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 09, 2026 is acknowledged. Currently claims 1-16 and 33-35 are pending. Claims 1 and 33 have been amended. Claim 17 is cancelled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 12, 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
Applicant's arguments filed June 09, 2026, have been fully considered but they are not persuasive.
On pages 7-11 of the Applicant’s remarks, the Applicant alleges that Lieb does not teach or suggest the newly added limitation of independent claims 1 and 33 because Lieb uses traditional multilevel statistical modeling as opposed to a machine learning algorithm as recited in the amended claims.
Applicant further alleges that Lieb is used to solely teach the “generating” limitation of claims 1 and 33 for the following reasons: First, Lieb does not generate an individualized “measure of longitudinal progression of left ventricular hypertrophy” for a subject. Second, Lieb does not use reference values “generated from analyzing medical images” of other subjects. Third, Lieb does not generate “a plurality of reference measures of left ventricular hypertrophy generated based on the plurality of reference values of differences in left ventricular mass”.
The examiner respectfully disagrees. The examiner does not use Lieb to teach a machine learning algorithm nor the “generating” limitation as recited in amended claims 1 and 33. As seen in the Office Action mailed January 14, 2026, Min teaches “generated using a machine learning algorithm, wherein the machine learning algorithm is trained on the plurality of reference values from analyzing medical images of the plurality of other subjects obtained at different times and the plurality of reference measures generated” ([0325] “In some embodiments, the system can be configured to utilize an AI, ML, and/or other algorithm. In some embodiments, the system is configured to analyze one or more of the aforementioned parameters individually, combined, and/or as a weighted measure. In some embodiments, one or more of these parameters derived from a medical image, either individually or combined, can be compared to one or more reference values derived or collected from other subjects,”). Min does not specifically disclose differences in left ventricular mass and measures of left ventricular hypertrophy based on differences in left ventricular mass. Therefore, Lieb is used to cure the deficiencies of Min ([pg. 3086] “To test these hypotheses, we investigated a large sample from the community. First, we evaluated the clinical correlates of LV mass longitudinally over a period of 16 years using multilevel modeling. Second, we analyzed correlates of short-term (4 years) change in LV mass (Figure 1).” wherein a measure of longitudinal progression is evaluating left ventricular (LV) mass longitudinally; wherein one of ordinary skill in the art would understand that left ventricular mass is a direct quantitative measure used to measure left ventricular hypertrophy) ([pg. 3086] “Short-term change in LV mass was evaluated in 2605 unique participants who attended at least 2 consecutive examinations at which echocardiography was performed. To maximize the number of observations, data on the change in LV mass from examination cycles 4 to 5 and from cycles 5 to 6 were pooled (n =4494 participants-observations; Figure 1).” wherein a difference is change in LV mass between the various examinations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the machine learning algorithm of Min to measure longitudinal progression of left ventricular hypertrophy of Lieb to improve diagnostic accuracy and speed as well to aid in early detection of left ventricular hypertrophy.
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 1-16 and 33-35 are rejected under 35 U.S.C. 103 as being unpatentable of Min et al., US 20210319558 A1, (hereinafter “Min”) in view of Lieb et al., “Longitudinal Tracking of Left Ventricular Mass Over the Adult Life Course: Clinical Correlates of Short- and Long-Term Change in the Framingham Offspring Study”, (hereinafter “Lieb”).
