DETAILED ACTION
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 .
Response to Amendment
The amendment filed 07/02/2026 has been entered. As directed, claims 1, 5, 9 and 10 have
been amended, claims 3-4, 7-8, 12-13 have been canceled and no claim has been added. Thus claims 1, 5, 9 and 10 remain pending in the application.
Response to Arguments
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment”:
Applicant argues:
Claims 1, 3-5, 7-10 and 12-13 stand rejected under 35 U.S.C. §101 as allegedly being directed to an abstract idea without significantly more. Applicant respectfully submits that the amended claims are patent-eligible. The Office Action has deemed the previous claims ineligible, primarily by characterizing the "identifying" and "creating training data" steps as abstract ideas falling within Mental Processes and/or Mathematical Concepts. Furthermore, the Office Action concluded that the claims lacked integration into a practical application and failed to present an inventive concept amounting to "significantly more." Applicant strongly disagrees with this characterization in light of the currently amended claims.
The Office Action asserts that the "identifying" and "creating training data" steps are described at a high level of generality. Applicant has amended claims 1, 5, 9, and 10 to include more specific and technically detailed limitations regarding the identification of the boundary layer, diffusion range, and wake flow diffusion range. These amendments the technical
nature and computational complexity of the claimed processes, demonstrating that the claimed
invention does not merely recite an abstract idea but constitutes a concrete, non-abstract
solution to a technical problem in fluid dynamics.
For example, claim 1 recites, inter alia, features of:
"the identifying of the position of the boundary layer includes obtaining, as the position of the boundary layer, a first angle formed by a first straight line and a reference axis along the flow direction when a front end of the object on an upstream side in a flow direction of the fluid is assumed as an origin of a height, by using a statistical model configured to output the first angle based on a Reynolds number and an object angle which is an angle formed by the reference axis and a second straight line connecting the front end and an end portion of the object adjacent to the front end, the first straight line being a straight line connecting the front end and a point where a flow velocity coincides with an initial velocity,
the identifying of the diffusion range includes obtaining, as the diffusion range, a height component at a position where the flow velocity is maximum when the front end of the object on the upstream side in the flow direction of the fluid is assumed as the origin of the height, by using a first regression model configured to output the height component based on the object angle and the Reynolds number, and the identifying of the flow velocity diffusion range includes obtaining, as the flow velocity diffusion range, a second angle formed by the reference axis and a third straight line connecting the end portion and a point where the flow velocity becomes zero on a downstream side of the object in the flow direction of the fluid, by using a second regression model configured to output a length from the object to a wake flow point based on the Reynolds number, wherein the second angle is derived from a functional relationship between the length and coordinates of the end portion."
Other independent claims have been same as claim 1. It is respectfully noted that this response is prepared in accordance with the guidance provided in "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" memorandum published on August 4, 2025 ("101 Reminder Memo”), as well as the precedential decision in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel and the subsequent "Advance notice of change to the MPEP in light of Ex Parte Desjardins" published on December 5, 2025 ("Desjardins Notice")[3]
Regarding the Examiner's opinion raised in the section "Response to Arguments" of the Office Action:
The Office Action acknowledged on page 3 that "training the neural network model..." cannot practically be performed by the human mind, even with the aid of pencil and paper. However, the Office Action maintained its stance that the mental process determination was based on the limitations directed to identifying a position of a boundary layer with respect to the object, a diffusion range of the fluid, and a flow velocity diffusion range of a wake flow of the fluid, and creating training data based on the identified values and corresponding label output. Specifically, the Office Action stated that "these limitations include observation, evaluation, and judgment that can be performed in the human mind" and that "the limitation does not recite a specific technological implementation that limits how the identification is performed" (Office Action, pages 3-4). Similarly, for creating training data, it was argued that "a person is capable of evaluating the determined data..., associating those values with a corresponding flow velocity field..., and organizing those associations into a dataset..." (Office Action, pages 3-4).
Applicant respectfully submits that the current amendments to Claim 1 (and correspondingly to Claims 5, 9, and 10) have explicitly cured these perceived deficiencies. The amended claims now meticulously detail the "identifying" steps, specifying that they involve "obtaining...by using a statistical model configured to output... " or "by using a first/second regression model configured to output...based on... "This language directly introduces a "specific technological implementation" for how the identification is performed, going far beyond mere human "observation, evaluation, and judgment." The statistical and regression models are computational tools designed to process extensive fluid dynamics data in accordance with physical principles (as detailed in the specification, paragraphs [0055]-[0130]), which require computational resources far beyond human capability.
Thus, the Office Action's reliance on broad statements that "these limitations include observation, evaluation, and judgment that can be performed in the human mind" appears to be no longer applicable to the amended claims. The models explicitly recited are themselves "technological implementations" that cannot be executed in the human mind. Consequently, the core premise of the Office Action's mental process argument - that these steps are performable by humans with pen and paper - is directly refuted by the intrinsic characteristics of the models now claimed, as these models necessitate computational execution beyond human capability.
