DETAILED ACTION
Status of Application
This action is a Non-Final Rejection. This action is in response to the application filed on June 8, 2023.
Claims 4, 6, 9-11, and 14 have been amended.
Claims 1-14 are pending and rejected.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on September 1, 2023; February 29, 2024; May 8, 2024; and November 26, 2024 have been considered by the examiner.
Claim Rejections - 35 USC § 112(b)
The following is a quotation 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 1-12 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 pre-AIA the applicant regards as the invention.
The follow claim limitations have been evaluated under the three-prong test set forth in MPEP § 2181, subsection I: a data acquisition unit, a model construction unit, an acquisition function construction unit, a design parameter group acquisition unit, and an output unit. However, the result is inconclusive. Thus, it is unclear whether these limitations should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because these units are only described in the Specification with respect to their functions. The boundaries of this claim limitations are ambiguous; therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
In response to this rejection, applicant must clarify whether these limitations should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Mere assertion regarding applicant’s intent to invoke or not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph is insufficient. Applicant may:
(a) Amend the claim to clearly invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by reciting “means” or a generic placeholder for means, or by reciting “step.” The “means,” generic placeholder, or “step” must be modified by functional language, and must not be modified by sufficient structure, material, or acts for performing the claimed function;
(b) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, should apply because the claim limitation recites a function to be performed and does not recite sufficient structure, material, or acts to perform that function;
(c) Amend the claim to clearly avoid invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by deleting the function or by reciting sufficient structure, material or acts to perform the recited function; or
(d) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply because the limitation does not recite a function or does recite a function along with sufficient structure, material or acts to perform that function.
For purposes of examination, these limitations are interpreted as software and not as invoking 35 U.S.C. 112(f).
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 1-14 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
No, with respect to claims 1-12, which recite a design assistance device comprising a data acquisition unit, a model construction unit, an acquisition function construction unit, a design parameter group acquisition unit, and an output unit. Figure 2 shows the design assistance device. The claimed units are shown within the processor of the design assistance device. However, the processor itself is not claimed. The Specification does not define these unit but instead describes their functions. Therefore, the broadest reasonable interpretation of each of these units includes software. Therefore, claims 1-12 are directed to software per se. Because software is not a statutory category, these claims are ineligible. To overcome this rejection, the claims should positively recite hardware such as a processor. Although these claims are ineligible at step 1, they are being analyzed below with respect to the other steps.
Yes, with respect to claim 13, which recites a method and, therefore, is directed to the statutory class of process.
Yes, with respect to claim 14, which recites a non-transitory computer readable recording medium and, therefore, is directed to the statutory class of manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a))
The following claims (Claims 1-12 are representative) identify the limitations that recite the abstract idea in regular text and that recite additional elements in bold:
1. A design assistance device obtaining a plurality of design parameters satisfying a target value set for each of a plurality of characteristic items indicating a characteristic of a product, an in-process product, a half-finished product, a component, or a trial product produced on the basis of a design parameter group including the plurality of design parameters, in order to apply to a method for optimizing a design parameter by repeating determination of the design parameter and production of the product, the in-process product, the half-finished product, the component, or the trial product based on the determined design parameter, in design of the product, the in-process product, the half-finished product, the component, or the trial product, the device comprising:
a data acquisition unit acquiring a plurality of performance data pieces including the design parameter group and an observation value of each of the plurality of characteristic items, for the produced product, in-process product, half-finished product, component, or trial product;
a model construction unit constructing a prediction model for predicting the observation value of the characteristic item as a probability distribution or an approximate or alternative index thereof on the basis of the design parameter group, on the basis of the performance data;
an acquisition function construction unit constructing a target-oriented acquisition function, which is a single acquisition function having the design parameter group as input and an index value of the design parameter group relevant to improvement of the characteristics indicated in all of the characteristic items as output, the target-oriented acquisition function including at least a target achievement probability term including a total achievement probability, which is a probability that the target values of all of the characteristic items are achieved and a probability calculated on the basis of the prediction model by using the design parameter group as a variable;
a design parameter group acquisition unit acquiring at least one design parameter group by optimization of the target-oriented acquisition function; and
an output unit outputting the design parameter group acquired by the design parameter group acquisition unit.
2. The design assistance device according to claim 1, wherein the design parameter group acquisition unit acquires at least one design parameter group for optimizing the output of the target-oriented acquisition function.
3. The design assistance device according to claim 1, wherein the design parameter group acquisition unit acquires a plurality of design parameter groups by a predetermined algorithm.
4. The design assistance device according to claim 1, wherein the total achievement probability is an infinite product of an achievement probability with respect to the target value of each of the characteristic items, and the achievement probability with respect to the target value of each of the characteristic items is based on the probability distribution of the observation value obtained by inputting the design parameter group to the prediction model of each of the characteristic items.
5. The design assistance device according to claim 4, wherein the target achievement probability term includes the total achievement probability or a logarithm of the total achievement probability.
6. The design assistance device according to claim 1, wherein the acquisition function construction unit constructs an acquisition function having the design parameter group as input and the index value of the design parameter group relevant to the improvement of the characteristic indicated in the characteristic item as output, for each of the characteristic items, and the target-oriented acquisition function further includes a term of a weighted sum of the acquisition function of each of the characteristic items.
7. The design assistance device according to claim 6, wherein the target-oriented acquisition function includes a sum of the term of the weighted sum of the acquisition function of each of the characteristic items and the target achievement probability term.
