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 December 30, 2024 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 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 Nishioka et al., US 20200155094 A1, (hereinafter “Nishioka”).
Regarding claim 1, Min teaches a computer-implemented method of determination of aortic stenosis for a subject based at least in part on one or more plaque parameters and one or more aortic leaflet parameters derived from medical image analysis, the method comprising:
accessing, by the computer system, a first medical image of a subject obtained at a first point in time, the first medical image comprising a portion of an aortic valve of the subject at the first point in time ([0225] “As illustrated in FIG. 3D, in some embodiments, the system at block 372 is configured to access a first set of plaque parameters derived from a medical image of a subject at a first point in time.” wherein a first medical image is a medical image at a first point in time) ([0179] “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.”);
analyzing, by the computer system, the first medical image of the subject to identify one or more aortic leaflets of the aortic valve of the subject using image segmentation ([0225] “As illustrated in FIG. 3D, in some embodiments, the system at block 372 is configured to access a first set of plaque parameters derived from a medical image of a subject at a first point in time.” wherein a first medical image is a medical image at a first point in time) ([0181] “Further, in some embodiments, parameters associated with the aortic valve can include thickness, volume, mass, calcifications, three-dimensional map of calcifications and density, eccentricity of calcification, classification by individual leaflet, and/or the like.”) ([0168] “Sequentially, in some embodiments, the algorithms that allow for segmentation of atherosclerosis, stenosis and vascular morphology—along with those that allow for segmentation of other cardiovascular structures, and thoracic structures—may serve as the inputs for the prognostic algorithms.”);
identifying, by the computer system, one or more regions of plaque within the one or more aortic leaflets ([0181] “Further, in some embodiments, parameters associated with the aortic valve can include thickness, volume, mass, calcifications, three-dimensional map of calcifications and density, eccentricity of calcification, classification by individual leaflet, and/or the like.” wherein parameters associated with the aortic valve pertain to plaque parameters);
generating, by the computer system, one or more plaque parameters of the one or more regions of plaque identified within the one or more aortic leaflets, the one or more plaque parameters comprising one or more of total plaque volume, low-density non-calcified plaque volume, non-calcified plaque volume, calcified plaque volume, proximity of plaque to the one or more aortic leaflets or plaque morphology ([0181] “Further, in some embodiments, parameters associated with the aortic valve can include thickness, volume, mass, calcifications, three-dimensional map of calcifications and density, eccentricity of calcification, classification by individual leaflet, and/or the like.” wherein parameters associated with the aortic valve pertain to plaque parameters) ([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.”);
generating, by the computer system, a preliminary risk assessment of aortic stenosis of the subject based at least in part on the one or more plaque parameters ([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 a preliminary risk assessment is a risk assessment based on calculated stenosis) ([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.”) ([0220] “For example, in some embodiments, the system can be configured to track the progression and/or regression of a disease by automatically and/or dynamically analyzing a plurality of medical images obtained from different times using one or more techniques discussed herein and comparing different parameters derived therefrom. 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.”),
wherein the preliminary risk assessment of aortic stenosis ([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 a preliminary risk assessment is a risk assessment based on calculated stenosis),
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:
one or more aortic leaflet parameters of the one or more aortic leaflets, the one or more aortic leaflet parameters comprising one or more of a gap between the one or more aortic leaflets or gradient of a boundary of the one or more aortic leaflets; and
a predetermined threshold is indicative of further assessment of aortic stenosis for the subject.