Regarding claim 1, Min teaches a computer-implemented method of longitudinal tracking of left ventricular hypertrophy in a subject based at least in part on image-based analysis of one or more cardiovascular structural features, the method comprising:
accessing, by a computer system, a first medical image of a subject, the first medical image comprising a portion of a myocardium of the subject, the first medical image obtained at a first point in time ([0279] “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 of a subject at a first point in time) ([0218] “As an example, in some embodiments, the imaging data for the coronary arteries may be integrated with the left ventricular mass, which can be segmented according to the amount and location of the artery it is subtended by. This combination of left ventricular fractional myocardial mass to coronary artery information may enhance the prediction of whether a future heart attack will be a large one or a small one.” wherein a portion of a myocardium is left ventricular fractional myocardial mass);
identifying, by the computer system, the left ventricle of the subject in the first medical image based at least in part on image segmentation ([0279] “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 of a subject at a first point in time) ([0218] “As an example, in some embodiments, the imaging data for the coronary arteries may be integrated with the left ventricular mass, which can be segmented according to the amount and location of the artery it is subtended by.”);
analyzing, by the computer system, the left ventricle of the subject identified in the first medical image to generate a first measure of left ventricular mass at the first point in time ([0231] “In some embodiments, parameters associated with the left ventricle can include size, mass, volume, shape, eccentricity, surface area, thickness, and/or the like.”) ([0279] “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 of a subject at a first point in time);
accessing, by a computer system, a second medical image of a subject, the second medical image comprising the portion of the myocardium of the subject, the second medical image obtained at a second point in time ([0282] “In some embodiments, at block 376, 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.” wherein the second medical image is the second medical image taken from the second point in time) ([0218] “As an example, in some embodiments, the imaging data for the coronary arteries may be integrated with the left ventricular mass, which can be segmented according to the amount and location of the artery it is subtended by. This combination of left ventricular fractional myocardial mass to coronary artery information may enhance the prediction of whether a future heart attack will be a large one or a small one.” wherein a portion of a myocardium is left ventricular fractional myocardial mass);
identifying, by the computer system, the left ventricle of the subject in the second medical image based at least in part on image segmentation ([0218] “As an example, in some embodiments, the imaging data for the coronary arteries may be integrated with the left ventricular mass, which can be segmented according to the amount and location of the artery it is subtended by.”) ([0282] “In some embodiments, at block 376, 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.” wherein the second medical image is the second medical image taken from the second point in time);
analyzing, by the computer system, the left ventricle of the subject identified in the second medical image to generate a second measure of left ventricular mass at the second point in time ([0231] “In some embodiments, parameters associated with the left ventricle can include size, mass, volume, shape, eccentricity, surface area, thickness, and/or the like.”) ([0282] “In some embodiments, at block 376, 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.” wherein the second medical image is the second medical image taken from the second point in time);
wherein the measure plurality of other subjects obtained at different times and the plurality of reference measures ([0325] “In some embodiments, the system can be configured to utilize an AI, ML, and/or other algorithm. In some embodiments, the system is configured to analyze one or more of the aforementioned parameters individually, combined, and/or as a weighted measure. In some embodiments, one or more of these parameters derived from a medical image, either individually or combined, can be compared to one or more reference values derived or collected from other subjects,”),
wherein the computer system comprises a computer processor and an electronic storage medium ([0014] “wherein the computer system comprises a computer processor and an electronic storage medium”).
Min does not specifically disclose determining a difference between the first measure of left ventricular mass at the first point in time and the second measure of left ventricular mass at the second point in time;
a measure of longitudinal progression of left ventricular hypertrophy for the subject based at least in part on the difference between the first measure of left ventricular mass and the second measure of left ventricular mass and a plurality of reference values of differences in left ventricular mass of a plurality of other subjects obtained at different times and a plurality of reference measures of left ventricular hypertrophy based on the plurality of reference values of differences in left ventricular mass, wherein the measure of longitudinal progression of left ventricular hypertrophy for the subject is based on differences in left ventricular mass and measures of left ventricular hypertrophy based differences in left ventricular mass.
However, Lieb teaches determining a difference between the first measure of left ventricular mass at the first point in time and the second measure of left ventricular mass at the second point in time ([pg. 3086] “Short-term change in LV mass was evaluated in 2605 unique participants who attended at least 2 consecutive examinations at which echocardiography was performed. To maximize the number of observations, data on the change in LV mass from examination cycles 4 to 5 and from cycles 5 to 6 were pooled (n =4494 participants-observations; Figure 1).” wherein a difference is change in LV mass between the various examinations);
a measure of longitudinal progression of left ventricular hypertrophy for the subject based at least in part on the difference between the first measure of left ventricular mass and the second measure of left ventricular mass and a plurality of reference values of differences in left ventricular mass of a plurality of other subjects obtained at different times and a plurality of reference measures of left ventricular hypertrophy based on the plurality of reference values of differences in left ventricular mass, wherein the measure of longitudinal progression of left ventricular hypertrophy for the subject is based on differences in left ventricular mass and measures of left ventricular hypertrophy based differences in left ventricular mass ([pg. 3086] “To test these hypotheses, we investigated a large sample from the community. First, we evaluated the clinical correlates of LV mass longitudinally over a period of 16 years using multilevel modeling. Second, we analyzed correlates of short-term (4 years) change in LV mass (Figure 1).” wherein a measure of longitudinal progression is evaluating left ventricular (LV) mass longitudinally; wherein one of ordinary skill in the art would understand that left ventricular mass is a direct quantitative measure used to measure left ventricular hypertrophy) ([pg. 3086] “Short-term change in LV mass was evaluated in 2605 unique participants who attended at least 2 consecutive examinations at which echocardiography was performed. To maximize the number of observations, data on the change in LV mass from examination cycles 4 to 5 and from cycles 5 to 6 were pooled (n =4494 participants-observations; Figure 1).” wherein a difference is change in LV mass between the various examinations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use longitudinal tracking to measure left ventricular hypertrophy of Lieb in the cardiovascular image-based machine learned analysis method of Min to detect subtle and early declines in heart muscle performance and to more accurately characterize left ventricular hypertrophy.
