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 .
DETAILED ACTION
Response to Amendment
In the amendment dated 07/16/2026, the following occurred: Claims 1-8 have been amended.
Claims 1-8 are pending and have been examined.
Priority
Acknowledgement is made of applicant’s claim to priority under 35 U.S.C. 371 to PCT Application No. PCT/KR2023/006673 filed 05/17/2023, which claims priority to Republic of Korea Application 10-2022-0096053 filed 08/02/2022.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 8 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (YES)
Claims 1 and 8 fall into at least one of the statutory categories (i.e., process or manufacture).
Step 2A1 (YES)
The limitations of acquiring target shape information of brain vessels of a subject patient, wherein the acquiring of the target shape information comprises: (i) acquiring pixel information from an original medical image of the subject patient acquired through medical imaging equipment, the pixel information comprising coordinate information and gray level information of the original medical image, (ii) acquiring coordinate information corresponding to a vascular region from the pixel information in which the gray level information has a value equal to or smaller than a predetermined value, and (iii) generating the target shape information, wherein the generating of the target shape information comprises: (A) generating, using a computer vision segmentation technique, a surface model that presents cerebrovascular shape information from the coordinate information corresponding to the vascular region, and (B) generating, using a computer vision skeleton technique, a one-dimensional (1D) model from the surface model; acquiring target blood flow information of the brain vessels of the subject patient, wherein the acquiring of the target blood flow information comprises: (i) acquiring a cerebrovascular boundary condition based on the target shape information, and (ii) generating… a fluid flow model from the 1D model, and (iii) calculating, using the fluid flow model, the target blood flow information based on the cerebrovascular boundary condition, wherein the target blood flow information comprises blood flow speed information and blood flow pressure information; acquiring target patient information of the subject patient, the target patient information comprising at least one of an age, a gender, an underlying disease or a race of the subject patient; inputting the acquired target shape information comprising the surface model and the 1D model, the target blood flow information comprising the blood flow speed information and the blood flow pressure information, and the target patient information into …; and acquiring a brain disease risk value output from… based on the target shape information, the target blood flow information, and the target patient information input into…, as drafted (claim 1 being representative), is a process that, under the broadest reasonable interpretation (BRI), covers performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting a device (Claim 1) or a computer-readable recording medium (CRM) implemented by a computer / said device (Claim 8), nothing in the claims precludes the steps from practically being performed in the mind.
For example, but for the CRM implemented by a computer, this claim encompasses a person thinking about acquiring target shape information (acquiring pixel information from an original medical image, the pixel information comprising coordinate information corresponding to a vascular region in which the gray level information has a value equal to or smaller than a predetermined value; and generating the target shape information by (A) generating a surface model using a computer vision segmentation technique and (B) generating a one-dimensional (1D) model from the surface model using a computer vision skeleton technique), acquiring target blood flow information (acquiring a cerebrovascular boundary condition; generating… a fluid flow model; and calculating, using the fluid flow model, the target blood flow information), acquiring target patient information, and inputting the acquired data for subsequent acquisition of a brain disease risk value output in the manner described in the identified abstract idea, supra. 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 claims recite an abstract idea.
