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 preliminary amendment dated 02/03/2025, the following occurred: Claim 8 has 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.
Information Disclosure Statement
The Information Disclosure Statement(s) (IDS)(s) submitted on 02/03/2025 follow(s) the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Appropriate correction is required.
Claim 1 recites “the brain disease risk analysis model”, which lacks adequate antecedent basis. The Examiner interprets the model as the trained version recited in the same step. The Examiner suggests amending to recite “the trained brain disease risk analysis model”. The rejection of independent claim 1 also applies to claims 2-3 and 8 mutatis mutandis. The rejection of independent claim 1 also applies to dependent claims 2-7.
Claim 6 recites “patients’ cerebrovascular shape information” / “cerebrovascular blood flow information of the patients”, which lacks antecedent basis. It is also unclear whether the patients are subject patients or training data patients.
Claim Rejections - 35 USC § 101
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 8 is rejected because it does not sufficiently recite a non-transitory computer readable storage medium. The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893 F.2d 319(Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. §101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. §101, Aug. 24, 2009; p. 2.
The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. §101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. §101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. §101 by adding the limitation "non-transitory" to the claim. Cf. Animals – Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. §101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998).
In furtherance of compact prosecution, Examiner will further consider the claims under 35 USC § 101 as if the claims were amended to be directed towards a computer hardware and not software per se.
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 a brain disease risk analysis model… acquiring target shape information of brain vessels of a subject patient; acquiring target blood flow information of the brain vessels of the subject patient; acquiring target patient information of the subject patient; and inputting the acquired target shape information, target blood flow information, and target patient information into … and acquiring a brain disease risk value output from…, 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 a brain disease risk analysis model, acquiring target shape information, acquiring 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 are directed to an abstract idea.
The claims further recite the additional element of acquiring and using a trained brain disease risk analysis model that implements the identified abstract idea (i.e., to apply data to an algorithm and output the results). The additional element is not described by the Applicant, is recited at a high-level of generality and is merely invoked as a tool to perform an existing process (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). 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. The claims are directed to an abstract idea.
Alternatively, or in addition, the implementation of the trained brain disease risk analysis model to apply data to an algorithm and report the results merely generally links the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (e.g., machine learning technology, or even computer technology as a machine learning model is not positively recited). 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, this additional element does not integrate the abstract idea into a practical application. Thus, the claim is 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. 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).
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 acquiring and using a trained brain disease risk analysis model to perform the method amounts no more than mere instructions to “apply it” with the exception by invoking an algorithm merely as a tool to perform an existing process (i.e., only recites the 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 algorithm (e.g., a trained machine learning algorithm) 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). Accordingly, alone or in combination, the additional element does not provide significantly more. Thus, the claims are not patent eligible.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of the trained brain disease risk analysis model is alternately or additionally considered generally linking the use of the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP § 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more; as such, the claim is 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 (i.e., reciting “the brain disease risk analysis model is trained”). When given its broadest reasonable interpretation in light of the disclosure, the training for either a mathematical or machine learning model, which causes the model to apply (1) cerebrovascular shape information including cerebrovascular location information or cerebrovascular diameter information, (2) cerebrovascular blood flow information including cerebrovascular speed information and cerebrovascular blood pressure information, (3) patient information including patients’ ages or genders, and (4) brain disease information to an algorithm and report the results using a brain disease risk analysis model and a type of math (e.g., computational fluid dynamics described in the spec. at para. 0055) represents the creation of mathematical interrelationships between data. As such, the training of the model represents 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.
Using a trained or untrained brain disease risk analysis model to perform the method amounts no more than mere instructions to “apply it” with the exception. See analysis, supra.
Claims 5-7 also merely further recite the additional element of using the brain disease risk analysis model (e.g., training, applying data to an algorithm to report the results). See analysis, supra.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Laksari et al. (US 2022/0093267 A1; “Laksari” herein) in view of Buckler et al. (US 2022/0012877 A1; “Buckler” herein).
