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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 14, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 5, 7, 11, 12 and 13 are rejected under 35 U.S.C 103 as being unpatentable over Rapaka et al. US Patent Publication No. US-20200160527-A1 (hereinafter Rapaka) in view of Hanratty US Patent Application Publication No. US-12444506-B2 (hereinafter Hanratty).
Regarding claim 1, Rapaka discloses a method for automated segmentation and analysis of an anatomy of interest from magnetic resonance image data, the method comprising (Rapaka in [0003] discloses, “systems and methods are provided for evaluating an aorta of a patient. A medical image of an aorta of a patient is received and the aorta is segmented from the medical image. A measurement plane is identified on the segmented aorta and a measurement is calculated at the measurement plane”. Additionally, Rapaka in [0019] discloses about MRI)): accessing magnetic resonance image data with a computer system, wherein the magnetic resonance image data comprise magnetic resonance images acquired from a subject using a magnetic resonance imaging (MRI) system (Rapaka in [0019] discloses, “workstation 102 may receive medical images of patient 106 from one or more medical imaging systems 104. Medical imaging system 104 may be of any modality, such as, e.g., a two-dimensional (2D) or three-dimensional (3D) computed tomography (CT), x-ray, magnetic resonance imaging (MRI)” wherein medical images of a patient equates to a subject).
Rapaka doesn’t disclose the following limitation as further recited in the claim.
Hanratty discloses generating segmented anatomy volume data from the magnetic resonance image data using the computer system, wherein the segmented anatomy volume data comprise a three-dimensional (3D) volume of an anatomy of interest that is segmented from the magnetic resonance image data (Hanratty in [Column – 17, Line 26 – 34] discloses, “3D surface mesh model generation module 120 may be executed by processor 102 for generating a 3D surface mesh model of the patient specific anatomical features within the medical images based on the results of the segmentation algorithm as well as the results of the anatomical feature identification algorithm, described above, and for extracting a 3D surface mesh model from the scalar volumes to generate a 3D printable model”. Hanratty in [Column – 28, Line 49 – 52] discloses about anatomy of interest, “a color map of the pixel intensities may be mapped directly to the 3D voxel intensities within only the segmentation to allow for specific volumetric visualization of the isolated anatomical feature”); generating centerline tracking data from the segmented anatomy volume data using the computer system, wherein the centerline tracking data comprise a centerline of the 3D volume of the anatomy of interest (Hanratty in [Column – 7, Line 50 – 62] discloses, “raycasting at predefined intervals along an axis ... direction of travel and determining distances between intersections of each ray cast and the 3D surface mesh model; calculating a center point at each interval by triangulating the distances between intersections of each ray cast ... adjusting the direction of travel at each interval based on a directional vector between adjacent calculated center points, such that raycasting at the predefined intervals occur in at least three directions perpendicular to the adjusted direction of travel at each interval; and calculating a centerline of the isolated anatomical feature based on the calculated center points from the start point to the end point”); generating, with the computer system, quantitative measures of the anatomy of interest based on the segmented anatomy volume data and the centerline tracking data (Hanratty in [Column – 18, Line 15 – 22] discloses, “FIGS. 3 to 24F, the physiological measurements associated with the selected pathologies determined by physiological information generation module 124 may include, but are not limited to, volume, cross-sectional area, diameter, centerline, surface, density, thickness, tortuosity, fracture size and location, blood clots, occlusions, and rate of growth over time of the anatomical feature and/or corresponding landmark”); and outputting at least one of the segmented anatomy volume data, the centerline tracking data, or the quantitative measures using the computer system (Hanratty in [Column – 13, Line 42 – 48] discloses, “User interface 108 may be used to receive inputs from, and/or provide outputs to, a user. For example, user interface 108 may include a touchscreen, display, switches, dials, lights, etc. Accordingly, user interface 108 may display information such as 3D surface mesh models, physiological measurements, heat maps, list of available medical devices for a patient specific pathology”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Hanratty into the system of Rapaka because centerline of the 3D volume provides a consistent reference path through the curved anatomy.