Step 2A2 (NO)
The judicial exception, the above-identified abstract idea, is not integrated into a practical application. In particular, the claims recite the additional element of a brain disease risk analysis device that implements the identified abstract idea (represented by claim 1). The additional element aforementioned is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer or computer component performing a generic computer or computer component function that facilitates the identified abstract idea) such that this amounts no more than mere instructions to apply the exception using a generic computer component (see Applicant’s disclosure, e.g., at Fig. 1 and para. 0044-0046). See MPEP § 2106.04(d)(I). Accordingly, alone or in combination, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims further recite the additional elements of acquiring and using a trained brain disease risk analysis model and a trained first neural network that both implement the identified abstract idea (i.e., to apply data to an algorithm and output the results). The additional elements are not described by the Applicant, are recited at a high-level of generality and are merely invoked as a tools to perform existing processes (MPEP § 2106.05(f)(2), see case involving a commonplace business method or mathematical algorithm being applied on a general-purpose computer within the “Other examples”), such that this amounts no more than mere instructions to apply the abstract idea on a general-purpose computer (see Specification at para. 57: “As an example, the brain disease risk analysis device 1000… may train a first neural network… using an artificial intelligence algorithm”; and at para. 0058: “As another example… may train a second neural network… using an artificial intelligence algorithm”). See MPEP § 2106.04(d)(I); and Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014). See also e.g. Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Alternatively, or in addition, the implementation of the trained brain disease risk analysis model and the trained first neural network to apply data to an algorithm and report the results merely generally links the use of the abstract idea (i.e., the trained models) to a particular technological environment or field of use (neural networks). MPEP § 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application. Thus, the claims are directed to an abstract idea.
Step 2B (NO)
The claims do not include additional elements 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 element of a brain disease risk analysis device to perform the method (represented by claim 1) amounts no more than mere instructions to apply the exception using a generic computer or generic computer component. Also, the claims recite only the idea of a solution or outcome (e.g., acquiring pixel information, acquiring coordinate information, generating a surface model using a computer vision segmentation technique, generating a 1D model using a computer vision skeleton technique, acquiring a boundary condition, calculating blood flow speed and pressure information using a fluid flow model), the results-based claim limitations attempting to cover any solution with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result. MPEP § 2106.05(f)(1). Mere instructions to apply an exception using generic computer(s) and/or generic computer component(s) cannot provide an inventive concept (“significantly more”). See MPEP § 2106.05(f).
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of acquiring and using a trained brain disease risk analysis model and a trained first neural network to perform the method amounts no more than mere instructions to “apply it” with the exception by invoking algorithms merely as tools to perform existing processes (i.e., only recites each algorithm as a tool to apply data to an algorithm and report the results), in this case to receive input data and output output data. The use of a trained model (e.g., a trained neural network) in its ordinary capacity to perform tasks in the identified abstract idea does not provide an inventive concept (“significantly more”). See MPEP § 2106.05(f). See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more). Accordingly, even in combination, the additional elements do not provide significantly more.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the trained brain disease risk analysis model and the trained first neural network to apply data to an algorithm and report the results were found to confine the use of the abstract idea (i.e., the trained models) to a particular technological environment or field of use (neural networks). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, the additional elements do not provide significantly more. As such, the claims are not patent eligible.
Dependent claims 2-7, when analyzed as a whole, are similarly rejected under 35 U.S.C. §101 because the additional limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. The claims, when considered alone or as an ordered combination, either (1) merely further define the abstract idea, (2) do not further limit the claim to a practical application, or (3) do not provide an inventive concept such that the claims are subject matter eligible.
Claim(s) 2-3 merely further describe(s) the additional element of using the trained brain disease risk analysis model (e.g., to apply data to an algorithm and report the results). See analysis, supra.
Claim 4 further recites the abstract idea including training of the brain disease risk model. The type of math utilized to perform the training is neither recited in the claim nor described by the Applicant. As such the Examiner is required to analyze the training given the broadest reasonable interpretation. The training of the brain disease risk model is considered to be part of the abstract idea because it falls under data manipulations that humans perform (i.e., fitting a model to data) and thus is interpreted to be part of the abstraction—the observations, evaluations, judgements, and opinions that fall under Mental Process. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”).
Alternatively, or in addition, when given its broadest reasonable interpretation in light of the disclosure, training performed by a type of math (e.g., computational fluid dynamics described in the spec. at para. 0055) represents the creation of mathematical interrelationships between data. See, e.g., Example 47, Claim 2. As such, the training for a model can represent a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. The Examiner notes that it is not clear whether each model can be trained in this manner described for the first neural network.
Claim 4 also recites use of the trained brain disease risk analysis model to perform the method, which amounts to no more than mere instructions to “apply it” with the exception. See analysis, supra.
