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 .
DETAILED ACTION
1. Claims 1-9 are presented for examination.
Specification
2. The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: STORAGE MEDIUM, OPTIMIZATION METHOD, AND INFORMATION PROCESSING APPARATUS FOR A SHAPE DESIGN.
Claim Objections
3. Claims 2, 5, and 8 are objected to because of the following informalities:
As per Claims 2, 5, and 8, the limitation "a plurality of the shapes" lacks proper antecedent basis, as the respective base claims recite only a singular shape of a design target item. For examination purposes, "a plurality of the shapes" is interpreted as a plurality of candidate shapes of the design target item, each candidate shape corresponding to a different disposition of the plurality of Gaussian functions.
Appropriate correction is required.
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.
4. Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1) Claim 1-3 is directed to a non-transitory computer-readable storage medium which is a statutory category of invention. Claim 4-6 is directed to steps or acts including identifying the shape of the design target item and falls within the statutory category of processes; and, the claim 7-9 is directed to an apparatus and falls within the statutory category of machines.
(Step 2A – Prong One) For the sake of identifying the abstract ideas, a copy of the claim is provided below. Abstract ideas are bolded.
The claims 1, 4 and 7 recite:
setting, for a region of a design target, a plurality of Gaussian functions as basis functions of a shape function that corresponds to a shape of a design target item in the region design (under its broadest reasonable interpretation, mathematical concepts); and
identifying the shape of the design target item indicated by the shape function obtained by combining the plurality of Gaussian functions identified to be disposed by identifying whether to dispose the plurality of Gaussian functions design (under its broadest reasonable interpretation, mathematical concepts and mental process).
Further the limitation dependent claims, under its broadest reasonable interpretation, is the abstract idea of mathematical concepts.
Therefore, the limitations, under its broadest reasonable interpretation, are the abstract idea of mathematical concepts.
(Step 2A – Prong Two: integration into practical application) This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements “A non-transitory computer-readable storage medium storing an optimization program that causes at least one computer to execute a process” (Claim 1), “computer” (Claim 4), and “apparatus comprising: one or more memories; and one or more processors coupled to the one or more memories and the one or more processors” (Claim 7) which is recited at high level generality and recited so generally that they represent more than mere instruction to apply the judicial exception on a computer (see MPEP 2106.05(f)). The limitation can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(d)). Further, the additional element of “computer”, “memories” and “processors” does not (1) improve the functioning of a computer or other technology, (2) is not applied with any particular machine (except for generic computer components), (3) does not effect a transformation of a particular article to a different state, and (4) is not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
(Step 2B - inventive concept) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements “A non-transitory computer-readable storage medium storing an optimization program that causes at least one computer to execute a process” (Claim 1), “computer” (Claim 4), and “apparatus comprising: one or more memories; and one or more processors coupled to the one or more memories and the one or more processors” (Claim 7) ) amount to no more than mere instruction to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component cannot provide an inventive concept.
Further dependent claims 2-3, 5-6 and 8-9 recite:
(Claim 2, 5 and 8) wherein the plurality of Gaussian functions are arbitrary disposed in a plurality of the shapes, wherein the identifying includes:
generating, for each of the shapes in which the plurality of Gaussian functions are arbitrarily disposed, a factorization machine based on training data with which a characteristic value in the shape is associated (under its broadest reasonable interpretation, mathematical concepts);
converting the generated factorization machine into quadratic unconstrained binary optimization (under its broadest reasonable interpretation, mathematical concepts); and
identifying whether to dispose the plurality of Gaussian functions by annealing for the converted quadratic unconstrained binary optimization (under its broadest reasonable interpretation, mathematical concepts).
(Claim 3, 6 and 9) wherein the setting includes setting a plurality of Gaussian functions of positive values and a plurality of Gaussian functions of negative values that respectively correspond to the Gaussian functions of positive values (under its broadest reasonable interpretation, mathematical concepts).