Summary of Citations (Hanratty)
[Column – 7, Line 50 – 62]; “raycasting at predefined intervals along an axis ... direction of travel and determining distances between intersections of each ray cast and the 3D surface mesh model; calculating a center point at each interval by triangulating the distances between intersections of each ray cast ... adjusting the direction of travel at each interval based on a directional vector between adjacent calculated center points, such that raycasting at the predefined intervals occur in at least three directions perpendicular to the adjusted direction of travel at each interval; and calculating a centerline of the isolated anatomical feature based on the calculated center points from the start point to the end point”.
[Column – 13, Line 42 – 48]; “User interface 108 may be used to receive inputs from, and/or provide outputs to, a user. For example, user interface 108 may include a touchscreen, display, switches, dials, lights, etc. Accordingly, user interface 108 may display information such as 3D surface mesh models, physiological measurements, heat maps, list of available medical devices for a patient specific pathology”.
[Column – 17, Line 26 – 34]; “3D surface mesh model generation module 120 may be executed by processor 102 for generating a 3D surface mesh model of the patient specific anatomical features within the medical images based on the results of the segmentation algorithm as well as the results of the anatomical feature identification algorithm, described above, and for extracting a 3D surface mesh model from the scalar volumes to generate a 3D printable model”.
[Column – 18, Line 15 – 22]; “FIGS. 3 to 24F, the physiological measurements associated with the selected pathologies determined by physiological information generation module 124 may include, but are not limited to, volume, cross-sectional area, diameter, centerline, surface, density, thickness, tortuosity, fracture size and location, blood clots, occlusions, and rate of growth over time of the anatomical feature and/or corresponding landmark”.
[Column – 28, Line 49 – 52]; “a color map of the pixel intensities may be mapped directly to the 3D voxel intensities within only the segmentation to allow for specific volumetric visualization of the isolated anatomical feature”.
Summary of Citations (Rapaka)
Paragraph [0003]; “systems and methods are provided for evaluating an aorta of a patient. A medical image of an aorta of a patient is received and the aorta is segmented from the medical image. A measurement plane is identified on the segmented aorta and a measurement is calculated at the measurement plane”.
Paragraph [0019]; “workstation 102 may receive medical images of patient 106 from one or more medical imaging systems 104. Medical imaging system 104 may be of any modality, such as, e.g., a two-dimensional (2D) or three-dimensional (3D) computed tomography (CT), x-ray, magnetic resonance imaging (MRI)”.
Regarding claim 2, Hanratty in the combination discloses the method of claim 1, wherein generating the segmented anatomy volume data comprises: accessing a machine learning model with the computer system (Hanratty in [Column – 14, Line 34 – 41] discloses, “Segmentation module 114 may be executed by processor 102 for automated segmentation of the medical images .... Specifically, segmentation module 114 may use machine learning based image segmentation techniques”), wherein the machine learning model has been trained on training data to segment anatomy from magnetic resonance images (Hanratty in [Column – 1, Line 62 – 65] discloses, “These models may be trained using curated semantically labeled datasets. To produce a 3D segmentation, a neural network or machine learning algorithm is trained to identify the anatomical features within a set of medical images”. Furthermore, Hanratty in [Column – 14, Line 7 – 11] discloses about MRI); and inputting the magnetic resonance image data to the machine learning model (Hanratty in [Column – 14, Line 7 – 11] discloses about inputting MRI medical image data. Furthermore, Hanratty in [Column – 14, Line 34 – 41] discloses, “Segmentation module 114 may be executed by processor 102 for automated segmentation of the medical images received by image receiver module 112 .... Specifically, segmentation module 114 may use machine learning based image segmentation techniques”), generating the segmented anatomy volume data as an output (Hanratty in [Column – 1, Line 62 – 65] discloses, “To produce a 3D segmentation, a neural network or machine learning algorithm is trained to identify the anatomical features within a set of medical images”).