Claim 5 merely further describes the abstract idea (e.g., training, the first training dataset, the second training dataset, the fourth training dataset, outputting the brain disease risk value approximating the brain disease-specific risk information included in the fourth training dataset) and the additional element of using the trained brain disease risk model (e.g., applying data to an algorithm to report the results.) See analysis, supra.
Claim 6 merely further describes the abstract idea (e.g., training on the basis of a first training dataset, a second training dataset, a third training dataset, and a fourth training dataset) and merely further recites the additional element of using the trained brain disease risk analysis model (e.g., applying data to an algorithm to report the results). See analysis, supra.
Claim 7 merely further describes the abstract idea (e.g., training, acquiring the first training dataset, the second training dataset, the third training dataset, and the fourth training dataset and outputting the brain disease risk value approximating the brain disease information included in the fourth training dataset) and merely further recites the additional element of using the trained brain disease risk analysis model (e.g., applying data to an algorithm to report the results). See analysis, supra.
Response to Arguments
Rejections under 35 U.S.C. §112(b)
Regarding the rejections, the Applicant has amended the claims to overcome the issues of indefiniteness. The amended claims do not appear to cause any new issues.
Rejections under 35 U.S.C. §101
Regarding the rejection of Claims 1-8, the Examiner has considered the Applicant’s arguments but does not find them persuasive for at least the following reasons. Applicant argues:
A1. “The amended Claim 1 recites steps that cannot, as a practical matter, be performed in the human mind. MPEP §2106.04(a)(2) states that a claim is not directed to a mental process "if the claim recites steps that cannot practically be performed in the human mind." The following elements in the amended Claim 1 clearly satisfy the standard: …” (Remarks, pg. 13).
Re. argument A1: The Examiner respectfully submits the basis of rejection. The identified claim elements, under the broadest reasonable interpretation (BRI), cover performance of a process that can be practically performed in the human mind with or without a physical aid. The claimed method requires the steps of “acquiring target shape information… acquiring pixel information… acquiring coordinate information… grey level information… generating the target shape information…”, etc. Mental Processes include observations, evaluations, judgements, and opinions. The mental process of predicting brain disease risk demonstrates that Applicant’s claim as a whole is directed to an abstract idea. See MPEP 2106.04(a)(2)(III). (See also Elec. Power Grp., LLC v. Alstom S.A., “[W]e have treated analyzing information by steps people go through in their minds . . . without more, as essentially mental processes within the abstract-idea category.”)
A2. “gray level threshold-based vascular region identification (Element (2)-(ii)): The step of "acquiring coordinate information corresponding to a vascular region from pixel information in which the gray level information has a value equal to or smaller than a predetermined value" requires computational processing and is physically impossible to perform mentally” (remarks, pg. 13) (emphasis omitted).
Re. argument A2: The Examiner respectfully disagrees. Given the broadest reasonable interpretation in light of the specification, the limitation can practically be performed in the human mind with or without the aid of a computer. The step of “acquiring coordinate information…” is a judgement in which the pixel information is observed to acquire the gray level information having a value equal to or smaller than a predetermined value. Value, the lightness or darkness of a color, can be determined subjectively by human observation. Visual artists do this commonly. Radiologists and pulmonologists also do this commonly. Whether or not this is done on a computer, it is merely recited in abstraction.
Note: A “mental process” group includes manual processes executed by computational devices; the rejection allows for the process to be either performable in the mind or with pen or paper (MPEP § 2106.04(a)(2)(III) citing CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)). The MPEP notes that collecting, analyzing, and presenting information (the recited pixel information, coordinate information, and gray level information) without changing the nature of the information is an abstract idea under the mental processes category (MPEP § 2106.04(a)(2)(III) Electric Power Group) – here, image data is being collected, analyzed, and/or presented (a historically manual process) presumably by an automated process on the computer and is therefore a manual process executed by computational devices.