Re. Claim 1, Laksari teaches a method of predicting a brain disease risk by a brain disease risk analysis device (Abstract and para. 0009 teach a probabilistic approach… incorporating continuous time-dependent and space-dependent volumetric perfusion maps directly into probabilities for prediction of infarct volumes / assessment of cerebral tissue viability for a stroke patient based at least in part on simulated cerebral blood flow (CBF). See also claim 18 and [0012], [0041] (device).), the method comprising:
acquiring a brain disease risk analysis model which has been trained ([0010], [0032] teach: To train a Binary Gaussian Process (BGP) classifier, we use two sources of input data. First, raw 3D perfusion measures from computational fluid dynamic (CFD) model simulations (CBF, CBV, MTT, TTP) and their mathematical time-domain characteristics (peak value, duration, change in time) as well as frequency-domain characteristics (power-spectral density and wavelet transform basis functions) are used. Secondly, we include… non-hemodynamic factors (age, gender, history of diabetes, and hypertension) into our data-driven model.);
acquiring target shape information of brain vessels of a subject patient ([0010] teaches an algorithm that, given a sequence of CTA images, … calculates each vessel's diameter, length, and tortuosity, and a connectivity matrix describing the branching patterns… calculates the vesselness probability map (local tubularity)… and determines 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... We then use these patient-specific vascular geometries... as input to the CFD model for blood flow simulations.);
acquiring target blood flow information of the brain vessels of the subject patient ([0012] teaches patient-specific boundary conditions; measuring blood flow velocity/pressure at the level of large cranial arteries; and using these measurements as input to the CDF model together with the patient’s CTA scans.);
acquiring target patient information of the subject patient ([0032] teaches including... non-hemodynamic factors (age, gender, history of diabetes, and hypertension) into our probabilistic data-driven model… integrating… factors likely to impact collateral flow (age and hypertension) into our predictor estimates.); and
inputting the acquired target shape information, target blood flow information, and target patient information into […] and acquiring a brain disease risk value output from […] (see [0010], [0012], [0032] (inputting). [0033] teaches extracting the best set of features… These parameters optimize the probabilistic BGP classifier… By using time-dependent perfusion calculations as input, the machine learning algorithm outperforms currently available parameters for stroke assessment. Fig. 1 and [0034] teach the stroke risk estimator module 140 develops stroke risk estimates (acquiring) based on patient data and ABCD2 standard. See also Fig. 3D and [0004].)
Laksari does not explicitly teach inputting the acquired target shape information, target blood flow information, and target patient information into the brain disease risk analysis model and acquiring a brain disease risk value output from the brain disease risk analysis model.
Buckler teaches
inputting… into the brain disease risk analysis model and acquiring a brain disease risk value output from the brain disease risk analysis model ([0150] teaches implementing a framework, e.g., one or more pre-trained / machine learned algorithms, which first identifies and quantifies biological properties/analytes utilizing a combination of (i) imaging features 122 from one or more acquired images 121A of a patient and (ii) non-imaging input data 121B for the patient (inputting) and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes (acquiring). See also [0151]-[0152] and [0351]. [0356]-[0375] teach the biological properties including one or more of tortuosity, flow, etc. [0377]-[0387] teach the conditions including one or more of perfusion/ischemia of the brain, risk stratification as a probability of event (value output), etc.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the noninvasive real-time patient-specific assessment of stroke severity of Laksari to train and utilize a machine learning model taking patient-specific data as input and reporting the results (e.g., probability of a Major Adverse Cardiac or Cerebrovascular Event (MACCE)) and to use this information as part of systems and methods for analyzing pathologies utilizing quantitative imaging as taught by Buckler (see Abstract), with the motivation of improving medical imaging diagnostics, personalized medicine, disease classification, patient outcomes, diagnostic accuracy, machine learning, etc. (see Buckler at least at para. 0006, 0011-0012, 0016-0017, 0041, 0044, 0063-0064, 0137)
Re. Claim 4, Laksari/Buckler teach the method of claim 1, wherein the brain disease risk analysis model is trained on the basis of a first training dataset related to cerebrovascular shape information including cerebrovascular location information or cerebrovascular diameter information (Buckler [0199], [0200], [0225], [0227] teaches analyte blob descriptors may be used, e.g., to capture location, shape or other structural characteristics (see Fig. 10); and the distribution of blob descriptors may be computed over the whole training set (see Fig. 11). See additionally Buckler [0074] and Table 5.), a second training dataset related to cerebrovascular blood flow information including cerebrovascular blood flow speed information or cerebrovascular blood pressure information (Buckler [0150], [0167], [0356], [0371]-[0372], [0375] teaches biological properties including flow (velocity), pressure, shear stress.), a third training dataset related to patient information including patients' ages or genders, and a fourth training dataset related to brain disease information (Buckler [0150], [0377]-[0387] teaches conditions determined based on the biological properties including one or more of perfusion/ischemia of brain tissue, perfusion/infarction of brain tissue, etc.) (Laksari [0007], [0010], [0032] teaches: To train a Binary Gaussian Process (BGP) classifier (model), we use two sources of input data. First, raw 3D brain perfusion measures (first and second training datasets) from computational fluid dynamic (CFD) model simulations (CBF, CBV, MTT, TTP) … are used. Secondly, we include… non-hemodynamic factors (age, gender, history of diabetes, and hypertension) (a third training dataset) into our data-driven model.)