Summary of Citations (Hanratty)
[Column – 1, Line 62 – 65]; “These models may be trained using curated semantically labeled datasets. To produce a 3D segmentation, a neural network or machine learning algorithm is trained to identify the anatomical features within a set of medical images”.
[Column – 14, Line 7 – 11]; “Image receiver module 112 may be executed by processor 102 for receiving standard medical images, e.g., 2D and/or 3D medical images, of one or more patient specific anatomical features taken from one or a combination of the following: CT, MRI, PET, and/or SPCET scanner”.
[Column – 14, Line 34 – 41]; “Segmentation module 114 may be executed by processor 102 for automated segmentation of the medical images received by image receiver module 112 .... Specifically, segmentation module 114 may use machine learning based image segmentation techniques”.
Regarding claim 4, Hanratty in the combination discloses the method of claim 1, wherein generating the centerline tracking data (Hanratty in [Column – 20, Line 17 – 25] discloses, “FIG. 5, exemplary method 500 for generating centerline measurements of a patient specific anatomical feature is provided ... centerline measurements of the patient specific anatomical feature associated with the selected pathology, may be generated from the generated 3D surface mesh model”) comprises selecting a starting point in the segmented anatomy volume data and tracking a line from the starting point to a next point to define a line segment of the centerline (Hanratty in [Column – 21, Line 5 – 9] discloses about starting point to a next point, “At step 510, a new direction of travel may be determined at each interval based on a directional vector extending from the previous center point of the previous interval and the current center point”. Additionally, Hanratty in [Column – 21, Line 9-11] discloses about line segment, “in FIG. 6, the new direction of travel at the first interval may be consistent with a directional vector extending from start point SP to center point CP1”).
Summary of Citations (Hanratty)
[Column – 20, Line 17 – 25]; “FIG. 5, exemplary method 500 for generating centerline measurements of a patient specific anatomical feature is provided ... centerline measurements of the patient specific anatomical feature associated with the selected pathology, may be generated from the generated 3D surface mesh model”.
[Column – 21, Line 5 – 9]; “At step 510, a new direction of travel may be determined at each interval based on a directional vector extending from the previous center point of the previous interval and the current center point”.
[Column – 21, Line 9-11]; “in FIG. 6, the new direction of travel at the first interval may be consistent with a directional vector extending from start point SP to center point CP1”.
Regarding claim 5, Hanratty in the combination discloses the method of claim 4, wherein the starting point is automatically determined from the segmented anatomy volume data (Hanratty in [Column – 19, Line 40 – 42] discloses, “The start and end points further may be close to a bounding box of the 3D surface mesh model, and on a common plane”).
Summary of Citations (Hanratty)
[Column – 19, Line 40 – 42]; “The start and end points further may be close to a bounding box of the 3D surface mesh model, and on a common plane”.
Regarding claim 7, Hanratty in the combination discloses the method of claim 1, wherein the quantitative measure comprises a maximum cross-sectional diameter of the 3D volume of the anatomy of interest measured from the centerline (Hanratty in [Column – 29, Line 24 – 27] discloses, “Once the 3D surface mesh model is generated from the automated segmentation, it will be possible to generate a number of measurements about the anatomy or pathology in the medical scan”. Furthermore, Hanratty in [Column – 8, Line 1 – 6] discloses, “determining start and end points of the isolated anatomical feature and a directional vector from the start point to the end point; establishing cutting planes at predefined intervals along the centerline based on the directional vector from the start point to the end point, each cutting plane perpendicular to a direction of travel of the centerline at each interval” wherein ‘cutting plane” equates to cross- sectional. Hanratty in [Column – 3, Line 1 – 6] discloses about determining diameter and maximum distance, “the following pseudocode outlines how vessel length, diameter and curvature information may be automatically collected without human intervention ... raycast at different equidistant angles -take longest distance”).
