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 Itu et al., US 20180336319 A1, (hereinafter “Itu”).
Regarding claim 2, Min teaches a computer-implemented method of predicting plaque progression based at least in part on a risk level of a region of plaque and a level of endothelial shear stress on the region of plaque determined based at least in part on a plurality of variables derived from non-invasive medical image analysis, the method comprising:
accessing, by a computer system, a medical image of a subject, the medical image comprising a portion of one or more arteries ([0207] “In some embodiments, at block 354, the system is configured to identify one or more arteries, plaque, and/or fat in the medical image, for example using AI, ML, and/or other algorithms.”);
analyzing, by the computer system, the medical image of the subject to identify one or more artery vessels and one or more regions of plaque within the one or more artery vessels ([0207] “In some embodiments, at block 354, the system is configured to identify one or more arteries, plaque, and/or fat in the medical image, for example using AI, ML, and/or other algorithms.”);
analyzing, by the computer system, the one or more artery vessels and the one or more regions of plaque to generate a plurality of variables, the plurality of variables comprising one or more of lesion length, remodeling index, plaque slice percentage, stenosis area percentage, presence of low-density plaque, stenosis diameter percentage, presence of positive remodeling, reference diameter after stenosis, reference diameter before stenosis, vessel length, lumen volume, number of chronic total occlusion (CTO), vessel volume, number of stenosis, total plaque volume, number of mild stenosis, low-density plaque volume, plaque morphology, embeddedness of a low density non- calcified plaque by non-calcified plaque or calcified plaque, distance between plaque and lumen wall or vessel wall, or eccentricity of plaque ([0312] “Features of embodiments of the system can include, for example, centerline and lumen/vessel extraction, plaque composition overlay, user identification of stenosis, vessel statistics calculated in real time, including vessel length, lesion length, vessel volume, lumen volume, plaque volume (non-calcified, calcified, low-density—non-calcified plaque and total), maximum remodeling index, and area/diameter stenosis (e.g., a percentage)”);
generating, by the computer system, a first weighted measure of the generated plurality of variables ([0616] “the first set of parameters obtained at a first point in time, wherein the first set of plaque parameters comprises volume, surface area, geometric shape, location, heterogeneity index, and radiodensity for one or more regions of plaque within the coronary region of the subject; generating, by the computer system, a first weighted measure of the accessed first set of plaque parameters;”);
determining, by the computer system, a risk level of a particular region of plaque of the one or more regions of plaque based at least in part on the first weighted measure of the generated plurality of variables ([0215] “In some embodiments, the system is configured to generate a risk assessment of coronary disease or cardiovascular event for the subject at block 366 using the weighted measure and/or using only some of these parameters.”) ([0215] “In some embodiments, the system can be configured to generate a weighted measure of one or more vascular morphology parameters, one or more quantified plaque parameters, one or more quantified fat parameters, calculated stenosis, risk of ischemia, and/or CAD-RADS score of the subject.”);
generating, by the computer system, a second weighted measure of the generated plurality of variables ([0616] “wherein the second set of plaque parameters comprises volume, surface area, geometric shape, location, heterogeneity index, and radiodensity for the one or more regions of plaque; generating, by the computer system, a second weighted measure of the determined second set of plaque parameters;”);
determining, by the computer system, ([0616] “wherein the second set of plaque parameters comprises volume, surface area, geometric shape, location, heterogeneity index, and radiodensity for the one or more regions of plaque; generating, by the computer system, a second weighted measure of the determined second set of plaque parameters;”); and
predicting, by the computer system, progression of the particular region of plaque based at least in part on the risk level ([0149] “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.”) ([0169] “In some embodiments, an automated or manual co-registration method can be combined with the imaging segmentation data to compare two or more images over time. In some embodiments, the comparison of these images can allow for determination of differences in coronary artery atherosclerosis, stenosis and vascular morphology over time, and can be used as an input variable for risk prediction.”),
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 level of endothelial shear stress.
