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 .
Claim Status
Applicant’s amendment filed on June 23, 2026 is acknowledged. Currently claims 1-7, 10-25, and 28-36 are pending. Claims 1, 10, 19, 22-24, and 28 have been amended. Claims 8-9 and 26-27 are cancelled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 25, 2026 is in compliance with the provisions of 27 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached by the examiner.
Response to Arguments
Regarding claims 1-7, 10-25, and 28-36, Applicant's arguments filed June 23, 2026, have been fully considered but they are not persuasive.
Applicant alleges that there is no established prima facie case of obviousness with respect to an least the normalizing limitation because combining imaging data with other cardiovascular disease phenotyping data, or weighting parameters, is not the same as “normalizing… the one or more relational plaque parameters for the subject by comparison to one or more physical properties of the subject, the one or more physical properties comprising one or more of body mass of the subject or left ventricular mass of the subject” as recited by amended claim 1. Applicant further states normalizing relational plaque parameters by comparison to body mass or left ventricular mass is a specific operation that can adjust plaque-derived parameters relative to a patient-specific physical characteristic of the subject.
The examiner respectfully disagrees. Applicant defines the process of “normalizing” in the context of the amended claims as normalizing relational plaque parameters by comparison to physical properties such as body mass or left ventricular mass, to adjust plaque-derived parameters relative to a patient-specific physical characteristic of the subject. Min teaches comparison to physical properties to adjust plaque-derived parameters relative to a patient-specific physical characteristic of the subject ([0167-0168] “In some embodiments, the imaging data of the coronary arteries can include measures of atherosclerosis, stenosis and vascular morphology. In some embodiments, this information can be combined with other cardiovascular disease phenotyping by quantitative characterization of left and right ventricles, left and right atria; aortic, mitral, tricuspid and pulmonic valves; aorta, pulmonary artery, pulmonary vein, coronary sinus and inferior and superior vena cava; epicardial or pericoronary fat; lung densities; bone densities; pericardium and others…In some embodiments, the imaging data may be combined with other data to identify areas within a coronary vessel that are normal and without plaque now but may be at higher likelihood of future plaque formation.” wherein the one or more physical properties are the imaging data and said imaging data can be combined with other data such as the one or more relational plaque parameters) ([0258] “In some embodiments, at block 420, the system can be configured to analyze one or more of the generated ratio of bad plaque to a vessel, whether by surface area or volume, total absolute volume of bad plaque, total absolute volume of plaque, blood chemistry and/or biomarker test results, and/or analysis results of one or more non-coronary cardiovascular system medical images to determine whether one or more of these parameters, either individually and/or combined, is above a predetermined threshold.” wherein one or more relational plaque parameters is the generated ratio of parameters). Min does not specifically disclose that one or more of the physical properties comprise body mass. However, Douthitt teaches one or more of the physical properties comprise body mass ([0035] “For example, in addition to a mapping (e.g., location information or topography) of the patient's anatomy, the imaging data can also include information related to any other discernable characteristic identified by the imaging technique. Specifically, the imaging data can include, inter alia, a degree of aortic angulation at the juxtarenal neck or other segment of the aorta; a degree, pattern, and location of atherosclerotic disease including plaque, calcification, and/or thrombus; morphometric characteristics of the vascular structure that influence size, position, angulation, or tortuosity such as vessel diameter (i.e., vascular lumen diameter); and/or vessel wall thickness, vessel length, location and number of branch arteries, and/or the like. In some embodiments, the electronic device can extract data associated with these characteristics and can store the extracted data in the memory (and/or a database). Moreover, the extracted data can be stored with and/or otherwise associated with other stored patient data and/or stored prosthetic data. For example, the electronic device can store anthropomorphic data of the patient such as body composition, body temperature, height, weight, body-mass index (BMI), abdominal circumference (absolute or normalized), age, and/or the like;”) ([0079] “For example, the data can be adjusted and/or updated based on patient data such as a degree of aortic angulation at the juxtarenal neck or other segment of the aorta; a degree, pattern, and location of atherosclerotic disease including plaque, calcification, and/or thrombus; morphometric characteristics of the vascular structure that influence size, position, angulation, or tortuosity such as vessel diameter (i.e., vascular lumen diameter); and/or vessel wall thickness, vessel length, location and number of branch arteries, and/or the like; anthropomorphic data of the patient such as body composition, body temperature, height, weight, BMI, abdominal circumference (absolute or normalized), age, and/or the like;” wherein imaging data can be adjusted or updated based on patient data such as body mas index).