Summary of Citations (Hanratty)
[Column – 29, Line 64 – 67 & Column – 30, Line 1 – 13]; “the following pseudocode outlines how vessel length, diameter and curvature information may be automatically collected without human intervention ... raycast at different equidistant angles -take longest distance”.
[Column – 8, Line 1 – 6]; “determining start and end points of the isolated anatomical feature and a directional vector from the start point to the end point; establishing cutting planes at predefined intervals along the centerline based on the directional vector from the start point to the end point, each cutting plane perpendicular to a direction of travel of the centerline at each interval”.
[Column – 29, Line 24 – 27]; “Once the 3D surface mesh model is generated from the automated segmentation, it will be possible to generate a number of measurements about the anatomy or pathology in the medical scan”.
Regarding claim 11, Hanratty in the combination discloses the method of claim 1, wherein the quantitative measures comprise a heat map that indicates regions where there are deformations in the anatomy of interest indicative of longitudinal changes in the subject measured relative to previously generated segmented anatomy volume data from the subject (Hanratty in [Column – 27, Line 43 – 55] discloses, “the analyzed physiological parameters of the isolated anatomical feature may be timestamped and recorded, such that over time, there is a chronological record of the physiological parameters for a specific patient. At step 2208, changes between the recorded/timestamped physiological parameters over time may be calculated to indicate, e.g., progression and prognosis of the selected pathology. ... a heat map may be generated to visually depict the changes between the recorded/timestamped physiological parameters over time, as shown in FIGS. 24A to 24F”).
Summary of Citations (Hanratty)
[Column – 27, Line 43 – 55]; “the analyzed physiological parameters of the isolated anatomical feature may be timestamped and recorded, such that over time, there is a chronological record of the physiological parameters for a specific patient. At step 2208, changes between the recorded/timestamped physiological parameters over time may be calculated to indicate, e.g., progression and prognosis of the selected pathology. ... a heat map may be generated to visually depict the changes between the recorded/timestamped physiological parameters over time, as shown in FIGS. 24A to 24F”.
Regarding claim 12, Hanratty in the combination discloses the method of claim 1, wherein the anatomy of interest is an aorta and the segmented anatomy volume data comprise a 3D model of an aortic arch of the aorta (Hanratty in [Column – 28, Line 12 – 19] discloses, “Moreover, the length of the anatomical feature, e.g., a vessel, may be calculated based on the output from the automated segmentation algorithm and subsequent 3D reconstruction. The data extracted from the 3D reconstruction may then be automatically analyzed to output a length from one specific anatomical landmark or abnormality to another, e.g., the length from the aortic arch to the thrombus in the case of a stroke”).
Summary of Citations (Hanratty)
[Column – 28, Line 12 – 19]; “Moreover, the length of the anatomical feature, e.g., a vessel, may be calculated based on the output from the automated segmentation algorithm and subsequent 3D reconstruction. The data extracted from the 3D reconstruction may then be automatically analyzed to output a length from one specific anatomical landmark or abnormality to another, e.g., the length from the aortic arch to the thrombus in the case of a stroke”.
Regarding claim 13, Rapaka in the combination discloses the method of claim 1, wherein outputting the at least one of the segmented anatomy volume data, the centerline tracking data, or the quantitative measures comprises overlaying the segmented anatomy volume data on the magnetic resonance image data and displaying the overlaid segmented anatomy volume data and magnetic resonance image data to a user using the computer system (Rapaka in [0036] discloses, the segmented aorta, aortic centerline, and locations of the one or more measurement planes may be shown overlaid on top of the originally received medical image in a curved multiplanar reconstruction (MPR) view”).
Summary of Citations (Rapaka)
Paragraph [0036]; the segmented aorta, aortic centerline, and locations of the one or more measurement planes may be shown overlaid on top of the originally received medical image in a curved multiplanar reconstruction (MPR) view”.