A3. “computer vision segmentation technique (Element (2)-(iii-A)): The step of "generating, using a computer vision segmentation technique, a surface model that presents cerebrovascular shape information" requires execution of specific computer vision algorithms to construct a three-dimensional surface representation of cerebrovascular structures from coordinate data and is beyond human mental capability” (remarks, pg. 14) (emphasis omitted).
Re. argument A3: A “mental process” group includes manual processes executed by computational devices; the rejection allows for the process to be either performable in the mind or with pen or paper (MPEP § 2106.04(a)(2)(III) citing CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)). The MPEP notes that collecting, analyzing (using a computer vision segmentation technique), and presenting information (a surface model that presents cerebrovascular shape information) without changing the nature of the information is an abstract idea under the mental processes category (MPEP § 2106.04(a)(2)(III) Electric Power Group) – here, image data is being collected, analyzed, and/or presented (a historically manual process) presumably by an automated process on the computer and is therefore a manual process executed by computational devices.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., computer vision algorithms, also the execution thereof to construct a three-dimensional surface representation of cerebrovascular structures from coordinate data) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Given the broadest reasonable interpretation in light of the Specification, the “technique” is instructions for generating “a surface model” and is part of the abstract idea.
A4. “computer vision skeleton technique (Element (2)-(iii-B)): The step of "generating, using a computer vision skeleton technique, a one-dimensional (ID) model from the surface model" requires algorithmic skeletonization of a three-dimensional surface model which cannot be performed mentally” (remarks, pg. 14) (emphasis omitted).
Re. argument A4: A “mental process” group includes manual processes executed by computational devices; the rejection allows for the process to be either performable in the mind or with pen or paper (MPEP § 2106.04(a)(2)(III) citing CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)). The MPEP notes that collecting (the surface model), analyzing (using a computer vision skeleton technique), and presenting information (generating a one-dimensional model) without changing the nature of the information is an abstract idea under the mental processes category (MPEP § 2106.04(a)(2)(III) Electric Power Group) – here, image data is being collected, analyzed, and/or presented (a historically manual process) presumably by an automated process on the computer and is therefore a manual process executed by computational devices.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a three-dimensional surface model, also computer vision algorithms) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Given the broadest reasonable interpretation in light of the Specification, this “technique” is instructions for generating “a one-dimensional model” and is also part of the abstract idea.
A5. “trained first neural network (Element (3)-(ii)): The step of "generating, through a trained first neural network, a fluid flow model from the ID model" requires execution of a trained artificial neural network to generate a computational fluid flow model from a one-dimensional vascular representation. Neural network inference is inherently a computational operation that cannot be performed in the human mind” (remarks, pg. 14) (emphasis omitted).
Re. argument A5: The Examiner respectfully submits that the step of “generating… a fluid flow model from the 1D model” is part of the identified mental process. Observing a 1D model and using best judgement, a person is thinking about generating a fluid flow model with or without the aid of pen and paper. The rejection allows for the process to be performable with pen or paper (MPEP § 2106.04(a)(2)(III) citing CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)). The MPEP notes that collecting (the 1D model), analyzing (using pen and paper), and presenting information (generating a fluid flow model) without changing the nature of the information is an abstract idea under the mental processes category (MPEP § 2106.04(a)(2)(III) Electric Power Group) – here, model data is being collected, analyzed, and/or presented (a historically manual process) presumably by an automated process on the computer and is therefore a manual process executed by computational devices.
As for the additional element, the use of the trained first neural network to generate a fluid flow model represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to generate the fluid flow model merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (neural networks) and thus fails to add an inventive concept to the claims.
A6. “fluid flow calculation using fluid flow model (Element (3)-(iii)): The step of "calculating, using the fluid flow model, the target blood flow information based on the cerebrovascular boundary condition" requires numerical computation of blood flow speed and pressure values across vascular networks using a computationally generated fluid flow model. This mathematical computation across complex vascular geometry is physically impossible to perform mentally” (remarks, pg. 14) (emphasis omitted).
