CTNF 18/170,673 CTNF 89845 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 Status This instant application No. 18/170673 has Claims 1 - 9 pending. Priority /Filing Date Applicant claimed Foreign Priority from Japanese Application No. JP2022-127479. The priority filing date of this application is August 9, 2022. Information Disclosure Statement As required by M.P.E.P. 609(C) , the Applicant’s submissions of the Information Disclosure Statements dated February 17, 2023, October 12, 2023 and June 13, 2025 are acknowledged by the Examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609 C(2) , a copy of each of the PTOL-1449s initialed and dated by the Examiner is attached to the instant Office action. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 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. 07-34-01 4. Claims 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 pre-AIA the applicant regards as the invention. i) As per Claim 1 line 12, Claim 8 line 12 and Claim 9 line 12, it is uncertain what the term “advanced” stands for. The examiner could not find any explanations of “advanced” in the spec or in any other claims- The term is indefinite because scope of "advanced" is not limited in the claim. For the purpose of examination, the examiner will interpret the claimed limitation as second time point velocity is faster than the first time point velocity. Appropriate correction is required. ii) As per Claim 1 line 3, Claim 8 line 4 and Claim 9 lines 5-6, it is uncertain what the term “a plurality of basic shapes forming an object” stands for. The examiner could not find any explanations of “basic” in the spec or in any other claims- The term is indefinite because scope of " basic " is not limited in the claim. For the purpose of examination, the examiner will interpret the claimed limitation as “a plurality of shapes forming an object” . Appropriate correction is required. iii) As per Claim 8 lines 15-17 and Claim 9 lines 15-17, the claims recite the limitation “estimate a three-dimensional behavior of the fluid for the object from a two-dimensional behavior of the fluid for the object ……”. Nowhere in the claims it is mentioned what is meant by “ a three-dimensional behavior of the fluid ” and “ a two-dimensional behavior of the fluid ”-making this limitation and overall meaning of these claim indefinite because scope of the term "behavior" is not limited in the claim. Appropriate correction is required. As per dependent claims, they are rejected for incorporating the above errors from their respective parent claim by dependency. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA 5. Claim s 1-6, 8-9 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Matsuo et al. hereafter Matsuo (“Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance”, J. Comput. Phys., 2021, pp 1-24) . Regarding Claim 1 , Matsuo disclose a flow behavior prediction system (Matsuo: abstract) comprising: a storage unit storing therein two-dimensional data of a plurality of basic shapes forming an object (Matsuo: Figure 1, page 3 section 2.1, paragraph 2: two-dimensional input) ; one or more hardware processors configured to function as: an acquisition unit acquiring first state data including a velocity of a fluid for the object at a first time point and a direction of the fluid at the first time point (Matsuo: page 4 last paragraph: three velocity components; page 7-8 section 3.1 Figure 6: z direction; Examiner’s Remark (ER): Note the different number of input sections which can be construed as different time points) ; and a three-dimensional estimation unit estimating second state data including a velocity of the fluid for the object at a second time point that is more advanced than the first time point and a direction of the fluid at the second time point using a three-dimensional estimation network to estimate a three-dimensional behavior of the fluid for the object from a two-dimensional behavior of the fluid for the object estimated from the two-dimensional data and the first state data (Matsuo: Figure 1, pages 3-4 section 2.1: combination of two- and three-dimensional CNN to obtain a three-dimensional output from two-dimensional input, as shown in Figure 1; pages 7-9 section 3.1 Figures 6 and 7: Examiner’s Remark (ER): Note reconstruct a three-dimensional velocity field from two-dimensional high-resolution cross sections as shown Figures 6 & 7) . Regarding Claims 8 and 9, the claims recite the same substantive limitations as Claim 1 and are rejected using the same teachings. Regarding Claim 2 , Matsuo further disclose the flow behavior prediction system according to claim 1, wherein the one or more hardware processors are configured to further function as a two-dimensional estimation unit estimating a two-dimensional behavior of the fluid for each of the basic shapes using a two dimensional estimation network to estimate the two dimensional behavior of the fluid for each of the basic shapes from the first state data and the two-dimensional data, and estimating the two dimensional