Regarding claim 2, Min in view of Lieb teaches the computer-implemented method of Claim 1, further comprising determining efficacy of treatment for hypertension for the subject between the first point in time and the second point in time (Min - [0015] “In some embodiments, a computer implemented method of tracking efficacy of a medical treatment for a plaque-based disease based on non-invasive medical image analysis using the normalization device… wherein the first medical image of the subject is obtained non-invasively at a first point in time, wherein the first set of plaque parameters comprises one or more of density, location, or volume of one or more regions of plaque from the medical image of the subject at the first point in time, and wherein the first set of vascular parameters comprises vascular remodeling of a vasculature at the first point in time; accessing, by the computer system, a second medical image of the subject, wherein the second medical image of the subject is obtained non-invasively at a second point in time after the subject is treated with a medical treatment, the second point in time being later than the first point in time”) (Min - [0337] “In some embodiments, these patient reports can be imported into an application that allows for following disease over time in relation to control of heart disease risk factors, such as diabetes or hypertension.”).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 3, Min in view of Lieb teaches the computer-implemented method of Claim 1, further comprising determining risk of hypertension of the subject based at least in part on the generated measure of longitudinal progression of left ventricular hypertrophy (Min - [0337] “In some embodiments, these patient reports can be imported into an application that allows for following disease over time in relation to control of heart disease risk factors, such as diabetes or hypertension.”) (Lieb - [pg. 3086] “To test these hypotheses, we investigated a large sample from the community. First, we evaluated the clinical correlates of LV mass longitudinally over a period of 16 years using multilevel modeling. Second, we analyzed correlates of short-term (4 years) change in LV mass (Figure 1).” wherein a measure of longitudinal progression is evaluating left ventricular (LV) mass longitudinally).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 4, Min in view of Lieb teaches the computer-implemented method of Claim 1, further comprising generating a proposed treatment for the subject to treat hypertension based at least in part on the generated measure of longitudinal progression of left ventricular hypertrophy (Min - [0229] “In some embodiments, the system can be configured to generate a proposed treatment for the subject based on the generated and/or updated risk assessment after comparison with the known datasets of coronary values.”) (Lieb - [pg. 3086] “To test these hypotheses, we investigated a large sample from the community. First, we evaluated the clinical correlates of LV mass longitudinally over a period of 16 years using multilevel modeling. Second, we analyzed correlates of short-term (4 years) change in LV mass (Figure 1).” wherein a measure of longitudinal progression is evaluating left ventricular (LV) mass longitudinally).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 5, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the difference between the first measure of left ventricular mass at the first point in time and the second measure of left ventricular mass at the second point in time being higher than a predetermined threshold is indicative of high left ventricular hypertrophy of the subject (Lieb - [pg. 3086] “Short-term change in LV mass was evaluated in 2605 unique participants who attended at least 2 consecutive examinations at which echocardiography was performed. To maximize the number of observations, data on the change in LV mass from examination cycles 4 to 5 and from cycles 5 to 6 were pooled (n =4494 participants-observations; Figure 1).” wherein a difference is change in LV mass between the various examinations) (Lieb - [pg. 3088] “Women and men with a higher CVD risk factor burden had higher baseline LV mass and a greater increase over time compared with participants with a lower CVD risk factor burden (Figure 3).” wherein high left ventricular hypertrophy is higher CVD risk factor burden).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 6, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the difference between the first measure of left ventricular mass at the first point in time and the second measure of left ventricular mass at the second point in time being lower than a predetermined threshold is indicative of low left ventricular hypertrophy of the subject (Lieb - [pg. 3086] “Short-term change in LV mass was evaluated in 2605 unique participants who attended at least 2 consecutive examinations at which echocardiography was performed. To maximize the number of observations, data on the change in LV mass from examination cycles 4 to 5 and from cycles 5 to 6 were pooled (n =4494 participants-observations; Figure 1).” wherein a difference is change in LV mass between the various examinations) (Lieb - [pg. 3088] “Women and men with a higher CVD risk factor burden had higher baseline LV mass and a greater increase over time compared with participants with a lower CVD risk factor burden (Figure 3).” wherein lower left ventricular hypertrophy is lower CVD risk factor burden).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 7, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the measure of longitudinal progression of left ventricular hypertrophy comprises one of low, medium, or high (Lieb - [pg. 3088] “Women and men with a higher CVD risk factor burden had higher baseline LV mass and a greater increase over time compared with participants with a lower CVD risk factor burden (Figure 3).” wherein one of low, medium, or high is higher or lower CVD risk factor).