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 August 25, 2023.
Claims 1-21 are pending.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on October 31, 2023 and January 15, 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.
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 1-7, 9, 12, 14-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable of Min et al., US 20210209757 A1 (hereinafter “Min”) in view of Sirohey et al., US 20080118122 A1, (hereinafter “Sirohey”).
Regarding claim 1, Min teaches a computer-implemented method of assessing a state of cardiovascular disease of a subject based on multi-dimensional information derived from non-invasive medical image analysis, the method comprising:
accessing, by a computer system, a medical image of a subject, wherein the medical image of the subject is obtained non-invasively ([0220] “As such, in some embodiments, the system can provide an automated disease tracking tool using non-invasive raw medical images as an input, which does not rely on subjective assessment.”);
analyzing, by the computer system, the medical image of the subject to identify one or more arteries, wherein the one or more arteries comprise one or more regions of plaque ([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.”);
determining, by the computer system, one or more vascular parameters associated with the subject by analyzing the one or more arteries identified from the medical image, wherein the one or more vascular parameters comprise one or more of vascular volume, diameter, area, cross-sectional area, surface area, length, location, or remodeling ([0129] “In particular, in some embodiments, the system can be configured to determine one or more vascular morphology parameters, such as for example arterial remodeling, curvature, volume, width, diameter, length, and/or the like.” wherein one or more vascular parameters are one or more vascular morphology parameters);
identifying, by the computer system, the one or more regions of plaque on the medical image ([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.”);
characterizing, by the computer system, one or more of the one or more identified regions of plaque as non-calcified plaque, wherein the one or more identified regions of plaque is characterized as non-calcified plaque when radiodensity values of one or more pixels within the one or more identified regions of plaque is below a predetermined threshold ([0157] “More specifically, in some embodiments, the system can be configured to identify as an initial set low-attenuated or non-calcified plaque by identifying pixels or regions with a radiodensity value that is below a predetermined threshold or within a predetermined range.”);
determining, by the computer system, one or more non-calcified plaque parameters for the one or more characterized non-calcified plaque, the one or more non- calcified plaque parameters comprising one or more of plaque density, radiodensity, location, volume, surface area, geometry, heterogeneity, diffusivity, or ratio between volume and surface area ([0293] “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.”);
analyzing, by the computer system, one or more of the one or more vascular parameters or one or more non-calcified plaque parameters by comparison to a dataset of values, the values comprising one or more of the one or more vascular parameters or one or more non-calcified plaque parameters derived from a population with varying states of cardiovascular disease ([0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein a population with varying states of cardiovascular disease is subjects with varying levels of risk);
generating, by the computer system, a ([0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein a population with varying states of cardiovascular disease is subjects with varying levels of risk); and
determining, by the computer system, an assessment of the state of cardiovascular disease of the subject ([0215] “In some embodiments, at block 366, the system can be configured to generate a risk assessment of cardiovascular disease or event for the subject. In some embodiments, the generated risk assessment can comprise a risk score indicating a risk of coronary disease for the subject. In some embodiments, the system can generate a risk assessment based on an analysis of one or more vascular morphology parameters, one or more quantified plaque parameters, one or more quantified fat parameters, calculated stenosis, risk of ischemia, CAD-RADS score, and/or the like.”),
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 generating a non-calcium score.
However, Sirohey teaches generating a non-calcium score ([0035] “A report is generated that uses the original calcium score for reporting the calcified plaque content as well as a new measure that called the soft plaque score that calculates the mass and volume of the soft plaque to compute a soft plaque score.” wherein a non-calcium score is a soft plaque score).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to generate a non-calcium score of Sirohey in the cardiovascular disease assessment of Min because non-calcified plaque is indicative of an unstable plaque or high-risk coronary artery disease. Generating a non-calcium score improves the overall accuracy of the assessment and the determination of risk of coronary artery disease.
