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 .
Notice to Applications
This communication is in response to the Application filed on March 22, 2024.
Claims 2-21 are pending.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on January 18, 2025 are in compliance with the provisions of 27 CFR 1.97. Accordingly, the information disclosure statements are being considered and attached by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 2-21 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 2, 5-6, 8, 12, 15-17, and 20-21 of Application no. 18/614580. Although the claims at issue are not identical, they are not patentably distinct from each other. (see Claim-Comparison Table below for claim 2 of the instant application against claims 2 and 5 of the reference application no. 18/614580).
Claim
Instant Application (18/614,588)
Claim
Reference Application (18/614580)
2
A computer-implemented method of determining a level of endothelial shear stress based at least in part on a plurality of variables derived from noninvasive medical image analysis, the method comprising:
2
A computer-implemented method of predicting plaque progression based at least in part on a risk level of a region of plaque and a level of endothelial shear stress on the region of plaque determined based at least in part on a plurality of variables derived from non-invasive medical image analysis, the method comprising:
Where the “predicting plaque progression based at least in part on a risk level of a region of plaque and a level of endothelial shear stress on the region of plaque” includes “determining a level of endothelial shear stress”.
2
accessing, by a computer system, a medical image of a subject, the medical image comprising a portion of one or more arteries;
2
accessing, by a computer system, a medical image of a subject, the medical image comprising a portion of one or more arteries;
2
analyzing, by the computer system, the medical image of the subject to identify one or more artery vessels and one or more regions of plaque within the one or more artery vessels
2
analyzing, by the computer system, the medical image of the subject to identify one or more artery vessels and one or more regions of plaque within the one or more artery vessels
2
analyzing, by the computer system, the one or more artery vessels and the one or more regions of plaque to generate a plurality of variables, the plurality of variables comprising one or more of lesion length, remodeling index, plaque slice percentage, stenosis area percentage, presence of low-density plaque, stenosis diameter percentage, presence of positive remodeling, reference diameter after stenosis, reference diameter before stenosis, vessel length, lumen volume, number of chronic total occlusion (CTO), vessel volume, number of stenosis, total plaque volume, number of mild stenosis, low-density plaque volume, plaque morphology, embeddedness of a low density non- calcified plaque by non-calcified plaque or calcified plaque, distance between plaque and lumen wall or vessel wall, or eccentricity of plaque;
2
analyzing, by the computer system, the one or more artery vessels and the one or more regions of plaque to generate a plurality of variables, the plurality of variables comprising one or more of lesion length, remodeling index, plaque slice percentage, stenosis area percentage, presence of low-density plaque, stenosis diameter percentage, presence of positive remodeling, reference diameter after stenosis, reference diameter before stenosis, vessel length, lumen volume, number of chronic total occlusion (CTO), vessel volume, number of stenosis, total plaque volume, number of mild stenosis, low-density plaque volume, plaque morphology, embeddedness of a low density non- calcified plaque by non-calcified plaque or calcified plaque, distance between plaque and lumen wall or vessel wall, or eccentricity of plaque;
2
generating, by the computer system, a weighted measure of the generated plurality of variables; and
2
generating, by the computer system, a second weighted measure of the generated plurality of variables;
Where the “weighted measure” is the “second weighted measure”.
2
determining, by the computer system, a level of endothelial shear stress for one or more regions of one or more artery vessels based at least in part of the weighted measure of the generated plurality of variables,
2
determining, by the computer system, a level of endothelial shear stress for the particular region of plaque based at least in part of the second weighted measure of the generated plurality of variables; and
Where the “particular region of plaque” is “one or more regions of one or more artery vessels” and the “weighted measure” is the “second weighted measure”.
2
wherein the level of endothelial shear stress for the one or more regions of the one or more artery vessels is determined using a machine learning algorithm trained based at least in part on a plurality of weighted measures of the plurality of variables generated from a plurality of medical images of a plurality of other subjects with known levels of endothelial shear stress,
5
The computer-implemented method of claim 2,
wherein the level of endothelial shear stress for the particular region of plaque is determined using a machine learning algorithm trained based at least in part on a plurality of second weighted measures of the plurality of variables generated from a plurality of medical images of a plurality of other subjects with known levels of endothelial shear stress
Where the “particular region of plaque” is “one or more regions of one or more artery vessels” and the “weighted measure” is the “second weighted measure”.