8. The design assistance device according to claim 6, wherein the target-oriented acquisition function includes a product of the term of the weighted sum of the acquisition function of each of the characteristic items and the target achievement probability term.
9. The design assistance device according to claim 6, wherein the acquisition function construction unit constructs the acquisition function of each of the characteristic items by any one of lower confidence bound (LCB), expected improvement (EI), and probability of improvement (PI).
10. The design assistance device according to claim 6, wherein the acquisition function construction unit constructs the acquisition function including a cost value relevant to a cost including at least any one of time and a cost according to the production of the product, the in-process product, the half-finished product, the component, or the trial product, generated in accordance with the design parameter group, the acquisition function for outputting the index value indicating that a degree of suitability of the design parameter group decreases as the cost value increases.
11. The design assistance device according to claim 1, wherein the prediction model is a regression model or a classification model having the design parameter group as input and the probability distribution of the observation value as output, and the model construction unit constructs the prediction model by machine learning using the performance data.
12. The design assistance device according to claim 11, wherein the prediction model is a machine learning model for predicting the probability distribution or the approximate or alternative index thereof of the observation value, by using any one of a posterior distribution of a prediction value based on a Bayesian theory, a distribution of a prediction value of a predictor configuring an ensemble, a theoretical formula of a prediction interval and a confidence interval of a regression model, a Monte Carlo dropout, and a distribution of a prediction of a plurality of predictors constructed in different conditions.
Yes. But for the recited additional elements as shown above in bold, the remaining limitations of the claims recite a method of optimizing a design parameter, i.e., a mental process. For example, claim 1 recites acquiring a plurality of performance data pieces (observation), constructing a prediction model for predicting an observation value (evaluation), constructing a target-oriented function (evaluation), acquiring at least one design parameter group by optimization of the target-oriented acquisition function (evaluation and judgment), and outputting the design parameter group (judgment). Thus, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d))
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components (i.e., additional elements that are in bold above) are recited at a high level of generality and are merely invoked as a tool to implement the steps. For example, only a programmed general purpose computing device is needed to implement the claimed process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05)
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claims, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claims do not provide an inventive concept.
As such, the claims are not patent eligible.
35 USC §§ 102 and 103
The claims are determined to be novel and nonobvious because the combination of claim limitations was not found in or made obvious by the prior art. Additionally, the claims were designated by the International Searching Authority in the PCT as novel and having an inventive step. The closest prior art is listed below.
Relevant Prior Art
The following references are relevant to Applicant’s invention:
Goel, U.S. Patent Application Publication Number 2009/0248368 A1. This reference teaches “According to one embodiment, the present invention is method of performing design optimization of a product, the method comprises at least the following: identifying a set of design variables, objectives and constraints for designing and optimizing a product; identifying a plurality of design of experiments (DOE) points, each of the DOE points includes a unique combination of design variables; creating a plurality of computer-aided engineering (CAE) analysis models corresponding to the plurality of the DOE points; obtaining analysis results by performing CAE analyses using the plurality of CAE analysis models; creating a radial basis function (RBF) based meta-model such that the RBF based meta-model can approximate the analysis results; obtaining an optimized design of the structural product using the RBF based meta-model, the optimized design is bounded by the set of design variables, objectives and constraints; and verifying the optimized design of the structural product by performing CAE analysis of a CAE analysis model created for the optimized design.” See paragraph 0011.
Nakazawa, U.S. Patent Number 5,974,246. This reference teaches a method of determining optimum product design parameters.
Okunev et al., U.S. Patent Application Publication Number 2022/0036273 A1. This reference teaches digital thread-driven sustainability design. Per the Abstract, “[t]he Digital Thread enables a set of predictive computational modeling tools for total lifecycle product design optimization, simulation and uncertainty and risk analysis integrated to access data through the Digital Thread. “
Kiarashinejad et al., U.S. Patent Application Publication Number 2022/0019716 A1. This reference teaches systems and methods for enhanced engineering design and optimizing incorporating double-step dimensionality-reduction in order to provide customized, automated solutions to the design and optimization of electromagnetic nanostructures.
Bacher et al., U.S. Patent Application Publication Number 2021/0141869 A1. This reference teaches automated analysis of mechanical designs.
Yabe, U.S. Patent Application Publication Number 2021/0034999 A1. This reference teaches an optimization device.
Fujimoto et al., U.S. Patent Application Publication Number 2020/0090058 A1. This reference teaches a model variable candidate generation device.
Anderson et al., U.S. Patent Application Publication Number 2020/0034513 A1. This reference teaches an automated robot design pipeline.
Hershenson et al., U.S. Patent Number 8,407,651 B2. This reference teaches optimizing design parameters of a circuit.
Dhir et al., U.S. Patent Application Publication Number 2003/0009317 A1. This reference teaches optimizing the design of a mechanical system. This includes creating models of the mechanical system and simulating the performance of the models to achieve a set of results.
Ito et al., U.S. Patent Application Publication Number 2022/0382938 A1. This reference teaches material design. A parameter group may be optimized by trial and error.
Email Communications
Per MPEP 502.03, Applicant may authorize email communications by filing Form PTO/SB/439, available at https://www.uspto.gov/sites/default/files/documents/sb0439.pdf, via the USPTO patent electronic filing system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH H ROSEN whose telephone number is (571) 270-1850 and email address is elizabeth.rosen@uspto.gov. The examiner can normally be reached Monday - Friday, 10 AM ET - 7 PM ET.
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/ELIZABETH H ROSEN/Primary Examiner, 3693
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