Re. argument A6: The Examiner respectfully submits that the step of “calculating, using the fluid flow model, the target blood flow information based on the cerebrovascular boundary condition” is part of the identified mental process. Observing the fluid flow model and the boundary condition while using best judgement, a person is thinking about calculating the target blood flow information with the aid of pen and paper (the amount of paper is not limited to one sheet). The rejection allows for the process to be performable with pen or paper (MPEP § 2106.04(a)(2)(III) citing CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)). Nothing precludes an engineer from practically performing a numerical computation on pen and paper to determine blood blow speed and pressure values. The MPEP notes that collecting (the fluid flow model and the boundary condition), analyzing (calculating with pen and paper), and presenting information (target blood flow information) without changing the nature of the information is an abstract idea under the mental processes category (MPEP § 2106.04(a)(2)(III) Electric Power Group) – here, model data is being collected, analyzed, and/or presented (historically a manual process) presumably by an automated process on the computer and is therefore a manual process executed by computational devices.
A7. “the amended Claim I is directed to particular machine integration as the amended Claim I is tied to a particular machine: medical imaging equipment that generates the original medical image, and a brain disease risk analysis device that processes the image through a specific technical operation” (remarks, pg. 14).
Re. argument A7: The Examiner respectfully disagrees. The claim limitation of “an original medical image of the subject patient acquired through medical imaging equipment” describes the data. While the abstract idea may be improved, an improved abstract idea is still an abstract idea. Only additional elements may provide an integration.
The brain disease risk analysis device is the computer recited in claim 8. It is implementing the "specific technical operation" that is the abstraction, which is insufficient to provide an integration of the abstract idea into a practical application according to our guidance. MPEP 2106.04(d)(2) indicates that a practical application may be present where the judicial exception is implemented using or in conjunction with a particular machine or manufacture. The instant claims do not recite a particular machine and, instead, recite that the abstract idea is implemented by a general-purpose computer (see Applicant’s Disclosure at Fig. 1 and Para. 0044-0046). MPEP 2106.05(b)(I) indicates that applying the judicial exception “by use of conventional computer functions does not qualify as a particular machine.”
A8. “the amended Claim 1 is directed to transformation of a particular article as the claimed method effects a transformation of a particular article - an original medical image of a subject patient's brain vessels - through a series of specific technical transformations…” (remarks, pg. 15).
Re. argument A8: The Examiner respectfully disagrees. MPEP 2106.04(d)(2) indicates that a practical application may be present where the claimed invention effects a transformation or reduction of a particular article to a different state or thing. MPEP 2106.05(c) thereafter describes that a transformation is present where a physical object or substance is transformed to a different state or thing. Notably, the mere manipulation of data has been deemed not to be a transformation within the meaning of the term “transformation.” See MPEP 2106.05(c): “mere manipulation of basic mathematical constructs i.e., the paradigmatic abstract idea, has not been deemed a transformation” (internal quotations omitted). Because no transformation is present in Applicant’s claimed invention, a practical application is not present.
A9. “produces a new and distinct representation of the underlying medical data. The brain disease risk value represents a specific, actionable medical assessment that did not previously exist in the original medical image” (Remarks, pg. 15) (emphasis omitted.)
Re. argument A9: Judicial exceptions, including abstract ideas, are still judicial exceptions, despite their novelty and non-obviousness. For example, Flook and Ultramercial included novel claims that were, nonetheless, abstract. Therefore, even if the claims are found to distinguish over the prior art, they are still directed to an abstract idea without significantly more as established in the two-part analysis set forth above.
A10. “the amended Claim 1 provides a technical solution to a specific technical problem: the inability of prior art systems to automatically and quantitatively predict brain disease risk from patient-specific cerebrovascular geometry and hemodynamic data. As to the technical problem, the Specification describes… conventional methods are limited in that they simply predict the possibility of a brain disease on the basis of a patient's symptoms and statistical data of patients, and do not comprehensively consider cerebrovascular shape information and cerebral blood flow information to diagnose or predict a brain disease…” (Remarks, pg. 15.)