Secondly, the Office Action asserts that the instant claims are "more analogous to Example 47 than Example 39" because Example 47 was found ineligible due to merely naming "known, generic algorithms" without specifying unique application, while Example 39 was eligible due to "a specific data processing technique for generating modified training sets" (Office Action, page 5-6). Applicant respectfully submits that the amended claims are now clearly analogous to the eligible scenario of Example 39, and distinct from the ineligible Example 47. The claims no longer rely on unspecific application of generic algorithms. Instead, the claims define a specific, non-generic data processing technique for generating specialized training data by identifying crucial fluid dynamic characteristics through complex statistical and regression modeling based on physical simulation conditions. This is precisely the kind of "specific data processing technique for generating modified training sets" that renders claims eligible, as in Example 39 and further supported by Ex parte Desjardins.
Thirdly, the Office Action asserts that "The Applicant's alleged improvement reflects to the abstract idea itself (i.e., identifying values and organizing them as training data) rather than a technological improvement in how the computer or model operates" (Office Action, page 11). This assertion fails to appreciate that the method of identifying these values and organizing them into training data is precisely where the technological improvement lies. By making the "identifying" steps more specific through the use of statistical and regression models, the claims demonstrate an improvement in how the computer processes and represents fluid dynamic information, which directly leads to an improvement in how the machine learning model itself operates-i.e., its accuracy and robustness in predicting flow velocity fields under variable inflow conditions. This is fundamentally an improvement in how the computer system and AI model function, not merely an improvement to an abstract idea. This aligns with the principles of Ex parte Desjardins, which recognizes improvements to the machine learning model itself as patent eligible (MPEP § 2106.05(a), examples xiii and xiv).
I. Step 2A Prong 1: Whether the Claims Recite a Judicial Exception
The Office Action asserts that the limitations of "identifying" fluid dynamic characteristics and "creating training data" are directed to abstract ideas, specifically Mental Processes and Mathematical Concepts. However, as explained above, this assertion is no longer applicable to the current amended claims, which incorporate highly specific and computationally intensive process steps.
The amended Claim 1 now explicitly specifies the use of "a statistical model" and "a first regression model" for identifying the position of the boundary layer and the diffusion range, respectively. Furthermore, for the flow velocity diffusion range, it refers to "a second regression model" that outputs a length, and specifies that the resulting angle "is derived from a functional relationship between the length and coordinates of the end portion." These are not mere mental operations or abstract mathematical concepts but concrete descriptions of how a computer- implemented system processes and interprets physical data using advanced computational models.
The "identifying" steps, as now detailed, involve sophisticated statistical modeling and regression analysis. These procedures necessitate processing large volumes of simulation data (e.g., as mentioned in paragraph [0170] of the specification, involving "two types of Reynolds numbers (Re=5, Re=50) x 126 different object shapes" for training data), parameter optimization, and complex calculations that are far beyond the practical capabilities of the human mind using pen and paper. As the 101 Reminder Memo clarifies, "a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s). " (MPEP 2106.04(a)(2)(III)). The explicit inclusion of statistical and regression modeling, which inherently require computational resources far beyond human capability, demonstrates that the claimed activity cannot practically be performed in the human mind.
Moreover, while mathematical concepts are inherent in statistical and regression modeling, the claims do not seek to preempt these mathematical concepts in the abstract. Instead, they apply these mathematical tools to extract physically meaningful fluid dynamic parameters (angles, height components, lengths) from simulated physical systems. As explained in MPEP 2106.04(a)(2)(I), a "claim does not recite a mathematical concept...if it is only based on or involves a mathematical concept." Here, the mathematical models are tools within a larger, concrete process for generating improved training data for fluid dynamics simulation, rather than the object of the claim themselves. The claimed method is not directed to the mathematical models in the abstract, but to their specific application to solve a technical problem.
Thus, we believe that the amended claims do not recite any judicial exception under Step 2A, Prong 1.
(see Response filed 07/02/2026 [pages 8-14]).
Applicant’s arguments with respect to the rejection under 35 U.S.C. § 101, Step 2A Prong 1 have been considered but are not persuasive.
Regarding Step 2A Prong 1, the Examiner acknowledges that amended claims recite details regarding identification of the position of the boundary layer, the diffusion range, and the flow velocity diffusion range of wake flow. In view of the amendments, the rejection in the current Office Action does not rely on the mental process for the judicial exception. However, the amended claims still recite mathematical concepts.
As explained in MPEP § 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a “series of mathematical calculations based on selected information” are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a “process of organizing information through mathematical correlations” are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of “managing a stable value protected life insurance policy by performing calculations and manipulating the results” as an abstract idea). MPEP § 2106.04(a)(2)(I)(A): A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.” MPEP § 2106.04(a)(2)(I)(C) recites: “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping … For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
In particular, claim 1 recites identifying the position of the boundary layer, the diffusion range, and the flow velocity diffusion range of wake flow using a statistical model, first and second regression models, Reynolds number, object angle, a functional relationship, angles, length, and coordinates. Under the broadest reasonable interpretation in light of the specification, the identifying limitations are directed to the mathematically derived values or parameters generated from models through mathematical relationship, equations or formulas and calculations. The specification supports that the corresponding determinations are performed using mathematical relationships, formulas, and calculations. See specification, [0055] to [0130] and equations (1) to (12). Therefore, these limitations are not only based on or involves a mathematical concept, but instead recite mathematical concepts and still fall within the mathematical concepts grouping of abstract ideas.
