally creating a model to predict learning performance.)
Therefore, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 further recites additional elements of
A non-transitory computer-readable recording medium having a program recorded thereon, (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract ideas. (see MPEP 2106.05(f)).)
A processor of an information processing apparatus having a storage device that stores a prescribed learning model (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract ideas. (see MPEP 2106.05(f)).)
The processor causes other respective information processing apparatuses to perform, on one or a plurality of data sets, machine learning by using the prescribed learning model according to respective combinations in which an instance number and a hyperparameter learned in parallel are arbitrarily changed (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)).)
acquire learning performance, corresponding to the respective combinations, from the respective information processing apparatuses; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
perform supervised learning by using learning data including the respective combinations and the learning performance corresponding to the respective combinations; (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)).)
by the supervised learning (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 9 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 9 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A non-transitory computer-readable recording medium having a program recorded thereon, uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
A processor of an information processing apparatus having a storage device that stores a prescribed learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
the processor causes other respective information processing apparatuses to perform, on one or a plurality of data sets, machine learning by using the prescribed learning model according to respective combinations in which an instance number and a hyperparameter learned in parallel are arbitrarily changed uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Acquire learning performance, corresponding to the respective combinations, from the respective information processing apparatuses 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)).
Perform supervised learning by using learning data including the respective combinations and the learning performance corresponding to the respective combinations uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
by the supervised learning uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 9 is subject-matter ineligible.
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 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) 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US 2023/0186168 A1) (hereafter referred to as Zhou) in view of Kawajiri (US 2023/0214668 A1) (hereafter referred to as Kawajiri).
Regarding claim 1, Zhou teaches
An information processing method performed by an information processing apparatus having a storage device storing a prescribed learning model, and a processor, (Zhou, page 13, paragraph 0064, “each of the plurality of computing devices may include a party within a federated learning environment” where “as shown in FIG. 3, computer system/server 12 in cloud computing node 10 is shown in the form of a general-purpose computing device. The components of computer system/server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16” (Zhou, page 11, paragraph 0049). Examiner notes that the party is the prescribed learning model.) the method comprising the steps of:
causing, by the processor, other respective information processing apparatuses to perform, on one or a plurality of data sets, machine learning by using the prescribed learning model according to respective combinations in which an instance number and a hyperparameter learned in parallel are arbitrarily changed (Zhou, page 15, paragraph 0096-0097, “The selection of hyperparameters is very important in a federated learning system, especially where there might be data distribution heterogeneity among parties. In response, a systematic approach is provided to perform hyperparameter tuning in a federated learning system which ensures the performance of the final global model without compromising local data privacy among the parties under both IID and non-IID settings…[0097] In one embodiment, a method to perform hyperparameter tuning in federated learning settings may include querying, by an aggregator, parties to perform hyperparameter optimization (HPO). Additionally, parties receiving the HPO query use their local dataset and the current model to run HPO to generate a set of features/loss pairs, and the set is sent to the aggregator” where “each of the plurality of computing devices may perform the plurality of HPO operations in parallel, separately from the other computing devices” (Zhou, page 13, paragraph 0065) Examiner notes that the instance number and hyperparameter are hyperparameters being tuned or changed. Examiner further notes that the combinations are the hyperparameters. Examiner further notes that the prescribed learning model are the parties or local models.);
acquiring, by the processor, learning performance, corresponding to the respective combinations, from the respective information processing apparatuses (Zhou, page 15, paragraph 0097, “parties receiving the HPO query use their local dataset and the current model to run HPO to generate a set of features/loss pairs, and the set is sent to the aggregator” where “performing the HPO operations may include training a local model at the computing device utilizing a local training data set and the generated hyperparameter values” (Zhou, page 14, paragraph 0078). Examiner notes that the feature/loss pairs are the learning performance corresponding to the combinations or hyperparameters. );
performing, by the processor, …learning by using learning data including the respective combinations and the learning performance corresponding to the respective combinations (Zhou, page 15, paragraph 0097-0098, “parties receiving the HPO query use their local dataset and the current model to run HPO to generate a set of features/loss pairs, and the set is sent to the aggregator. [0098] Further, the aggregator uses the collected features/loss pairs to select the best global hyperparameters for FL training, and shares these best global hyperparameters with all parties. The parties use the features/loss pairs to select the best local hyperparameters for local training with the received best global hyperparameters” where “performing the HPO operations may include training a local model at the computing device utilizing a local training data set and the generated hyperparameter values” (Zhou, page 14, paragraph 0078). Examiner notes that the learning data is the training data and the generated hyperparameters. Examiner further notes that the feature/loss pairs are the learning performance.)
