DETAILED ACTION
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 .
Status of the Claims
Claims 31-50, as amended, are currently pending and have been considered below.
Drawings
The drawings are objected to because of the following informalities:
Figure 4B, item 480 recites “the perivscular” which should recite “the perivascular.”
Figure 5A, item 515 recites “coronary artieries” which should recite “coronary arteries.”
Figure 5B, item 555 recites “Quantfying” which should recite “Quantifying.”
Figure 6, item 625 recites “vessel juxaposed” which should recite “vessel juxtaposed.”
Figure 6, item 615 recites “low attnuation” which should recite “low attenuation.”
Figure 7, item 625 recites “vessel juxaposed” which should recite “vessel juxtaposed.”
Figure 7, item 615 recites “low attnuation” which should recite “low attenuation.”
Figure 11, item 951 recites “Hounsfield Unts” which should recite “Hounsfield Units.”
Figure 11, item 961 recites “Hounsfield Unts” which should recite “Hounsfield Units.”
Figure 12, item 1231 recites “Hounsfield Unts” which should recite “Hounsfield Units.”
Figure 12, item 1241 recites “Hounsfield Unts” which should recite “Hounsfield Units.”
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0004], line 22 recites the “Because of this limitation, the SYNAX score is sometimes performed” which appears to contain a typographical error and should recite “Because of this limitation, the SYNTAX score is sometimes performed.”
Paragraph [0011], the last sentence does not end with a period.
Paragraph [0030], the sentence does not end with a period.
Paragraph [0055], line 7, the sentence does not end with a period.
Paragraph [0055], line 12, the sentence does not end with a period.
Paragraph [0055], line 14, the sentence does not end with a period.
Paragraph [0105], line 4 recites “vessel wall 663, and juxaposed” which appears to contain a typographical error and should recite, “vessel wall, 663, and juxtaposed.”
Paragraph [0109], line 3 contains a duplicate phrase “of the of the”.
Paragraph [0110], page 40, line 20, the sentence does not end with a period.
Paragraph [0110], page 40, lines 25 and 27, the respective sentences do not end with a period.
Appropriate correction is required.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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.
Claim(s) 31, 32, 34, 36, 38-45 and 48-50 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabir, Adeel, et al. "Measuring noncalcified coronary atherosclerotic plaque using voxel analysis with MDCT angiography: phantom validation." American Journal of Roentgenology 190.4 (2008): W242-W246, hereinafter, “Sabir”, in view of Obaid, Daniel R., et al. "Atherosclerotic Plaque Composition and Classification Identified by Coronary Computed Tomography: Assessment of Computed Tomography–Generated Plaque Maps Compared with Virtual Histology Intravascular Ultrasound and Histology." Circulation: Cardiovascular Imaging 6.5 (2013): 655-664, hereinafter, “Obaid”, and further in view of Dey, Damini, et al. "Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study." European radiology 28.6 (2018): 2655-2664, hereinafter, “Dey”.
As per claim 31, Sabir discloses a computer-implemented method for facilitating assessment of risk of coronary plaque by analyzing density values of coronary plaque of a subject, the method comprising:
accessing, by a computer system, image data of a computed tomography (CT) image obtained along a vessel of the subject (Sabir, page 242. Objective, evaluate the accuracy and reproducibility of a voxel analysis technique for measuring noncalcified plaque in the coronary arteries; Sabir, page 243, Volume Measurement, reconstructed images from both micro-CT and MDCT … Hounsfield density profiles; Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen));
wherein determining the coronary plaque data comprises:
identifying a lumen wall and a vessel wall (Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Volume Measurements);
identifying, based at least in part on the identified lumen wall and vessel wall, a region of coronary plaque in a region of a vessel of the subject (Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Volume Measurements);
determining density values of pixels within the identified region of coronary plaque (Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Volume Measurements); and
wherein the computer system comprises a computer processor and an electronic storage medium (Sabir, page 242, Introduction, computer application; Sabir, page 243, Volume measurements, Hounsfield unit values ... were determined by the computer).
Sabir does not explicitly disclose the following limitations as further recited however Obaid discloses
generating, based at least in part on the determined density values in the identified region of coronary plaque, quantification of different types of plaque (Obaid, page 657, Figure 2B, Ratio of Plaque/Lumen attenuation (HU) … defined plaque tissue types and lumen; Obaid, page 661, Discussion, we defined plaque components based on the ratio of attenuation of plaque to luminal contrast; Obaid, page 656, Introduction, Plaque Maps were tested for diagnostic accuracy in a separate in vivo validation patient cohort; Obaid, page 657, Figure 2B, Ratio of Plaque/Lumen attenuation, necrotic core, fibrous tissue calcification, lumen).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Obaid and Sabir because they are in the same field of endeavor. One skilled in the art would have been motivated to include the parameters as taught by Obaid in the system of Sabir in order to provide a means to determine and distinguish the different types and effects of plaque including necrotic core, fibrous tissue and calcification for individual subjects (Obaid, page 661, Discussion, different plaque components show significant differences in HU attenuation).
Sabir and Obaid do not explicitly disclose the following limitations as further recited however Dey discloses
determining, by the computer system, based at least in part on applying a machine learning algorithm to the image data, coronary plaque data of the subject (Dey, page 2656, Introduction, correlated quantitative plaque measures from CTA can be effectively combined to predict lesion-specific ischaemia using machine learning; Dey, pages 2657-2658, Machine learning integration),
determining whether the region of the vessel is likely to be ischemic based at least in part on the quantification of the different types of plaque (Dey, page 2656, Introduction, correlated quantitative plaque measures from CTA can be effectively combined to predict lesion-specific ischaemia [high risk] using machine learning; Dey, pages 2657-2658, Machine learning integration),
wherein the determined coronary plaque data and whether the region of the vessel is likely to be ischemic is configured to be used to assess risk of coronary plaque of the subject (Dey, page 2656, Introduction, correlated quantitative plaque measures from CTA can be effectively combined to predict lesion-specific ischaemia [high risk] using machine learning; Dey, pages 2657-2658, Machine learning integration).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Dey with Sabir and Obaid because they are in the same field of endeavor. One skilled in the art would have been motivated to include the quantitative CTA machine learning integration as taught by Dey in the system of Sabir and Obaid in order to automate feature selection to improve the prediction of a high-risk plaques in patients (Dey, page 2657, Machine learning integration).