Therefore, the examiner asserts that Min in view of Douthitt teaches normalizing, by the computer system, the one or more relational plaque parameters for the subject by comparison to one or more physical properties of the subject, the one or more physical properties comprising one or more of body mass of the subject or left ventricular mass of the subject (Min - [0167-0168] “In some embodiments, the imaging data of the coronary arteries can include measures of atherosclerosis, stenosis and vascular morphology. In some embodiments, this information can be combined with other cardiovascular disease phenotyping by quantitative characterization of left and right ventricles, left and right atria; aortic, mitral, tricuspid and pulmonic valves; aorta, pulmonary artery, pulmonary vein, coronary sinus and inferior and superior vena cava; epicardial or pericoronary fat; lung densities; bone densities; pericardium and others…In some embodiments, the imaging data may be combined with other data to identify areas within a coronary vessel that are normal and without plaque now but may be at higher likelihood of future plaque formation.” wherein the one or more physical properties are the imaging data and said imaging data can be combined with other data such as the one or more relational plaque parameters) (Min - [0258] “In some embodiments, at block 420, the system can be configured to analyze one or more of the generated ratio of bad plaque to a vessel, whether by surface area or volume, total absolute volume of bad plaque, total absolute volume of plaque, blood chemistry and/or biomarker test results, and/or analysis results of one or more non-coronary cardiovascular system medical images to determine whether one or more of these parameters, either individually and/or combined, is above a predetermined threshold.” wherein one or more relational plaque parameters is the generated ratio of parameters) (Douthitt - [0035] “For example, in addition to a mapping (e.g., location information or topography) of the patient's anatomy, the imaging data can also include information related to any other discernable characteristic identified by the imaging technique. Specifically, the imaging data can include, inter alia, a degree of aortic angulation at the juxtarenal neck or other segment of the aorta; a degree, pattern, and location of atherosclerotic disease including plaque, calcification, and/or thrombus; morphometric characteristics of the vascular structure that influence size, position, angulation, or tortuosity such as vessel diameter (i.e., vascular lumen diameter); and/or vessel wall thickness, vessel length, location and number of branch arteries, and/or the like. In some embodiments, the electronic device can extract data associated with these characteristics and can store the extracted data in the memory (and/or a database). Moreover, the extracted data can be stored with and/or otherwise associated with other stored patient data and/or stored prosthetic data. For example, the electronic device can store anthropomorphic data of the patient such as body composition, body temperature, height, weight, body-mass index (BMI), abdominal circumference (absolute or normalized), age, and/or the like;”) (Douthitt - [0079] “For example, the data can be adjusted and/or updated based on patient data such as a degree of aortic angulation at the juxtarenal neck or other segment of the aorta; a degree, pattern, and location of atherosclerotic disease including plaque, calcification, and/or thrombus; morphometric characteristics of the vascular structure that influence size, position, angulation, or tortuosity such as vessel diameter (i.e., vascular lumen diameter); and/or vessel wall thickness, vessel length, location and number of branch arteries, and/or the like; anthropomorphic data of the patient such as body composition, body temperature, height, weight, BMI, abdominal circumference (absolute or normalized), age, and/or the like;” wherein imaging data can be adjusted or updated based on patient data such as body mas index).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include body mass of Douthitt in the physical properties of Min to elucidate a correlation between body mass and the accumulation of vascular plaque as well as to remove any confounding effects of body size when analyzing plaque characteristics which would aid in improving the accuracy of the overall cardiovascular disease assessment of Min.
The applicant additionally alleges that the examiner’s mapping of the “vessel” threshold limitation to Sethuraman is deficient because Sethuraman’s threshold is not applied to “the one or more arteries identified from the medical image to determine one or more arterial sections of interest above the vessel threshold” for downstream analysis of plaque parameters and cardiovascular disease assessment as recited by amended claim 1. Furthermore, the applicant states that the motivation used in the Office Action is unsupported by Sethuraman never mentions plaque analysis, plaque parameters, or cardiovascular disease assessment.