Re. Claim 5, Laksari/Buckler teach the method of claim 4, wherein the brain disease risk analysis model is configured to acquire the first training dataset, the second training dataset, the third training dataset, and the fourth training dataset (see claims 1 and 4 prior art rejections. Buckler [0150], [0356]-[0375] teaches biological properties can include one or more of angiogenesis, neovascularization, inflammation, etc. (the fourth training dataset related to brain disease information)) and output a brain disease risk prediction value (Buckler [0150], [0377]-[0387] teaches conditions determined based on the biological properties including one or more of perfusion/ischemia of brain tissue, perfusion/infarction of brain tissue, risk stratification as probability of event (risk prediction value), etc.), and the brain disease risk analysis model is trained to output the brain disease risk prediction value approximating the brain disease information included in the fourth training dataset (see claims 1 and 4 prior art rejections. The Examiner interprets the training data as including biological properties (inputs) related to brain disease information and conditions (outputs) related to probability of an event, e.g., perfusion/ischemia of brain tissue.)
Re. Claim 8, the subject matter of claim 8 is essentially defined in terms of a manufacture (presumably), which is technically corresponding to method claim 1. Since claim 8 is analogous to claim 1, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 1. Also, Laksari teaches a computer-readable recording medium on which a program for causing a computer to execute the method… is recorded (para. 0041).
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Laksari in view of Buckler and Sanders et al. (US 2018/0078139 A1; “Sanders” herein).
Re. Claim 2, Laksari/Buckler teach the method of claim 1, wherein the acquiring of the target shape information further comprises:
acquiring shape information of a standard blood vessel (Buckler [0079] teaches providing for phenotype classification of a plaque based on an enriched radiological data set (standard). In particular, the phenotype classification(s) may include distinguishing stable plaque from unstable plaque, e.g., where the ground truth basis for the classification is based on factors such as (i) luminal narrowing (possibly augmented by additional measures such as tortuosity and/or ulceration), (ii) calcium content (possibly augmented by depth, shape, and/or other complex presentations), (iii) lipid content (possibly augmented by measures of cap thickness and/or other complex presentations), (iv) anatomic structure or geometry, and/or (v) IPH or other content); and
acquiring […] the target shape information and […] (see claim 1 prior art rejection. Note: Laksari [0012] teaches the simulation of cerebral blood flow in the brain using the CFD model requires data concerning inlet boundary conditions of blood flow velocity/pressure… using these as input to the CFD model, together with the patient’s CTA scans, the device will provide 3D estimate maps and confidence levels for infarct and salvageable tissue.), and
the acquiring of the brain disease risk value further comprises inputting […] the target shape information […] into the brain disease risk analysis model and acquiring a brain disease risk value output from the brain disease risk analysis model (see claim 1 prior art rejection. Note: Laksari [0031] teaches, traditionally, simple thresholds have been used to categorize perfusion images into infarct core or salvageable penumbra depending on reperfusion status… this approach of binary classification is based on a predefined cut-off perfusion value and assumes that a single lumped parameter is capable of predicting risk levels of infarction.)
Laksari/Buckler may not teach
acquiring a difference between the target shape information and the shape information of the standard blood vessel, or
inputting the difference between the target shape information and the shape information of the standard blood vessel.
