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 .
This Office Action is in response to claims filed on 02/27/2023
Claims 1-17 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/27/2023 and 09/17/2025 are being considered by the examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hideyuki Nakagawa, US2022/0138194 A1, Published: May 5, 2022, (hereafter Nakagawa).
Regarding claim 1. Nakagawa teaches an information processing apparatus comprising:
a regression model generator configured to, by combining two or more of a plurality of variables, generate a plurality of terms that include combinations of two or more of the plurality of variables, respectively, and generate a regression model that regresses a property variable or an objective variable by the plurality of terms, the objective variable indicating an output of an objective function that includes the property variable (Par 62, gaussian process regression model is calculated) (Par 144, Gaussian process regression model is generated);
a subgroup generator configured to generate, based on coefficients of the plurality of terms included in the regression model, at least one or more subgroups that are one or more of the combinations of variables included in at least one or more of the terms (Fig 7, SA3, Determine r-dimensional search space) (Par 48, D-dimensional parameters divided into groups, x0 to x4, x5 to x9), respectively; and
a subspace search processor configured to perform search for each of subspaces spanned by the subgroups based on an optimization criterion for the objective function, and generate pieces of first design value data that include values of the plurality of variables for the subspaces (Fig 1, Search Space determination unit) (Fig 7, SA7, Search the r-dimensional search space) (Par 40, optimize the objective function within the search space).
Regarding claim 2. Nakagawa teaches the information processing apparatus according to claim 1, wherein the regression model generator generates the regression model based on data sets (Par 61, N data sets acquired, Gaussian process regression is modeled),
the data sets including pieces of second design value data that include values of the plurality of variables acquired by sampling, respectively (Fig 2, Data set) (Fig 5, 20 dimensional data set), and
output values of the property variable or output values of the objective function based on the pieces of second design value data, respectively (Fig 5, output y) (Fig 7, Output of the objective function, SA9).
Regarding claim 3. Nakagawa teaches the information processing apparatus according to claim 2, wherein the subgroup generator performs selection of the terms in descending order of absolute values of the coefficients (Par 50, x0 is selected in step SA2, may change, and x1 is selected) and
generation of the subgroups that are the combinations of variables included in the terms in order of selection until the plurality of variables are included in at least any of the subgroups (Par 48, divided into groups, first, second, third group).
Regarding claim 4. Nakagawa teaches the information processing apparatus according to claim 3, wherein the subspace search processor searches the subspaces in descending order of the absolute values of the coefficients (Par 58, restricting the search range to the r-dimensional search space, has a high potential for minimizing the observed value).
Regarding claim 6. Nakagawa teaches the information processing apparatus according to claim 1, wherein the regression model generator generates the regression model using at least one of ridge regression, lasso regression, elastic net regression, decision tree regression, random forest regression, K-proximity regression, support vector regression and a neural network (Par 57, Random forest regression).
Regarding claim 7. Nakagawa teaches the information processing apparatus according to claim 2, wherein the subspace search processor decides values of variables other than the variables that span the subspaces among the plurality of variables wherein the decided values are any of the pieces of first design value data or any of the pieces of second design value data that gives an optimal output value of the property variable or the objective function among the output values included in the data sets (Par 76, acquires an optimal parameter value xbest associated with a minimum observed value of the objective function),
generates the pieces of first design value data that include the values of the variables acquired by the search and the value of the other variables and adds, to the data sets, pieces of data that include the pieces of first design value data and values of the property variable or output values of the objective variable based on the pieces of first design value data (Par 76, the optimal parameter values xbest is output, maximizes manufacturing yield).
Regarding claim 8. Nakagawa teaches the information processing apparatus according to claim 7, wherein the subspace search processor searches a space spanned by any of the subgroups that has the next largest coefficient absolute value based on the data sets to which the pieces of data are added (Par 118, using data set with a relatively short distance from the search space, efficiency in parameter optimization is improved) (Par 144, maximizes the acquisition function, belongs to an r-dimensional affine subspace, search space).
Regarding claim 9. Nakagawa teaches the information processing apparatus according to claim 7, wherein the subspace search processor continuously performs search of the subspace a plurality of times (Par 53, iteration of the loop regarding the first condition and the loop regarding the second condition) (Par 142, optimal parameter value is achieved by iteration).
Regarding claim 10. Nakagawa teaches the information processing apparatus according to claim 7, wherein, after the search for all the subspaces ends, a series of the processes by the regression model generator, the subgroup generator and the subspace search processor is repeated one or more times based on the data sets (Fig 7, multiple iterations based on first condition and second condition) (Par 53, data sets, increases through the iteration of the loop regarding the first and second condition) (Par 71, first condition defines the number of times of performing parameter search within the r-dimensional search space).
Regarding claim 11. Nakagawa teaches the information processing apparatus according to claim 10, wherein the subgroup generator increases the number of the subgroups to be generated as the number of repetitions of the series of the processes increases (Par 52, threshold for distance may be changed for every loop regarding step SA11 or SA12, in accordance with the number of iterations of that loop, the greater the number of iterations, the smaller the threshold, thus selecting a large threshold increases the number of the groups).
