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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR
1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/06/2025 has been entered.
Response to Amendment
The amendment filed 11/06/2025 has been entered. As directed, claims 10 has been
amended, no claim is canceled and added. Thus claims 1, 3-5, 7-10 and 12-13 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:
…
First, Claim 1 recites at least "training the neural network model by inputting the input data included in the training data to the neural network model so 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". The training of a neural network model inherently involves processing large-scale, high-dimensional datasets and optimizing a vast number of parameters through computationally intensive calculations. Such operations, due to their sheer scale and complexity, cannot practically be performed by the human mind, even with the aid of pencil and paper. The "August 4, 2025, USPTO Memorandum clarifies that Al-related limitations that exceed the practical capabilities of the human mind are not considered mental processes. As highlighted in the memorandum, "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)." The claimed invention, which is fundamentally tied to the training of a complex computational model, falls squarely into this category and is therefore not a mental process. "
(see Response filed 11/06/2025 [page 8]).
In response to Applicant's argument, the Examiner agrees 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 did not characterize the training step as a mental process. See Final Office Action dated 8/11/2025, pages 15 and 19. Rather, 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. Under broadest reasonable interpretation in light of specification, and apart from the recitation of generic computing components, these limitations include observation, evaluation, and judgment that can be performed in the human mind. Please refer to the current Office Action for the details of analysis under 35 U.S.C. § 101, Step 2A, Prong One.
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment”:
Applicant argues:
Second, illustrative Example 39 (affirming eligibility) from the January 2019 Subject Matter Eligibility Examples2 provides a relevant analogy. The Examiner may rely on Example 47 of the July 2024 Subject Matter Eligibility Examples, but Claim 2 of Example 47 was found to recite an abstract idea because it merely named known, generic algorithms-"a backpropagation algorithm and a gradient descent algorithm" - without specifying how they were uniquely applied. It was a classic "apply it" scenario.
In stark contrast, the presently claimed method recites a highly specific and unconventional training process that is not merely the application of a known formula. The claim specifies a series of concrete, ordered steps: …
This unique combination of steps is not a generic instruction. It is a specific mechanism designed to achieve a particular technological result. Specifically, the claimed method improves the existing technology of flow velocity field estimation by addressing the problem of decreased accuracy when fluid inflow velocity is variable. This is achieved through the novel approach of creating specialized training data based on specific fluid dynamic characteristics - boundary layer position, fluid diffusion range, and wake flow velocity diffusion range - instead of simply using object shape data. This outcome is a tangible improvement in the quality of the data representation generated by the computer, which directly improves the accuracy of subsequent data analysis tasks as disclosed in the present specification. Thus, the claim does not simply state to "train a model"; it details a specific, inventive training framework that transforms the computer into a specialized tool for producing a superior data structure.
Therefore, much like the claim in Example 39 that was deemed eligible for their innovative approach to training data refinement to solve a technical problem, Claim 1 of the present application should also be affirmed as patent-eligible, as it similarly provides a specific, unconventional, and technical solution through a unique training data creation process, resulting in a demonstrable improvement in an existing technological field.
(see Response filed 11/06/2025 [pages 8-10]).
In response to applicant's argument, the examiner respectfully disagrees that “the claim in Example 39 that was deemed eligible for their innovative approach to training data refinement to solve a technical problem, Claim 1 of the present application should also be affirmed as patent-eligible, as it similarly provides a specific, unconventional, and technical solution through a unique training data creation process, resulting in a demonstrable improvement in an existing technological field.”
Example 39 was eligible because the claim did not recite a judicial exception, but instead recited a specific data processing technique for generating modified training sets. By contrast, Example 47 was found ineligible because the claim clearly recited mathematical concepts and mental process, and merely applied them using a generic computer and trained neural network. The instant claims are more analogous to Example 47 because the steps of identifying parameters and creating training data, which under BRI recites mental process and/or mathematical concepts. The neural network is then generically trained and used to generate a prediction, without reciting a specific improvement to how the model operates. Therefore, the claims are more analogous to Example 47 than Example 39 for purposes of Step 2A, Prong One. Applicant’s arguments regarding alleged technological improvement are addressed below under Step 2A, Prong Two and Step 2B.
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment”:
Applicant argues:
Third, even assuming arguendo that the claim recites a judicial exception, it is respectfully submitted that the claims are directed to a patent eligible subject matter under Step 2A, Prong 2 or Step 2B. For example, the claim recites specific steps of generating specific, highly-structured training data for a neural network model to solve a specific technical problem in aerodynamic simulation. These specific steps extract critical fluid dynamic characteristics (boundary layer position, fluid diffusion range, wake flow velocity diffusion range) that are specifically tailored to address the technical problem in conventional aerodynamic analysis methods, which primarily rely on object shape (SDF) alone (paragraph [0033]).