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 8, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the measure of longitudinal progression of left ventricular hypertrophy comprises a scaled value of a continuum of scaled values (Lieb - [pg. 3086] “LV mass was natural-logarithmically transformed to normalize its distribution and stabilize its variance in men and women, permitting pooled sex analysis.” wherein a scaled value of a continuum of scaled values is LV mass that was natural-logarithmically transformed to normalize its distribution).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 9, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the first medical image and the second medical image are obtained at about a same point during a cardiac cycle (Min - [0361] “In some embodiments, the processed CT image data can visualize and compare the artery morphologies over time, i.e., throughout the cardiac cycle.” It is obvious to try to have the images obtained at about at same point in time in the cardiac cycle. There is no significant difference and will still yield an expected success of obtaining the image).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 10, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the first medical image and the second medical image are obtained at a point during a cardiac cycle when movement of the myocardium is expected to be below a predetermined threshold (Min - [0361] “In some embodiments, the processed CT image data can visualize and compare the artery morphologies over time, i.e., throughout the cardiac cycle”.) (Min - [0491] “In some embodiments, the first medical image and the second medical image each comprise a CT image and the one or more first variable acquisition parameters and the one or more second variable acquisition parameters comprise one or more of a kilovoltage (kV), kilovoltage peak (kVp), a milliamperage (mA), or a method of gating.” wherein the variable acquisition parameters is the point during a cardiac cycle when movement of the myocardium is expected to be below a predetermined threshold).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 11, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the second point in time is more than 12 months after the first point in time (Lieb - [pg. 3087] “Generalized estimating equations, which account for relatedness among participants, were used to determine clinical correlates of change in LV mass during a mean follow-up period of 4 years.” wherein the second point in time is 4 years after the first point in time).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 12, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the second point in time is more than 24 months after the first point in time (Lieb - [pg. 3087] “Generalized estimating equations, which account for relatedness among participants, were used to determine clinical correlates of change in LV mass during a mean follow-up period of 4 years.” wherein the second point in time is 4 years after the first point in time).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 13, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the second point in time is more than 36 months after the first point in time (Lieb - [pg. 3087] “Generalized estimating equations, which account for relatedness among participants, were used to determine clinical correlates of change in LV mass during a mean follow-up period of 4 years.” wherein the second point in time is 4 years after the first point in time).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 14, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the second point in time is more than 48 months after the first point in time (Lieb - [pg. 3087] “Generalized estimating equations, which account for relatedness among participants, were used to determine clinical correlates of change in LV mass during a mean follow-up period of 4 years.” wherein the second point in time is 4 years after the first point in time).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 15, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein the first medical image and the second medical image comprise a computed tomography (CT) image (Min - [0186] “The medical image can comprise an image obtain using one or more modalities such as for example, CT, Dual-Energy Computed Tomography (DECT), Spectral CT, photon-counting 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). In some embodiments, the medical image comprises one or more of a contrast-enhanced CT image, non-contrast CT image, MR image, and/or an image obtained using any of the modalities described above.”).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 16, Min in view of Lieb teaches the computer-implemented method of Claim 1, wherein one or more of the first medical or the second medical image comprises an image obtained using 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 - [0186] “The medical image can comprise an image obtain using one or more modalities such as for example, CT, Dual-Energy Computed Tomography (DECT), Spectral CT, photon-counting 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). In some embodiments, the medical image comprises one or more of a contrast-enhanced CT image, non-contrast CT image, MR image, and/or an image obtained using any of the modalities described above.”).
The motivation for combining Min and Lieb is the same motivation as used for claim 1.
Regarding claim 33, the claim recites similar limitations to claim 1 but in the form of a system. Therefore, claim 33 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 34, the claim recites similar limitations to claim 2 but in the form of a system. Therefore, claim 34 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 35, the claim recites similar limitations to claim 3 but in the form of a system. Therefore, claim 35 recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA PEARSON whose telephone number is (703)-756-5786. The examiner can normally be reached Monday - Friday 8:00 - 5:30.
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