The examiner respectfully disagrees. Min teaches the one or more arteries identified from the medical image to determine one or more arterial sections of interest ([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.”). However, the amended claim 1 features a vessel threshold that is applied to the medical images of Min, to determine which of the one or more arterial sections of interest of Min, are above said vessel threshold. Min does not specifically disclose said vessel threshold, however, Sethuraman teaches a vessel threshold ([0054] “Additionally or alternatively to being used for the indication of quality, the determination may be used to provide suggestions to the user for improving the acquisition. For example, if a vessel diameter and/or beam density within the vessel is below a threshold value, the ultrasound system, such as system 100, may prompt the user to select a different vessel and/or imaging settings (e.g., increase beam density) to acquire flow measurements.” wherein a vessel threshold is a vessel diameter below a threshold value). Sethuraman merely cures the deficiency of Min and is used to teach a vessel threshold that is applied to identify more specific arterial sections of interest of Min, based upon their vessel diameter, to then initiate the downstream analysis of Min.
Sethuraman teaches the use of a vessel threshold to identify vessels for downstream image analysis. One of ordinary skill in the art would reasonably understand to apply a vessel threshold used for Doppler ultrasound imaging of Sethuraman to the one or more arteries in medical images of Min because vessel thresholds in Doppler ultrasound imaging can aid in filtering out false positives and improve the accuracy of image analysis. Therefore, applying said vessel threshold of Sethuraman to the medical images of Min identifies representative arterial areas of interest for more accurate downstream plaque image analysis.
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, 10-25, and 28-36 are rejected under 35 U.S.C. 103 as being unpatentable of Min et al., US 20210209757 A1, (hereinafter “Min”) in view of Sethuraman et al., US 20240164756 A1, (hereinafter “Sethuraman”) in further view of Douthitt et al., US 20170027683 A1, (hereinafter “Douthitt”).
Regarding claim 1, Min teaches a computer-implemented method of assessing a state of cardiovascular disease of a subject based on one or more normalized relational plaque parameters 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 ([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.”);
applying, by the computer system, ([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 arterial sections of interest, wherein the one or more vascular parameters comprise one or more of vessel 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, one or more regions of plaque in the arterial sections of interest ([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 plaque parameters associated with the one or more regions of plaque, wherein the one or more plaque parameters comprise one or more of plaque density, composition, calcification, radiodensity, location, volume, surface area, geometry, heterogeneity, diffusivity, or ratio between volume and surface area ([0210] “In some embodiments, the system can be configured to determine one or more vascular morphology and/or quantified plaque parameters at block 208. 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.”);
determining, by the computer system, one or more relational plaque parameters for the subject, the one or more relational plaque parameters determined by comparing the one or more plaque parameters to the one or more vascular parameters ([0258] “In some embodiments, at block 420, the system can be configured to analyze one or more of the generated ratio of bad plaque to a vessel, whether by surface area or volume, total absolute volume of bad plaque, total absolute volume of plaque, blood chemistry and/or biomarker test results, and/or analysis results of one or more non-coronary cardiovascular system medical images to determine whether one or more of these parameters, either individually and/or combined, is above a predetermined threshold.” wherein one or more relational plaque parameters is the generated ratio of parameters);