2
wherein the determined level of endothelial shear stress is configured to be used to determine a risk of arterial disease for the subject, and
2
predicting, by the computer system, progression of the particular region of plaque based at least in part on the risk level and the level of endothelial shear stress for the particular region of plaque,
Where the “progression of the particular region of plaque based at least in part on the risk level and the level of endothelial shear stress for the particular region of plaque” is “determine a risk of arterial disease” because claim 9 discloses predicting the progression of the particular region of plaque determines a risk of arterial disease:
9. The computer-implemented method of claim 2, further comprising:
determining, by the computer system, a risk of arterial disease for the subject based at least in part on the predicted progression of the particular region of plaque.
2
wherein the computer system comprises a computer processor and an electronic storage medium.
2
wherein the computer system comprises a computer processor and an electronic storage medium.
Claims 2 of the instant application, and 2 of the reference application no. 18/614580, are nearly identical. Therefore, it would have been obvious to one of ordinary skill in the art at the date of filing to substitute the method, system, and non-transitory computer readable medium steps of the reference application with that of the instant application, which would produce known results with a reasonable expectation for success.
Independent claims 12 and 17 of the instant application recite similar limitations to claim 2 but in the form of a system and non-transitory computer readable medium. Therefore, claims 12 and 17 of the instant application are rejected over claims 12 and 17 of the reference application no. 18/614580 for similar rationale and reasoning to claim 2 (see the Claim-Comparison Table below for claim 2 of the instant application against claims 2 and 5 of the reference application no. 18/614580).
Dependent claims 5-6 and 10 of the instant application are equivalent in scope with claims 6, 8, and 2 of the reference application no. 18/614580 respectively.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 2-21 are rejected under 35 U.S.C. 103 as being unpatentable of Min et al., US 20210209757 A1, (hereinafter “Min”) in view of Itu et al. US 20180336319 A1, (hereinafter “Itu”).
Regarding claim 2, Min teaches a computer-implemented method of determining a level of endothelial shear stress based at least in part on a plurality of variables derived from noninvasive medical image analysis, the method comprising:
accessing, by a computer system, a medical image of a subject, the medical image comprising a portion of one or more arteries ([0207] “In some embodiments, at block 354, the system is configured to identify one or more arteries, plaque, and/or fat in the medical image, for example using AI, ML, and/or other algorithms.”);
analyzing, by the computer system, the medical image of the subject to identify one or more artery vessels and one or more regions of plaque within the one or more artery vessels ([0207] “In some embodiments, at block 354, the system is configured to identify one or more arteries, plaque, and/or fat in the medical image, for example using AI, ML, and/or other algorithms.”);
analyzing, by the computer system, the one or more artery vessels and the one or more regions of plaque to generate a plurality of variables, the plurality of variables comprising one or more of lesion length, remodeling index, plaque slice percentage, stenosis area percentage, presence of low-density plaque, stenosis diameter percentage, presence of positive remodeling, reference diameter after stenosis, reference diameter before stenosis, vessel length, lumen volume, number of chronic total occlusion (CTO), vessel volume, number of stenosis, total plaque volume, number of mild stenosis, low-density plaque volume, plaque morphology, embeddedness of a low density non- calcified plaque by non-calcified plaque or calcified plaque, distance between plaque and lumen wall or vessel wall, or eccentricity of plaque ([0312] “Features of embodiments of the system can include, for example, centerline and lumen/vessel extraction, plaque composition overlay, user identification of stenosis, vessel statistics calculated in real time, including vessel length, lesion length, vessel volume, lumen volume, plaque volume (non-calcified, calcified, low-density—non-calcified plaque and total), maximum remodeling index, and area/diameter stenosis (e.g., a percentage)”);
generating, by the computer system, a weighted measure of the generated plurality of variables ([0616] “the first set of parameters obtained at a first point in time, wherein the first set of plaque parameters comprises volume, surface area, geometric shape, location, heterogeneity index, and radiodensity for one or more regions of plaque within the coronary region of the subject; generating, by the computer system, a first weighted measure of the accessed first set of plaque parameters;”); and
determining, by the computer system, a level ([0616] “the first set of parameters obtained at a first point in time, wherein the first set of plaque parameters comprises volume, surface area, geometric shape, location, heterogeneity index, and radiodensity for one or more regions of plaque within the coronary region of the subject; generating, by the computer system, a first weighted measure of the accessed first set of plaque parameters;”),
wherein the level ([0294] “For example, using a machine learning process has been trained on thousands of CT scans determine information depicted in the CT images, and/or utilizing analyst to review and enhance the results of the machine learning process, and the example user interfaces described herein can provide the determined information to another analyst or a medical practitioner.”),
wherein the determined level ([0215] “In some embodiments, the system is configured to generate a risk assessment of coronary disease or cardiovascular event for the subject at block 366 using the weighted measure and/or using only some of these parameters.”), and
wherein the computer system comprises a computer processor and an electronic storage medium ([0012] “wherein the computer system comprises a computer processor and an electronic storage medium”).