and generating, by the processor, a prediction model that predicts learning performance for each combination of an instance number and a hyperparameter (Zhou, page 13, paragraph 0071, “in one embodiment, the aggregator may determine the optimal global hyperparameters. In another embodiment, the optimal global hyperparameters may be sent to each of the plurality of computing devices. For example, each of the plurality of computing devices may determine a local loss surface utilizing their local HPO results” where “performing the HPO operations may include evaluating, at the computing device, the trained local model on local test data to compute a loss value for each of the generated hyperparameter values” (Zhou, page 14, paragraph 0080). Examiner notes that the loss is the learning performance for each combination of hyperparameters within the HPO. The prediction model is the trained local model. ).
Zhou does not explicitly teach, but Kawajiri does teach
performing…supervised learning by using learning data (Kawajiri, page 12, paragraph 0050, “The first NN 16 can be trained by supervised learning. In this case, the first NN 16 is trained by using learning data including input data and teaching data corresponding to the input data.)
generating…a prediction model…by the supervised learning (Kawajiri, page 12, paragraph 0050, “The first NN 16 can be trained by supervised learning. In this case, the first NN 16 is trained by using learning data including input data and teaching data corresponding to the input data.)
Zhou and Kawajiri are considered analogous to the claimed invention because they both tune hyperparameters in machine learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Zhou to use supervised learning. Doing so is advantageous because “the accuracy of the output data with respect to the input data can be improved” (Kawajiri, page 12, paragraph 0050).
Regarding claim 2, Zhou in view of Kawajiri teaches the information processing method according to claim 1. Zhou further teaches
wherein the processor predicts, for each of the combinations, learning performance obtained when the prescribed data set is input to the prediction model and machine learning of the prescribed learning model is performed (Zhou, page 13, paragraph 0071, “in one embodiment, the aggregator may determine the optimal global hyperparameters. In another embodiment, the optimal global hyperparameters may be sent to each of the plurality of computing devices. For example, each of the plurality of computing devices may determine a local loss surface utilizing their local HPO results” where “performing the HPO operations may include evaluating, at the computing device, the trained local model on local test data to compute a loss value for each of the generated hyperparameter values” (Zhou, page 14, paragraph 0080). Examiner notes that the loss is the learning performance for each combination of hyperparameters within the HPO. The prediction model is the trained local model. ).
Regarding claim 3, Zhou in view of Kawajiri teaches the information processing method according to claim 1. Zhou further teaches
wherein the acquisition of the learning performance includes acquiring a learning time together with the learning performance (Zhou, page 13, paragraph 0065, “the performance metric of the HPO query may include one or more of predictive machine learning metrics including absolute or relative accuracy or loss and resource metrics including runtime and memory utilization.” Examiner notes that the loss is the learning performance and the runtime is the learning time.),
the performing of the …learning includes performing …learning by using learning data including the respective combinations and learning performance and learning times corresponding to the respective combinations (Zhou, page 15, paragraph 0097-0098, “parties receiving the HPO query use their local dataset and the current model to run HPO to generate a set of features/loss pairs, and the set is sent to the aggregator. [0098] Further, the aggregator uses the collected features/loss pairs to select the best global hyperparameters for FL training, and shares these best global hyperparameters with all parties. The parties use the features/loss pairs to select the best local hyperparameters for local training with the received best global hyperparameters” where “performing the HPO operations may include training a local model at the computing device utilizing a local training data set and the generated hyperparameter values” (Zhou, page 14, paragraph 0078) and “the performance metric of the HPO query may include one or more of predictive machine learning metrics including absolute or relative accuracy or loss and resource metrics including runtime and memory utilization” (Zhou, page 13, paragraph 0065). Examiner notes that the learning data is the training data and the generated hyperparameters. Examiner further notes that the loss is the learning performance and the learning time is the runtime.)