As per claim 32, Sabir, Obaid and Dey disclose the computer-implemented method of claim 31, further comprising determining a risk level of coronary plaque (Dey, page 2656, Introduction, correlated quantitative plaque measures from CTA can be effectively combined to predict lesion-specific ischaemia [high risk] using machine learning; Dey, pages 2657-2658, Machine learning integration).
As per claim 34, Sabir, Obaid and Dey disclose the computer-implemented method of claim 32, wherein a coronary plaque is identified as high risk if the coronary plaque is likely to cause ischemia (Dey, page 2656, Introduction, correlated quantitative plaque measures from CTA can be effectively combined to predict lesion-specific ischaemia using machine learning; Dey, pages 2657-2658, Machine learning integration).
As per claim 36, Sabir, Obaid and Dey disclose the computer-implemented method of claim 32, wherein a coronary plaque is identified as high risk if the coronary plaque comprises a radiodensity value below a predetermined threshold (Dey, Abstract, CTA data sets were analysed by semi-automated software to quantify stenosis and non-calcified (NCP), low-density NCP (LD-NCP, < 30 HU), calcified and total plaque volumes, contrast density difference (CDD, maximum difference in luminal attenuation per unit area) and plaque length ... Eighty patients had ischaemia by FFR (FFR ≤ 0.80) in 100 vessels. Information gain for predicting ischaemia was highest for CDD (0.172), followed by LD-NCP (0.125), NCP (0.097), and total plaque volumes (0.092).
As per claim 38, Sabir, Obaid and Dey disclose the computer-implemented method of claim 31, wherein determining the coronary plaque data further comprises determining a gradient of the density values in the region of coronary plaque (Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Figure 1A-1D; Sabir, page 244, Figures 2A-2D).
As per claim 39, Sabir, Obaid and Dey disclose the computer-implemented method of claim 31, wherein determining the coronary plaque data further comprises quantifying density values in a region of perivascular tissue of the subject (Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Volume Measurements).
As per claim 40, Sabir, Obaid and Dey disclose the computer-implemented method of claim 39, wherein the coronary plaque data is further determined using the determined density values in the region of perivascular tissue (Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Volume Measurements).
As per claim 41, Sabir, Obaid and Dey disclose the computer-implemented method of claim 39, wherein the coronary plaque data is further determined based on a gradient of density values in the region of perivascular tissue (Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Volume Measurements).
As per claim 42, Sabir, Obaid and Dey disclose the computer-implemented method of claim 39, wherein the coronary plaque data is further determined by determining a ratio of a gradient of the density values in the region of coronary plaque and the gradient of the density values in the region of perivascular tissue (Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Obaid, page 657, Figure 2B, Ratio of Plaque/Lumen attenuation (HU) … defined plaque tissue types and lumen; Obaid, page 661, Discussion, we defined plaque components based on the ratio of attenuation of plaque to luminal contrast).
As per claim 43, Sabir, Obaid and Dey disclose the computer-implemented method of claim 39, wherein the coronary plaque data is further determined by generating a ratio between determined density values in the region of coronary plaque and determined density values in the region of perivascular tissue (Obaid, page 657, Figure 2B, Ratio of Plaque/Lumen attenuation (HU) … defined plaque tissue types and lumen; Obaid, page 661, Discussion, we defined plaque components based on the ratio of attenuation of plaque to luminal contrast).
As per claim 44, Sabir discloses a system for facilitating assessment of risk of coronary plaque by analyzing density values of coronary plaque of a subject, the system comprising:
one or more computer readable storage devices configured to store a plurality of computer executable instructions; and one or more hardware computer processors in communication with the one or more computer readable storage devices and configured to execute the plurality of computer executable instructions (Sabir, page 242, Introduction, computer application; Sabir, page 243, Volume measurements, Hounsfield unit values ... were determined by the computer) in order to cause the system to:
access image data of a computed tomography (CT) image obtained along a vessel of the subject (Sabir, page 242. Objective, evaluate the accuracy and reproducibility of a voxel analysis technique for measuring noncalcified plaque in the coronary arteries; Sabir, page 243, Volume Measurement, reconstructed images from both micro-CT and MDCT … Hounsfield density profiles; Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen));
wherein determining the coronary plaque data comprises:
identifying a lumen wall and a vessel wall (Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Volume Measurements);
identifying, based at least in part on the identified lumen wall and vessel wall, a region of coronary plaque in a region of a vessel of the subject (Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Volume Measurements);
determining density values of pixels within the identified region of coronary plaque (Sabir, page 243, Figures 1A-1D; Sabir, page 244, Figures 2A-2D; Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Volume Measurements).
Sabir does not explicitly disclose the following limitations as further recited however Obaid discloses
generating, based at least in part on the determined density values in the identified region of coronary plaque, quantification of different types of plaque (Obaid, page 657, Figure 2B, Ratio of Plaque/Lumen attenuation (HU) … defined plaque tissue types and lumen; Obaid, page 661, Discussion, we defined plaque components based on the ratio of attenuation of plaque to luminal contrast; Obaid, page 656, Introduction, Plaque Maps were tested for diagnostic accuracy in a separate in vivo validation patient cohort; Obaid, page 657, Figure 2B, Ratio of Plaque/Lumen attenuation, necrotic core, fibrous tissue calcification, lumen).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Obaid and Sabir because they are in the same field of endeavor. One skilled in the art would have been motivated to include the parameters as taught by Obaid in the system of Sabir in order to provide a means to determine and distinguish the different types and effects of plaque including necrotic core, fibrous tissue and calcification for individual subjects (Obaid, page 661, Discussion, different plaque components show significant differences in HU attenuation).