Re. argument A10: The Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicate that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by the brain disease risk analysis device (a computer). The problem of ignoring certain patient data in analysis was not a problem caused by the computer, is it a problem that existed and/or exists regardless of whether a computer is involved in the process. At best, Applicant’s identified problem is a medical or mathematics problem. Because no technological problem is present, the claims do not provide a practical application.
A11. “The amended Claim I requires a specific ordered combination of operations. The ordered combination is not a generic instruction to apply a trained model to patient data. Rather, the amended Claim I defines a specific implementation for deriving cerebrovascular shape information and blood-flow information from medical image data and using those technically generated inputs to acquire a patient-specific brain disease risk value” (Remarks, pg. 16.)
Re. argument A11: While the operations may be a specific ordered combination (i.e., a specific implementation), the identified abstract idea is still an abstract idea. Only additional elements can provide an integration or an inventive concept. The Examiner respectfully asserts that, when viewed either individually or as an ordered combination, the additional elements do not provide significantly more to the abstract idea and the claims are not subject matter eligible.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using the trained brain disease risk analysis model and the trained first neural network to apply data to an algorithm and report the results were found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained models) to a particular technological environment or field of use (neural networks). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
A12. “In particular, the combination of computer vision segmentation technique, computer vision skeleton technique, and trained first neural network-based fluid flow model generation is not well-understood, routine, or conventional in the field of brain disease risk analysis. In addition, the prior art… does not teach or suggest this specific ordered combination” (Remarks, pg. 16.)
Re. argument A12: As a preliminary matter, the recited computer vision techniques and trained first neural network-based fluid flow model generation are part of the identified abstract idea. See analysis, supra. MPEP 2106.05(d) states: “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry (emphasis added).” Further, MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).” As such, it is only the additional elements identified by the Examiner to not be part of the abstract idea that are analyzed to determine whether they represent well-understood, routine, conventional activities in the field of the invention.
In that regard, the additional elements of the claims do not provide significantly more based on this inquiry.
Taking these in turn, whether the additional elements of the claim provide an improvement was analyzed/addressed in the 2A2 analysis. The additional elements did not provide an improvement since these, alone or in combination, amount no more than mere instructions to apply the exception using a generic computer or component. The technological environment to which the claims are confined (a general-purpose computer performing generic computer functions according to Applicant’s disclosure at Fig. 1 and Para. 0044-0046, 0057-0058) is recited at a high level of generality and has been found by the courts to be insufficient to provide a practical application (see MPEP 2106.05(d)(II); Alice Corp.)
Finally, none of the additional elements of the claim were found to represent extra-solution activity and thus no well-understood, routine, conventional analysis is required. MPEP 2106.07(a) states “At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.” This was not asserted. As such, when viewed either individually or as an ordered combination, the additional elements do not provide significantly more to the abstract idea; and the claims are not subject matter eligible.
Regarding the rejection of Claims 2-8, the Applicant has not offered any arguments with respect to these claims other than to reiterate the argument(s) present for analogous claim 1 or claim(s) from which they depend. As such, the rejection of these claims is respectfully maintained.
Subject Matter Free of Prior Art
The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within the independent claims 1 and 8 as follows:
Acquiring coordinate information corresponding to a vascular region from the pixel information in which the gray level information has a value equal to or smaller than a predetermined value.
generating, using a computer vision segmentation technique, a surface model that presents cerebrovascular shape information from the coordinate information corresponding to the vascular region.
Generating, using a computer vision skeleton technique, a one-dimensional (1D) model from the surface model.
