DETAILED ACTION
Status of Application
This action is a Final Rejection. This action is in response to the amendment and response filed on August 18, 2026.
Claims 1, 13, 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 July 17, 2026 and August 31, 2026 have been considered by the examiner.
Response to Arguments
The rejection under 35 U.S.C. § 112(b) is withdrawn in light of Applicant’s amendments.
Regarding the rejection under 35 U.S.C. § 101, Applicant argues that “the claimed invention improves the technological field of computer-aided product design and manufacturing by enabling the optimization of multiple characteristics and design variables with a significantly lower processing load and fewer physical experiments.” Remarks at 12. However, Applicant is describing an improvement to the abstract idea and not to the technology that is being used to implement the abstract idea. For example, the use of existing technology to improve the efficiency or accuracy of designing does not provide a technological improvement. Additionally, the ability to handle large processing loads is inherent in the use of the computing device. See MPEP 2106.05(f)(2) (“Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).”). See also MPEP 2106.05(a) (“Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: … ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) ….”).
Applicant further argues that the claims, as amended, integrate the abstract idea because of additional elements that are relevant to “Controlling a Manufacturing Apparatus.” See Remarks at 14-15. However, although the claims have been amended to recite “output the proposed design parameter group to a manufacturing apparatus in a plant or a laboratory, and cause the manufacturing apparatus to produce the product, the in-process product, the half-finished product, the component, or the trial product based on the proposed design parameter group,” there is no indication in the Specification that the output is being used to control a manufacturing apparatus. As such, this limitation is interpreted to mean that someone may use the output to assist in manufacturing. Additionally, even if interpreted to mean that the output controls a manufacturing apparatus, this limitation is recited at a high level with no written description as to the manner in which a manufacturing apparatus is controlled.
Applicant further argues that the additional elements are not well-understood, routine, or conventional. Remarks at 15-16. However, the rejection does not allege that any additional elements are insignificant extra-solution activity or well-understood, routine, or conventional.
As such, the rejection under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 13, and 14 were amended to recite “output the proposed design parameter group to a manufacturing apparatus in a plant or a laboratory, and cause the manufacturing apparatus to produce the product, the in-process product, the half-finished product, the component, or the trial product based on the proposed design parameter group.” On page 10 of the Remarks, Applicant pointed to paragraphs 0035-0037 and 0080-0083 as providing support for the claim amendments. These paragraphs are reproduced below:
[0035] FIG. 1 is a diagram illustrating the outline of a material design process, which is an example of a design process of a product, an in-process product, a half-finished product, a component, or a trial product to which a design assistance device according to an embodiment is applied. Note that, hereinafter, the "product, the in-process product, the half-finished product, the component, or the trial product" will be described as the "product or the like". A design assistance device 10 according to this embodiment can be applied to a design process of any product or the like including a plurality of25 characterist items indicating the characteristic of the product or the like and a target value of each of the characteristic items. The design assistance device 10 can be applied to a method for optimizing a design parameter and an object variable of the product or the like by repeating the determination of the design parameter and the production of the product, the in-process product, the half-finished product, the component, or the trial product based on the determined design parameter. Specifically, the design assistance device 10, for example, can be applied to the design of a product such as a vehicle and a chemical, the optimization of a molecular structure of the chemical, and the like, in addition to the development and the design of the material. In this embodiment, as described above, design assistance processing of the design assistance device 10 will be described with an example of the material design as an example of the design of the product or the like.
[0036] As illustrated in FIG. 1, the design assistance processing of the design assistance device 10 is applied to material production and an experiment in a plant, a laboratory A, and the like. That is, the material is produced in accordance with a set design parameter group x, in the plant, the laboratory A, and the like, and an observation value y of a plurality of characteristic items indicating the characteristic of the material is acquired on the basis of the produced material. Note that, the material production and the experiment in the plant and the laboratory A may be a simulation. In this case, the design assistance device 10 provides the design parameter group x for executing the next simulation.
[0037] The design assistance device 10 optimizes the plurality of characteristic items and the design parameter, on the basis of performance data including the design parameter group x and the observation value y of the plurality of characteristic items of the material produced on the basis of the design parameter group x. Specifically, the design assistance device 10 outputs the design parameter group x for the next production and experiment, with a possibility that a more suitable characteristic is obtained, on the basis of the design parameter group x and the observation value y relevant to the produced material.
[0080] In addition, as an example, the design parameter group acquisition unit 14 may acquire a plurality of design parameter groups by a predetermined algorithm. Specifically, the design parameter group acquisition unit 14 may acquire the plurality of design parameter groups by applying a batch Bayesian optimization method to the target-oriented acquisition function. The batch Bayesian optimization method, for example, may be a method such as local penalization, but the method is not limited.
[0081] The output unit 15 outputs the design parameter group acquired by the design parameter group acquisition unit 14. That is, the output unit 15 outputs the design parameter group obtained on the basis of the performance data in the first (t = 1) to (T-1)-th (t = T-1) material production, as the design parameter group xT for the T-th material production.
[0082] In addition, in a case where the plurality of design parameter groups are acquired by the design parameter group acquisition unit 14, the output unit 15 outputs the acquired design parameter group as the design parameter group for the material production for N times subsequent to the (T-1)-th material production. The design parameter group for the material production for a plurality of times may be available to simultaneous experiment and material production.
[0083] An output mode is not limited, and the output unit 15, for example, outputs the design parameter group candidate by displaying the design parameter group candidate on a predetermined display device or by storing the design parameter group candidate in a predetermined storage means.
None of these paragraphs provide support for “caus[ing] the manufacturing apparatus to produce the product, the in- process product, the half-finished product, the component, or the trial product based on the proposed design parameter group.” Additionally, the full Specification was reviewed and support could not be found for this limitation. Therefore, the claim amendments are new matter.
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)
Yes, with respect to claims 1-12, which recite a design assistance device (i.e., system) that comprises at least one processor and, therefore, are directed to the statutory class of machine or manufacture.
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:
at least one processor,
wherein the at least one processor is configured to:
acquire 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;
construct 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;
construct 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;
acquire at least one design parameter group as a proposed design parameter group by optimization of the target-oriented acquisition function; and
output the proposed design parameter group to a manufacturing apparatus in a plant or a laboratory, and cause the manufacturing apparatus to produce the product, the in-process product, the half-finished product, the component, or the trial product based on the proposed design parameter group.
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 as a proposed 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 (i.e., the claimed at least one processor) 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. Although the claims have been amended to recite “output the proposed design parameter group to a manufacturing apparatus in a plant or a laboratory, and cause the manufacturing apparatus to produce the product, the in-process product, the half-finished product, the component, or the trial product based on the proposed design parameter group,” there is no indication in the Specification that the output is being used to control a manufacturing apparatus. As such, this limitation is interpreted to mean that someone may use the output to assist in manufacturing. 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.
Andino et al., U.S. Patent Application Publication Number 2004/0246440 A1. This reference teaches an automatic lens design and manufacturing system. Specifically, it teaches “[m]ore preferably, the computer system further comprises a signal module for converting said CAD output file into control signals that control the computer-controllable manufacturing device to produce said ophthalmic lens.” See paragraph 0079.
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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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