Claim 3 is rejected under 35 U.S.C 103 as being unpatentable over Rapaka in view of Hanratty and further in view of Katakol “Fully automated pipeline for measurement of the thoracic aorta using joint segmentation and localization neural network” (hereinafter Katakol).
Regarding claim 3, Hanratty in the combination discloses the method of claim 2.
Rapaka and Hanratty don’t disclose the following limitation as further recited in the claim.
Katakol discloses the machine learning model comprises a neural network implementing a U-Net architecture (Katakol in [Section 2.3, Paragraph – 1] discloses, “The network utilizes a 3D UNet14 stem (with shared parameters θ) fshared θ separate branches gseg and two ϕ and gll ψ for segmentation and landmark localization, respectively (Fig. 2)”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Katakol into the system of Rapaka in view of Hanratty because it would allow the model in the system to take MRI images as input and automatically identify and segment the anatomy of interest.
Summary of Citations (Katakol)
[Section 2.3, Paragraph – 1]; “The network utilizes a 3D UNet14 stem (with shared parameters θ) fshared θ separate branches gseg and two ϕ and gll ψ for segmentation and landmark localization, respectively (Fig. 2)”.
Claims 8 – 10 are rejected under 35 U.S.C 103 as being unpatentable over Rapaka in view of Hanratty and further in view of Markl Patent Publication No. WO-2025097129-A1 (hereinafter Markl).
Regarding claim 8, Hanratty in the combination discloses the method of claim 1.
Rapaka and Hanratty don’t disclose the following limitation as further recited in the claim.
Markl discloses the quantitative measure comprises a heat map that indicates regions where there are deformations in the anatomy of interest relative to normative measures (Markl in [0032] discloses, “the heatmaps can indicate longitudinal measurement changes or measurement comparisons between an individual subject with reference data or population norms. As one non-limiting example, a heatmap is provided in FIG. 7C in which the heatmap indicates regions of the aorta that have abnormal diameter. In this implementation, the heatmap identifies regions with a diameter of ± 2<J.sub.SD in which // represents the mean and <J.sub.SD represents the standard deviation for the corresponding region within a healthy control population”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Markl into the system of Rapaka in view of Hanratty because it would allow the system to show where the patient’s anatomy differs from expected normal anatomy.
Summary of Citations (Markl)
Paragraph [0032]; “the heatmaps can indicate longitudinal measurement changes or measurement comparisons between an individual subject with reference data or population norms. As one non-limiting example, a heatmap is provided in FIG. 7C in which the heatmap indicates regions of the aorta that have abnormal diameter. In this implementation, the heatmap identifies regions with a diameter of ± 2<J.sub.SD in which // represents the mean and <J.sub.SD represents the standard deviation for the corresponding region within a healthy control population”.
Regarding claim 9, Markl in the combination discloses the method of claim 8, wherein the heat map comprises a 3D heat map (Markl in [0016] discloses, “the mapped aorta 3D imaging data can be referenced or compared to an appropriate control population (e.g., matched for age. sex, race, ethnicity, risk profile, etc.) to generate a personalized aorta heatmap”).
Summary of Citations (Markl)
Paragraph [0016]; “the mapped aorta 3D imaging data can be referenced or compared to an appropriate control population (e.g., matched for age. sex, race, ethnicity, risk profile, etc.) to generate a personalized aorta heatmap”.
Regarding claim 10, Markl in the combination discloses the method of claim 8, wherein the normative measures comprise measures from at least one of an age-matched cohort, a sex-matched cohort, a race-matched cohort, or a disease- matched cohort (Markl in [0017] discloses, “calculation of personalized aorta heatmaps by referencing the mapped aorta data to an appropriate control population (e.g., matched for age, sex, race ethnicity’, risk profile, etc.)”).
Summary of Citations (Markl)
Paragraph [0017]; “calculation of personalized aorta heatmaps by referencing the mapped aorta data to an appropriate control population (e.g., matched for age, sex, race ethnicity’, risk profile, etc.)”.