Regarding Applicant’s argument that “the amended claims are now clearly analogous to the eligible scenario of Example 39, and distinct from the ineligible Example 47” is not persuasive because the claim of Example 39 does not recite any of the judicial exceptions. Instead, the amended claims do not merely recite training a model or generating training data. The claims require mathematically identifying the input parameters used for the training data. Thus, even if training a neural network model is not considered as a mathematical concept, the amended claims still recite mathematical concepts through the identifying limitations. The creating and training limitations are addressed as additional elements under Step 2A Prong 2 and Step 2B.
Regarding Applicant’s argument that “an improvement in how the computer system and AI model function, not merely an improvement to an abstract idea, is more properly addressed under Step 2A Prong 2. For Step 2A Prong 1, the Examiner maintains that the amended claims recite mathematical concepts because the claimed the identified limitations are directed to mathematically derived values or parameters generated from models through mathematical relationship, equations or formulas and calculations. Therefore, Applicant’s arguments do not overcome the determination that claims 1, 5, 9, and 10 recite a judicial exception under Step 2A Prong 1.
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment”:
Applicant argues:
II. Step 2A Prong 2: Integration into a Practical Application and Improvement in Technology
Even if, arguendo, any part of the amended claims were considered to recite a judicial exception, the claims clearly integrate that exception into a practical application and provide a demonstrable improvement to technology. The Office Action's prior analysis, which characterized the additional elements as "mere instructions to implement an abstract idea on a computer" or "insignificant extra-solution activity" (Office Action, page 9), fails to properly consider the technical detail now present in the claims and their specific impact.
The claimed invention provides a technological solution to a specific technical problem in aerodynamic simulation: the decreased accuracy of flow velocity field estimation under variable fluid inflow conditions due to conventional methods relying solely on object shape (Specification, paragraph [0033]). The current amendments introduce highly specific limitations that demonstrably improve upon existing technology. Specifically, the steps of identifying fluid dynamic characteristics using statistical and regression models to generate specialized training data represent an innovative approach to data representation for machine learning models. This is not merely reporting a result but fundamentally altering how the input data for the machine learning model is structured and derived from physical parameters.
This improvement aligns directly with the guidance provided in Ex parte Desjardins, which affirmed that claims reflecting "an improvement in the functioning of a computer, or an improvement to other technology or technical field" are patent-eligible. Ex parte Desjardins specifically recognized claims to "an improved way of training a machine learning model" as patent-eligible when the specification identifies improvements to how the machine learning model itself operates (MPEP § 2106.04(d), subsection III). The claimed method directly enhances the machine learning model's capability by providing a more robust and physically informed training data structure, which leads to improved accuracy and generalization performance in fluid flow estimation (Specification, paragraphs [0172]-[0179]). This is a concrete improvement in the specific field of aerodynamic simulation.
The Office Action's previous position that improvements only reflect the abstract idea itself is inconsistent with the Desjardins Notice, which emphasizes that improvements to software design can lead to non-abstract improvements in computer technology. The detailed and specific process of obtaining fluid dynamic characteristics through complex models and then creating training data incorporating these characteristics is a technical contribution that transforms the abstract idea, if any, into a practical application. It is not "mere data gathering" but a sophisticated data generation and structuring process that is integral to the improved performance of the "neural network model for estimating a flow velocity field".
In particular, each identifying step in the amended claims corresponds directly to a specific computational model described in technical detail in the specification: the statistical model for the boundary layer angle is described at paragraphs [0055]-[0070] and equation (1); the first regression model for the diffusion range height component is described at paragraphs [0081]-[0088] and equations (4)-(7); and the second regression model for the wake flow length is described at paragraphs [0090]-[0096] and equations (8)-(12). This directly satisfies MPEP 2106.04(d)(1)'s requirement that the specification describe the invention such that the improvement would be apparent to one of ordinary skill, and that the claim include the components that provide the improvement described in the specification.
Therefore, we believe that the amended claims integrate any potential judicial exception into a practical application and provide a demonstrable improvement to technology (Step 2A - Prong 2: Yes).
(see Response filed 07/02/2026 [pages 14-15]).
Regarding Step 2A Prong 2, Applicant argues that the amended claims integrate the judicial exception into a practical application because the claims allegedly provide a technological solution to decreased accuracy of flow velocity field estimation under variable inflow conditions. This argument is not persuasive. The alleged improvement relies on the same limitations identified above as mathematical concepts, i.e., identifying the position of the boundary layer, the diffusion range, and the flow velocity diffusion range of wake flow using statistical and regression models, Reynolds number, object angle, and functional relationships. An improvement to the mathematical identification of data values, or to the mathematically derived input data, is not an improvement to computer functionality or another technology. As explained in MPEP 2106.05(a), II.: "it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology." (emphasis added).
Applicant further argues that the claims improve the input data structure for a machine learning model by generating specialized training data. This argument is not persuasive. Claims recite creating training data by using the mathematically identified parameters or values as input data and associating values or parameters with a corresponding flow velocity field label. The claim then recites training the neural network model by inputting the training data and updating parameters so that the model output corresponds to the label. These limitations use the mathematically identified values parameters in a machine learning training environment, but do not impose a meaningful limit on the mathematical concepts themselves. The claim does not recite that the creating or training steps improve the operation of the computer, improve the operation of the neural network model itself, or apply the mathematically identified values in a practical technological process.
