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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 21 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 21 recites “the computer readable medium” in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the computer readable medium” will be read as “the non-transitory computer readable medium”.
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., 20210209757 A1, (hereinafter “Min”) in view of Vaidya et al., US 20230071558 A1, (hereinafter “Vaidya”).
Regarding claim 2, Min teaches a computer-implemented method of determining a likelihood of vulnerable plaque features based at least in part on a plurality of variables derived from non-invasive medical image analysis, the computer-implemented 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.”);
generating, by the computer system, one or more quantified vascular parameters based at least in part on the identified one or more arteries ([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.”);
analyzing, by the computer system, the identified one or more arteries to identify one or more regions of plaque based at least in part on density ([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.”);
generating, by the computer system, one or more quantified plaque parameters based at least in part on the identified one or more regions of plaque ([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.”); and
wherein the ([0129] “In some embodiments, at block 112, the system can be further configured to analyze the identified vessels, coronary arteries, and/or plaque, for example using an AI and/or ML algorithm. 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. In some embodiments, the system can be configured to determine one or more plaque parameters, such as for example volume, surface area, geometry, radiodensity, ratio or function of volume to surface area, heterogeneity index, and/or the like of one or more regions of plaque shown within the medical image.”),
wherein the machine learning algorithm is trained using a dataset comprising one or more quantified vascular parameters and one or more quantified plaque parameters derived from a plurality of other medical images ([0129] “In some embodiments, at block 112, the system can be further configured to analyze the identified vessels, coronary arteries, and/or plaque, for example using an AI and/or ML algorithm. 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. In some embodiments, the system can be configured to determine one or more plaque parameters, such as for example volume, surface area, geometry, radiodensity, ratio or function of volume to surface area, heterogeneity index, and/or the like of one or more regions of plaque shown within the medical image.”), and
wherein the ([0223] “For example, for the non-calcified plaque-dominant mixed response, the non-calcified plaque can further include necrotic core, fibrofatty plaque and/or fibrous plaque as separate categories within the overall umbrella of non-calcified plaque. Similarly, calcified plaques can be categorized as lower density calcified plaques, medium density calcified plaques and high density calcified plaques.”),
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 determining a likelihood of presence of vulnerable plaque features for the one or more regions of plaque without direct identification of vulnerable plaque features from the medical image; and
using training data with known presence of absence of vulnerable plaque features for a region of plaque.
However, Vaidya teaches determining a likelihood of presence of vulnerable plaque features for the one or more regions of plaque without direct identification of vulnerable plaque features from the medical image ([0024] “In one embodiment, the assessment of the lesion is a classification of the lesion as being vulnerable (or high risk) plaque or non-vulnerable plaque. For example, the classification may be represented as a probability score indicating a probability that the plaque is vulnerable (or non-vulnerable) or indicating a degree of vulnerability.”); and
using training data with known presence of absence of vulnerable plaque features for a region of plaque ([0025] “The classifier network is trained during a prior offline or training stage using a set of training data. The training data is annotated with ground truths to identify the classification. The ground truth annotations may be based on clinical interpretation of medical images or follow-up data where certain plaque rupture events are used to identify specific positive examples.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine plaque vulnerability of Vaidya in the computer-based plaque risk analysis method of Min because determining vulnerable plaque aids in more accurately predicting acute cardiovascular events.
Regarding claim 3, Min in view of Vaidya teaches the computer-implemented method of Claim 2, wherein the one or more quantified plaque parameters comprises one or more of percent atheroma volume of total plaque, total plaque volume, percent atheroma volume of low-density non-calcified plaque, percent atheroma volume of non-calcified plaque, percent atheroma volume, low-density non-calcified plaque volume, percent atheroma volume of total calcified plaque, non-calcified plaque volume, total calcified plaque volume, percent atheroma volume of total non-calcified plaque, percent atheroma volume of low-density calcified plaque, percent atheroma volume of high-density calcified plaque, total non-calcified plaque volume, low-density calcified plaque volume, percent atheroma volume of medium-density calcified plaque, high-density calcified plaque volume, medium-density calcified plaque volume, number of high-risk plaque regions, number of segments with calcified plaque, number of segments with non-calcified plaque, plaque area, plaque burden, necrotic core percentage, necrotic core volume, fatty fibrous volume, fatty fibrous percentage, dense calcium percentage, presence of thin-cap fibroatheroma (TCFA), low-density calcium percentage, medium-density calcified percentage, high- density calcified percentage, presence of two-feature positive plaques, or number of two-feature positive plaques (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.”).