normalizing, by the computer system, the one or more relational plaque parameters for the subject by comparison to one or more physical properties of the subject, the one or more physical properties comprising one or more of body mass of the subject or left ventricular mass of the subject ([0167-0168] “In some embodiments, the imaging data of the coronary arteries can include measures of atherosclerosis, stenosis and vascular morphology. In some embodiments, this information can be combined with other cardiovascular disease phenotyping by quantitative characterization of left and right ventricles, left and right atria; aortic, mitral, tricuspid and pulmonic valves; aorta, pulmonary artery, pulmonary vein, coronary sinus and inferior and superior vena cava; epicardial or pericoronary fat; lung densities; bone densities; pericardium and others…In some embodiments, the imaging data may be combined with other data to identify areas within a coronary vessel that are normal and without plaque now but may be at higher likelihood of future plaque formation.” wherein the one or more physical properties are the imaging data and said imaging data can be combined with other data such as the one or more relational plaque parameters) ([0258] “In some embodiments, at block 420, the system can be configured to analyze one or more of the generated ratio of bad plaque to a vessel, whether by surface area or volume, total absolute volume of bad plaque, total absolute volume of plaque, blood chemistry and/or biomarker test results, and/or analysis results of one or more non-coronary cardiovascular system medical images to determine whether one or more of these parameters, either individually and/or combined, is above a predetermined threshold.” wherein one or more relational plaque parameters is the generated ratio of parameters);
analyzing, by the computer system, the normalized one or more relational plaque parameters for the subject by comparison to a dataset of values, the values comprising a plurality of normalized one or more relational plaque parameters derived from applying ([0258] “In some embodiments, at block 420, the system can be configured to analyze one or more of the generated ratio of bad plaque to a vessel, whether by surface area or volume, total absolute volume of bad plaque, total absolute volume of plaque, blood chemistry and/or biomarker test results, and/or analysis results of one or more non-coronary cardiovascular system medical images to determine whether one or more of these parameters, either individually and/or combined, is above a predetermined threshold. For example, in some embodiments, the system can be configured to analyze one or more of the foregoing parameters individually by comparing them to one or more reference values of healthy subjects and/or subjects at risk of a cardiovascular event. In some embodiments, the system can be configured to analyze a combination, such as a weighted measure, of one or more of the foregoing parameters by comparing the combined or weighted measure thereof to one or more reference values of healthy subjects and/or subjects at risk of a cardiovascular event.” wherein one or more relational plaque parameters is the generated ratio of parameters and normalizing is weighting); and
determining, by the computer system, an assessment of a state of cardiovascular disease of the subject based at least in part on analysis of the normalized one or more relational plaque parameters for 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.”) ([0258] “In some embodiments, at block 420, the system can be configured to analyze one or more of the generated ratio of bad plaque to a vessel, whether by surface area or volume, total absolute volume of bad plaque, total absolute volume of plaque, blood chemistry and/or biomarker test results, and/or analysis results of one or more non-coronary cardiovascular system medical images to determine whether one or more of these parameters, either individually and/or combined, is above a predetermined threshold. For example, in some embodiments, the system can be configured to analyze one or more of the foregoing parameters individually by comparing them to one or more reference values of healthy subjects and/or subjects at risk of a cardiovascular event. In some embodiments, the system can be configured to analyze a combination, such as a weighted measure, of one or more of the foregoing parameters by comparing the combined or weighted measure thereof to one or more reference values of healthy subjects and/or subjects at risk of a cardiovascular event.” wherein one or more relational plaque parameters is the generated ratio of parameters and normalizing is weighting),
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 vessel threshold.
However, Sethuraman teaches a vessel threshold ([0054] “Additionally or alternatively to being used for the indication of quality, the determination may be used to provide suggestions to the user for improving the acquisition. For example, if a vessel diameter and/or beam density within the vessel is below a threshold value, the ultrasound system, such as system 100, may prompt the user to select a different vessel and/or imaging settings (e.g., increase beam density) to acquire flow measurements.” wherein a vessel threshold is a vessel diameter below a threshold value).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a vessel threshold of Sethuraman in the cardiovascular disease assessment of Min to because the diameter of a vessel directly correlates to plaque data. Therefore, a vessel threshold aids in identifying vessels of interest that are relevant to plaque analysis.
Min in view of Sethuraman does not specifically disclose body mass of the subject.