Applicant’s reliance on Ex parte Desjardins is also not persuasive. Desjardins does not support for the proposition that any claim involving training data or machine learning training is patent eligible. In Desjardins, the claims reflected improvements disclosed in the specification to how the machine learning model itself operates, including learning new tasks while protecting knowledge about previous tasks and reducing storage capacity or system complexity. In contrast, the amended claims use mathematically identified values or parameters as training data or model input, but do not recite a comparable improvement to the operation of the neural network model, computer functionality, or another technological process. Accordingly, Desjardins is distinguishable.
Applicant also argues that the claimed process is not mere data gathering because the specification describes specific models and equations. However, the issue under Step 2A Prong 2 is not whether the specification describes mathematical detail. The issue is whether the additional claim elements apply the mathematical concepts in a manner that meaningfully limits the exception and reflects a practical application. However, the additional elements merely use a computer and neural network model as tools to apply the mathematically identified parameters in the field of flow velocity field estimation. See MPEP 2106.05(f) and 2106.05(h).
Accordingly, the amended claims do not integrate the judicial exception into a practical application under Step 2A Prong 2.
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment”:
Applicant argues:
III. Step 2B: Inventive Concept and Significantly More
Even if, arguendo, the claimed invention were to be considered directed to an abstract idea after Step 2A Prong 2, the additional elements in the amended claims, considered individually and in combination, amount to significantly more than any abstract idea, thereby providing an inventive concept. The Office Action asserts that the steps of training and estimating fluid velocity fields are "well-understood, routine, and conventional" activity (Office15 Action, page 22) based on references like Tompson ("Accelerating Eulerian Fluid Simulation With Convolutional Networks," published in 2017) and Guo ("Convolutional Neural Networks for Steady Flow Approximation," published in 2016). However, this assertion appears to fail to appreciate the unconventionality of the overall claimed framework.
The inventive concept of the claimed invention resides not merely in training a neural network or estimating a fluid flow field, but in the specific, non-conventional upstream processing required to generate specialized training data. This involves identifying specific fluid dynamic characteristics (boundary layer angle, diffusion height, wake flow angle) using novel statistical and regression models based on physical parameters like Reynolds number and object angle. The generation of such highly structured training data, tailored to address the technical problem of variable inflow velocities, is far from "well-understood, routine, and conventional" activity in the field of fluid dynamics simulation using machine learning.
While individual components (e.g., a regression model, a neural network) might be known, their non-conventional and non-generic arrangement and interaction within the claimed framework collectively provide an inventive concept. As established in BASCOM Global Internet Services v. AT&T Mobility LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016) (citing MPEP § 2106.05(d)), an inventive concept can arise from such an arrangement of known components that yields a new technical solution. Here, the synergistic combination of specific fluid dynamic parameter identification with specialized data structuring for neural network training constitutes such an inventive arrangement.
The references cited by the Office Action (Tompson and Guo) generally discuss the application of convolutional neural networks to fluid simulation. However, they do not disclose how to generate the specific, physics-informed input features for training data, particularly incorporating concepts like boundary layer angles, diffusion heights, and wake flow parameters derived from physical conditions via statistical/regression models.
For example, Tompson's accelerating Eulerian Fluid Simulation with Convolutional Networks primarily focuses on using convolutional networks to accelerate the pressure projection step within Eulerian fluid simulations. It proposes an unsupervised learning framework for approximating a specific step of a numerical solver. While it uses deep learning, its core inventive contribution lies in speeding up a known simulation sub-routine and its approach to data generation (synthetic data with random initial conditions) differs significantly from the claimed invention's method of extracting physics-informed features from fluid parameters. Tompson's work does not teach or suggest the identification and modeling of specific fluid dynamic characteristics (boundary layer, diffusion range, wake flow) as input features to address variable inflow velocity problems, which is central to the claimed invention.
Guo's Convolutional Neural Networks for Steady Flow Approximation discloses the use of CNNs as surrogate models for predicting non-uniform steady laminar flow. It leverages Signed Distance Functions (SDF) as geometry representation for CNN inputs. While it aims for faster predictions than traditional CFD, its approach is a direct substitution of CNN for a CFD solver and does not involve the sophisticated, physics-driven feature engineering and training data creation methods of the claimed invention. There is no disclosure of deriving boundary layer positions, diffusion ranges, or wake flow parameters using statistical/regression models based on Reynolds numbers and object angles to adapt to variable inflow conditions.
Therefore, the cited references, while related to fluid simulation and machine learning, do not individually or in combination render the specific pre-processing and training data generation methods, as detailed in the amended claims, as "well-understood, routine, and conventional." The inventive concept in the present claims lies in this unconventional, physics-informed data preparation that directly addresses a long-standing technical problem in fluid dynamics simulations.
Thus, we believe that the subject matter of the claims amounts to significantly more than the judicial exception (Step 2B: Yes).
In view of the amendments made to the claims and the foregoing arguments, Applicant respectfully requests that the rejection under 35 U.S.C. § 101 for claims 1, 5, 9, and 10 be withdrawn. As the Office Action has indicated that "Claims 1, 3-5 and 7-9 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action", the Applicant would appreciate any suggestions for claim amendments that could further clarify patent eligibility.
(see Response filed 07/02/2026 [pages 15-18]).