Min does not specifically disclose a level of endothelial shear stress.
However, Itu teaches endothelial shear stress ([0069] “Since the experimental conditions can be controlled exactly in in vitro studies, an ML model can be trained to predict the formation, evolution and rupture of plaques using a database built from such studies as shown in the workflow 900 presented in FIG. 9. At step 905, in vitro studies are performed related to plaque formation, evolution, and rupture. Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.”).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to determine the level of endothelial shear stress of Itu in the arterial disease risk analysis method of Min because endothelial shear stress plays a crucial role in plaque progression. Thus, the risk of arterial disease can be more accurately analyzed and determined.
Regarding claim 3, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the level of endothelial shear stress comprises one of low, medium, or high (Itu - [0069] “Since the experimental conditions can be controlled exactly in in vitro studies, an ML model can be trained to predict the formation, evolution and rupture of plaques using a database built from such studies as shown in the workflow 900 presented in FIG. 9. At step 905, in vitro studies are performed related to plaque formation, evolution, and rupture. Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.” wherein the level of endothelial shear stress is a form of hemodynamics measured to relate to either plaque formation, evolution, and rupture; wherein low, medium or high correspond to plaque formation, evolution, and rupture respectively).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 4, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the level of endothelial shear stress is determined on a continuous scale (Itu - [0072] “Furthermore, the above described in vitro studies may be performed at normal or microfluidic scale, and in 2D or in 3D.” wherein a continuous scale is a microfluidic scale).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 5, Min in view of Itu teaches the computer-implemented method of claim 2, further comprising:
generating, by the computer system, a graphical representation of the determined level of endothelial shear stress for the one or more regions of the one or more artery vessels (Itu - [0069] “Since the experimental conditions can be controlled exactly in in vitro studies, an ML model can be trained to predict the formation, evolution and rupture of plaques using a database built from such studies as shown in the workflow 900 presented in FIG. 9. At step 905, in vitro studies are performed related to plaque formation, evolution, and rupture. Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.”) (Itu - [0091] “Computed results can be visualized on the scanner, or on another device, such as an imaging workstation. All of the above mentioned measures of interest related to plaque may be visualized including, without limitation, risk of a cardiovascular event related to atherosclerotic plaque” wherein shear stress can be visualized).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 6, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the plurality of variables are generated by using an artificial intelligence (AI) and/or machine learning (ML) algorithm trained on a plurality of medical images with the plurality of variables pre-identified (Min - [0119] “For example, in some embodiments, the system can be configured to utilize one or more machine learning and/or artificial intelligence algorithms to automatically and/or dynamically analyze a medical image to identify, quantify, and/or classify one or more coronary arteries and/or plaque.”) (Min - [0294] “For example, using a machine learning process has been trained on thousands of CT scans determine information depicted in the CT images, and/or utilizing analyst to review and enhance the results of the machine learning process, and the example user interfaces described herein can provide the determined information to another analyst or a medical practitioner.”).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 7, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the level of endothelial shear stress for the one or more regions of the one or more artery vessels is determined without using computational fluid dynamics (Min - [0071] “The model 1000 may also include various measurement devices for determining the hemodynamic measures including Pressure Transducers 1040, Flow Meters 1045, and a Doppler Probe 1035 for measuring the fluid velocity. Additionally, a pressure or flow measurement catheter (not shown in FIG. 1000) may be used to add the influence in high degree stenosis or small diameter vessels on the measured quantity.” wherein the disclosed tools are not computational fluid dynamics).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 8, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the level of endothelial shear stress for the one or more regions of the one or more artery vessels is determined without using an invasive measurement of the subject (Itu - [0069] “Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.”) (Itu - [0039] “Examples of non-invasive patient data that may be acquired at step 105 include, without limitation, demographics and patient history. The non-invasive patient data can be acquired via a wide range of sensors, comprising medical equipment and devices (stethoscope, blood pressure meter, imaging scanners, laboratory diagnostic, etc.) as well as non-medical grade devices (including, without limitation, wearables) for the measurements of physiological signals. In some embodiments, the non-invasive patient data includes biochemical signals as produced, for example, by blood tests and molecular measurements (e.g., “omics” such as proteomics, transcriptomics, genomics, metabolomics, lipidomics, and epigenomics). Additionally, the non-invasive patient data may span a wide range of biometrics signals, and can be driven by the individual as in the Quantified Self movement, promoting self-monitoring and self-sensing through wearable sensors and wearable computing. Furthermore, features extracted based on radio-genomics may be generated and used as non-invasive patient data (e.g., imaging biomarkers that are linked with the genomics of a pathology).” wherein shear stress is measured non-invasively).