and the generation of the prediction model includes generating a prediction model that predicts learning performance and a learning time for each combination of an instance number and a hyperparameter by the … learning (Zhou, page 13, paragraph 0071, “in one embodiment, the aggregator may determine the optimal global hyperparameters. In another embodiment, the optimal global hyperparameters may be sent to each of the plurality of computing devices. For example, each of the plurality of computing devices may determine a local loss surface utilizing their local HPO results” where “performing the HPO operations may include evaluating, at the computing device, the trained local model on local test data to compute a loss value for each of the generated hyperparameter values” (Zhou, page 14, paragraph 0080) and “the performance metric of the HPO query may include one or more of predictive machine learning metrics including absolute or relative accuracy or loss and resource metrics including runtime and memory utilization” (Zhou, page 13, paragraph 0065). Examiner notes that the loss is the learning performance for each combination of hyperparameters within the HPO and the runtime is the learning time. The prediction model is the trained local model. ).
Zhou does not explicitly teach, but Kawajiri does teach
the performing of the supervised learning includes performing supervised learning by using learning data (Kawajiri, page 12, paragraph 0050, “The first NN 16 can be trained by supervised learning. In this case, the first NN 16 is trained by using learning data including input data and teaching data corresponding to the input data.)
the generation of the prediction model includes generating a prediction model…by the supervised learning (Kawajiri, page 12, paragraph 0050, “The first NN 16 can be trained by supervised learning. In this case, the first NN 16 is trained by using learning data including input data and teaching data corresponding to the input data.)
Zhou and Kawajiri are considered analogous to the claimed invention because they both tune hyperparameters in machine learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Zhou to use supervised learning. Doing so is advantageous because “the accuracy of the output data with respect to the input data can be improved” (Kawajiri, page 12, paragraph 0050).
Regarding claim 4, Zhou in view of Kawajiri teaches the information processing method according to claim 3. Zhou further teaches
wherein the processor predicts, for each of the combinations, learning performance and a learning time obtained when a prescribed data set is input to the prediction model and machine learning of the prescribed learning model is performed (Zhou, page 13, paragraph 0071, “in one embodiment, the aggregator may determine the optimal global hyperparameters. In another embodiment, the optimal global hyperparameters may be sent to each of the plurality of computing devices. For example, each of the plurality of computing devices may determine a local loss surface utilizing their local HPO results” where “performing the HPO operations may include evaluating, at the computing device, the trained local model on local test data to compute a loss value for each of the generated hyperparameter values” (Zhou, page 14, paragraph 0080) and “the performance metric of the HPO query may include one or more of predictive machine learning metrics including absolute or relative accuracy or loss and resource metrics including runtime and memory utilization” (Zhou, page 13, paragraph 0065). Examiner notes that the loss is the learning performance for each combination of hyperparameters within the HPO and the runtime is the learning time. The prediction model is the trained local model.).
Regarding claim 5, Zhou in view of Kawajiri teaches the information processing method according to claim 3. Zhou further teaches
wherein the processor, with the learning performance being a first variable and with the learning time being a second variable, generates relationship information in which the first and second variables and the instance number and hyperparameter are associated with each other (Zhou, page 13, paragraph 0071, “i