Sanders teaches
acquiring a difference between the target shape information and the shape information of the standard blood vessel (Fig. 2 teaches receiving a patient-specific anatomic model of their vascular system; truncating the patient-specific anatomic model at locations where appropriate boundary conditions (see Laksari) may be applied; apply boundary conditions to the truncated patient-specific anatomic model to estimate blood flow characteristics using CFD; split the truncated model into one or more regions based on estimated, measured, and/or simulated blood flow characteristics; generate a reduced order model for each region (shape information of the standard blood vessel); estimate impedance values for each reduced order model based on the geometrical characteristics of the region and blood flow properties; for each of the one or more points in each region, determine the error, e.g., difference, of the estimated impedance values of the reduced order model from the determined impedance values of the truncated patient-specific anatomical model (target shape information).) and
inputting the difference between the target shape information and the shape information of the standard blood vessel into the brain disease risk analysis model (Figs. 2 and 4C teach training a machine learning algorithm using the errors of the impedance values (Fig. 2); and using the trained machine learning algorithm to update the simplified geometry of the reduced order and/or lumped parameter models (Fig. 4C).)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the noninvasive real-time patient-specific assessment of stroke severity of Laksari/Buckler to acquire various patient-specific and comparative models, each comprised of shape and blood flow information, and to use this information as part of systems and methods for estimating/determining blood flow characteristics of a patient using reduced order modeling and machine learning as taught by Sanders (see Abstract), with the motivation of improving computational precision, accuracy and efficiency for medical imaging, diagnostics, treatment planning (see Sanders at para. 0002-0003).
Re. Claim 3, Laksari/Buckler/Sanders teach the method of claim 2, wherein the acquiring of the target shape information further comprises:
acquiring blood flow information of the standard blood vessel (Laksari [0012] teaches generic inlet and outflow boundary conditions (standard blood flow information). Buckler [0079] teaches providing for phenotype classification of a plaque based on an enriched radiological data set; and the phenotype classification(s) may include distinguishing stable plaque from unstable plaque, e.g., where the ground truth basis for the classification is based on various factors.); and
acquiring […] the target blood flow information and […] (Laksari [0012] teaches the simulation of cerebral blood flow in the brain using the CFD model requires, e.g., patient-specific boundary conditions of blood flow velocity/pressure; and using these as input to the CFD model, together with the patient’s CTA scans, the device will provide 3D estimate maps and confidence levels for infarct and salvageable tissue), and
the acquiring of the brain disease risk value further comprises inputting […] the target blood flow information […] into the brain disease risk analysis model and acquiring a brain disease risk value output from the brain disease risk analysis model (see claim 1 prior art rejection. Note: Laksari [0031] teaches, traditionally, simple thresholds have been used to categorize perfusion images into infarct core or salvageable penumbra depending on reperfusion status… this approach of binary classification is based on a predefined cut-off perfusion value and assumes that a single lumped parameter is capable of predicting risk levels of infarction.)
Laksari/Buckler may not teach
acquiring a difference between the target blood flow information and the blood flow information of the standard blood vessel, or
inputting the difference between the target blood flow information and the blood flow information of the standard blood vessel.
Sanders teaches
acquiring a difference between the target blood flow information and the blood flow information of the standard blood vessel (Fig. 2 teaches receiving a patient-specific anatomic model of their vascular system; truncating the patient-specific anatomic model at locations where appropriate boundary conditions (see Laksari) may be applied; apply boundary conditions to the truncated patient-specific anatomic model to estimate blood flow characteristics using CFD; split the truncated model (target blood flow information) into one or more regions based on estimated, measured, and/or simulated blood flow characteristics; generate a reduced order model for each region (blood flow information of the standard blood vessel); estimate impedance values for each reduced order model based on the geometrical characteristics of the region and blood flow properties; for each of the one or more points in each region, determine the error, e.g., difference, of the estimated impedance values of the reduced order model from the determined impedance values of the truncated patient-specific anatomical model.) and
inputting the difference between the target blood flow information and the blood flow information of the standard blood vessel into the brain disease risk analysis model (Figs. 2 and 4C teach training a machine learning algorithm using the errors of the impedance values (Fig. 2); and using the trained machine learning algorithm to update the simplified geometry of the reduced order and/or lumped parameter models (Fig. 4C).)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the noninvasive real-time patient-specific assessment of stroke severity of Laksari/Buckler to acquire various patient-specific and comparative models, each comprised of shape and blood flow information, and to use this information as part of systems and methods for estimating/determining blood flow characteristics of a patient using reduced order modeling and machine learning as taught by Sanders (see Abstract), with the motivation of improving computational precision, accuracy and efficiency for medical imaging, diagnostics, treatment planning (see Sanders at para. 0002-0003).