Regarding Step 2B, Applicant argues that the inventive concept resides in the specific upstream processing used to generate specialized training data, including identifying boundary layer angle, diffusion height, and wake flow angle using statistical and regression models based on Reynolds number and object angle. This argument is not persuasive. These are the same limitations identified above as reciting mathematical concepts. The mathematical concepts themselves cannot supply the inventive concept required at Step 2B. The relevant inquiry is whether the additional elements, individually or in ordered combination, amount to significantly more than the identified mathematical concepts.
Applicant further argues that the claimed framework is unconventional because it uses the mathematically identified fluid dynamic characteristics to generate specialized training data. This argument is not persuasive. The additional creating limitation merely organizes the mathematically identified parameters as input data and associates the input data with a corresponding flow velocity field label. The additional training limitation merely inputs the training data into a neural network model and updates model parameters such that the model output corresponds to the label. These additional limitations do not recite an inventive computer implementation, an improved neural network architecture, or an unconventional training mechanism beyond the identified mathematical concepts.
Applicant’s reliance on BASCOM is also not persuasive. The claims do not recite a non-conventional arrangement of computer components or a particular technological architecture that changes how the computer or network operates. Rather, the alleged arrangement is the combination of mathematically identifying fluid dynamic parameters, arranging the parameters into training data, and applying the training data in a neural network environment. This ordered combination does not add significantly more because the alleged non-conventional aspect remains the mathematical parameter identification itself.
Applicant also argues that Thompson and Guo do not disclose Applicant’s specific physics informed input features, including boundary layer positions, diffusion ranges, or wake flow parameters using statistical/regression models based on Reynolds numbers and object angles to adapt to variable inflow conditions. This argument is not persuasive because the cited references are not relied upon to show that Applicant’s particular mathematical feature identification was well-understood, routine, and conventional. Rather, the references are relied upon to show that the additional machine learning activities, including creating or using training data, training neural network models, and predicting or estimating flow velocity fields using neural network models, were known in the art.
Further, newly applied reference Ambati is relied upon to show that training data, training a machine learning model, and using the trained model to generate predictions were known machine learning activities. Guo is relied upon to show that, in the fluid simulation field, creating training data with input features and flow velocity field labels, training CNN models, and predicting flow velocity fields were known. Thus, the additional elements beyond the mathematical concepts merely apply the mathematically identified parameters in a known machine learning training and prediction environment. Even if Applicant’s particular mathematical feature identification is not disclosed by the cited references, the additional elements beyond the mathematical concepts merely apply the mathematically identified parameters in a known machine learning training and prediction environment. Therefore, the additional elements, considered individually and in combination, do not amount to significantly more than the judicial exception.
Accordingly, as discussed above, the amended claims remain directed to an abstract idea including mathematical concepts. The additional elements, considered individually and in combination, do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Therefore, the rejection under 35 U.S.C. 101 for claims 1, 5, 9 and 10 is maintained.
Claim Rejections - 35 USC § 112
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.
Claims 1, 5, 9 and 10 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “… along the flow direction when a front end of the object on an upstream side in a flow direction of the fluid …” There is insufficient antecedent basis for this limitation in the claim. Although the limitation later recites “ a flow direction of the fluid,” the later recitation does not provide antecedent basis for the earlier use of “the flow direction.” Therefore, it is unclear whether “the flow direction” refers to the direction of the previously recited inflow velocity, the subsequently recited flow direction of the fluid, or another direction. For the purpose of substantive examination, the examiner interprets the limitation as reciting “… along a flow direction of the fluid when a front end of the object on an upstream side in the flow direction of the fluid …”
Claims 5, 9 and 10 also recite similar limitation and are rejected for the same reason.
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.
The claim(s) claims 1, 5, 9 and 10 are rejected under 35 USC § 101 because the claimed
invention is directed to judicial exception an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates, and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1 is directed to non-transitory computer-readable storage medium and fall within the statutory category of manufacture;
Yes, Claims 5 is directed to machine learning device and falls within the statutory category of machine;
Yes, Claim 9 is directed to method and falls within the statutory category of process;
Yes, Claims 10 is directed to non-transitory computer-readable storage medium and falls within the statutory category of manufacture.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
As explained in MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a “series of mathematical calculations based on selected information” are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a “process of organizing information through mathematical correlations” are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of “managing a stable value protected life insurance policy by performing calculations and manipulating the results” as an abstract idea).