[0033] However, in such a conventional aerodynamic analysis method, the SDF shape
does not include information on an inflow velocity of the fluid. Therefore, it is not
possible to model a case in which the inflow velocity of the fluid is variable, and there is
a problem that a plurality of pieces of flow velocity field data is generated for the same
SDF shape and learning accuracy decreases.
As explicitly stated in paragraph [0033], the claimed invention directly addresses a known technical problem in conventional aerodynamic analysis methods, namely that the conventional method "does not include information on an inflow velocity of the fluid" and therefore "it is not possible to model a case in which the inflow velocity of the fluid is variable, and there is a problem that a plurality of pieces of flow velocity field data is generated for the same SDF shape and learning accuracy decreases" (paragraph [0033]).
To solve this problem, the claimed invention provides a technical solution: … This is not merely an abstract idea but a concrete methodology to improve the accuracy of the neural network model for flow velocity field estimation, particularly when fluid inflow velocity is variable.
As explained above, the functionality of Claim 1 is analogous to USPTO's Example 39, which was found to be patent-eligible. In Example 39, a neural network is trained using an initial training set, and then a second, updated training set (including false positives) is created for retraining to provide a robust facial detection model that limits misdetections. The key innovative aspect in Example 39 lies in the generation of refined training data ("a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training") to improve the model's performance.
Similarly, Claim 1's innovation resides in the creation of specialized training data that incorporates "the position of a boundary layer ..., a diffusion range of the fluid, and a flow velocity diffusion range of a wake flow of the fluid" based on both object shape and inflow velocity. This data, which goes beyond mere object shape (SDF) data used in conventional methods, allows the neural network model to learn and estimate flow velocity fields more accurately and robustly, especially for unknown input data and variable fluid inflow velocities.
This specific and non-generic approach to training data generation for a neural network model, which is tailored to solve a technical issue in fluid dynamics, clearly distinguishes the claimed invention from claims that merely implement abstract ideas on a computer. The claimed invention fundamentally improves the quality of the training data and, consequently, the accuracy and robustness of the machine learning model for fluid flow simulation.
Dependent Claims 3, 4, 7, 8, 12, and 13 further enhance the patent eligibility of the claimed invention by adding specific technical limitations to their respective independent claims. For instance, Claims 3, 7, and 12 further define "the diffusion range of the fluid" as a height component at a position where the flow velocity is maximum, calculated based on a first angle and a Reynolds number. This specific definition, detailed in for example paragraphs [0081]- [0088] of the present specification, grounds the claim firmly in a technical field and clearly cannot be performed mentally due to its reliance on precise physical parameters and mathematical relationships. Similarly, Claims 4, 8, and 13 further detail "the flow velocity diffusion range of the wake flow of the fluid" as an angle formed by specific physical points, as elaborated in for example paragraphs [0090]-[0096]. These additional technical features provide concrete, non-abstract limitations that contribute to the practical application and technological improvement of the flow velocity field estimation, further demonstrating that these claims are not directed to abstract ideas.
Applicant further contends that Claims 5, 9, and 10 are likewise patent-eligible for substantially the same reasons as Claim 1. These claims recite the same technical processing steps of acquiring simulation conditions, identifying specific fluid dynamic characteristics, creating specialized training data, and training/estimating a flow velocity field using a neural network model. These intricate steps, involving complex calculations and data generation, cannot practically be performed by the human mind, nor do they represent mere abstract mathematical concepts in isolation. Instead, they constitute a concrete application and improvement of technology, providing a detailed and unconventional solution to the technical problem of accurately estimating flow velocity fields under variable inflow conditions, as discussed in detail for Claim 1. Therefore, the arguments set forth for Claim 1 apply with equal force to Claims 5, 9, and 10.
Thus, Applicant believes that claims do not recite any judicial exception (Step 2A - Prong1: No), and that the subject matter of the claims integrates the abstract idea into a practical application (Step 2A - Prong 2: Yes), or amounts to significantly more than the judicial exception (Step 2B: Yes).
(see Response filed 11/06/2025 [pages 10-13]).
In response to applicant's argument, the examiner respectfully disagrees that “the claims are directed to a patent eligible subject matter under Step 2A, Prong 2 or Step 2B.”
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).
Further, In order to determine if additional element is integrating the abstract idea into a practical application, See MPEP 2106.04(d)(1), “first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel").” In other words, the specification should describe the claimed improvement over the background invention or existing technology, and the claimed improvement should be reflected at least in the additional elements (emphasis added) by specifying how the claimed improvement perform the additional element different from existing technology, functioning of a computer or existing technical field.