Sabir and Obaid do not explicitly disclose the following limitations as further recited however Dey discloses
determine, based at least in part on applying a machine learning algorithm to the image data, coronary plaque data of the subject (Dey, page 2656, Introduction, correlated quantitative plaque measures from CTA can be effectively combined to predict lesion-specific ischaemia using machine learning; Dey, pages 2657-2658, Machine learning integration),
determining whether the region of the vessel is likely to be ischemic based at least in part on the quantified different types of plaque (Dey, page 2656, Introduction, correlated quantitative plaque measures from CTA can be effectively combined to predict lesion-specific ischaemia [high risk] using machine learning; Dey, pages 2657-2658, Machine learning integration),
wherein the determined coronary plaque data and whether the region of the vessel is likely to be ischemic is configured to be used to assess risk of coronary plaque of the subject (Dey, page 2656, Introduction, correlated quantitative plaque measures from CTA can be effectively combined to predict lesion-specific ischaemia [high risk] using machine learning; Dey, pages 2657-2658, Machine learning integration).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Dey with Sabir and Obaid because they are in the same field of endeavor. One skilled in the art would have been motivated to include the quantitative CTA machine learning integration as taught by Dey in the system of Sabir and Obaid in order to automate feature selection to improve the prediction of a high-risk plaques in patients (Dey, page 2657, Machine learning integration).
As per claim 45, Sabir, Obaid and Dey disclose the system of claim 44, wherein the system is further caused to determine a risk level of the coronary plaque (Dey, page 2656, Introduction, correlated quantitative plaque measures from CTA can be effectively combined to predict lesion-specific ischaemia [high risk] using machine learning; Dey, pages 2657-2658, Machine learning integration).
As per claim 48, Sabir, Obaid and Dey disclose the system of claim 45, wherein a coronary plaque is identified as high risk if the coronary plaque comprises a radiodensity value below a predetermined threshold (Dey, Abstract, CTA data sets were analysed by semi-automated software to quantify stenosis and non-calcified (NCP), low-density NCP (LD-NCP, < 30 HU), calcified and total plaque volumes, contrast density difference (CDD, maximum difference in luminal attenuation per unit area) and plaque length ... Eighty patients had ischaemia by FFR (FFR ≤ 0.80) in 100 vessels. Information gain for predicting ischaemia was highest for CDD (0.172), followed by LD-NCP (0.125), NCP (0.097), and total plaque volumes (0.092).
As per claim 49, Sabir, Obaid and Dey disclose the system of claim 44, wherein the system is further caused to determine plaque data by determining a gradient of the density values in the region of coronary plaque (Sabir, page 242, Introduction, we developed a semiautomatic computer application based on the gradient change in Hounsfield units across coronary artery regions (epicardial fat, arterial wall, noncalcified plaque, and lumen); Sabir, page 243, Figure 1A-1D; Sabir, page 244, Figures 2A-2D).
As per claim 50, Sabir, Obaid and Dey disclose the system of claim 44, wherein the quantification of different types of plaque is configured to be used to compare the quantification of different types of plaques to a reference population to facilitate assessment of a risk for coronary artery disease for the subject (Obaid, page 656, Computed Tomography, Curved multiplanar reconstructions of coronary arteries were compared with longitudinal reconstructed IVUS data sets ... CT images were matched with corresponding VH-IVUS frames and multiple regions of interests (ROIs) sampled on CT images in areas preclassified by VH-IVUS as necrotic core, fibrous plaque, calcified plaque, or lumen, resulting in CT attenuation values for each plaque component within these ROIs (expressed in Hounsfield units, HU) ... The ratio of plaque attenuation to its corresponding contrast attenuation was calculated for all sampled areas and used to assign ranges of plaque/contrast attenuation ratios to each plaque component. Curved multiplanar reconstructions of the coronary artery segments that corresponded to those chosen for VH-IVUS analysis were created and used to produce Plaque Maps: Obaid, page 657, Results, we first imaged 57 patients with 3-vessel VH-IVUS followed by coronary CT and co-registered 108 frames from 108 plaques (Figure 1). We sampled 855 ROIs on CT images that corresponded with defined tissue types on VH-IVUS (236 necrotic core, 215 fibrous plaque, 260 calcified plaque, 144 lumen). Median HU values with Q1 to Q3 range for each component were: necrotic core, 39 (−35 to 159); fibrous plaque, 106 (0–294); arterial lumen, 362 (246–545); calcified plaque, 770 (358–1587; Figure 2A). Using a linear mixed-effects model, there were significant differences in attenuation between the different components).
Claim(s) 33 and 46 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabir, Adeel, et al. "Measuring noncalcified coronary atherosclerotic plaque using voxel analysis with MDCT angiography: phantom validation." American Journal of Roentgenology 190.4 (2008): W242-W246, hereinafter, “Sabir”, in view of Obaid, Daniel R., et al. "Atherosclerotic Plaque Composition and Classification Identified by Coronary Computed Tomography: Assessment of Computed Tomography–Generated Plaque Maps Compared with Virtual Histology Intravascular Ultrasound and Histology." Circulation: Cardiovascular Imaging 6.5 (2013): 655-664, hereinafter, “Obaid”, in view of Dey, Damini, et al. "Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study." European radiology 28.6 (2018): 2655-2664, hereinafter, “Dey” as applied to claim 32 and 45 above, and further in view of Okubo, Ryo, et al. "Pericoronary adipose tissue ratio is a stronger associated factor of plaque vulnerability than epicardial adipose tissue on coronary computed tomography angiography." Heart and vessels 32.7 (2017): 813-822, hereinafter, “Okubo”.