Regarding claim 2, Min in view of Sirohey teaches the computer-implemented method of Claim 1, wherein one or more of the one or more identified regions of plaque is further characterized as non-calcified plaque when distribution of radiodensity values of one or more pixels within the one or more identified regions of plaque is above a predetermined threshold (Min - [0157] “More specifically, in some embodiments, the system can be configured to identify as an initial set low-attenuated or non-calcified plaque by identifying pixels or regions with a radiodensity value that is below a predetermined threshold or within a predetermined range.” wherein above a predetermined threshold is within a predetermined range).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 3, Min in view of Sirohey teaches the computer-implemented method of Claim 1, further comprising characterizing, by the computer system, one or more of the one or more identified regions of plaque as calcified plaque, wherein the one or more identified regions of plaque is characterized as calcified plaque when radiodensity values of one or more pixels within the one or more identified regions of plaque is above a predetermined threshold (Min - [0161] “In particular, in some embodiments, the system can be configured to identify calcified or high-attenuated plaque from the medical image or non-contrast CT image by identifying pixels or regions within the image that have a radiodensity value above a predetermined threshold and/or within a predetermined range.”).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 4, Min in view of Sirohey teaches the computer-implemented method of Claim 3, further comprising determining, by the computer system, one or more calcified plaque parameters for the one or more characterized calcified plaque, the one or more calcified plaque parameters comprising one or more of plaque density, radiodensity, location, volume, surface area, geometry, heterogeneity, diffusivity, or ratio between volume and surface area (Min - [0210] “For example, in some embodiments, the system can be configured to determine a geometry and/or volume of a region of plaque and/or a vessel at block 201, a ratio or function of volume to surface area of a region of plaque at block 203, a heterogeneity or homogeneity index of a region of plaque at block 205, radiodensity of a region of plaque and/or a composition thereof by ranges of radiodensity values at block 207, a ratio of radiodensity to volume of a region of plaque at block 209, and/or a diffusivity of a region of plaque at block 211.”) (Min - [0247] “For example, in some embodiments, if the comparison of one or more plaque parameters reveals that plaque is stabilizing or showing high radiodensity values as a whole for the subject without generation of any new plaque, then the system can report that the change in calcium score is positive.” wherein the one or more calcified plaque parameters are one or more plaque parameters).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 5, Min in view of Sirohey teaches The computer-implemented method of Claim 4, further comprising analyzing, by the computer system, one or more of the one or more calcified plaque parameters by comparison to a dataset of values of the one or more calcified plaque parameters derived from a population with varying states of cardiovascular disease (Min - [0249] “In some embodiments, the system is configured to utilize an AI, ML, and/or other algorithm to characterize the change in calcium score based on one or more plaque parameters derived from a medical image. For example, in some embodiments, the system can be configured to utilize an AI and/or ML algorithm that is trained using a CNN and/or using a dataset of known medical images with identified plaque parameters combined with calcium scores.” wherein one or more calcified plaque parameters are one or more plaque parameters) (Min - [0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein a population with varying states of cardiovascular disease is subjects with varying levels of risk).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 6, Min in view of Sirohey teaches the computer-implemented method of Claim 5, further comprising generating, by the computer system, a calcium score for the subject, the calcium score generated based at least in part by analyzing one or more of the one or more calcified plaque parameters by comparison to the dataset of values of the one or more calcified plaque parameters (Min - [0249] “In some embodiments, the system is configured to utilize an AI, ML, and/or other algorithm to characterize the change in calcium score based on one or more plaque parameters derived from a medical image. For example, in some embodiments, the system can be configured to utilize an AI and/or ML algorithm that is trained using a CNN and/or using a dataset of known medical images with identified plaque parameters combined with calcium scores.” wherein one or more calcified plaque parameters are one or more plaque parameters).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 7, Min in view of Sirohey teaches the computer-implemented method of Claim 6, further comprising generating, by the computer system, a weighted measure of the non-calcium score and the calcium score (Min - [0231] “In some embodiments, the system can be configured to compare the plaque parameters individually and/or combining one or more of them as a weighted measure.” wherein the scores can be calculated as a weighted measure) (Siroehy - [0035] “A report is generated that uses the original calcium score for reporting the calcified plaque content as well as a new measure that called the soft plaque score that calculates the mass and volume of the soft plaque to compute a soft plaque score.” wherein a non-calcium score is a soft plaque score) (Min - [0249] “In some embodiments, the system is configured to utilize an AI, ML, and/or other algorithm to characterize the change in calcium score based on one or more plaque parameters derived from a medical image.”).