Claim 14 is rejected under 35 U.S.C 103 as being unpatentable over Rapaka in view of Hanratty and further in view of Walker US Patent Publication No. US-20140074502-A1 (hereinafter Walker).
Regarding claim 14, Hanratty in the combination discloses the method of claim 13, wherein the magnetic resonance images contained in the magnetic resonance image data comprise DICOM images (Hanratty in [Column – 14, Line 7 – 13] discloses, “Image receiver module 112 may be executed by processor 102 for receiving standard medical images, e.g., 2D and/or 3D medical images, of one or more patient specific anatomical features taken from one or a combination of the following: CT, MRI, PET, and/or SPCET scanner. The medical images may be formatted in a standard compliant manner such as with DICOM”).
Rapaka further discloses overlaying the segmented anatomy volume data on the magnetic resonance image data comprises (Rapaka in [0036] discloses, the segmented aorta, aortic centerline, and locations of the one or more measurement planes may be shown overlaid on top of the originally received medical image in a curved multiplanar reconstruction (MPR) view”).
Rapaka and Hanratty don’t disclose the following limitation as further recited in the claim.
Walker discloses converting the segmented anatomy volume data to a data structure that is compatible with the DICOM images (Walker in [0030] discloses, “a conversion engine can perform image recognition on received data (e.g., medical image data and/or other types of structured clinical reports) by identifying data therein, segmenting the report, using a library to perform value object extraction on the segments, and converting the value objects to a standard DICOM format”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Walker into the system of Rapaka in view Hanratty because it would make the automated results more usable within existing medical imaging system and DICOM viewers.
Summary of Citations (Rapaka)
Paragraph [0036]; the segmented aorta, aortic centerline, and locations of the one or more measurement planes may be shown overlaid on top of the originally received medical image in a curved multiplanar reconstruction (MPR) view”.
Summary of Citations (Walker)
Paragraph [0030]; “a conversion engine can perform image recognition on received data (e.g., medical image data and/or other types of structured clinical reports) by identifying data therein, segmenting the report, using a library to perform value object extraction on the segments, and converting the value objects to a standard DICOM format”.
Summary of Citations (Hanratty)
[Column – 14, Line 7 – 13]; “Image receiver module 112 may be executed by processor 102 for receiving standard medical images, e.g., 2D and/or 3D medical images, of one or more patient specific anatomical features taken from one or a combination of the following: CT, MRI, PET, and/or SPCET scanner. The medical images may be formatted in a standard compliant manner such as with DICOM”.
Claim 15 is rejected under 35 U.S.C 103 as being unpatentable over Rapaka in view of Hanratty and further in view of Elbaz Patent Publication No. WO-2020102154-A1 (hereinafter Elbaz).
Regarding claim 15, Hanratty in the combination discloses the method of claim 1.
Rapaka and Hanratty don’t disclose the following limitation as further recited in the claim.
Elbaz discloses outputting the at least one of the segmented anatomy volume data, the centerline tracking data, or the quantitative measures comprises displaying at least one of the segmented anatomy volume data and the centerline tracking data in one of a virtual reality environment, an augmented reality environment, or an extended reality environment (Elbaz in [0024] discloses, “the reference point can include a centerline of the segmented volume or another linear or curvilinear path extending through all or a part of the segmented volume. For instance, the centerline, or other linear or curvilinear path, can be generated by inputting the segmented volume to a curve detection algorithm in order to generate the centerline or other linear or curvilinear path”. Furthermore, Elbaz in [0058] discloses about displaying in VR, “display 404 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, a virtual reality ("VR”) system, an augmented reality ("AR”) system, and so on”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Elbaz into the system of Rapaka in view of Hanratty because 3D clinical visualization can make the location and pathology easier to assess and help in planning treatment and surgery.
Summary of Citations (Elbaz)
Paragraph [0024]; “the reference point can include a centerline of the segmented volume or another linear or curvilinear path extending through all or a part of the segmented volume. For instance, the centerline, or other linear or curvilinear path, can be generated by inputting the segmented volume to a curve detection algorithm in order to generate the centerline or other linear or curvilinear path”.