MPEP 2106.04(a)(2)(I)(A): A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Further, MPEP recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Claim 1: The limitation of “identifying, based on the shape of the object and the inflow velocity included in the acquired simulation conditions stored in a memory of the computer, a position of a boundary layer with respect to the object, a diffusion range of the fluid, and a flow velocity diffusion range of a wake flow of the fluid … the identifying of the position of the boundary layer includes obtaining, as the position of the boundary layer, a first angle formed by a first straight line and a reference axis along the flow direction when a front end of the object on an upstream side in a flow direction of the fluid is assumed as an origin of a height, by using a statistical model configured to output the first angle based on a Reynolds number and an object angle which is an angle formed by the reference axis and a second straight line connecting the front end and an end portion of the object adjacent to the front end, the first straight line being a straight line connecting the front end and a point where a flow velocity coincides with an initial velocity, the identifying of the diffusion range includes obtaining, as the diffusion range, a height component at a position where the flow velocity is maximum when the front end of the object on the upstream side in the flow direction of the fluid is assumed as the origin of the height, by using a first regression model configured to output the height component based on the object angle and the Reynolds number, and the identifying of the flow velocity diffusion range includes obtaining, as the flow velocity diffusion range, a second angle formed by the reference axis and a third straight line connecting the end portion and a point where the flow velocity becomes zero on a downstream side of the object in the flow direction of the fluid, by using a second regression model configured to output a length from the object to a wake flow point based on the Reynolds number, wherein the second angle is derived from a functional relationship between the length and coordinates of the end portion,” as drafted, under its broadest reasonable interpretation (BRI) in light of specification, can be considered to represent mathematical concepts including mathematical relationships, equations or formular and calculations. Specifically, the claim recites obtaining the identified boundary layer position, diffusion range, and wake flow diffusion range of wake flow through a statistical model, first and second regression models, and a functional relationship. The specification supports that the models and relationships are implemented through defined mathematical equations and calculations for the Reynolds number, object angle, boundary layer angle, diffusion thickness, and wake flow angle. See specification, [0055] – [0130] and equations (1) - (12). Accordingly, the identified limitations are directed to mathematically derived values or parameters generated from the models through mathematical relationship, equations or formulas and calculations. Therefore, at least the identifying limitations of claim 1 recite mathematical concepts within the mathematical concepts grouping of abstract idea. See MPEP 2106.4(a)(2)(I).
The elements of claims 5, 9 and 10 are substantially the same as those of claim 1. Therefore, the elements of claims 5, 9 and 10 are rejected due to the same reasons as outlined above for claim 1.
Therefore, claims 1, 5, 9 and 10 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
Step 2A Prong 2: Claims 1, 5, 9 and 10: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements: "A non-transitory computer-readable storage medium for storing a machine-learning program of training a neural network model for estimating a flow velocity field, the machine learning program comprising instructions which, when executed by a computer, cause a processor of the computer to perform processing, the processing comprising" and “A machine learning device of training a neural network model for estimating a flow velocity field, the machine learning device comprising: a memory; and a processor coupled to the memory, the processor being configured to perform processing, the processing including” and “A machine learning method implemented by a computer of training a neural network model for estimating a flow velocity field, the method comprising” and “A non-transitory computer-readable storage medium for storing a flow velocity field estimation program of estimating a flow velocity field using a flow velocity field estimation model, the flow velocity field estimation program comprising instructions which, when executed by a computer, cause a processor of the computer to perform processing, the processing comprising” which are merely recitations of instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception, which does not integrate judicial exception into a practical application (see MPEP §2106.05(f)).
Further, the following additional element: “acquiring simulation conditions including a shape of an object and an inflow velocity of fluid” which are merely recitations of insignificant extra-solution activity such as data gathering (i.e., acquiring or receiving data), which does not integrate a judicial exception into practical application (see MPEP § 2106.05(g)).
Further, the following additional elements: “creating training data that includes, as input data, the position of the boundary layer, the diffusion range of the fluid, and the flow velocity diffusion range of the wake flow of the fluid, and that includes, as a label corresponding to the input data, a flow velocity field under the simulation conditions; and training the neural network model by inputting the input data included in the training data to the neural network model to update parameters of the neural network model such that the neural network model outputs, as a prediction result, the flow velocity field indicated by the label of the training data from the position of the boundary layer, the diffusion range of the fluid, and the flow velocity diffusion range indicated by the input data of the training data,” which are merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
In particular, the creating limitation merely recites using the mathematically identified position of the boundary layer, diffusion range, and flow velocity diffusion range of wake flow as input data and associating the input data with a corresponding flow velocity field label. The limitation does not recite a particular manner of generating the training data, transforming the mathematically identified values into a different technological form, or a particular data structure that improves computer functionality. Rather, the limitation merely states the result of preparing or organizing the identified parameters or values and corresponding label into training data.
Further, the training limitation likewise merely recites inputting the training data to a neural network model and updating model parameters such that the model output corresponds to the label of the training data. The limitation does not recite a particular manner in which the training operation improves the functioning of the computer or improves the operation of the neural network model. Rather, the limitation merely states the result of training the model to output the labeled flow velocity field from the mathematically idented input parameters.
Therefore, these additional limitations merely use a computer and neural network model in their ordinary capacity to apply the recited mathematical concepts. The limitations use the mathematically identified parameters for training data preparation and model training, but do not impose a meaningful limitation on the judicial exception and do not integrate the judicial exception into a practical application.
Alternatively, the limitations merely link the use of the judicial exception to a particular technological environment or field of use, such as machine learning or neural network training for flow velocity filed estimation. See MPEP § 2106.05(h). The recitation of creating training data and training a neural network model links the use of the mathematically identified parameters to a machine learning context, but does not meaningfully limit the mathematical concepts or integrate the judicial exception into a practical application. Accordingly, the additional limitations does not integrate the judicial exception into a practical application. The Federal Circuit held that “patents that do no more than claim the application of generic machine learning to new data environments without disclosing improvements to the machine learning models to be applied, are patent ineligible under 35 U.S.C. § 101.” See Recentive Analytics, Inc. v, Fox Corp.