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 9, Min in view of Sirohey teaches The computer-implemented method of Claim 1, wherein the medical image is obtained using an imaging technique comprising one or more of computed tomography (CT), x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), magnetic resonance (MR) imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron- emission tomography (PET), single photon emission computed tomography (SPECT), or near- field infrared spectroscopy (NIRS) (Min - [0015] “In some embodiments of a computer-implemented method of quantifying and classifying coronary plaque within a coronary region of a subject based on non-invasive medical image analysis, the medical image is obtained using an imaging technique comprising one or more of CT, x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), MR imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron-emission tomography (PET), single photon emission computed tomography (SPECT), or near-field infrared spectroscopy (NIRS).”).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 12, Min in view of Sirohey teaches the computer-implemented method of Claim 7, wherein the assessment of the state of cardiovascular disease of the subject is determined based at least in part on the generated weighted measure of the non-calcium score and the calcium score (Min - [0231] “In some embodiments, the system can be configured to compare the plaque parameters individually and/or combining one or more of them as a weighted measure.” wherein the scores can be calculated as a weighted measure) (Sirohey - [0035] “A report is generated that uses the original calcium score for reporting the calcified plaque content as well as a new measure that called the soft plaque score that calculates the mass and volume of the soft plaque to compute a soft plaque score.” wherein a non-calcium score is a soft plaque score) (Min - [0249] “In some embodiments, the system is configured to utilize an AI, ML, and/or other algorithm to characterize the change in calcium score based on one or more plaque parameters derived from a medical image.”).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 14, Min in view of Sirohey teaches the computer-implemented method of Claim 1, further comprising generating, by the computer system, a treatment for cardiovascular disease for the subject based at least in part on the determined assessment of the state of cardiovascular disease (Min - [0149] “In some embodiments, at block 214, the system is configured to generate a proposed treatment plan for the subject based on the analysis, such as for example the classification of plaque derived automatically from a raw medical image. In particular, in some embodiments, the system can be configured to assess or predict the risk of atherosclerosis, stenosis, and/or ischemia of the subject based on a raw medical image and automated image processing thereof.”).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 15, Min in view of Sirohey teaches the computer-implemented method of Claim 14, wherein the treatment for cardiovascular disease comprises medical intervention, medical treatment, or lifestyle change (Min - [0260] “For example, in some embodiments, the system can be configured to generate a proposed treatment plan for the subject based on the change in calcium score and/or characterization thereof for the subject. In some embodiments, the generated treatment plan can include use of statins, lifestyle changes, and/or surgery.”).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 17, Min in view of Sirohey teaches The computer-implemented method of Claim 1, further comprising:
determining, by the computer system, one or more relational parameters, the relational parameters comprising one or more of a ratio of surface area of plaque to surface of vessel, ratio of volume of plaque to volume of vessel, or a ratio of thickness of plaque to thickness of vessel (Min - [0253] “In some embodiments, the system is configured to determine whether a patient is at risk for a cardiovascular event based on an absolute amount or volume or a ratio of the amount or volume bad plaque buildup in the patient's artery vessels compared to the total volume of some or all of the artery vessels.” wherein one or more relational parameters is ratio of volume of plaque to volume of vessel); and
analyzing, by the computer system, one or more of the one or more relational parameters by comparison to a dataset of values, the values comprising one or more of the one or more relational parameters derived from a population with varying states of cardiovascular disease (Min - [0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein one or more relational parameters is ratio and a population with varying states of cardiovascular disease is subjects with varying levels of risk),
wherein the non-calcium score for the subject is further generated based at least in part on the analyzed one or more relational parameters (Sirohey - [0035] “A report is generated that uses the original calcium score for reporting the calcified plaque content as well as a new measure that called the soft plaque score that calculates the mass and volume of the soft plaque to compute a soft plaque score.” wherein a non-calcium score is a soft plaque score) (Min - [0253] “In some embodiments, the system is configured to determine whether a patient is at risk for a cardiovascular event based on an absolute amount or volume or a ratio of the amount or volume bad plaque buildup in the patient's artery vessels compared to the total volume of some or all of the artery vessels.” wherein one or more relational parameters is ratio of volume of plaque to volume of vessel).
The motivation for combining Min and Sirohey is the same motivation as used for claim 1.
Regarding claim 18, the claim recites similar limitations to claim 1 but in the form of a system. Therefore, claim 18 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 19, the claim recites similar limitations to claims 3-6 but in the form of a system. Therefore, claim 19 recites similar limitations to claims 3-6 and is rejected for similar rationale and reasoning (see the analysis for claims 3-6 above).