As per claim 33, Sabir, Obaid and Dey disclose the computer-implemented method of claim 32, but do not explicitly disclose the following limitation as further recited however Okubo discloses wherein a coronary plaque is identified as high risk if the coronary plaque is likely to rapidly progress (Okubo, page 819, Discussion, Several studies have demonstrated that EAT is more closely associated with the formation, progression and severity of coronary atherosclerosis when compared with other adipose tissues … local adipose tissue has a potential role in the progression of coronary plaque … PAT may also contribute to the formation or progression of vulnerable plaques by influencing several inflammatory markers … possibly indicating that adipose tissue promotes plaque progression or plaque vulnerability associated with plaque rupture).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Okubo with Sabir, Obaid and Dey because they are in the same field of endeavor. One skilled in the art would have been motivated to include the comparison with previously classified patient data as taught by Okubo in the system of Sabir, Obaid and Dey in order to provide an alternate means to calculate and determine plaque vulnerability (Okubo, Abstract; Okubo, page 819, Discussion).
As per claim 46, Sabir, Obaid and Dey disclose the system of claim 45, but do not explicitly disclose the following limitations as further recited however Okubo discloses wherein a coronary plaque is identified as high risk if the coronary plaque is likely to rapidly progress (Okubo, page 819, Discussion, Several studies have demonstrated that EAT is more closely associated with the formation, progression and severity of coronary atherosclerosis when compared with other adipose tissues … local adipose tissue has a potential role in the progression of coronary plaque … PAT may also contribute to the formation or progression of vulnerable plaques by influencing several inflammatory markers … possibly indicating that adipose tissue promotes plaque progression or plaque vulnerability associated with plaque rupture).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Okubo with Sabir, Obaid and Dey because they are in the same field of endeavor. One skilled in the art would have been motivated to include the comparison with previously classified patient data as taught by Okubo in the system of Sabir, Obaid and Dey in order to provide an alternate means to calculate and determine plaque vulnerability (Okubo, Abstract; Okubo, page 819, Discussion).
Claim(s) 35, 37 and 47 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabir, Adeel, et al. "Measuring noncalcified coronary atherosclerotic plaque using voxel analysis with MDCT angiography: phantom validation." American Journal of Roentgenology 190.4 (2008): W242-W246, hereinafter, “Sabir”, in view of Obaid, Daniel R., et al. "Atherosclerotic Plaque Composition and Classification Identified by Coronary Computed Tomography: Assessment of Computed Tomography–Generated Plaque Maps Compared with Virtual Histology Intravascular Ultrasound and Histology." Circulation: Cardiovascular Imaging 6.5 (2013): 655-664, hereinafter, “Obaid”, in view of Dey, Damini, et al. "Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study." European radiology 28.6 (2018): 2655-2664, hereinafter, “Dey as applied to claim 32 and 45 above, and further in view of Antonopoulos, Alexios S., et al. "Detecting human coronary inflammation by imaging perivascular fat." Science Translational Medicine 9.398 (2017): eaal2658, hereinafter, “Antonopoulos”.
As per claim 35, Sabir, Obaid and Dey disclose the computer-implemented method of claim 32, but do not explicitly disclose the following limitation as further recited however Antonopoulos discloses wherein a coronary plaque is identified as high risk if the coronary plaque is likely not to calcify (Antonopoulos, page 9, Discussion, linked EpAT thickness with coronary microvascular function in patients with nonobstructive CAD and EpAT volume with coronary calcification and presence of noncalcified plaques).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Antonopoulos with Sabir, Obaid and Dey because they are in the same field of endeavor. One skilled in the art would have been motivated to modify the teachings of Sabir, Obaid and Dey to include the coronary calcium scoring as taught by Antonopoulos in order to validate the attenuation index to determine plaque burden and its relation to vascular inflammation (Antonopoulos, page 5, Validating FAI against established imaging biomarkers and coronary atherosclerotic plaque burden).
As per claim 37, Sabir, Obaid and Dey disclose the computer-implemented method of claim 32, but do not explicitly disclose the following limitation as further recited however Antonopoulos discloses wherein a coronary plaque is identified as high risk if the coronary plaque is likely not to respond, regress or stabilize to medical therapy (Antonopoulos, Abstract, This methodology can be implemented in clinical practice to noninvasively detect plaque instability in the human coronary vasculature; Antonopoulos, page 10, Discussion, We also provide a tool that enables noninvasive detection of vulnerable (highly inflamed) atherosclerotic plaques in the human coronary arteries. This approach can be used to analyze historical CTAs that have previously been performed in patients for diagnostic purposes. If the potential prognostic value of this new imaging phenotyping approach of PVAT is confirmed in clinical studies with prospective follow-up, then it could have a major impact on risk stratification).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Antonopoulos with Sabir, Obaid and Dey because they are in the same field of endeavor. One skilled in the art would have been motivated to modify the teachings of Sabir, Obaid and Dey to include the analysis of historical data that has previously been performed in patients as taught by Antonopoulos in order to provide an additional means to detect vulnerable plaques (Antonopoulos, page 10, Discussion).
As per claim 47, Sabir, Obaid and Dey disclose the system of claim 45, but do not explicitly disclose the following limitation as further recited however Antonopoulos discloses wherein a coronary plaque is identified as high risk if the coronary plaque is likely not to calcify (Antonopoulos, page 9, Discussion, linked EpAT thickness with coronary microvascular function in patients with nonobstructive CAD and EpAT volume with coronary calcification and presence of noncalcified plaques).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Antonopoulos with Sabir, Obaid and Dey because they are in the same field of endeavor. One skilled in the art would have been motivated to modify the teachings of Sabir, Obaid and Dey to include the coronary calcium scoring as taught by Antonopoulos in order to validate the attenuation index to determine plaque burden and its relation to vascular inflammation (Antonopoulos, page 5, Validating FAI against established imaging biomarkers and coronary atherosclerotic plaque burden).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 31-50 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-14, 16, 23-25, 27, 28, 30 and 33 of U.S. Patent No. 12,138,093. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 31-50 of the current application are encompassed by the scope of claims 1, 4-14, 16, 23-25, 27, 28, 30 and 33 of U.S. Patent No. 12,138,093 in that claims 1, 4-14, 16, 23-25, 27, 28, 30 and 33 of U.S. Patent No. 12,138,093 contain similar limitations as claims 31-50 of the current application and are therefore not patentably distinct from claims 1, 4-14, 16, 23-25, 27, 28, 30 and 33 of U.S. Patent No. 12,138,093.