However, Douthitt teaches body mass of the subject ([0035] “For example, in addition to a mapping (e.g., location information or topography) of the patient's anatomy, the imaging data can also include information related to any other discernable characteristic identified by the imaging technique. Specifically, the imaging data can include, inter alia, a degree of aortic angulation at the juxtarenal neck or other segment of the aorta; a degree, pattern, and location of atherosclerotic disease including plaque, calcification, and/or thrombus; morphometric characteristics of the vascular structure that influence size, position, angulation, or tortuosity such as vessel diameter (i.e., vascular lumen diameter); and/or vessel wall thickness, vessel length, location and number of branch arteries, and/or the like. In some embodiments, the electronic device can extract data associated with these characteristics and can store the extracted data in the memory (and/or a database). Moreover, the extracted data can be stored with and/or otherwise associated with other stored patient data and/or stored prosthetic data. For example, the electronic device can store anthropomorphic data of the patient such as body composition, body temperature, height, weight, body-mass index (BMI), abdominal circumference (absolute or normalized), age, and/or the like;”) ([0079] “For example, the data can be adjusted and/or updated based on patient data such as a degree of aortic angulation at the juxtarenal neck or other segment of the aorta; a degree, pattern, and location of atherosclerotic disease including plaque, calcification, and/or thrombus; morphometric characteristics of the vascular structure that influence size, position, angulation, or tortuosity such as vessel diameter (i.e., vascular lumen diameter); and/or vessel wall thickness, vessel length, location and number of branch arteries, and/or the like; anthropomorphic data of the patient such as body composition, body temperature, height, weight, BMI, abdominal circumference (absolute or normalized), age, and/or the like;” wherein imaging data can be adjusted or updated based on patient data such as body mas index).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include body mass of Douthitt in the physical properties of Min in view of Sethuraman to elucidate a correlation between body mass and the accumulation of vascular plaque which would aid in improving the accuracy of the overall cardiovascular disease assessment of Min in view of Sethuraman.
Regarding claim 2, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 1, wherein the vessel threshold comprises about 2.0 mm in diameter (Sethuraman - [0054] “Additionally or alternatively to being used for the indication of quality, the determination may be used to provide suggestions to the user for improving the acquisition. For example, if a vessel diameter and/or beam density within the vessel is below a threshold value, the ultrasound system, such as system 100, may prompt the user to select a different vessel and/or imaging settings (e.g., increase beam density) to acquire flow measurements.” wherein a vessel threshold is a vessel diameter below a threshold value) (Min - [Table US 00005] “Calcified plaques are identified in each coronary artery ≥1.5 mm in mean vessel diameter” wherein about 2.0 mm in diameter is >1.5 mm in mean vessel diameter).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 3, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 1, wherein the one or more relational plaque parameters comprises percent atheroma volume (PAV) (Min - [0258] “In some embodiments, at block 420, the system can be configured to analyze one or more of the generated ratio of bad plaque to a vessel, whether by surface area or volume, total absolute volume of bad plaque, total absolute volume of plaque, blood chemistry and/or biomarker test results, and/or analysis results of one or more non-coronary cardiovascular system medical images to determine whether one or more of these parameters, either individually and/or combined, is above a predetermined threshold.” wherein one or more relational plaque parameters is the generated ratio of parameters) (Min - [0232] “In some embodiments, the state of plaque progression as determined by the system can include one of four categories, including rapid plaque progression, non-rapid calcium dominant mixed response, non-rapid non-calcium dominant mixed response, or plaque regression. In some embodiments, the system is configured to classify the state of plaque progression as rapid plaque progression when a percent atheroma volume increase of the subject is more than 1% per year.”).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 4, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 3, wherein the PAV comprises total plaque volume over vessel volume of the one or more arterial sections of interest (Min - [0121] “For example, in some embodiments, the system can provide a specific numerical value for the volume of stable and/or unstable plaque, the ratio thereof against the total vessel volume, percentage of stenosis, and/or the like, using for example radiodensity values of pixels and/or regions within a medical image.” wherein the PAV is the a specific numerical value) (Min - [0232] “In some embodiments, the state of plaque progression as determined by the system can include one of four categories, including rapid plaque progression, non-rapid calcium dominant mixed response, non-rapid non-calcium dominant mixed response, or plaque regression. In some embodiments, the system is configured to classify the state of plaque progression as rapid plaque progression when a percent atheroma volume increase of the subject is more than 1% per year.”).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 5, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 3, wherein the PAV comprises non-calcified plaque volume over vessel volume of the one or more arterial sections of interest (Min - [0121] “For example, in some embodiments, the system can provide a specific numerical value for the volume of stable and/or unstable plaque, the ratio thereof against the total vessel volume, percentage of stenosis, and/or the like, using for example radiodensity values of pixels and/or regions within a medical image.” wherein the PAV is the a specific numerical value) (Min - [0232] “In some embodiments, the state of plaque progression as determined by the system can include one of four categories, including rapid plaque progression, non-rapid calcium dominant mixed response, non-rapid non-calcium dominant mixed response, or plaque regression. In some embodiments, the system is configured to classify the state of plaque progression as rapid plaque progression when a percent atheroma volume increase of the subject is more than 1% per year.”).