Paragraph [0058]; “display 404 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, a virtual reality ("VR”) system, an augmented reality ("AR”) system, and so on”.
Claim 16 is rejected under 35 U.S.C 103 as being unpatentable over Rapaka in view of Hanratty and further in view of Li Patent Publication No. US-11837354-B2 (hereinafter Li).
Regarding claim 16, Hanratty in the combination discloses the method of claim 1.
Rapaka and Hanratty don’t disclose the following limitation as further recited in the claim.
Li discloses the magnetic resonance image data comprise magnetic resonance images acquired from the subject without use of a contrast agent (Li in [Column – 2, Line 1 – 6] discloses, “in liver cancer diagnosis, non-contrast enhanced MR imaging (NCEMRI) obtained without CA injection can barely distinguish areas of hemangioma (a benign tumor) and hepatocellular carcinoma (HCC, a malignant tumor)”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Li into the system of Rapaka in view of Hanratty because it would allow the system supports a more consistent workflow for automatic aortic measurement.
Summary of Citations (Li)
[Column – 2, Line 1 – 6]; “in liver cancer diagnosis, non-contrast enhanced MR imaging (NCEMRI) obtained without CA injection can barely distinguish areas of hemangioma (a benign tumor) and hepatocellular carcinoma (HCC, a malignant tumor)”.
Claims 21 – 23 are rejected under 35 U.S.C 103 as being unpatentable over Rapaka in view of Hanratty and Elbaz and further in view of Markl.
Regarding claim 21, Hanratty in the combination discloses the method of claim 15.
Rapaka, Hanratty and Elbaz don’t disclose the following limitation as further recited in the claim.
Markl discloses the quantitative measures comprise a heat map that indicates regions where there are deformations in the aortic arch relative to normative measures (Markl in [0016] discloses, “The aorta heatmap can be used to visualize significant differences in regional aortic dimensions compared to the control population”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Markl into the system of Rapaka in view of Hanratty and Elbaz because it would allow the system would be able to show where the patients aorta differs from expected normal anatomy in terms of size or shape.
Regarding claim 22, Markl in the combination discloses the method of claim 21, wherein the normative measures comprise measures from at least one of an age-matched cohort, a sex-matched cohort, a race-matched cohort, or a disease-matched cohort (Markl in [0017] discloses, “calculation of personalized aorta heatmaps by referencing the mapped aorta data to an appropriate control population (e.g., matched for age, sex, race ethnicity’, risk profile, etc.)”).
Summary of Citations (Markl)
Paragraph [0017]; “calculation of personalized aorta heatmaps by referencing the mapped aorta data to an appropriate control population (e.g., matched for age, sex, race ethnicity’, risk profile, etc.)”.
Regarding claim 23, Hanratty in the combination discloses the method of claim 15.
Rapaka, Hanratty and Elbaz don’t disclose the following limitation as further recited in the claim.
Markl discloses the quantitative measures comprise a heat map that indicates regions where there are deformations in the aortic arch indicative of longitudinal changes in the subject measured relative to previously generated segmented anatomy volume data from the subject (Markl in [0032] discloses, “the heatmaps can indicate longitudinal measurement changes or measurement comparisons between an individual subject with reference data or population norms. As one non-limiting example, a heatmap is provided in FIG. 7C in which the heatmap indicates regions of the aorta that have abnormal diameter”).
Summary of Citations (Markl)
Paragraph [0032]; “the heatmaps can indicate longitudinal measurement changes or measurement comparisons between an individual subject with reference data or population norms. As one non-limiting example, a heatmap is provided in FIG. 7C in which the heatmap indicates regions of the aorta that have abnormal diameter”.
Allowable Subject Matter
Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter.