Claims 31-50 of the current application recite similar limitations as claims 1, 4-14, 16, 23-25, 27, 28, 30 and 33 of U.S. Patent No. 12,138,093 as follows:
Current Application No. 18/905,755
U.S. Patent No. 12,138,093
31. A computer-implemented method for facilitating assessment of risk of coronary plaque by analyzing density values of coronary plaque of a subject, the method comprising: accessing, by a computer system, image data of a computed tomography (CT) image obtained along a vessel of the subject; determining, by the computer system, based at least in part on applying a machine learning algorithm to the image data, coronary plaque data of the subject, wherein determining the coronary plaque data comprises: identifying a lumen wall and a vessel wall; identifying, based at least in part on the identified lumen wall and vessel wall, a region of coronary plaque in a region of a vessel of the subject; determining density values of pixels within the identified region of coronary plaque; and generating, based at least in part on the determined density values in the identified region of coronary plaque, quantification of different types of plaque; and determining whether the region of the vessel is likely to be ischemic based at least in part on the quantification of the different types of plaque, wherein the determined coronary plaque data and whether the region of the vessel is likely to be ischemic is configured to be used to assess risk of coronary plaque of the subject, wherein the computer system comprises a computer processor and an electronic storage medium.
1. A computer-implemented method for assessing risk of coronary plaque by analyzing radiodensity values of coronary plaque of a subject, the method comprising: accessing image information for the subject, the image information comprising image data of computed tomography (CT) images obtained along a vessel of the subject, the image data comprising radiodensity values of coronary plaque; determining, based at least in part on applying a machine learning algorithm to the image information, coronary plaque information of the subject, wherein determining the coronary plaque information comprises: identifying a lumen wall and a vessel wall; identifying, based at least in part on the identified lumen wall and vessel wall, a region of coronary plaque in a region of a vessel of the subject; determining radiodensity values of pixels within the identified region of coronary plaque; and generating, based at least in part on the determined radiodensity values in the identified region of coronary plaque, metrics of coronary plaque comprising quantification of different types of plaque; and determining whether the region of the vessel is likely to be ischemic based at least in part on one or more of the identified lumen wall and vessel wall, the region of coronary plaque, or the quantified different types of plaque, wherein the method is performed by one or more computer hardware processors configured to execute computer-executable instructions on a non-transitory computer storage medium.
Claim 32 of the current application corresponds to claim 4 of U.S. Patent No. 12,138,093. Claim 33 of the current application corresponds to claim 5 of U.S. Patent No. 12,138,093. Claim 34 of the current application corresponds to claim 6 of U.S. Patent No. 11,751,831. Claim 35 of the current application corresponds to claim 7 of U.S. Patent No. 12,138,093. Claim 36 of the current application corresponds to claim 8 of U.S. Patent No. 12,138,093. Claim 37 of the current application corresponds to claim 9 of U.S. Patent No. 12,138,093. Claim 38 of the current application corresponds to claim 10 of U.S. Patent No. 12,138,093. Claim 39 of the current application corresponds to claim 11 of U.S. Patent No. 12,138,093. Claim 40 of the current application corresponds to claim 12 of U.S. Patent No. 12,138,093. Claim 41 of the current application corresponds to claim 13 of U.S. Patent No. 12,138,093. Claim 42 of the current application corresponds to claim 14 of U.S. Patent No. 12,138,093. Claim 43 of the current application corresponds to claim 16 of U.S. Patent No. 12,138,093. Claim 44 of the current application corresponds to claim 23 of U.S. Patent No. 12,138,093. Claim 45 of the current application corresponds to claim 24 of U.S. Patent No. 12,138,093. Claim 46 of the current application corresponds to claim 25 of U.S. Patent No. 12,138,093. Claim 47 of the current application corresponds to claim 27 of U.S. Patent No. 12,138,093. Claim 48 of the current application corresponds to claim 28 of U.S. Patent No. 12,138,093. Claim 49 of the current application corresponds to claim 30 of U.S. Patent No. 12,138,093. Claim 50 of the current application corresponds to claim 33 of U.S. Patent No. 12,138,093.
The table above shows that, although the corresponding claims are not identical, claims 31-50 of the current invention are not patentably distinct from claims 1, 4-14, 16, 23-25, 27, 28, 30 and 33 of U.S. Patent No. 12,138,093.
Claims 31-35, 37-47 and 49 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11, 13, 21-23, 25 and 27 of U.S. Patent No. 11,751,831. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 31-35, 37-47 and 49 of the current application are encompassed by the scope of claims 1-11, 13, 21-23, 25 and 27 of U.S. Patent No. 11,751,831 in that claims 1-11, 13, 21-23, 25 and 27 of U.S. Patent No. 11,751,831 contain similar limitations as claims 31-35, 37-47 and 49 of the current application and are therefore not patentably distinct from claims 1-11, 13, 21-23, 25 and 27 of U.S. Patent No. 11,751,831.