Considering the claim both individually and in combination, there is no element or combination of elements recited contains any “inventive concept” or adds “significantly more” to transform the abstract concept into a patent-eligible application.
Claim Rejections - 35 USC § 102
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.
5. Claims 1, 3, 4, 6, 7, and 9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Otomo ("A 3-D Topology Optimization of Magnetic Cores for Wireless Power Transfer Device", listed on IDS filed 06/26/2023).
As per Claim 1, 5, and 7, Otomo teaches a non-transitory computer-readable medium storage/method/ apparatus storing an optimization program that causes at least one computer to execute a process (Abstract “robust optimization method”; Introduction “we pro pose a novel optimization method to make the optimal solutions robust against misalignment in the coils.”: Inherency considered):
setting, for a region of a design target, a plurality of Gaussian functions as basis functions of a shape function that corresponds to a shape of a design target item in the region (Sec. II-A
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; Sec. III-A
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: normalized Gaussian functions deployed over the design region serve as the basis functions of the shape function that corresponds to the shape of the design target item in the region); and
identifying the shape of the design target item indicated by the shape function obtained by combining the plurality of Gaussian functions identified to be disposed by identifying whether to dispose the plurality of Gaussian functions (Sec. II-A
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; Sec. I “the ON/OFF method based on the normalized Gaussian network (NGnet) with aid of the genetic algorithm (GA) and 2-D finite-element method (FEM) has been shown effective for the optimization of the magnetic core of a rotating machine”: the material shape is identified from the value of the shape function formed by combining the deployed Gaussian functions under an ON/OFF determination over the design region; Examiner's Note – the claimed identifying whether to dispose the plurality of Gaussian functions corresponds to Otomo's ON/OFF determination over the deployed Gaussian basis functions, as each deployed Gaussian function is arranged or not arranged according to that determination).
As per Claim 3, 6 and 9, Otomo teaches wherein the setting includes setting a plurality of Gaussian functions of positive values and a plurality of Gaussian functions of negative values that respectively correspond to the Gaussian functions of positive values (Sec. II-A, eqs. (1) and (4); Sec. II-B "To find current density localized in a coil, we modify the shape function (1) as follows”, eq. (5): the shape function is expressed as α tanh of the weighted sum of the deployed normalized Gaussian basis functions and expressly assumes values over the range −α ≤ Tz(x) ≤ α, whereby the deployed weighted Gaussian terms assume positive values and correspondingly negative values and the shape function assigns ferrite where its value is non-negative and air where its value is negative; Examiner's Note – a weighting coefficient of either sign multiplying a deployed normalized Gaussian yields a Gaussian function of positive values or a corresponding Gaussian function of negative values at the same deployment).
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.
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.
6. Claims 2, 5, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Otomo ("A 3-D Topology Optimization of Magnetic Cores for Wireless Power Transfer Device", listed on IDS filed 06/26/2023) as applied to claims 1, 3, 4, 6, 7, and 9 above, and further in view of Kitai ("Expanding the horizon of automated metamaterials discovery via quantum annealing", listed on IDS filed 05/22/2026).
As per Claim 2, 5 and 8, Otomo fails to teach explicitly wherein the plurality of Gaussian functions are arbitrary disposed in a plurality of the shapes;
generating, for each of the shapes in which the plurality of Gaussian functions are arbitrarily disposed, a factorization machine based on training data with which a characteristic value in the shape is associated;
converting the generated factorization machine into quadratic unconstrained binary optimization; and
identifying whether to dispose the plurality of Gaussian functions by annealing for the converted quadratic unconstrained binary optimization.