That is, while the combination teaches acquiring coordinate information corresponding to a vascular region from the pixel information in which the gray level information has a computed intensity value (encoded as gray scale information), and while the second reference teaches generating, using a computer vision segmentation technique, a surface model that presents cerebrovascular shape information from some of the coordinate information corresponding to the vascular region, the combination of prior art references does not teach or suggest generating, using the computer vision segmentation technique, the surface model that presents cerebrovascular shape information for the vascular region of the original medical image where the pixel information includes gray level values equal to or smaller than a predetermined value.
Also, while the combination teaches generating, using a computer vision skeleton technique, a 3D model from the surface model, the combination does not teach generating a 1D model from the surface model.
The most remarkable prior art of record is as follows:
Laksari et al. (US 2022/0093267 A1) for teaching performance of a skeletonization algorithm to determine the vessel centerlines (3D skeletonization), local diameters at each point on the centerline, and the branching connections at each vessel bifurcation. The result is a 3D map of vessel centerline and corresponding diameter for the entire brain vasculature, from the large cranial vessels down to vessels with diameters at the level of CTA image resolution. Visualizing the 3D vascular distribution and architecture alone will be also helpful in highlighting vessel blockage sites for clinical use. We then use these patient-specific vascular geometries, including each branch's length, diameter, angle, and branching structure, as input to the computational fluid dynamic (CFD) model for blood flow simulations (the recited “generating, using a computer vision skeleton technique, a… model”) (para. 0010) … Also for teaching the simulation of cerebral blood flow in the brain using the CFD model (the required “fluid flow model”) requires data concerning inlet boundary conditions at the cranial arteries (the recited “cerebrovascular boundary condition”) and measuring blood flow velocity/pressure at the level of large cranial arteries (the recited “calculating, using the fluid flow model, … blood flow speed information and blood flow pressure information”) (para. 0012).
Buckler et al. (US 2022/0012877 A1) for teaching calculating imaging features 122 from the acquired images 121A of the patient (para. 0151); analyzing the vascular composition (the recited “shape information”) based on the image intensity and other image features (the recited “acquiring pixel information from an original medical image of the subject acquired”) … an analyte blob model may be employed for analyzing composition of particular sub-regions (the recited “vascular region”) … in 2D, 3D, or 4D images… the blob (the recited “surface model”) may utilize an anatomically aligned coordinate system (the recited “acquiring coordinate information corresponding to a vascular region from the pixel information”) … The model may advantageously account for the observed image intensity at a pixel or voxel (para. 0170); the values of pixels could be simple image intensities; use of gray scale encoding (the recited “pixel information in which gray level information has a value”) (para. 0331); and neural networks (para. 0065, 0150).
Sanders et al. (US 2018/0078139 A1) for teaching system and methods for estimation of blood flow characteristics using reduced order model and machine learning (see abstract, Figs. 2, 4C and para. 0002-0003.)
Zimmerman (US 2022/0384045 A1) for teaching artificial intelligence based cardiac event predictor systems and methods (see Abstract, Figs. 5A-5B and para. 0038-0039.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Van Haaften et al. (EP 4,312,184 A1) for teaching a system for evaluating stenosis progression risk including (1) a feature detector or neural network trained to automatically identify a proposed location B-B’ for a stent based on the position of a narrowing of the lumen of a vessel in the brain (necessarily input) (para. 0013, 0052) and (2) calculating stenosis progression risk values at the positions along the vessel based on calculated vessel wall shear stress values, blood flow simulation, and angiographic image data; and outputting a stenosis progression risk map (acquiring), the map including the risk values for the vessel (Abstract and para. 0078). Motivation: improving diagnostic support, evaluation of the disease progression risk for vessels in the vasculature (e.g., of the brain), medical image processing, and target shape modeling accuracy (see para. 0007, 0013, 0027, 0062).
Pack et al. (EP 3,654,281 A1) for teaching deep learning for arterial analysis and assessment (See Abstract).
Mourad et al. (US 2005/0015009 A1) for teaching systems and methods for determining intracranial pressure non-invasively and acoustic transducer assemblies for use in such systems.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/J.M.W./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
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