Claims 31-35, 37-47 and 49 of the current application recite similar limitations as claims 1-11, 13, 21-23, 25 and 27 of U.S. Patent No. 11,751,831 as follows:
Current Application No. 18/905,755
U.S. Patent No. 11,751,831
31. A computer-implemented method for facilitating assessment of risk of coronary plaque by analyzing density values of coronary plaque of a subject, the method comprising: accessing, by a computer system, image data of a computed tomography (CT) image obtained along a vessel of the subject; determining, by the computer system, based at least in part on applying a machine learning algorithm to the image data, coronary plaque data of the subject, wherein determining the coronary plaque data comprises: identifying a lumen wall and a vessel wall; identifying, based at least in part on the identified lumen wall and vessel wall, a region of coronary plaque in a region of a vessel of the subject; determining density values of pixels within the identified region of coronary plaque; and generating, based at least in part on the determined density values in the identified region of coronary plaque, quantification of different types of plaque; and determining whether the region of the vessel is likely to be ischemic based at least in part on the quantification of the different types of plaque, wherein the determined coronary plaque data and whether the region of the vessel is likely to be ischemic is configured to be used to assess risk of coronary plaque of the subject, wherein the computer system comprises a computer processor and an electronic storage medium.
1. A computer-implemented method for risk assessment of coronary plaque by analyzing metrics generated from radiodensity values of coronary plaque of a subject and previously stored metrics generated from radiodensity values of coronary plaque of a collection of a plurality of other subjects, the method comprising: generating image information for the subject, the image information including image data of computed tomography (CT) scans along a vessel of the subject, the image data having radiodensity values of coronary plaque and radiodensity values of perivascular tissue located adjacent to the coronary plaque, wherein generating the image information comprises: accessing CT scan parameters stored for the subject in a database, the CT scan parameters comprising: a CT scanner type; and one or more of contrast type used in the subject or an injection rate of contrast into the subject; and automatically determining radiodensity values of coronary plaque in the image data; determining, based on applying a machine learning algorithm to the image information, coronary plaque information of the subject, wherein determining the coronary plaque information comprises: quantifying, using the image information, radiodensity values in a region of coronary plaque of the subject; and generating metrics of coronary plaque of the subject using the quantified radiodensity values in the region of coronary plaque; accessing a database of coronary plaque information of a collection of a plurality of other subjects, the coronary plaque information in the database including a collection of metrics generated from radiodensity values of a region of coronary plaque in the plurality of other subjects; and generating a risk assessment of the coronary plaque information of the subject by comparing the metrics of the coronary plaque and one or more characteristics of the subject to the collection of metrics of the coronary plaque of the plurality of other subjects, in the database, having one or more of the one or more characteristics of the subject, wherein the method is performed by one or more computer hardware processors configured to execute computer-executable instructions on a non-transitory computer storage medium.
Claim 32 of the current application corresponds to claim 2 of U.S. Patent No. 11,751,831. Claim 33 of the current application corresponds to claim 3 of U.S. Patent No. 11,751,831. Claim 34 of the current application corresponds to claim 4 of U.S. Patent No. 11,751,831. Claim 35 of the current application corresponds to claim 5 of U.S. Patent No. 11,751,831. Claim 37 of the current application corresponds to claim 6 of U.S. Patent No. 11,751,831. Claim 38 of the current application corresponds to claim 7 of U.S. Patent No. 11,751,831. Claim 39 of the current application corresponds to claim 8 of U.S. Patent No. 11,751,831. Claim 40 of the current application corresponds to claim 9 of U.S. Patent No. 11,751,831. Claim 41 of the current application corresponds to claim 10 of U.S. Patent No. 11,751,831. Claim 42 of the current application corresponds to claim 11 of U.S. Patent No. 11,751,831. Claim 43 of the current application corresponds to claim 13 of U.S. Patent No. 11,751,831. Claim 44 of the current application corresponds to claim 21 of U.S. Patent No. 11,751,831. Claim 45 of the current application corresponds to claim 22 of U.S. Patent No. 11,751,831. Claim 46 of the current application corresponds to claim 23 of U.S. Patent No. 11,751,831. Claim 47 of the current application corresponds to claim 25 of U.S. Patent No. 11,751,831. Claim 49 of the current application corresponds to claim 27 of U.S. Patent No. 11,751,831.
The table above shows that, although the corresponding claims are not identical, claims 31-35, 37-47 and 49 of the current invention are not patentably distinct from claims 1-11, 13, 21-23, 25 and 27 of U.S. Patent No. 11,751,831.
Claims 31, 33, 34, 39, 40, 43, 44 and 50 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 6, 16, 18, 19 and 30 of U.S. Patent No. 11,759,161. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 31, 33, 34, 39, 40, 43, 44 and 50 of the current application are encompassed by the scope of claims 1, 6, 16, 18, 19 and 30 of U.S. Patent No. 11,759,161 in that claims 1, 6, 16, 18, 19 and 30 of U.S. Patent No. 11,759,161 contain similar limitations as claims 31, 33, 34, 39, 40, 43, 44 and 50 of the current application and are therefore not patentably distinct from claims 1, 6, 16, 18, 19 and 30 of U.S. Patent No. 11,759,161.
Claims 31, 33, 34, 39, 40, 43, 44 and 50 of the current application recite similar limitations as claims 1, 6, 16, 18, 19 and 30 of U.S. Patent No. 11,759,161 as follows:
Current Application No. 18/905,755
U.S. Patent No. 11,759,161
31. A computer-implemented method for facilitating assessment of risk of coronary plaque by analyzing density values of coronary plaque of a subject, the method comprising: accessing, by a computer system, image data of a computed tomography (CT) image obtained along a vessel of the subject; determining, by the computer system, based at least in part on applying a machine learning algorithm to the image data, coronary plaque data of the subject, wherein determining the coronary plaque data comprises: identifying a lumen wall and a vessel wall; identifying, based at least in part on the identified lumen wall and vessel wall, a region of coronary plaque in a region of a vessel of the subject; determining density values of pixels within the identified region of coronary plaque; and generating, based at least in part on the determined density values in the identified region of coronary plaque, quantification of different types of plaque; and determining whether the region of the vessel is likely to be ischemic based at least in part on the quantification of the different types of plaque, wherein the determined coronary plaque data and whether the region of the vessel is likely to be ischemic is configured to be used to assess risk of coronary plaque of the subject, wherein the computer system comprises a computer processor and an electronic storage medium.