behavior of the fluid for the object from the two-dimensional behavior of the fluid for the each of the basic shapes (Matsuo: Figure 1, Figure 2, pages 3-4 section 2.1: combination of two- and three-dimensional CNN to obtain a three-dimensional output from two-dimensional input, as shown in Figure 1; page 5 section 2.2: a machine learning model G, which is distinct against the model for three-dimensional reconstruction F, is utilized to reconstruct two-dimensional high-resolution sections q 2D,HR from their two-dimensional low-resolution counterparts q 2D,LR ; pages 7-9 section 3.1 Figures 6 and 7: Examiner’s Remark (ER): Note reconstruct a three-dimensional velocity field from two-dimensional high-resolution cross sections as shown Figures 6 & 7) . Regarding Claim 3 , Matsuo further disclose the flow behavior prediction system according to claim 2, wherein the two-dimensional estimation unit estimates the two-dimensional behavior of the fluid for the object from the two-dimensional behavior of the fluid for the each of the basic shapes using a combining network that combines two-dimensional behaviors of the fluid for the respective basic shapes (Matsuo: pages 5-6 section 2.1: 2D-3D convolutional neural network with the super-resolution analysis; page 5 section 2.2: a machine learning model G, which is distinct against the model for three-dimensional reconstruction F, is utilized to reconstruct two-dimensional high-resolution sections q 2D,HR from their two-dimensional low-resolution counterparts q 2D,LR ) . Regarding Claim 4 , Matsuo further disclose the flow behavior prediction system according to claim 1, wherein parameters of the three-dimensional estimation network are subjected to machine learning using teacher data in which input data acquired by simulating two dimensional states of the fluid for the two-dimensional data of the basic shapes is associated with output data acquired by simulating a three-dimensional state of the fluid for the object (Matsuo: Figure 1, pages 3-4 section 2.1: convolutional neural network; combination of two- and three-dimensional CNN to obtain a three-dimensional output from two-dimensional input, as shown in Figure 1; page 5 section 2.2: a machine learning model G, which is distinct against the model for three-dimensional reconstruction F, is utilized to reconstruct two-dimensional high-resolution sections q 2D,HR from their two-dimensional low-resolution counterparts q 2D,LR ; Figure 13, page 14 last paragraph-page 15 first paragraph: training of the present two-dimensional CNN-based super resolution model) . Regarding Claim 5 , Matsuo further disclose the flow behavior prediction system according to claim 2, wherein parameters of the two-dimensional estimation network are subjected to machine learning using teacher data in which input data indicating the two-dimensional data of the each of the basic shapes is associated with output data acquired by simulating a two-dimensional state of the fluid around the two-dimensional data of the each of the basic shapes (Matsuo: Figure 1, pages 3-4 section 2.1: convolutional neural network; combination of two- and three-dimensional CNN to obtain a three-dimensional output from two-dimensional input, as shown in Figure 1; page 5 section 2.2: a machine learning model G, which is distinct against the model for three-dimensional reconstruction F, is utilized to reconstruct two-dimensional high-resolution sections q 2D,HR from their two-dimensional low-resolution counterparts q 2D,LR ; Figure 13, page 14 last paragraph-page 15 first paragraph: training of the present two-dimensional CNN-based super resolution model) . Regarding Claim 6 , Matsuo further disclose the flow behavior prediction system according to claim 3, wherein the combining network includes parameters to combine two-dimensional states of the fluid for pieces of two-dimensional data of the respective basic shapes and extract features of a two-dimensional state of the fluid for entire two-dimensional data of the basic shapes (Matsuo: Figure 1, pages 3-4 section 2.1: convolutional neural network; combination of two- and three-dimensional CNN to obtain a three-dimensional output from two-dimensional input, as shown in Figure 1; page 5 section 2.2: a machine learning model G, which is distinct against the model for three-dimensional reconstruction F, is utilized to reconstruct two-dimensional high-resolution sections q 2D,HR from their two-dimensional low-resolution counterparts q 2D,LR ) , and the parameters are subjected to machine learning using teacher data in which input data acquired by simulating the two-dimensional states of the fluid for the pieces of two-dimensional data of the respective basic shapes is associated with output data indicating the two-dimensional state of the fluid for the entire two-dimensional data of the basic shapes (Matsuo: Figure 13, page 14 last paragraph-page 15 first paragraph: training of the present two-dimensional CNN-based super resolution model) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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 of this title, 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. 