Regarding claim 20, the claim recites similar limitations to claims 7 and 12 but in the form of a system. Therefore, claim 20 recites similar limitations to claims 7 and 12 and is rejected for similar rationale and reasoning (see the analysis for claims 7 and 12 above).
Claims 8, 10-11, 13, 16 and 21 are rejected under 35 U.S.C. 103 as being unpatentable of Min et al., US 20210209757 A1 (hereinafter “Min”) in view of Sirohey et al., US 20080118122 A1, (hereinafter “Sirohey”) in further view of Nickisch et al., US 20210110543 A1, (hereinafter “Nickisch”).
Regarding claim 8, Min in view of Sirohey teaches the computer-implemented method of Claim 7, wherein the weighted measure of the non-calcium score and the calcium score is generated (Min - [0231] “In some embodiments, the system can be configured to compare the plaque parameters individually and/or combining one or more of them as a weighted measure.” wherein the scores can be calculated as a weighted measure) (Sirohey - [0035] “A report is generated that uses the original calcium score for reporting the calcified plaque content as well as a new measure that called the soft plaque score that calculates the mass and volume of the soft plaque to compute a soft plaque score.” wherein a non-calcium score is a soft plaque score) (Min - [0249] “In some embodiments, the system is configured to utilize an AI, ML, and/or other algorithm to characterize the change in calcium score based on one or more plaque parameters derived from a medical image.”).
Min in view of Sirohey does not specifically disclose weighting the non-calcium score between 0 and 1 and by weighting the calcium score between 0 and 1.
However, Nickisch teaches weighting the non-calcium score between 0 and 1 and by weighting the calcium score between 0 and 1 ([0002] “Fractional flow reserve (FFR) is an invasive measure in the catheterization laboratory (Cath Lab) to quantify, via an FFR index, the hemodynamic significance of a coronary lesion due to calcified or soft plaque…The FFR value is an absolute number between 0 and 1, where a value 0.50 indicates that a given stenosis causes a 50% drop in blood pressure.” wherein the non-calcium and calcium scores are FFR values).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to weight the measured non-calcium and calcium scores of Min in view of Sirohey between 0 and 1, of Nickisch, to further classify and characterize the identified regions of plaque as non-calcified or calcified, thereby improving the accuracy of the overall cardiovascular disease assessment of Min in view of Sirohey.
Regarding claim 10, Min in view of Sirohey teaches the computer-implemented method of Claim 7, wherein the weighted measure of the non-calcium score and the calcium score is generated (Min - [0231] “In some embodiments, the system can be configured to compare the plaque parameters individually and/or combining one or more of them as a weighted measure.” wherein the scores can be calculated as a weighted measure) (Sirohey - [0035] “A report is generated that uses the original calcium score for reporting the calcified plaque content as well as a new measure that called the soft plaque score that calculates the mass and volume of the soft plaque to compute a soft plaque score.” wherein a non-calcium score is a soft plaque score) (Min - [0249] “In some embodiments, the system is configured to utilize an AI, ML, and/or other algorithm to characterize the change in calcium score based on one or more plaque parameters derived from a medical image.”).
Min in view of Sirohey does not specifically disclose weighting the non-calcium score 1 and the calcium score 0.
However, Nickisch teaches weighting the non-calcium score 1 and the calcium score 0 ([0002] “Fractional flow reserve (FFR) is an invasive measure in the catheterization laboratory (Cath Lab) to quantify, via an FFR index, the hemodynamic significance of a coronary lesion due to calcified or soft plaque…The FFR value is an absolute number between 0 and 1, where a value 0.50 indicates that a given stenosis causes a 50% drop in blood pressure.” wherein the non-calcium and calcium scores are FFR values; It is obvious to one of ordinary skill in the art to generate the non-calcium score as 1 and the calcium score as 0 to indicate no blockage vs significant blockage).
The motivation for combining Min, Sirohey, and Nickisch is the same motivation as used for claim 8.