1. A computer-implemented method for risk assessment of coronary plaque by analyzing metrics generated from density values of coronary plaque and density values of perivascular tissue of a subject and previously stored metrics generated from density values of coronary plaque and density values of perivascular tissue of other people, the method comprising: generating image information for the subject, the image information including image data of computed tomography (CT) scans along a vessel of the subject, the image data having density values of coronary plaque and density values of perivascular tissue, wherein generating the image information comprises: accessing CT scan parameters stored for the subject in a database, the CT scan parameters comprising: one or more of a CT scanner type, CT scanner tube amperage, or CT scanner peak tube voltage of the CT scanner used to generate the CT scans; one or more of the CT scanner noise, CT scanner signal-to-noise ratio, or CT scanner contrast-to-noise ratio of the CT scanner used to generate the CT scans; and one or more of contrast type used in the subject for the CT scans or an injection rate of contrast into the subject; and automatically determining density values of coronary plaque and density values of perivascular tissue in the image data; determining, based on applying a machine learning algorithm to the image information, coronary plaque information of the subject, wherein determining the coronary plaque information comprises: quantifying, using the image information, density values in a region of coronary plaque of the subject; quantifying, using the image information density values in a region of perivascular tissue of the subject; and generating metrics of coronary plaque of the subject using the quantified density values in the region of coronary plaque and the quantified density values in the region of perivascular tissue; accessing a database of coronary plaque information of other people, the coronary plaque information in the database including metrics generated from density values of a region of coronary plaque in the other people and density values of a region of perivascular tissue in the other people; and generating a risk assessment of the coronary plaque information of the subject by comparing the metrics of the coronary plaque of the subject to the metrics of the coronary plaque of other people in the database, wherein the method is performed by one or more computer hardware processors configured to execute computer-executable instructions on a non-transitory computer storage medium.
Claim 33 of the current application corresponds to claim 18 of U.S. Patent No. 11,759,161. Claim 34 of the current application corresponds to claim 16 of U.S. Patent No. 11,759,161. Claim 39 of the current application corresponds to claim 1 of U.S. Patent No. 11,759,161. Claim 40 of the current application corresponds to claim 1 of U.S. Patent No. 11,759,161. Claim 43 of the current application corresponds to claim 6 of U.S. Patent No. 11,759,161. Claim 44 of the current application corresponds to claim 19 of U.S. Patent No. 11,759,161. Claim 50 of the current application corresponds to claim 30 of U.S. Patent No. 11,759,161.
The table above shows that, although the corresponding claims are not identical, claims 31, 33, 34, 39, 40, 43, 44 and 50 of the current invention are not patentably distinct from claims 1, 6, 16, 18, 19 and 30 of U.S. Patent No. 11,759,161.
Claims 31, 33-35, 37-39 and 41-43 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, 6, 7, 11, 13-16 and 20 of U.S. Patent No. 11,350,899. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 31, 33-35, 37-39 and 41-43 of the current application are encompassed by the scope of claims 1, 4, 6, 7, 11, 13-16 and 20 of U.S. Patent No. 11,350,899 in that claims 1, 4, 6, 7, 11, 13-16 and 20 of U.S. Patent No. 11,350,899 contain similar limitations as claims 31, 33-35, 37-39 and 41-43 of the current application and are therefore not patentably distinct from claims 1, 4, 6, 7, 11, 13-16 and 20 of U.S. Patent No. 11,350,899.
Claims 31, 33-35, 37-39 and 41-43 of the current application recite similar limitations as claims 1, 4, 6, 7, 11, 13-16 and 20 of U.S. Patent No. 11,350,899 as follows:
Current Application No. 18/905,755
U.S. Patent No. 11,350,899
31. A computer-implemented method for facilitating assessment of risk of coronary plaque by analyzing density values of coronary plaque of a subject, the method comprising: accessing, by a computer system, image data of a computed tomography (CT) image obtained along a vessel of the subject; determining, by the computer system, based at least in part on applying a machine learning algorithm to the image data, coronary plaque data of the subject, wherein determining the coronary plaque data comprises: identifying a lumen wall and a vessel wall; identifying, based at least in part on the identified lumen wall and vessel wall, a region of coronary plaque in a region of a vessel of the subject; determining density values of pixels within the identified region of coronary plaque; and generating, based at least in part on the determined density values in the identified region of coronary plaque, quantification of different types of plaque; and determining whether the region of the vessel is likely to be ischemic based at least in part on the quantification of the different types of plaque, wherein the determined coronary plaque data and whether the region of the vessel is likely to be ischemic is configured to be used to assess risk of coronary plaque of the subject, wherein the computer system comprises a computer processor and an electronic storage medium.
1. A method for characterization of coronary plaque tissue data and perivascular tissue data using image information that includes image data gathered from a computed tomography (CT) scan along a blood vessel, the image data including radiodensity values of coronary plaque and perivascular tissue located adjacent to the coronary plaque, the method comprising: quantifying, in the image data, radiodensity values in a region of coronary plaque; quantifying, in the image data, radiodensity values in a region of corresponding perivascular tissue adjacent to the coronary plaque; determining a slope of a gradient of the quantified radiodensity values in the region of coronary plaque; determining a slope of a gradient of the quantified radiodensity values in the region of perivascular tissue; characterizing the coronary plaque by analyzing the slope of the gradient of the quantified radiodensity values in the region of coronary plaque, the slope of the gradient of the quantified radiodensity values in the region of perivascular tissue, and previously classified gradients of radiodensity values in image data from other subjects, wherein the method is performed by one or more computer hardware processors configured to execute computer-executable instructions on a non-transitory computer storage medium.