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 6, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 3, wherein the PAV comprises low-density non-calcified plaque volume over vessel volume of the one or more arterial sections of interest (Min - [0232] “In some embodiments, the system is configured to classify the state of plaque progression as non-rapid non-calcium dominant mixed response when a percent atheroma volume increase of the subject is less than 1% per year and non-calcified plaque represents more than 50% of total new plaque formation.” wherein non-calcified plaque volume over vessel volume is non-rapid non-calcium dominant plaque).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 7, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 1, wherein the vessel threshold comprises about 2.0 mm in diameter, and wherein the one or more relational plaque parameters comprises percent atheroma volume (PAV) (Sethuraman - [0054] “Additionally or alternatively to being used for the indication of quality, the determination may be used to provide suggestions to the user for improving the acquisition. For example, if a vessel diameter and/or beam density within the vessel is below a threshold value, the ultrasound system, such as system 100, may prompt the user to select a different vessel and/or imaging settings (e.g., increase beam density) to acquire flow measurements.” wherein a vessel threshold is a vessel diameter below a threshold value) (Min - [Table US 00005] “Calcified plaques are identified in each coronary artery ≥1.5 mm in mean vessel diameter” wherein about 2.0 mm in diameter is >1.5 mm in mean vessel diameter) (Min - [0258] “In some embodiments, at block 420, the system can be configured to analyze one or more of the generated ratio of bad plaque to a vessel, whether by surface area or volume, total absolute volume of bad plaque, total absolute volume of plaque, blood chemistry and/or biomarker test results, and/or analysis results of one or more non-coronary cardiovascular system medical images to determine whether one or more of these parameters, either individually and/or combined, is above a predetermined threshold.” wherein one or more relational plaque parameters is the generated ratio of parameters) (Min - [0232] “In some embodiments, the state of plaque progression as determined by the system can include one of four categories, including rapid plaque progression, non-rapid calcium dominant mixed response, non-rapid non-calcium dominant mixed response, or plaque regression. In some embodiments, the system is configured to classify the state of plaque progression as rapid plaque progression when a percent atheroma volume increase of the subject is more than 1% per year.”).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 10, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 9, wherein the left ventricular mass of the subject is determined based at least in part on the medical image of the subject (Min - [0179] “In some embodiments, at block 310, the system can be configured to further identify one or more other cardiovascular structures from the medical image and/or determine one or more parameters associated with the same. For example, the one or more additional cardiovascular structures can include the left ventricle, right ventricle, left atrium, right atrium, aortic valve, mitral valve, tricuspid valve, pulmonic valve, aorta, pulmonary artery, inferior and superior vena cava, epicardial fat, and/or pericardium.”) (Min - [0180] “In some embodiments, parameters associated with the left ventricle can include size, mass, volume, shape, eccentricity, surface area, thickness, and/or the like.”).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 11, Min in view of Sethuraman and Douthitt 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, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 12, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 1, wherein the vessel threshold varies by vessel (Sethuraman - [0054] “Additionally or alternatively to being used for the indication of quality, the determination may be used to provide suggestions to the user for improving the acquisition. For example, if a vessel diameter and/or beam density within the vessel is below a threshold value, the ultrasound system, such as system 100, may prompt the user to select a different vessel and/or imaging settings (e.g., increase beam density) to acquire flow measurements.” wherein a vessel threshold is a vessel diameter below a threshold value).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 13, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 1, wherein the vessel threshold is determined based at least in part on quality of the medical image (Sethuraman - [0054] “Additionally or alternatively to being used for the indication of quality, the determination may be used to provide suggestions to the user for improving the acquisition. For example, if a vessel diameter and/or beam density within the vessel is below a threshold value, the ultrasound system, such as system 100, may prompt the user to select a different vessel and/or imaging settings (e.g., increase beam density) to acquire flow measurements.” wherein a vessel threshold is a vessel diameter below a threshold value).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 14, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 1, wherein the assessment of the state of cardiovascular disease of the subject is further determined based at least in part on the one or more vascular parameters or the one or more plaque parameters (Min - [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.”).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 15, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 1, further comprising:
generating, by the computer system, a weighted measure of one or more of the one or more vascular parameters, one or more plaque parameters, or one or more relational plaque parameters (Min - [0197] “In some embodiments, the system can be configured to generate a weighted measure of one or more vascular morphology parameters and/or quantified plaque parameters determined and/or derived from raw medical images.”),
wherein the assessment of the state of cardiovascular disease of the subject is further determined based at least in part on the generated weighted measure (Min - [0215] “In some embodiments, the system can be configured weight one or more of these parameters logarithmically, algebraically, and/or utilizing another mathematical transform. 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.”).