Further, the following additional elements of claim 10: “estimating the flow velocity field corresponding to the simulation conditions, by inputting, into the flow velocity field estimation model, the identified position of the boundary layer, the identified diffusion range of the fluid, and the identified flow velocity diffusion range of the wake flow of the fluid, the flow velocity field estimation model being a neural network model trained by using training data that includes, as input data, a specific position of the boundary layer, a specific diffusion range of the fluid, and a specific flow velocity diffusion range of the wake flow of the fluid, and that includes, as a label corresponding to the input data, a specific flow velocity field so that the neural network model outputs, as a prediction result, the specific flow velocity field indicated by the label of the training data from the specific position of the boundary layer, the specific diffusion range of the fluid, and the specific flow velocity diffusion range indicated by the input data of the training data,” which are merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). In particular, the estimating limitation merely recites inputting the mathematically identified position of the boundary layer, diffusion range, and flow velocity diffusion range of wake flow into a flow velocity field estimation model to generate an estimated flow velocity field. The limitation does not recite a particular manner by which the estimation operation improves computer functionality, improves the operation of the estimation model, or imposes any meaningful technological limit on the mathematically identified parameters. Rather the limitation merely states the result of using the identified parameters as model input and generating a predicted flow velocity field as model output.
The recitation of the flow velocity field estimation model is “a neural network model trained by using training data” likewise does not integrate the judicial exception into a practical application. This limitation merely characterizes the estimation model by reference to training data including specific mathematically identified parameters and a corresponding label. The claim does not recite a particular manner by which the training data changes the operation of the computer or changes the operation of the neural network model in a technological way. Rather, the limitation merely identifies the type of input data and label used to configure the model before the claimed estimation is performed.
Therefore, these additional limitations merely use a computer and neural network model in their ordinary capacity to apply the recited mathematical concepts. The limitations do not impose a meaningful limitation on the judicial exception and do not integrate the judicial exception into a practical application.
Alternatively, the limitations merely link the use of the judicial exception to a particular technological environment or field of use, such as a neural network based flow velocity field estimation environment. See MPEP § 2106.05(h). The recitation of inputting the mathematically identified parameters into a neural network model to obtain an estimated flow velocity field links the use of the mathematical concepts to a machine learning estimation context, but does not meaningfully limit the mathematical concepts. Accordingly, the additional limitations do not integrate the judicial exception into a practical application. The Federal Circuit held that “patents that do no more than claim the application of generic machine learning to new data environments without disclosing improvements to the machine learning models to be applied, are patent ineligible under 35 U.S.C. § 101.” See Recentive Analytics, Inc. v, Fox Corp.
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 5, 9 and 10 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 1, 5, 9 and 10: the claims do not include additional elements, alone or in combination, 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 amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)) ;
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
Further, the courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); …;
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); …;
iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); …
Further, the additional elements “creating training data … training the neural network model …” and “estimating a flow velocity field …,” do not amount to significantly more than the judicial exception. As shown in the references Ambati (US20180293501A1) teaches Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. A computer may include a machine learning model that can be trained to implement a complex function that is configured to generate one or more predictions based on a set of inputs. The trained machine learning model is configured to act like a black box: it receives production data, the production data is applied to the complex function, and one or more prediction labels are outputted (see BACKGROUND OF THE INVENTION). Thus, Ambati evidences that creating or using training data, training a machine learning model, and outputting a prediction result were well understood, routine and conventional computer activities. Guo (“Convolutional Neural Networks for Steady Flow Approximation,” published in 2016) discloses CNN based surrogate models for predicting velocity fields in 2D or 3D non-uniform steady laminar flow, and further teaches that training data may be generated from simulation results, that knowledge of fluid dynamics behavior may be extracted by learning the relationship between an input feature vector extracted from geometry and ground truth data output from a full CFD simulation, and that LBM is used to generated training data for the CNNs. Guo also discloses that 2D primitives are projected into a Cartesian grid and that the LBM simulation computes a velocity field as labels (See ABSTRACT, INTRODUCTION, RELATED WORK and EXPERIMENT SETUP). Accordingly, Guo evidences that creating training data with input features and corresponding flow velocity field labels, training CNN models using the training data, and using the trained model to predict a flow velocity field were known machine learning activities in the fluid simulation filed.
Therefore, even if the cited references do not teach particular mathematical feature identification, the cited references show that the additional elements of creating training data, training a neural network model, and estimating or predicting a flow velocity field were well understood, routine, and conventional activities. Accordingly, the additional elements beyond the mathematical concepts do not amount to significantly more than the judicial exception.
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1, 5, 9 and 10 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Allowable Subject Matter
Claims 1, 5 and 9 would be allowable if rewritten or amended to overcome the rejection(s)
under 35 U.S.C. 101 and 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 1, 5 and 9, the closest prior arts found, Nabi US 20210311089 A1 disclose of A uniform horizontal wind flow 1500 of velocity U in +ζ direction is incident on a cylinder 1502. The wind velocity U may also correspond to upstream velocity. The wind flow can also be referred to as fluid flow [0131] and one technique involves “training” the machine learning program on the converged CFD data for a variety of shapes and complex terrains that are representative of typical sites. More than hundreds of such simulations maybe required for the training. Once the program is trained, a process, for example, using Gaussian Process regression, or deep learning techniques, is utilized to infer the velocities and pressures, as well as the horizontal gradient of vertical velocity, for a new complex terrain shape based on all of the previous complex terrain shapes [0153]. Platzer US 5975462 disclose of a position of a boundary layer with respect to the object, a diffusion range of the fluid, and a flow velocity diffusion range of a wake flow of the fluid (fig.1 and 1c). “The importance of being thin” by Stephen H. Davis, published in 2017 disclose of Steady boundary layer over a flat plate and fig.12 associated with a front end of the object on an upstream side in a flow direction of the fluid is assumed as an origin of a height, an angle formed by a straight line connecting the front end and a point where a flow velocity coincides with an initial velocity and by a reference axis along the flow direction.