However, Nishioka teaches:
one or more aortic leaflet parameters of the one or more aortic leaflets, the one or more aortic leaflet parameters comprising one or more of a gap between the one or more aortic leaflets or gradient of a boundary of the one or more aortic leaflets ([0078] “When the reference surface 50 and the reference surface 51 are set, the setting function 353 sets a boundary line between two adjacent valve leaflets. The boundary line, for example, indicates the boundary between the two adjacent valve leaflets.” wherein one or more aortic leaflet parameters is the boundary line); and
a predetermined threshold is indicative of further assessment of aortic stenosis for the subject ([0129] “Therefore, according to the medical image processing apparatus 300, when the threshold value is made larger than 0, it is possible to allow the user to easily understand the contact region between the valve leaflet 77a and the valve leaflet 77b, which is considered to be close to the state of being separated as well as the separation region where the valve leaflet 77a and the valve leaflet 77b are separated from each other.” wherein the predetermined threshold is 0).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use aortic leaflet parameters of Nishioka in the aortic stenosis analysis method of Min because a narrow gap between the leaflets reduces blood flow, causing aortic stenosis. Therefore, using aortic leaflet parameters aids in more accurately analyzing and calculating the risk of aortic stenosis.
Regarding claim 3, Min in view of Nishioka teaches the computer-implemented method of claim 2, further comprising:
accessing, by the computer system, a second medical image of the subject obtained at a second point in time when the preliminary risk assessment of aortic stenosis is above the predetermined threshold, the second medical image comprising a portion of the aortic valve of the subject at the second point in time (Min - [0243] “In particular, in some embodiments, at block 390, the system can be configured to dynamically and/or automatically derive a second set of plaque parameters from the second medical image taken from the second point in time.”) (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.” wherein a preliminary risk assessment is a risk assessment based on calculated stenosis) (Nishioka - [0129] “Therefore, according to the medical image processing apparatus 300, when the threshold value is made larger than 0, it is possible to allow the user to easily understand the contact region between the valve leaflet 77a and the valve leaflet 77b, which is considered to be close to the state of being separated as well as the separation region where the valve leaflet 77a and the valve leaflet 77b are separated from each other.” wherein the predetermined threshold is 0) (Min - [0179] “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.”);
analyzing, by the computer system, the second medical image of the subject to identify one or more aortic leaflets of the aortic valve of the subject using image segmentation (Min - [0243] “In particular, in some embodiments, at block 390, the system can be configured to dynamically and/or automatically derive a second set of plaque parameters from the second medical image taken from the second point in time.”) (Min - [0181] “Further, in some embodiments, parameters associated with the aortic valve can include thickness, volume, mass, calcifications, three-dimensional map of calcifications and density, eccentricity of calcification, classification by individual leaflet, and/or the like.”) (Min - [0168] “Sequentially, in some embodiments, the algorithms that allow for segmentation of atherosclerosis, stenosis and vascular morphology—along with those that allow for segmentation of other cardiovascular structures, and thoracic structures—may serve as the inputs for the prognostic algorithms.”); and
determining, by the computer system, a gap between the one or more aortic leaflets at the second point in time, wherein the gap between the one or more aortic leaflets at the second point in time being below a predetermined threshold is indicative of aortic stenosis (Nishioka - [0084] “On the other hand, as illustrated in FIG. 11, when the aortic valve 60 is not completely closed, the setting function 353 calculates a center of gravity 67 of a region surrounded by the three valve leaflets 60a to 60c on the reference surface 50. The region surrounded by the valve leaflets 60a to 60c is a non-contact region where the valve leaflets 60a to 60c do not contact with one another and a non-contact region where the valve leaflets 60a to 60c do not exist.” wherein a second point in time is when the aortic valve is not completely closed) (Nishioka - [0124] “In the graph 82a, a threshold value 98 for determining that the valve leaflet 83c and the valve leaflet 83a are separated from each other is set to 1 mm.” wherein the predetermined threshold is 1 mm).
The motivation for combining Min and Nishioka is the same motivation as used for claim 2.