The motivation for combining Min and Vaidya is the same motivation as used for claim 2.
Regarding claim 4, Min in view of Vaidya teaches the computer-implemented method of Claim 2, wherein the one or more quantified vascular parameters comprises one or more of vessel length, segment length, lesion length, lumen volume, number of chronic total occlusion (CTO), vessel volume, number of stenosis, severity of stenosis, remodeling index, minimum lumen diameter, maximum lumen diameter, mean lumen diameter, stenosis area percentage, stenosis diameter percentage, number of mild stenosis, number of moderate stenosis, number of zero stenosis, number of severe stenosis, presence of high-risk anatomy, presence of positive remodeling, inflammation, macrophage infiltration, number of severe stenosis excluding CTO, vessel area, lumen area, diameter stenosis percentage, presence of ischemia, number of stents, reference lumen diameter before stenosis, perivascular fat attenuation, or reference lumen diameter after stenosis (Min - [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.”).
The motivation for combining Min and Vaidya is the same motivation as used for claim 2.
Regarding claim 5, Min in view of Vaidya teaches the computer-implemented method of Claim 2, wherein the determined likelihood of presence of vulnerable plaque features comprises a binary output of likelihood or unlikelihood of presence of vulnerable plaque features (Vaidya - [0024] “In one embodiment, the assessment of the lesion is a classification of the lesion as being vulnerable (or high risk) plaque or non-vulnerable plaque. For example, the classification may be represented as a probability score indicating a probability that the plaque is vulnerable (or non-vulnerable) or indicating a degree of vulnerability.”).
The motivation for combining Min and Vaidya is the same motivation as used for claim 2.
Regarding claim 6, Min in view of Vaidya teaches the computer-implemented method of Claim 2, further comprising determining a risk of artery disease for the subject based at least in part on the determined likelihood of presence of vulnerable plaque features (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.”) (Vaidya - [0024] “In one embodiment, the assessment of the lesion is a classification of the lesion as being vulnerable (or high risk) plaque or non-vulnerable plaque. For example, the classification may be represented as a probability score indicating a probability that the plaque is vulnerable (or non-vulnerable) or indicating a degree of vulnerability.”).
The motivation for combining Min and Vaidya is the same motivation as used for claim 2.
Regarding claim 7, Min in view of Vaidya teaches the computer-implemented method of Claim 6, wherein the artery disease comprises at least one of coronary artery disease (CAD) or peripheral artery disease (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 and Vaidya is the same motivation as used for claim 2.
Regarding claim 8, Min in view of Vaidya teaches the computer-implemented method of Claim 6, wherein the risk of artery disease for the subject is determined based at least in part on comparing the determined likelihood of presence of vulnerable plaque features against a dataset comprising varying risks of artery disease and known presence or absence of vulnerable plaque features derived from a reference population (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.”) (Vaidya - [0017] “Embodiments described herein provide for the fully automated assessment of coronary vulnerable plaque in coronary CT images. Radiomic features extracted inside a region of interest around coronary lesions in an input medical image are used to differential vulnerable (or high-risk) plaque from non-vulnerable plaque in coronary arteries.”).
The motivation for combining Min and Vaidya is the same motivation as used for claim 2.
Regarding claim 9, Min in view of Vaidya teaches the computer-implemented method of Claim 6, further comprising determining a proposed treatment for the subject based at least in part on the determined risk of artery disease (Min - [0130] “Further, in some embodiments, at block 114, the system can be configured to generate one or more treatment plans for the subject based on the analysis results. In some embodiments, the system can be configured to utilize one or more AI and/or ML algorithms to identify and/or analyze vessels or plaque, derive one or more quantification metrics and/or classifications, and/or generate a treatment plan.”).