Regarding claim 12. Nakagawa teaches the information processing apparatus according to claim 10, wherein the subgroup generator decreases the number of the subgroups to be generated as the number of repetitions of the series of the processes increases (Par 52, threshold for distance may be changed for every loop regarding step SA11 or SA12, in accordance with the number of iterations of that loop, the greater the number of iterations, the smaller the threshold, thus selecting a small threshold decreases the number of the groups).
Regarding claim 13. Nakagawa teaches the information processing apparatus according to claim 10, wherein the subgroup generator changes the number of the subgroups to be generated when the number of repetitions of the series of the processes reaches a predetermined number of times (Par 48, iterated a predetermined number of times).
Regarding claim 14. Nakagawa teaches the information processing apparatus according to claim 1, further comprising an output device configured to output information about the terms and the coefficients of the terms that are included in the regression model (Par 76, optimal parameter value is output, xbest, displayed) (Par 77, that minimizes the observed value of the objective function).
Regarding claim 15. Nakagawa teaches the information processing apparatus according to claim 1, wherein a target apparatus is a semiconductor apparatus (Par 155, realized, by hardware, integrated circuit (IC)).
Regarding claim 16. Nakagawa teaches an information processing method comprising:
combining two or more of a plurality of variables and generating a plurality of terms that include combinations of two or more of the plurality of variables, respectively, and generating a regression model that regresses a property variable or an objective variable by the plurality of terms, the objective variable indicating an output of an objective function that includes the property variable (Par 62, gaussian process regression model is calculated) (Par 144, Gaussian process regression model is generated);
generating, based on coefficients of the plurality of terms included in the regression model, at least one or more subgroups that are one or more of the combinations of variables included in at least one or more of the terms (Fig 7, SA3, Determine r-dimensional search space) (Par 48, D-dimensional parameters divided into groups, x0 to x4, x5 to x9), respectively; and
performing search for each of subspaces spanned by the subgroups based on an optimization criterion for the objective function, and generating pieces of first design value data that include values of the plurality of variables for the subspaces (Fig 1, Search Space determination unit) (Fig 7, SA7, Search the r-dimensional search space) (Par 40, optimize the objective function within the search space).
Regarding claim 17. Nakagawa teaches a non-transitory computer readable medium having a computer program stored therein which causes a computer to perform processes (Par 152, computer readable storage medium) comprising:
combining two or more of a plurality of variables and generating a plurality of terms that include combinations of two or more of the plurality of variables, respectively, and generating a regression model that regresses a property variable or an objective variable by the plurality of terms, the objective variable indicating an output of an objective function that includes the property variable (Par 62, gaussian process regression model is calculated) (Par 144, Gaussian process regression model is generated);
generating, based on coefficients of the plurality of terms included in the regression model, at least one or more subgroups that are one or more of the combinations of variables included in at least one or more of the terms (Fig 7, SA3, Determine r-dimensional search space) (Par 48, D-dimensional parameters divided into groups, x0 to x4, x5 to x9), respectively; and
performing search for each of subspaces spanned by the subgroups based on an optimization criterion for the objective function, and generating pieces of first design value data that include values of the plurality of variables for the subspaces (Fig 1, Search Space determination unit) (Fig 7, SA7, Search the r-dimensional search space) (Par 40, optimize the objective function within the search space).
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hideyuki Nakagawa, US2022/0138194 A1, Published: May 5, 2022, (hereafter Nakagawa), in views of Dipanwita Bhattacharjee, NPL, “Thorough Descriptor Search to Machine Learn the Lattice Thermal Conductivity of Half-Heusler Compounds”, Published: June 29, 2022 (hereafter Bhattacharjee).
Regarding claim 5. Nakagawa teaches the information processing apparatus according to claim 1,
Nakagawa does not teach wherein each of the terms includes at least one of a product of the variables, reciprocals of the variables, a product of the reciprocals of the variables and a compound function of the variables.
Bhattacharjee teaches wherein each of the terms includes at least one of a product of the variables, reciprocals of the variables, a product of the reciprocals of the variables and a compound function of the variables (Page 8917, Fig 3, combined elements and compound, combined compound and compound thermal) (Page 8917, Col 2, Par 2, descriptor set indicating high relevance of compound features, E, E+C, C+CT, and E+C+CT).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Nakagawa to incorporate the teachings of Bhattacharjee for the terms to include compound function of variables because ML models built with compound descriptors reach high accuracy with fewer number of descriptors (Bhattacharjee, Abstract)
Conclusion
The prior art made of record, listed on PTO-892, and not relied upon is considered pertinent to applicant's disclosure.
Maruo et al., US2022/0215137 A1, discloses a multi-objective optimization, a pareto solution set from a model generated based on a plurality of objective functions. The model being a regression model.
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/A.C./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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