Regarding claim 4, Min in view of Nishioka teaches the computer-implemented method of claim 3, further comprising: generating, by the computer system, a risk level of aortic stenosis for the subject based at least on comparing the determined gap between the one or more aortic leaflets at the second point in time to a plurality of reference values of gaps between one or more aortic leaflets generated from a plurality of other subjects with varying levels of aortic stenosis (Min - [0710] “The system of embodiment 234, wherein the one or more computer hardware processors are further configured to execute the computer-executable instructions to generate and display on the user interface a cartoon artery tree, the cartoon artery tree being a non-patient specific graphical representation of an artery tree, wherein portions of the artery tree are displayed in a color that corresponds to a risk level…[0711] “The system of embodiment 255, wherein the risk level is based on stenosis.”) (Nishioka - [0084] “On the other hand, as illustrated in FIG. 11, when the aortic valve 60 is not completely closed, the setting function 353 calculates a center of gravity 67 of a region surrounded by the three valve leaflets 60a to 60c on the reference surface 50. The region surrounded by the valve leaflets 60a to 60c is a non-contact region where the valve leaflets 60a to 60c do not contact with one another and a non-contact region where the valve leaflets 60a to 60c do not exist.” wherein a second point in time is when the aortic valve is not completely closed) (Nishioka - [0075] “Furthermore, the setting function 353 sets the reference surface 51 for each combination of two adjacent valve leaflets.” wherein reference values of gaps are reference surfaces for two adjacent valve leaflets) (Min - [0220] “For example, in some embodiments, the system can be configured to track the progression and/or regression of a disease by automatically and/or dynamically analyzing a plurality of medical images obtained from different times using one or more techniques discussed herein and comparing different parameters derived therefrom. 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.” wherein a plurality of other subjects with varying levels is a plurality of medical images obtained from different times using one or more techniques).
The motivation for combining Min and Nishioka is the same motivation as used for claim 2.
Regarding claim 5, Min in view of Nishioka teaches the computer-implemented method of claim 3, further comprising: generating, by the computer system, an assessment of risk of coronary artery disease (CAD) or major adverse cardiovascular event (MACE) of the subject based at least in part on the gap between the one or more aortic leaflets at the second point in time (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.”) (Nishioka - [0084] “On the other hand, as illustrated in FIG. 11, when the aortic valve 60 is not completely closed, the setting function 353 calculates a center of gravity 67 of a region surrounded by the three valve leaflets 60a to 60c on the reference surface 50. The region surrounded by the valve leaflets 60a to 60c is a non-contact region where the valve leaflets 60a to 60c do not contact with one another and a non-contact region where the valve leaflets 60a to 60c do not exist.” wherein a second point in time is when the aortic valve is not completely closed).
The motivation for combining Min and Nishioka is the same motivation as used for claim 2.
Regarding claim 6, Min in view of Nishioka teaches the computer-implemented method of claim 3, wherein the first point in time and the second point in time comprise different points in a cardiac cycle of the subject (Min - [0302] “In some embodiments, the processed CT image data can visualize and compare the artery morphologies over time, i.e., throughout the cardiac cycle.” wherein the first point and second points are from artery morphologies over time) (Nishioka - [0081] “FIG. 10 and FIG. 11 illustrate the cases where the object to be processed is the aortic valve 60 composed of three valve leaflets 60a to 60c. FIG. 10 illustrates a case where the aortic valve 60 is completely closed and FIG. 11 illustrates a case where the aortic valve 60 is not completely closed.” wherein the first point is when the aortic valve is closed and the second point is when the aortic valve is not closed).
The motivation for combining Min and Nishioka is the same motivation as used for claim 2.
Regarding claim 7, Min in view of Nishioka teaches the computer-implemented method of claim 3, wherein the second medical image is obtained from an imaging modality comprising one or more of CT, x-ray, ultrasound, echocardiography, 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 - [0243] “In particular, in some embodiments, at block 390, the system can be configured to dynamically and/or automatically derive a second set of plaque parameters from the second medical image taken from the second point in time.”) (Min - [0386] “In addition to CT scanners, other types of medical imagers can also be used to capture medical images. These can include, for example, 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 Nishioka is the same motivation as used for claim 2.