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 9, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the determined level of endothelial shear stress is configured to be used to determine a treatment for arterial disease for the subject (Itu - [0069] “Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.”) (Min - [0162] “For example, in some embodiments, the system can be configured to generate a treatment plan for an arterial disease, renal artery disease, abdominal atherosclerosis, carotid atherosclerosis, and/or the like, and the medical image being analyzed can be taken from any one or more regions of the subject for such disease analysis.”).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 10, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the plurality of variables further comprises one or more of: percent atheroma volume of total plaque, percent atheroma volume of low-density non-calcified plaque, percent atheroma volume of non-calcified plaque, percent atheroma volume, low-density non-calcified plaque volume, percent atheroma volume of total calcified plaque, non-calcified plaque volume, total calcified plaque volume, percent atheroma volume of total non-calcified plaque, percent atheroma volume of low-density calcified plaque, percent atheroma volume of high-density calcified plaque, total non-calcified plaque volume, low-density calcified plaque volume, percent atheroma volume of medium-density calcified plaque, high-density calcified plaque volume, medium-density calcified plaque volume, number of high-risk plaque regions, number of segments with calcified plaque, number of segments with non-calcified plaque, plaque area, plaque burden, necrotic core percentage, necrotic core volume, fatty fibrous volume, fatty fibrous percentage, dense calcium percentage, low-density calcium percentage, medium density calcified percentage, high-density calcified percentage, presence of two-feature positive plaques, number of two-feature positive plaques, segment length, severity of stenosis, minimum lumen diameter, maximum lumen diameter, mean lumen diameter, number of moderate stenosis, number of zero stenosis, number of severe stenosis, presence of high-risk anatomy, number of severe stenosis excluding CTO, vessel area, lumen area, diameter stenosis percentage, presence of ischemia, number of stents, reference lumen diameter before stenosis, perivascular fat attenuation, or reference lumen diameter after stenosis (Min - [0293] “In some embodiments, a patient-specific report generated by the system includes a quantified measure of various plaque and/or vascular morphology-related parameters shown within the vessel. In some embodiments, for each or some of the arteries included in the report, the system is configured to generate and/or derive from a medical image of the patient and include in a patient-specific report a quantified measure of the total plaque volume, total low-density or non-calcified plaque volume, total non-calcified plaque value, and/or total calcified plaque volume.”).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 11, Min in view of Itu teaches the computer-implemented method of claim 2, further comprising:
accessing, by the computer system, a subsequent medical image of the subject, the subsequent medical image comprising one or more artery vessels and one or more regions of plaque within the one or more artery vessels (Min - [0276] “In some embodiments, the system is configured to insert into the patient-specific report dynamically generated illustrations or images of patient artery vessels in order to highlight specific vessels and/or portions of vessels that contain or are likely to contain vascular disease that require review or further analysis.”);
analyzing, by the computer system, the subsequent medical image of the subject to generate the plurality of variables (Min - [0293] “In some embodiments, a patient-specific report generated by the system includes a quantified measure of various plaque and/or vascular morphology-related parameters shown within the vessel. In some embodiments, for each or some of the arteries included in the report, the system is configured to generate and/or derive from a medical image of the patient and include in a patient-specific report a quantified measure of the total plaque volume, total low-density or non-calcified plaque volume, total non-calcified plaque value, and/or total calcified plaque volume.”);