Claim 31 of the current application corresponds to claims 1 and 16 of U.S. Patent No. 11,350,899. Claim 33 of the current application corresponds to claim 13 of U.S. Patent No. 11,350,899. Claim 34 of the current application corresponds to claim 11 of U.S. Patent No. 11,350,899. Claim 35 of the current application corresponds to claims 14 and 20 of U.S. Patent No. 11,350,899. Claim 37 of the current application corresponds to claim 15 of U.S. Patent No. 11,350,899. Claim 38 of the current application corresponds to claim 4 of U.S. Patent No. 11,350,899. Claim 39 of the current application corresponds to claim 4 of U.S. Patent No. 11,350,899. Claim 41 of the current application correspond to claim 4 of U.S. Patent No. 11,350,899. Claim 42 of the current application corresponds to claim 6 of U.S. Patent No. 11,350,899. Claim 43 of the current application corresponds to claim 7 of U.S. Patent No. 11,350,899.
The table above shows that, although the corresponding claims are not identical, claims 31, 33-35, 37-39 and 41-43 of the current application are not patentably distinct from claims 1, 4, 6, 7, 11, 13-16 and 20 of U.S. Patent No. 11,350,899.
Claims 31, 33-35, 37, 38 and 44 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 11, 18, 20-22 and 24 of U.S. Patent No. 10,813,612. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 31, 33-35, 37, 38 and 44 of the current application are encompassed by the scope of claims 1, 2, 11, 18, 20-22 and 24 of U.S. Patent No. 10,813,612 in that claims 1, 2, 11, 18, 20-22 and 24 of U.S. Patent No. 10,813,612 contain similar limitations as claims 31, 33-35, 37, 38 and 44 of the current application and are therefore not patentably distinct from claims 1, 2, 11, 18, 20-22 and 24 of U.S. Patent No. 10,813,612.
Claims 31, 33-35, 37, 38 and 44 of the current application recite similar limitations as claims 1, 2, 11, 18, 20-22 and 24 of U.S. Patent No. 10,813,612 as follows:
Current Application No. 18/905,755
U.S. Patent No. 10,813,612
31. A computer-implemented method for facilitating assessment of risk of coronary plaque by analyzing density values of coronary plaque of a subject, the method comprising: accessing, by a computer system, image data of a computed tomography (CT) image obtained along a vessel of the subject; determining, by the computer system, based at least in part on applying a machine learning algorithm to the image data, coronary plaque data of the subject, wherein determining the coronary plaque data comprises: identifying a lumen wall and a vessel wall; identifying, based at least in part on the identified lumen wall and vessel wall, a region of coronary plaque in a region of a vessel of the subject; determining density values of pixels within the identified region of coronary plaque; and generating, based at least in part on the determined density values in the identified region of coronary plaque, quantification of different types of plaque; and determining whether the region of the vessel is likely to be ischemic based at least in part on the quantification of the different types of plaque, wherein the determined coronary plaque data and whether the region of the vessel is likely to be ischemic is configured to be used to assess risk of coronary plaque of the subject, wherein the computer system comprises a computer processor and an electronic storage medium.
1. A method for characterization of coronary plaque tissue data and perivascular tissue data using image data gathered from a computed tomography (CT) scan along a blood vessel, the image data including radiodensity values of coronary plaque and perivascular tissue located adjacent to the coronary plaque, the method comprising: quantifying, in the image data, radiodensity values in regions of coronary plaque; quantifying, in the image data, radiodensity values in at least one region of corresponding perivascular tissue adjacent to the coronary plaque; determining gradients of the quantified radiodensity values within the coronary plaque and the quantified radiodensity values within the corresponding perivascular tissue, wherein the determined gradients comprise one or more gradients of the quantified radiodensity values along a line in the image data, wherein the line extends from at least one region of the coronary plaque to the at least one region of corresponding perivascular tissue; determining one or more ratios between the quantified radiodensity values within the coronary plaque and the quantified radiodensity values within the corresponding perivascular tissue; characterizing the coronary plaque by analyzing one or more of: the gradients of the quantified radiodensity values within the coronary plaque and the quantified radiodensity values within the corresponding perivascular tissue, or the one or more ratios between the quantified radiodensity values within the coronary plaque and the quantified radiodensity values within the corresponding perivascular tissue; determining a plot of a change in the quantified radiodensity values relative to baseline radiodensity values in each of one or more concentric layers of the perivascular tissue with respect to a distance from an outer wall of the blood vessel up to an end distance; determining an area of a region bound by the plot of the change in the quantified radiodensity values and a plot of the baseline radiodensity values with respect to the distance from the outer wall of the blood vessel up to the end distance; and dividing said area by the quantified radiodensity values measured at the distance from the outer wall of the blood vessel, wherein the distance is less than a radius of the blood vessel or is a distance from an outer surface of the blood vessel above which quantified radiodensity values of adipose tissue drops by more than 5% compared to baseline radiodensity values of adipose tissue in a blood vessel of the same type free of disease, wherein the method is performed by one or more computer hardware processors configured to execute computer-executable instructions on a non-transitory computer storage medium.
2. The method of claim 1, wherein the perivascular tissue comprises at least one of coronary artery lumen, fat, coronary plaque or myocardium.
Claim 33 of the current application corresponds to claim 20 of U.S. Patent No. 10,813,612. Claim 34 of the current application corresponds to claim 18 of U.S. Patent No. 10,813,612. Claim 35 of the current application correspond to claim 21 of U.S. Patent No. 10,813,612. Claim 37 of the current application corresponds to claim 22 of U.S. Patent No. 10,813,612. Claims 38 and 41 of the current application correspond to claim 11 U.S. Patent No. 10,813,612. Claim 44 of the current application corresponds to claim 24 of U.S. Patent No. 10,813,612.
The table above shows that, although the corresponding claims are not identical, claims 31, 33-35, 37, 38 and 44 of the current application are not patentably distinct from claims 1, 2, 11, 18, 20-22 and 24 of U.S. Patent No. 10,813,612.
Conclusion
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/TRACY MANGIALASCHI/Primary Examiner, Art Unit 2668
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