6. Claim 7 is rejected under 35 U.S.C. 103 as being obvious over Matsuo et al. hereafter Matsuo (“Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance”, J. Comput. Phys., 2021, pp 1-24), in view of Barr et al. hereafter Barr (Pub. No.: US 2020/0387579 A1) . Regarding Claim 7 , Matsuo disclose the flow behavior prediction system according to claim 1. However, Matsuo do not explicitly disclose: wherein the first state data further includes a temperature around the object, the two-dimensional behavior and the three-dimensional behavior of the fluid further include a temperature distribution around the object, and the three-dimensional estimation unit estimates the second state data further including the temperature around the object at the second time point. Barr disclose: wherein the first state data further includes a temperature around the object, the two-dimensional behavior and the three-dimensional behavior of the fluid further include a temperature distribution around the object (Barr: [0026], [0037], [0053], [0059]: the output may include a graph or other visual data of one or more of velocity, turbulence, density, pressure, and temperature, included in the predicted CFD flow). and the three-dimensional estimation unit estimates the second state data further including the temperature around the object at the second time point (Barr: [0026], [0037], [0053], [0059]: the output may include a graph or other visual data of one or more of velocity, turbulence, density, pressure, and temperature, included in the predicted CFD flow) . Matsuo and Barr are analogous art because they are from the same field of endeavor. They both relate to CFD flow. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the above CNN-based reconstruction application, as taught by Matsuo, and incorporating the use of temperature distribution data in the analysis, as taught by Barr. One of ordinary skill in the art would have been motivated to do this modification in order to predicting a flow about an object through the use of a predictive model, as suggested by Barr (Barr: abstract). Conclusion 07-96 AIA 7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Fukami et al. (“Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data”, 2020, arXiv, pp 1-13) teaches a customized convolutional neural network based autoencoder called a hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow fields while preserving the contribution order of the latent vectors. Hasegawa et al. (“Machine-learning-based reduced order modeling for unsteady flows around bluff bodies of various shapes”, 2020, arXiv, pp 1-18) teaches method to construct a reduced order model with machine learning for unsteady flows by combining a convolutional neural network autoencoder (CNN-AE) and a long short-term memory (LSTM), which are trained in a sequential manner. Toshiaki et al. (Japanese Pub. No.: JP2020119189(A)) conceptually presents a rapid prediction of behavior of a liquid by a particle method by acquiring an input vector based on the position and rate of each of the particles of a liquid in a first time point and inputs the input vector to a machine learning model and generating an output vector indicative of the position and velocity of each of said plurality of particles at a second time point after a given time step from said first time point. Murata et al. (“Nonlinear mode decomposition with convolutional neural networks for fluid dynamics”, Journal of Fluid Mechanics, 2019, pp 1-15) teaches a new nonlinear mode decomposition method to visualize the decomposed flow fields, named the mode decomposing convolutional neural network autoencoder (MDCNN-AE). The proposed method is applied to a flow around a circular cylinder at R eD =100 as a test case. 8. Examiner’s Remarks: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. Correspondence Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to IFTEKHAR A KHAN whose telephone number is (571)272-5699. The examiner can normally be reached on M-F from 9:00AM-6:00PM (CST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached on (571)272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR to authorized users only. 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To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form . /IFTEKHAR A KHAN/Primary Examiner, Art Unit 2187 Application/Control Number: 18/170,673 Page 2 Art Unit: 2187 Application/Control Number: 18/170,673 Page 3 Art Unit: 2187 Application/Control Number: 18/170,673 Page 4 Art Unit: 2187 Application/Control Number: 18/170,673 Page 5 Art Unit: 2187 Application/Control Number: 18/170,673 Page 6 Art Unit: 2187 Application/Control Number: 18/170,673 Page 7 Art Unit: 2187 Application/Control Number: 18/170,673 Page 8 Art Unit: 2187 Application/Control Number: 18/170,673 Page 9 Art Unit: 2187 Application/Control Number: 18/170,673 Page 10 Art Unit: 2187 Application/Control Number: 18/170,673 Page 11 Art Unit: 2187