Regarding claim 8, Min in view of Nishioka teaches the computer-implemented method of claim 2, further comprising: generating, by the computer system, a risk level of aortic stenosis for the subject based at least on comparing the preliminary risk assessment of aortic stenosis of the subject to a plurality of reference values of preliminary risk assessments of aortic stenosis generated from a plurality of other subjects with varying levels of aortic stenosis (Min - [0710] “The system of embodiment 234, wherein the one or more computer hardware processors are further configured to execute the computer-executable instructions to generate and display on the user interface a cartoon artery tree, the cartoon artery tree being a non-patient specific graphical representation of an artery tree, wherein portions of the artery tree are displayed in a color that corresponds to a risk level…[0711] “The system of embodiment 255, wherein the risk level is based on stenosis.”) (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.” wherein a preliminary risk assessment is a risk assessment based on calculated stenosis) (Nishioka - [0075] “Furthermore, the setting function 353 sets the reference surface 51 for each combination of two adjacent valve leaflets.” wherein reference values of gaps are reference surfaces for two adjacent valve leaflets) (Min - [0220] “For example, in some embodiments, the system can be configured to track the progression and/or regression of a disease by automatically and/or dynamically analyzing a plurality of medical images obtained from different times using one or more techniques discussed herein and comparing different parameters derived therefrom. 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.” wherein a plurality of other subjects with varying levels is a plurality of medical images obtained from different times using one or more techniques).
The motivation for combining Min and Nishioka is the same motivation as used for claim 2.
Regarding claim 9, Min in view of Nishioka teaches the computer-implemented method of claim 2, wherein one or more of low- density non-calcified plaque volume, non-calcified plaque volume, or calcified plaque volume is determined based at least in part on analyzing density of one or more pixels corresponding to the one or more regions of plaque in the first medical image (Min - [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.”) (Min - [0225] “As illustrated in FIG. 3D, in some embodiments, the system at block 372 is configured to access a first set of plaque parameters derived from a medical image of a subject at a first point in time.” wherein a first medical image is a medical image at a first point in time) (Min - [0391] “In some embodiments, the normalization device may have different known materials with different densities adjacent to each other (e.g., as described with reference to FIG. 12F). This configuration may address an issue present in some CT images where the density of a pixel influences the density of the adjacent pixels and that influence changes with the density of each of the individual pixel.”).
The motivation for combining Min and Nishioka is the same motivation as used for claim 2.
Regarding claim 10, Min in view of Nishioka teaches the computer-implemented method of claim 2, wherein the first medical image is obtained from an imaging modality comprising one or more of CT, x-ray, ultrasound, echocardiography, 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 - [0225] “As illustrated in FIG. 3D, in some embodiments, the system at block 372 is configured to access a first set of plaque parameters derived from a medical image of a subject at a first point in time.” wherein a first medical image is a medical image at a first point in time) (Min - [0386] “In addition to CT scanners, other types of medical imagers can also be used to capture medical images. These can include, for example, 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 Nishioka is the same motivation as used for claim 2.
Regarding claim 11, Min in view of Nishioka teaches the computer-implemented method of claim 2, wherein the aortic leaflets are in a closed configuration at the first point in time (Nishioka - [0082] “As illustrated in FIG. 10, when the aortic valve 60 is completely closed, the setting function 353 sets the boundary line 64 between the valve leaflet 60c and the valve leaflet 60a on the reference surface 50.” wherein a first point in time is when the aortic valve is closed).
The motivation for combining Min and Nishioka 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 (Min - [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 5 but in the form of a system. Therefore, claim 15 recites similar limitations to claim 5 and is rejected for similar rationale and reasoning (see the analysis for claim 5 above).
Regarding claim 16, the claim recites similar limitations to claim 6 but in the form of a system. Therefore, claim 16 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 17, the claim recites similar limitations to claim 2 but in the form of a non-transitory computer readable medium (Min - [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 5 but in the form of a non-transitory computer readable medium. Therefore, claim 20 recites similar limitations to claim 5 and is rejected for similar rationale and reasoning (see the analysis for claim 5 above).
Regarding claim 21, the claim recites similar limitations to claim 6 but in the form of a non-transitory computer readable medium. Therefore, claim 21 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Conclusion
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666