However, In light of record taken as a whole, applicant's claims 1, 5 and 9 are considered to be patentable distinct over the prior art. In particular, the prior arts do not disclose, teach or suggest in combination of “… the identifying of the position of the boundary layer includes obtaining, as the position of the boundary layer, a first angle formed by a first straight line and a reference axis along the flow direction when a front end of the object on an upstream side in a flow direction of the fluid is assumed as an origin of a height, by using a statistical model configured to output the first angle based on a Reynolds number and an object angle which is an angle formed by the reference axis and a second straight line connecting the front end and an end portion of the object adjacent to the front end, the first straight line being a straight line connecting the front end and a point where a flow velocity coincides with an initial velocity, the identifying of the diffusion range includes obtaining, as the diffusion range, a height component at a position where the flow velocity is maximum when the front end of the object on the upstream side in the flow direction of the fluid is assumed as the origin of the height, by using a first regression model configured to output the height component based on the object angle and the Reynolds number, and the identifying of the flow velocity diffusion range includes obtaining, as the flow velocity diffusion range, a second angle formed by the reference axis and a third straight line connecting the end portion and a point where the flow velocity becomes zero on a downstream side of the object in the flow direction of the fluid, by using a second regression model configured to output a length from the object to a wake flow point based on the Reynolds number, wherein the second angle is derived from a functional relationship between the length and coordinates of the end portion,” in the way disclosed in claims 1, 5 and 9.
Claims 10 would be allowable if rewritten or amended to overcome the rejection(s)
under 35 U.S.C. 101 and 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 10, the closest prior arts found, Nabi US 20210311089 A1 disclose of A uniform horizontal wind flow 1500 of velocity U in +ζ direction is incident on a cylinder 1502. The wind velocity U may also correspond to upstream velocity. The wind flow can also be referred to as fluid flow [0131] and one technique involves “training” the machine learning program on the converged CFD data for a variety of shapes and complex terrains that are representative of typical sites. More than hundreds of such simulations maybe required for the training. Once the program is trained, a process, for example, using Gaussian Process regression, or deep learning techniques, is utilized to infer the velocities and pressures, as well as the horizontal gradient of vertical velocity, for a new complex terrain shape based on all of the previous complex terrain shapes [0153]. Platzer US 5975462 disclose of a position of a boundary layer with respect to the object, a diffusion range of the fluid, and a flow velocity diffusion range of a wake flow of the fluid (fig.1 and 1c). “The importance of being thin” by Stephen H. Davis, published in 2017 disclose of Steady boundary layer over a flat plate and fig.12 associated with a front end of the object on an upstream side in a flow direction of the fluid is assumed as an origin of a height, an angle formed by a straight line connecting the front end and a point where a flow velocity coincides with an initial velocity and by a reference axis along the flow direction.
However, In light of record taken as a whole, applicant's claim 10 considered to be patentable distinct over the prior art. In particular, the prior arts do not disclose, teach or suggest in combination of “… the identifying of the position of the boundary layer includes obtaining, as the position of the boundary layer, a first angle formed by a first straight line and a reference axis along the flow direction when a front end of the object on an upstream side in a flow direction of the fluid is assumed as an origin of a height, by using a statistical model configured to output the first angle based on a Reynolds number and an object angle which is an angle formed by the reference axis and a second straight line connecting the front end and an end portion of the object adjacent to the front end, the first straight line being a straight line connecting the front end and a point where a flow velocity coincides with an initial velocity, the identifying of the diffusion range includes obtaining, as the diffusion range, a height component at a position where the flow velocity is maximum when the front end of the object on the upstream side in the flow direction of the fluid is assumed as the origin of the height, by using a first regression model configured to output the height component based on the object angle and the Reynolds number, and the identifying of the flow velocity diffusion range includes obtaining, as the flow velocity diffusion range, a second angle formed by the reference axis and a third straight line connecting the end portion and a point where the flow velocity becomes zero on a downstream side of the object in the flow direction of the fluid, by using a second regression model configured to output a length from the object to a wake flow point based on the Reynolds number, wherein the second angle is derived from a functional relationship between the length and coordinates of the end portion,” in the way disclosed in claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mihora US 5651516 A discloses an apparatus and method of stabilizing unstable shock waves on the surface of a body induce shock waves to form prematurely at a particular location on a surface of the body and fix that location such that shock waves will form consistently and persistently at that location on the surface of the body.
Sinha US 5961080 A discloses a system to detect and control steady and unsteady boundary layer separation.
Rodriguez US 20120232860 A1 discloses a fluid-flow simulation over a computer-generated aircraft surface is generated using a diffusion technique.
Rodriguez US 20150370933 A1 discloses Fluid-flow simulation over a computer-generated aircraft surface is generated using inviscid and viscous simulations.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/YI . HAO/
Examiner, Art Unit 2187
/JOHN E JOHANSEN/Examiner, Art Unit 2187
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