Regarding claimed additional limitations under Step 2A, Prong Two. Please refer to the current Office Action for the details of analysis under 35 U.S.C. § 101, Step 2A, Prong Two. However, the additional limitations are merely adding a recitation of insignificant extra-solution activities such as data gathering activity, and 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, which do not integrate a judicial exception into 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 also Recentive Analytics, Inc. v, Fox Corp.
The additional limitations do not specify how the alleged improvement performs the additional elements in a manner that is different from existing technology, functioning of a computer or existing technical field or any known technical field. For example, how the claimed additional limitations of acquiring data, training neural network model, and predicting flow velocity utilizing the trained neural network model are technologically distinct from generic or conventional computer functions such as data gathering and data processing steps. Likewise, the claim does not provide any details indicating that receiving, training and predicting functions using any non-conventional techniques or specifies any technical improvement over known data gathering and data processing methods. In contrast, the claimed limitation merely discloses the receiving, training and predicting steps at a high level generality would not consider as an improvement in the functioning of a computer, or an improvement to other technology or technical field.
As previously explained, Example 39 was found eligible at step 2A, Prong One because the claim did not recite a judicial exception. In contrast, the instant claims recite mental process and mathematical concepts. Further, the instant claims do not recite any specific improvement to the functioning of the neural network, such as a modified training mechanism, altered architecture, or technical refinement to the learning process. Rather, the claims apply a conventional neural network to selected/identified data inputs.
The Applicant 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. The claims do not specify any non-conventional technical mechanism by which the neural network is structurally or functionally altered. Therefore, the additional limitations do not integrate the judicial exception into a practical application.
Regarding Step 2B, for similar reasons, the additional elements do not amount to significantly more than the judicial exception. The claims do not recite any unconventional neural network architecture, new training algorithm, or specific technical implementation more than applying generic machine learning techniques to selected/identified parameters.
Moreover, the dependent claims merely further define the identified parameters, such as defining a diffusion range of the fluid, and a flow velocity diffusion range of a wake flow of the fluid. The claimed limitations do not provide any additional technological mechanism that improve the computer functionality or the operation of the neural network. Therefore, the dependent claims do not disclose any additional limitations that integrate the abstract idea into practical application, nor amount to significantly more than abstract idea. The dependent claims are therefore ineligible under 35 U.S.C. § 101.
For the reasons discussed above, applicant’s arguments have been considered but are not
persuasive. The claims are directed to abstract ideas (mental process and/or mathematical concepts), do not integrate judicial exception into a practical application, and do not recite additional elements that amount to significantly more than the judicial exception. Accordingly, the rejection of claims 1, 3-5, 7-10 and 12-13 under 35 U.S.C. § 101 is maintained.
Claim Objections
Claim 10 is objected to because of the following informalities:
Claim 10 recites “estimating a flow velocity field corresponding to the simulation conditions ...” in lines 10, should read as “estimating the flow velocity field corresponding to the simulation conditions ...” (examiner note: antecedent basis provided in the preamble by “estimating a flow velocity field”).
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The claims 1, 3-5, 7-10 and 12-13 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 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1, 3 and 4 are directed to non-transitory computer-readable storage medium and fall within the statutory category of manufacture;
Yes, Claims 5, 7 and 8 are 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, 12 and 13 are 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:
Claim 1: The limitations of “identifying, based on the shape of the object and the inflow velocity included in the acquired simulation conditions, 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 “… wherein the position of the boundary layer includes, 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, 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,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. For example, a person is capable of observing and evaluating the shape of object and the inflow velocity, and determine the position of a boundary layer, the diffusion range of the fluid, and the flow velocity diffusion range of a wake flow of the fluid, including determining the relative angle defined between the front end of the object and a point where the flow velocity coincides with an initial velocity along a reference axis. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). Examiner note: the limitation does not recite a specific technological implementation that limits how the identification is performed. Therefore, the limitation does not include a constraint that would preclude performance in the human mind or with pen and paper, and is reasonably considered as a mental process. See MPEP 2106.4(a)(2)(III).
Claim 1: The limitations of “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,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. For example, a person is capable of evaluating the determined data (e.g., position of the boundary layer, diffusion range of the fluid, etc.), associating those values with a corresponding flow velocity field under the simulation conditions, and organizing those associations into a dataset (for example, by writing them down or arranging them in tabular form), such that the flow velocity field is labeled as the expected result corresponding to the determined input values. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). Examiner note: the limitation does not recite a specific technological implementation that limits how the training data is created. Therefore, the limitation does not include a constraint that would preclude performance in the human mind or with pen and paper, and is reasonably considered as a mental process. See MPEP 2106.4(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under step 2A, Prong One.