Regarding claim 6, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Rapaka (US-20200160527-A1), Hanratty (US-12444506-B2), Walker (US-20140074502-A1), Li (US-11837354-B2), Markl (WO-2025097129-A1), Elbaz (WO-2020102154-A1), Gamechi (Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT) and Narikiyo (JP-2022175748-A) doesn’t disclose the limitation, searching for an updated plane using a wobble function to apply inclination angles to the plane to find the updated plane as an angled plane with minimum cross-sectional area. Narikiyo in [Page - 12, Paragraph – 7] discloses about searching by changing orientation or rotation of the cross section. Furthermore, Gamechi discloses about center of mass on [Page – 4615, Paragraph – 2 (right)], “To start tracing the centerline of the aorta, aortic seed points were extracted as the center of mass of the coarse initial aorta segmentation at the axial slice”. But Narikiyo and none of the prior art in the combination discloses about wobble function to find updated plane as an angled plane with minimum cross-sectional area.
Claims 17 – 20 are allowed.
The following is an examiner’s statement of reasons for allowance:
Regarding claim 17, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Rapaka (US-20200160527-A1), Hanratty (US-12444506-B2), Walker (US-20140074502-A1), Li (US-11837354-B2), Markl (WO-2025097129-A1), Elbaz (WO-2020102154-A1), Gamechi (Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT) and Narikiyo (JP-2022175748-A) doesn’t disclose the limitation, searching for an updated plane using a wobble function to apply inclination angles to the plane to find the updated plane as an angled plane with minimum cross-sectional area. Hanratty in [Column – 28, Line 12 – 19] discloses about segmented volume of an aortic arch, “Moreover, the length of the anatomical feature, e.g., a vessel, may be calculated based on the output from the automated segmentation algorithm and subsequent 3D reconstruction. The data extracted from the 3D reconstruction may then be automatically analyzed to output a length from one specific anatomical landmark or abnormality to another, e.g., the length from the aortic arch to the thrombus in the case of a stroke”. Hanratty in [Column – 20, Line 17 – 25] discloses about generating centerline, “FIG. 5, exemplary method 500 for generating centerline measurements of a patient specific anatomical feature is provided ... centerline measurements of the patient specific anatomical feature associated with the selected pathology, may be generated from the generated 3D surface mesh model”. Hanratty in [Column – 21, Line 5 – 9] discloses about starting point to a next point, “At step 510, a new direction of travel may be determined at each interval based on a directional vector extending from the previous center point of the previous interval and the current center point”. Additionally, Hanratty in [Column – 21, Line 9-11] discloses about line segment, “in FIG. 6, the new direction of travel at the first interval may be consistent with a directional vector extending from start point SP to center point CP1”. Narikiyo in [Page - 12, Paragraph – 7] discloses about searching by changing orientation or rotation of the cross section. Furthermore, Gamechi discloses about center of mass on [Page – 4615, Paragraph – 2 (right)], “To start tracing the centerline of the aorta, aortic seed points were extracted as the center of mass of the coarse initial aorta segmentation at the axial slice”. But Hanratty, Narikiyo and none of the prior art in the combination discloses about wobble function to find updated plane as an angled plane with minimum cross-sectional area.
Regarding claims 18 – 20, claims 18 – 20 is allowed by virtually being dependent upon claim 17.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to ZAID MUHAMMAD SALEH whose telephone number is (703)756-1684.
The examiner can normally be reached M-F 8 am - 5 pm ET. Examiner interviews are available
via telephone, in-person, and video conferencing using a USPTO supplied web-based
collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO
Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.If attempts to
reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be
reached on (571)272-7332. The fax phone number for the organization where this application or
proceeding is assigned is 571-273-8300. Information regarding the status of published or
unpublished applications may be obtained from Patent Center. Unpublished application
information in Patent Center is available to registered users. To file and manage patent
submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit
https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center
and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For
additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
If you would like assistance from a USPTO Customer Service Representative, call 800-786
9199 (IN USA OR CANADA) or 571-272-1000.
/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
09/17/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
Prosecution Insights