Kitai teaches wherein the plurality of Gaussian functions are arbitrary disposed in a plurality of the shapes (pg. 4 "Our algorithm is capable of solving a black-box optimization problem over binary variables representing a structure of materials."; pg. 9 "the first 50 structures, which were randomly selected as the initial data, are common in all three methods": a plurality of candidate shapes is obtained from arbitrary, randomly selected dispositions of the binary design units; in the combination, the design units are the deployed Gaussian basis functions of Otomo);
generating, for each of the shapes in which the plurality of Gaussian functions are arbitrarily disposed, a factorization machine based on training data with which a characteristic value in the shape is associated (pg. 4 " First, a factorization machine (FM)30 is trained with available data to model the material’s property of interest. …In the next step, a new point is added to training data, and the FM is retrained.": a factorization machine is generated from training data in which each sampled shape is associated with its evaluated characteristic value);
converting the generated factorization machine into quadratic unconstrained binary optimization (pg. 4 "Selection of a new candidate with respect to an acquisition function based on a trained FM boils down to a QUBO and solved by the quantum annealer."; pg. 18 "The key of our idea is to use a machine learning regression model, which can be expressed by QUBO, and the next candidate material structure with a high acquisition function can be rapidly selected by the quantum annealer.": the generated factorization machine is expressed as, i.e., converted into, a quadratic unconstrained binary optimization); and
identifying whether to dispose the plurality of Gaussian functions by annealing for the converted quadratic unconstrained binary optimization (pg. 19 "We utilized the D-Wave 2000Q quantum annealer to select the next candidate material in automated materials discovery.": annealing over the converted QUBO selects the binary values that identify which design units are applied, i.e., whether to dispose the plurality of Gaussian functions of the combination). In particular, Kitai teaches an iterative optimization loop in which a factorization machine is trained on training data associating binary candidate structures with an evaluated property value, the trained factorization machine is expressed as a quadratic unconstrained binary optimization, and a quantum annealer selects the next candidate structure.
Otomo and Kitai are analogous art because they are all related to computational optimization of the shape or structure of a design object.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Kitai into Otomo's invention for the purpose of the topology optimization to provide an improved method with gigantic computational power for selection of candidate structures whereby the structure with a high FOM can be obtained with small number of evaluations, simulations or experiments (Kitai: Abstract, pg. 19).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Maruo (US 2022/0129605 A1) teaches teaches a non-transitory computer-readable storage medium storing an optimization program that causes at least one computer to execute a process ([0184] "The control unit 101 realizes various functions"; [0190] "stored in the auxiliary storage device 103"; [0190] "and executed by the control unit 101”) including optimizing the shape of an object arranged in a design region by dividing the object into a plurality of elements and specifying, for each element, whether to arrange the element ([0097] "using the objective function equation based on a contribution to a predetermined characteristic of the object in each element of a plurality of elements obtained by dividing the object"; [0106] "the shape of the object is optimized by specifying whether to arrange each element of the object"; [0107] "the shape of the object can be searched in a wide range in consideration of all the combinations that can arrange each element in the design region"), the objective function being expressed in a quadratic unconstrained binary optimization format and minimized by annealing ([0143] "there is a case where it is needed for the objective function to be expressed by a quadratic unconstrained binary optimization"; [0153] "the energy value in the Ising model equation can be efficiently optimized")
Kushibe (US 2022/0114470 A1) teaches an optimization apparatus that finds the ground state of an Ising model that represents a target problem by running a simulation, with a value corresponding to noise added to some of the coefficients used in the simulation.
Tsukamoto (US 11,526,740 B2) teaches an optimization apparatus that searches for a combination of bit values of each neuron at which energy becomes minimum for an evaluation function obtained by converting an optimization problem, using a probabilistic search method such as simulated annealing.
Watanabe (US 12,235,926 B2) teaches Ising machines that each search for a solution of a subproblem among subproblems obtained by dividing a problem represented by an Ising model.
Matsumori ("Application of QUBO solver using black-box optimization to structural design for resonance avoidance”) teaches optimizing a structural design by estimating a binary quadratic model from data sets of binary design variables and evaluated objective values using a factorization machine and solving the resulting quadratic unconstrained binary optimization with an annealing-based solver.
Wang ("Radial basis functions and level set method for structural topology optimization”) teaches representing the level set function for structural topology optimization as an expansion in radial basis functions, the sign of the level set function distinguishing material from void.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/ Primary Examiner, Art Unit 2188
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