In MPEP 2106.04(II)(B): A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
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, 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; 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,” as drafted, is a process that, 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. As described in the specification, [0055] – [0130] and equations (1) - (12), the identified parameters and the corresponding flow velocity field used in creating the training data are determined based on defined mathematical relationships and calculations. Therefore, the identifying and creating steps encompass mathematical relationships, equations or formular and calculations. 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" and “A machine learning method implemented by a computer of training a neural network model for estimating a flow velocity field” 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” 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, the flow velocity field estimation program comprising instructions which, when executed by a computer, cause a processor of the computer to perform processing” which are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception with the broadest reasonable interpretation, 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” is mere recitations of insignificant extra-solution activity, such as data gathering (i.e., acquire/receive data), which does not integrate a judicial exception into practical application. The insignificant extra-solution activities are further addressed below under step 2B as also being Well-Understood, Routine, and Conventional (WURC). See MPEP § 2106.05(g).
Further, the following additional elements: “training the neural network model by inputting the input data included in the training data to the neural network model so 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. It merely recites applying the identified parameter (abstract idea) using a generic computing component. The claim does not recite any specific improvement to the neural network architecture, training technique, or computation mechanism. Instead, it generically instructs a computer to perform training and prediction based on the identified parameters. Therefore, the limitation does not integrate into judicial exception into practical application. See MPEP 2106.05(f). 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 also Recentive Analytics, Inc. v, Fox Corp.
Further, the recitation of “training the neural network model …” merely limits the judicial exception to a particular technological environment or field of use. Therefore, limiting an abstract idea to a neural network model does not integrate the exception into a practical application. See MPEP § 2106.05(h).
Further, the following additional elements: “estimating a 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. It merely recites training a neural network model using specific parameters and corresponding labeled outputs, and subsequently apply that trained model to generate a predicted flow velocity field from the identified parameters. The claim does not recite any specific improvement to the neural network architecture, any training technique, or computation mechanism for performing estimation. Instead, it generically instructs a computer to train a neural network using specific parameters and labeled data, and then use the trained model to generate a prediction from input parameters. Therefore, the limitation does not integrate into judicial exception into practical application. See MPEP 2106.05(f). Further, 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 also 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)) ; …
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); …
For example, the additional elements “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 Tompson (“Accelerating Eulerian Fluid Simulation With Convolutional Networks,” published in 2017) and Guo (“Convolutional Neural Networks for Steady Flow Approximation,” published in 2016). Therefore, the steps of training the neural network model and using the neural network model to estimate a fluid velocity field are well-understood and conventional.
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.
Dependent claims 3-4, 7-8 and 12-13 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself (and/or mathematical operations) or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 3-4, 7-8 and 12-13 are also rejected for incorporating the deficiency of their independent claims 1, 5 and 10.
Claim 3 recites “The non-transitory computer-readable storage medium according to claim 1, wherein the diffusion range of the fluid includes, 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, a height component at a position where the flow velocity is maximum, the height component being calculated based on a first angle and a Reynolds number, the first angle being an angle formed by a straight line connecting the front end and an end portion of the object adjacent to the front end and by the reference axis along the flow direction.”
This merely further defines the diffusion range of the fluid, which is used for identification and training data creation as recited in claim 1; therefore, it is merely an extension of mental process. Therefore, the claim 3 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 4 recites “The non-transitory computer-readable storage medium according to claim 3, wherein the flow velocity diffusion range of the wake flow of the fluid includes, 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, an angle formed by a straight line connecting the end portion of the object adjacent to the front end and a point where the flow velocity becomes zero on a downstream side of the object in the flow direction of the fluid and by the reference axis.”
This merely further defines the flow velocity diffusion range of the wake flow of the fluid, which is used for identification and training data creation as recited in claim 1; therefore, it is merely an extension of mental process. Therefore, the claim 4 does not recite patent eligible subject matter under 35 U.S.C. § 101.
The elements of claims 7-8 and 12-13 are substantially the same as those of claims 3-4. Therefore, the elements of claims 7-8 and 12-13 are rejected due to the same reasons as outlined above for claims 3-4.
Allowable Subject Matter
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 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 training the neural network model by inputting the input data included in the training data to the neural network model so 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, wherein the position of the boundary layer includes, 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, 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, in the way disclosed in claims 1, 5 and 9. Dependent claims 3-4 and 7-8 are allowed as being dependent from allowed claims 1 and 5.
Claims 10 and 12-13 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 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 estimating a 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, wherein the position of the boundary layer includes, 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, 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, in the way disclosed in claim 10. Dependent claims 12-13 are allowed as being dependent from allowed 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.
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187