The motivation for combining Min, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 16, Min in view of Sethuraman and Douthitt 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, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 17, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 16, 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, Sethuraman, and Douthitt is the same motivation as used for claim 1.
Regarding claim 19, the claim recites similar limitations to claim 1 but in the form of a system. Therefore, claim 19 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 20, the claim recites similar limitations to claim 2 but in the form of a system. Therefore, claim 20 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 21, the claim recites similar limitations to claim 3 but in the form of a system. Therefore, claim 21 recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above).
Regarding claim 22, the claim recites similar limitations to claim 4 but in the form of a system. Therefore, claim 22 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above).
Regarding claim 23, the claim recites similar limitations to claim 5 but in the form of a system. Therefore, claim 23 recites similar limitations to claim 5 and is rejected for similar rationale and reasoning (see the analysis for claim 5 above).
Regarding claim 24, the claim recites similar limitations to claim 6 but in the form of a system. Therefore, claim 24 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 25, the claim recites similar limitations to claim 7 but in the form of a system. Therefore, claim 25 recites similar limitations to claim 7 and is rejected for similar rationale and reasoning (see the analysis for claim 7 above).
Regarding claim 28, the claim recites similar limitations to claim 10 but in the form of a system. Therefore, claim 28 recites similar limitations to claim 10 and is rejected for similar rationale and reasoning (see the analysis for claim 10 above).
Regarding claim 29, the claim recites similar limitations to claim 11 but in the form of a system. Therefore, claim 29 recites similar limitations to claim 11 and is rejected for similar rationale and reasoning (see the analysis for claim 11 above).
Regarding claim 30, the claim recites similar limitations to claim 12 but in the form of a system. Therefore, claim 30 recites similar limitations to claim 12 and is rejected for similar rationale and reasoning (see the analysis for claim 12 above).
Regarding claim 31, the claim recites similar limitations to claim 13 but in the form of a system. Therefore, claim 31 recites similar limitations to claim 13 and is rejected for similar rationale and reasoning (see the analysis for claim 13 above).
Regarding claim 32, the claim recites similar limitations to claim 14 but in the form of a system. Therefore, claim 32 recites similar limitations to claim 14 and is rejected for similar rationale and reasoning (see the analysis for claim 14 above).
Regarding claim 33, the claim recites similar limitations to claim 15 but in the form of a system. Therefore, claim 33 recites similar limitations to claim 15 and is rejected for similar rationale and reasoning (see the analysis for claim 15 above).
Regarding claim 34, the claim recites similar limitations to claim 16 but in the form of a system. Therefore, claim 34 recites similar limitations to claim 16 and is rejected for similar rationale and reasoning (see the analysis for claim 16 above).
Regarding claim 35, the claim recites similar limitations to claim 17 but in the form of a system. Therefore, claim 35 recites similar limitations to claim 17 and is rejected for similar rationale and reasoning (see the analysis for claim 17 above).
Claims 18 and 36 are rejected under 35 U.S.C. 103 as being unpatentable of Min et al., US 20210209757 A1, (hereinafter “Min”) in view of Sethuraman et al., US 20240164756 A1, (hereinafter “Sethuraman”) in further view of Nickisch et al., US 20210110543 A1, (hereinafter “Nickisch”).
Regarding claim 18, Min in view of Sethuraman and Douthitt teaches the computer-implemented method of Claim 16, further comprising tracking, by the computer system, (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.”).
Min in view of Sethuraman and Douthitt and does not specifically disclose tracking efficacy of the treatment at a later point in time after treatment.
However, Nickisch teaches tracking efficacy of the treatment at a later point in time after treatment ([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 Sethuraman and Douthitt.
Regarding claim 36, the claim recites similar limitations to claim 18 but in the form of a system. Therefore, claim 36 recites similar limitations to claim 18 and is rejected for similar rationale and reasoning (see the analysis for claim 18 above).
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