However, Itu teaches determining a level of endothelial shear stress ([0069] “Since the experimental conditions can be controlled exactly in in vitro studies, an ML model can be trained to predict the formation, evolution and rupture of plaques using a database built from such studies as shown in the workflow 900 presented in FIG. 9. At step 905, in vitro studies are performed related to plaque formation, evolution, and rupture. Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a level of endothelial shear stress of Itu in the plaque progression prediction method of Min because endothelial shear stress plays a crucial role in plaque initiation, progression, and rupture. Thus, a level of endothelial shear stress can be used to better characterize and predict plaque progression.
Regarding claim 3, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the progression of the particular region of plaque is predicted using a machine learning algorithm trained based at least in part on a plurality of first weighted measures and a plurality of second weighted measures generated from a plurality of medical images of a plurality of other subjects with known progressions of plaque (Min - [0168] “In some embodiments, the benefit that is predicted by these algorithms may be for reduced progression, determination of type of plaque progression (progression, regression or mixed response),”) (Min - [0616] “the first set of parameters obtained at a first point in time, wherein the first set of plaque parameters comprises volume, surface area, geometric shape, location, heterogeneity index, and radiodensity for one or more regions of plaque within the coronary region of the subject; generating, by the computer system, a first weighted measure of the accessed first set of plaque parameters;”) (Min - [0616] “wherein the second set of plaque parameters comprises volume, surface area, geometric shape, location, heterogeneity index, and radiodensity for the one or more regions of plaque; generating, by the computer system, a second weighted measure of the determined second set of plaque parameters;”).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 4, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the risk level of the particular region of plaque is determined using a machine learning algorithm trained based at least in part on a plurality of first weighted measures generated from a plurality of medical images of a plurality of other subjects with identified risks of plaque (Min - [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.”) (Min - [0215] “In some embodiments, the system is configured to generate a risk assessment of coronary disease or cardiovascular event for the subject at block 366 using the weighted measure and/or using only some of these parameters.”) (Min - [0215] “In some embodiments, the system can be configured to generate a weighted measure of one or more vascular morphology parameters, one or more quantified plaque parameters, one or more quantified fat parameters, calculated stenosis, risk of ischemia, and/or CAD-RADS score of the subject.”).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 5, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the level of endothelial shear stress for the particular region of plaque is determined using a machine learning algorithm trained based at least in part on a plurality of second weighted measures of the plurality of variables generated from a plurality of medical images of a plurality of other subjects with known levels of endothelial shear stress (Itu - [0069] “Since the experimental conditions can be controlled exactly in in vitro studies, an ML model can be trained to predict the formation, evolution and rupture of plaques using a database built from such studies as shown in the workflow 900 presented in FIG. 9. At step 905, in vitro studies are performed related to plaque formation, evolution, and rupture. Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.”) (Min - [0616] “wherein the second set of plaque parameters comprises volume, surface area, geometric shape, location, heterogeneity index, and radiodensity for the one or more regions of plaque; generating, by the computer system, a second weighted measure of the determined second set of plaque parameters;”).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 6, Min in view of Itu teaches the computer-implemented method of claim 2, further comprising:
generating, by the computer system, a graphical representation of the determined level of endothelial shear stress for the particular region of plaque (Itu - [0069] “Since the experimental conditions can be controlled exactly in in vitro studies, an ML model can be trained to predict the formation, evolution and rupture of plaques using a database built from such studies as shown in the workflow 900 presented in FIG. 9. At step 905, in vitro studies are performed related to plaque formation, evolution, and rupture. Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.”) (Itu - [0091] “Computed results can be visualized on the scanner, or on another device, such as an imaging workstation. All of the above mentioned measures of interest related to plaque may be visualized including, without limitation, risk of a cardiovascular event related to atherosclerotic plaque” wherein shear stress can be visualized).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 7, Min in view of Itu teaches the computer-implemented method of claim 2, further comprising:
generating, by the computer system, a graphical representation of the determined level of endothelial shear stress for the particular region of plaque, the determined risk level of the particular region of plaque, and the predicted progression of the particular region of plaque (Itu - [0069] “Since the experimental conditions can be controlled exactly in in vitro studies, an ML model can be trained to predict the formation, evolution and rupture of plaques using a database built from such studies as shown in the workflow 900 presented in FIG. 9. At step 905, in vitro studies are performed related to plaque formation, evolution, and rupture. Next, at steps 910 and 915 features of interest and plaque-related measures of interest are extracted. The features of interest may include, without limitation, the geometry of the artificial vessel, the hemodynamics in the artificial vessel (e.g., flow rate, velocity, pressure, shear stress, etc.) obtained through direct or indirect measurements, and plaque composition.”) (Itu - [0091] “Computed results can be visualized on the scanner, or on another device, such as an imaging workstation. All of the above mentioned measures of interest related to plaque may be visualized including, without limitation, risk of a cardiovascular event related to atherosclerotic plaque” wherein shear stress can be visualized).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 8, Min in view of Itu teaches the computer-implemented method of claim 2, wherein the plurality of variables are generated by using an artificial intelligence (AI) and/or machine learning (ML) algorithm trained on a plurality of medical images with the plurality of variables pre-identified (Min - [0119] “For example, in some embodiments, the system can be configured to utilize one or more machine learning and/or artificial intelligence algorithms to automatically and/or dynamically analyze a medical image to identify, quantify, and/or classify one or more coronary arteries and/or plaque.”) (Min - [0294] “For example, using a machine learning process has been trained on thousands of CT scans determine information depicted in the CT images, and/or utilizing analyst to review and enhance the results of the machine learning process, and the example user interfaces described herein can provide the determined information to another analyst or a medical practitioner.”).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 9, Min in view of Itu teaches the computer-implemented method of claim 2, further comprising:
determining, by the computer system, a risk of arterial disease for the subject based at least in part on the predicted progression of the particular region of plaque (Min - [0149] “In particular, in some embodiments, the system can be configured to assess or predict the risk of atherosclerosis, stenosis, and/or ischemia of the subject based on a raw medical image and automated image processing thereof.”) (Min - [0169] “In some embodiments, an automated or manual co-registration method can be combined with the imaging segmentation data to compare two or more images over time. In some embodiments, the comparison of these images can allow for determination of differences in coronary artery atherosclerosis, stenosis and vascular morphology over time, and can be used as an input variable for risk prediction.”).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 10, Min in view of Itu teaches the computer-implemented method of claim 9, further comprising:
generating, by the computer system, a graphical representation of the determined risk of arterial disease for the subject (Min - [0132] “In some embodiments, at block 116, the system is configured to automatically and/or dynamically generate a Graphical User Interface (GUI) or other visualization of the analysis results at block 116, which can include for example identified vessels, regions of plaque, coronary arteries, quantified metrics or parameters, risk assessment, proposed treatment plan, and/or any other analysis result discussed herein.”).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 11, Min in view of Itu teaches the computer-implemented method of claim 9, further comprising:
determining, by the computer system, a proposed treatment for arterial disease for the subject based at least in part on the predicted progression of the particular region of plaque (Min - [0288] “In some embodiments, at block 514, the system can be configured to generate a proposed treatment plan for the patient based on the determined progression of plaque and/or disease.”).
The motivation for combining Min and Itu is the same motivation as used for claim 2.
Regarding claim 12, the claim recites similar limitations to claim 2 but in the form of a system comprising a non-transitory computer storage medium configured to at least store computer executable instructions; and one or more computer hardware processors in communication with the first non-transitory computer storage medium, the one or more computer hardware processors configured to execute the computer-executable instructions to perform the method of claim 2 (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 having program instructions for causing a hardware processor 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 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
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666