The motivation for combining Min and Vaidya is the same motivation as used for claim 2.
Regarding claim 10, Min in view of Vaidya teaches the computer-implemented method of Claim 2, wherein the one or more regions of plaque comprise a necrotic core and non-calcified plaque (Min - [0223] “For example, for the non-calcified plaque-dominant mixed response, the non-calcified plaque can further include necrotic core, fibrofatty plaque and/or fibrous plaque as separate categories within the overall umbrella of non-calcified plaque. Similarly, calcified plaques can be categorized as lower density calcified plaques, medium density calcified plaques and high density calcified plaques.”).
The motivation for combining Min and Vaidya is the same motivation as used for claim 2.
Regarding claim 11, Min in view of Vaidya teaches the computer-implemented method of Claim 2, wherein the one or more regions of plaque comprise one or more of low density non-calcified plaque or non-calcified plaque (Min - [0195] “Types of atherosclerosis can be determined binarily (calcified vs. non-calcified plaque), ordinally (dense calcified plaque, calcified plaque, fibrous plaque, fibrofatty plaque, necrotic core, or admixtures of plaque types), or continuously (by attenuation density on a Hounsfield unit scale or similar).”).
The motivation for combining Min and Vaidya is the same motivation as used for claim 2.
Regarding claim 12, Min in view of Vaidya teaches the computer-implemented method of Claim 2, wherein the one or more arteries comprises one or more coronary arteries, carotid arteries, lower extremity arteries, upper extremity arteries, or aorta (Min - [0239] “In some embodiments, the medical image of the subject can comprise the coronary region, coronary arteries, carotid arteries, renal arteries, abdominal aorta, cerebral arteries, lower extremities, and/or upper extremities of the subject.”).
The motivation for combining Min and Vaidya is the same motivation as used for claim 2.
Regarding claim 13, Min in view of Vaidya teaches the computer-implemented method of Claim 2, wherein the one or more regions of plaque are identified as low density non-calcified plaque when a radiodensity value is between about -189 and about 30 Hounsfield units, wherein the one or more regions of plaque are identified as non-calcified plaque when a radiodensity value is between about 31 and about 350 Hounsfield units, and wherein the one or more regions of plaque are identified as calcified plaque when a radiodensity value is between about 351 and about 2500 Hounsfield units (Min - [0358] See below table).
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The motivation for combining Min and Vaidya is the same motivation as used for claim 2.
Regarding claim 14, Min in view of Vaidya teaches the computer-implemented method of Claim 2, wherein the medical image comprises a Computed Tomography (CT) image (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 Vaidya is the same motivation as used for claim 2.
Regarding claim 15, Min in view of Vaidya teaches the computer-implemented method of Claim 2, wherein the medical image is obtained using an imaging technique comprising one or more of CT, x-ray, ultrasound, echocardiography, 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 Vaidya is the same motivation as used for claim 2.
Regarding claim 16, 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 non-transitory computer storage medium, the one or more computer hardware processors configured to execute the computer-executable instructions 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 16 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 17, the claim recites similar limitations to claim 6 but in the form of a system. Therefore, claim 17 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 18, the claim recites similar limitations to claim 8 but in the form of a system. Therefore, claim 18 recites similar limitations to claim 8 and is rejected for similar rationale and reasoning (see the analysis for claim 8 above).
Regarding claim 19, the claim recites similar limitations to claim 9 but in the form of a system. Therefore, claim 19 recites similar limitations to claim 9 and is rejected for similar rationale and reasoning (see the analysis for claim 9 above).
Regarding claim 20, the claim recites similar limitations to claim 12 but in the form of a system. Therefore, claim 20 recites similar limitations to claim 12 and is rejected for similar rationale and reasoning (see the analysis for claim 12 above).
Regarding claim 21, the claim recites similar limitations to claim 2 but in the form of a non-transitory computer readable medium having program instructions for causing a hardware processor to perform a method of claim 2 (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 21 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Conclusion
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
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