Regarding claim 11, Min in view of Sirohey teaches the computer-implemented method of Claim 7, wherein the weighted measure of the non-calcium score and the calcium score is generated (Min - [0231] “In some embodiments, the system can be configured to compare the plaque parameters individually and/or combining one or more of them as a weighted measure.” wherein the scores can be calculated as a weighted measure) (Sirohey - [0035] “A report is generated that uses the original calcium score for reporting the calcified plaque content as well as a new measure that called the soft plaque score that calculates the mass and volume of the soft plaque to compute a soft plaque score.” wherein a non-calcium score is a soft plaque score) (Min - [0249] “In some embodiments, the system is configured to utilize an AI, ML, and/or other algorithm to characterize the change in calcium score based on one or more plaque parameters derived from a medical image.”).
Min in view of Sirohey does not specifically disclose weighting the non-calcium score 0 and the calcium score 1.
However, Nickisch teaches weighting the non-calcium score 0 and the calcium score 1 ([0002] “Fractional flow reserve (FFR) is an invasive measure in the catheterization laboratory (Cath Lab) to quantify, via an FFR index, the hemodynamic significance of a coronary lesion due to calcified or soft plaque…The FFR value is an absolute number between 0 and 1, where a value 0.50 indicates that a given stenosis causes a 50% drop in blood pressure.” wherein the non-calcium and calcium scores are FFR values; It is obvious to one of ordinary skill in the art to generate the non-calcium score as 0 and the calcium score as 1 to indicate significant blockage vs no blockage).
The motivation for combining Min, Sirohey, and Nickisch is the same motivation as used for claim 8.
Regarding claim 13, Min in view of Sirohey and Nickisch teaches the computer-implemented method of Claim 8, wherein the assessment of the state of cardiovascular disease of the subject is determined by comparing the generated weighted measure of the non-calcium score and the calcium score against a dataset of values of weighted measures of non-calcium score and calcium score for a population with varying states of cardiovascular disease (Min - [0216] “In some embodiments, the system can be configured to generate a risk assessment of coronary disease or cardiovascular event by comparing one or more vascular morphology parameters, one or more quantified plaque parameters, one or more quantified fat parameters, calculated stenosis, risk of ischemia, and/or CAD-RADS score of the subject to a database of known such parameters derived from medical images of other subjects, including for example healthy subjects and/or subjects at risk of a cardiovascular event.”) (Sirohey - [0035] “A report is generated that uses the original calcium score for reporting the calcified plaque content as well as a new measure that called the soft plaque score that calculates the mass and volume of the soft plaque to compute a soft plaque score.” wherein a non-calcium score is a soft plaque score) (Min - [0249] “In some embodiments, the system can be configured to characterize a change in calcium score by accessing known datasets of the same stored in a database.”) (Min - [0231] “In some embodiments, the system can be configured to compare the plaque parameters individually and/or combining one or more of them as a weighted measure.” wherein the scores can be calculated as a weighted measure).
The motivation for combining Min, Sirohey, and Nickisch is the same motivation as used for claim 8.
Regarding claim 16, Min in view of Sirohey teaches the computer-implemented method of Claim 14, further comprising tracking, by the computer system, efficacy of the treatment by determining assessment of the state of cardiovascular disease of the subject (Min - [0149] “In some embodiments, at block 214, the system is configured to generate a proposed treatment plan for the subject based on the analysis, such as for example the classification of plaque derived automatically from a raw medical image. In particular, in some embodiments, the system can be configured to assess or predict the risk of atherosclerosis, stenosis, and/or ischemia of the subject based on a raw medical image and automated image processing thereof.”).
Min in view of Sirohey does not specifically disclose tracking efficacy of the treatment by determining assessment of the state of cardiovascular disease of the subject at a later point in time after treatment.
However, Nickisch teaches ([0035] “Documented patient outcome such as cardiac events or survival data can be used in a similar way to adapt parameters taking the previously obtained model prediction and the medical treatment into account. For example, if a lesion was considered insignificant via a CT-FFR assessment and that lesion caused a major cardiac event, then the assessment can be reconsidered and parameters such as the FFR threshold can be updated to better match the outcome.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to track the efficacy of the treatment plan of Nickisch to confirm the effectiveness of the generated treatment plan, thereby improving the accuracy of the overall cardiovascular disease assessment of Min in view of Sirohey.
Regarding claim 21, the claim recites similar limitations to claim 8 but in the form of a system. Therefore, claim 21 recites similar limitations to claim 8 and is rejected for similar rationale and reasoning (see the analysis for claim 8 above).
Conclusion
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666