determining, by the computer system, a subsequent level of endothelial shear stress for one or more regions of one or more artery vessels based at least in part on the plurality of variables generated from analyzing the subsequent medical image (Itu - [0069] “Since the experimental conditions can be controlled exactly in in vitro studies, an ML model can be trained to predict the formation, evolution and rupture of plaques using a database built from such studies as shown in the workflow 900 presented in FIG. 9. At step 905, in vitro studies are performed related to plaque formation, evolution, and rupture. Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.”) (Itu - [Claim 17] “comparing the patient-specific measurements related to atherosclerotic plaque to the predicted measurements related to atherosclerotic plaque” wherein the subsequent level of endothelial shear stress is the measured shear stress of predicted measurements) (Min - [0293] “In some embodiments, a patient-specific report generated by the system includes a quantified measure of various plaque and/or vascular morphology-related parameters shown within the vessel. In some embodiments, for each or some of the arteries included in the report, the system is configured to generate and/or derive from a medical image of the patient and include in a patient-specific report a quantified measure of the total plaque volume, total low-density or non-calcified plaque volume, total non-calcified plaque value, and/or total calcified plaque volume.”) (Min - [0276] “In some embodiments, the system is configured to insert into the patient-specific report dynamically generated illustrations or images of patient artery vessels in order to highlight specific vessels and/or portions of vessels that contain or are likely to contain vascular disease that require review or further analysis.”); and
generating, by the computer system, a graphical representation of a comparison of the level of endothelial shear stress and the level of subsequent level of endothelial shear stress for one or more regions of one or more artery vessels, wherein the graphical representation of the comparison is configured to be used to track progression of arterial disease for the subject (Itu - [0069] “Since the experimental conditions can be controlled exactly in in vitro studies, an ML model can be trained to predict the formation, evolution and rupture of plaques using a database built from such studies as shown in the workflow 900 presented in FIG. 9. At step 905, in vitro studies are performed related to plaque formation, evolution, and rupture. Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.”) (Itu - [0091] “Computed results can be visualized on the scanner, or on another device, such as an imaging workstation. All of the above mentioned measures of interest related to plaque may be visualized including, without limitation, risk of a cardiovascular event related to atherosclerotic plaque” wherein shear stress can be visualized) (Itu - [Claim 17] “comparing the patient-specific measurements related to atherosclerotic plaque to the predicted measurements related to atherosclerotic plaque” wherein the level and subsequent level of endothelial shear stress are the measured shear stress of patient-specific measurements and predicted measurements respectively).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 12, the claim recites similar limitations to claim 2 but in the form of a system comprising a non-transitory computer storage medium configured to at least store computer executable instructions; and one or more computer hardware processors in communication with the first non-transitory computer storage medium, the one or more computer hardware processors configured to execute the computer-executable instructions to perform the method of claim 2 ([0378] “For example, by one or more computer hardware processors in communication with the one or more non-transitory computer storage mediums, executing the computer-executable instructions stored on one or more non-transitory computer storage mediums.”). Therefore, claim 12 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 13, the claim recites similar limitations to claim 3 but in the form of a system. Therefore, claim 13 recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above).
Regarding claim 14, the claim recites similar limitations to claim 4 but in the form of a system. Therefore, claim 14 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above).
Regarding claim 15, the claim recites similar limitations to claim 6 but in the form of a system. Therefore, claim 15 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 16, the claim recites similar limitations to claim 7 but in the form of a system. Therefore, claim 16 recites similar limitations to claim 7 and is rejected for similar rationale and reasoning (see the analysis for claim 7 above).
Regarding claim 17, the claim recites similar limitations to claim 2 but in the form of a non-transitory computer readable medium having program instructions for causing a hardware processor to perform a method of claim 2 ([0378] “For example, by one or more computer hardware processors in communication with the one or more non-transitory computer storage mediums, executing the computer-executable instructions stored on one or more non-transitory computer storage mediums.”). Therefore, claim 17 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 18, the claim recites similar limitations to claim 3 but in the form of a non-transitory computer readable medium. Therefore, claim 18 recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above).
Regarding claim 19, the claim recites similar limitations to claim 4 but in the form of a non-transitory computer readable medium. Therefore, claim 19 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above).
Regarding claim 20, the claim recites similar limitations to claim 6 but in the form of a non-transitory computer readable medium. Therefore, claim 20 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 21, the claim recites similar limitations to claim 7 but in the form of a non-transitory computer readable medium. Therefore, claim 21 recites similar limitations to claim 7 and is rejected for similar rationale and reasoning (see the analysis for claim 7 above).
Conclusion
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666