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Laksari in view of Buckler and Sanders and Zimmerman (US 2022/0384045 A1).
Re. Claim 6, Laksari/Buckler/Sanders teach the method of claim 3, wherein the brain disease risk analysis model is trained (Laksari [0007], [0010], [0032] teaches: To train a Binary Gaussian Process (BGP) classifier (model), we use two sources of input data. First, raw 3D brain perfusion measures from computational fluid dynamic (CFD) model simulations (CBF, CBV, MTT, TTP) … are used) on the basis of a first training dataset composed of […] cerebrovascular shape information and standard cerebrovascular shape information, a second training dataset composed of […] cerebrovascular blood flow information of the […] and standard cerebrovascular blood flow information (Laksari [0010], [0012] teaches a 3D map of patient-specific cerebrovascular geometries (cerebrovascular shape); and data concerning inlet and outflow boundary conditions… may be patient-specific or specified generically for all patients or specific classes of patients (cerebrovascular blood flow). Buckler Fig. 1, [0155] teaches the cohort tool module 130 enables defining a cohort of patients for group analyses thereof, e.g., based on a selected set of criteria related to the cohort study in question… cohort analysis may be for a set of subjects for which ground truth or references exist (standard), and this type of cohort may be further decomposed into a training set or "development" set and a test or "holdout" set… Development sets may be supported so as to train 112 the algorithms and models within analyzer module 120.), a third training dataset related to the patient information including patients' ages or genders (Laksari [0007], [0010], [0032] teaches: To train a Binary Gaussian Process (BGP) classifier (model), we use two sources of input data… Secondly, we include… non-hemodynamic factors (age, gender, history of diabetes, and hypertension) into our data-driven model.), and a fourth training dataset related to brain disease information (Buckler [0150], [0356]-[0375] teaches biological properties can include one or more of angiogenesis, neovascularization, inflammation, etc. Alternately, Buckler [0150], [0377]-[0387] teaches conditions determined based on the biological properties including one or more of perfusion/ischemia of brain tissue, perfusion/infarction of brain tissue, etc.)
Laksari/Buckler/Sanders may not teach training data includes differences between patients’ data… and standard data.
Zimmerman teaches
training data including differences between patients’ data and standard data (Fig. 5A-5B, [0038] teach training a convolutional neural network on a plurality of patients, wherein the plurality of patients include at least patients having a recorded ECG within a diagnosis threshold and patients having a recorded ECG outside a diagnosis threshold (differences between patients’ data and standard data); wherein the diagnosis threshold is compared against the time between the date of diagnosis of aortic stenosis and the date of the recorded ECG; and providing the trained CNN as the trained model. See also [0039].)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the noninvasive real-time patient-specific assessment of stroke severity of Laksari/Buckler/Sanders to use training data specifying a diagnosed patient cohort and a healthy patient cohort to train a disease risk classification model and to use this information as part of a method and system for predicting the likelihood of a patient suffering a cardiac event as taught by Zimmerman (see Abstract, Fig. 4A and para. 0125), with the motivation of improving patient health risk analysis, medical event monitoring, event detection, treatment planning, and patient outcomes (see Zimmerman at para. 0005-0007).
Re. Claim 7, Laksari/Buckler/Sanders teach the method of claim 6, wherein the brain disease risk analysis model is configured to acquire the first training dataset, the second training dataset, the third training dataset, and the fourth training dataset (see claim 6 prior art rejection) and output a brain disease risk prediction value (see claim 1 prior art rejection), and the brain disease risk analysis model is trained to output the brain disease risk prediction value approximating the brain disease information included in the fourth training dataset (see claims 1 and 6 prior art rejections. The Examiner interprets a machine learning classifier as trained on the training inputs in the claim 6 prior art rejection to output one or more conditions of Buckler, e.g., a probability of an event and the event being the condition of perfusion/ischemia of brain tissue.)
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.
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/J.M.W./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
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