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 .
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Response to Amendment
The amendment filed on May 20, 2026 has been entered.
The amendment of claims 1, 9, and 15 has been acknowledged.
Response to Arguments
Applicant's arguments filed on May 20, 2026, with respect to using a model, have been fully considered but they are not persuasive.
Applicant’s Representative submits that the prior art of record does not teach a trained model.
The examiner respectfully disagrees. The claim does not require that the borders to be unknown. The claim only recites using a trained model to take an image as an input to generate estimation information. A training process can use known borders, e.g., using a standard known model to generate an automated model for fitting a shape.
The prior art of record (Chenal et al. [US 2002/0072671 A1], in view of Min et al. [US 2021/0319558 A1]) teaches that the method is for automatically tracing a tissue border and fitting to a standard model that maximizes correlation (Chenal Abstract, ¶¶0004, ¶¶0028-¶¶0030) and further teaches using a trained algorithm and neural network for identifying and tracking vessels, coronary arteries, and/or regions of plaque (Min ¶¶0175).
In addition, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Note that Applicant’s arguments regarding Min’s CT image data is not persuasive because Min teaches that the image data is not limited to CT (see Min ¶¶0172) and the information regarding change over time in displacement is already taught by the primary reference in addition to Min (see Chenal ¶¶0033; Min ¶¶0465, Min ¶¶0892, & item 7 below).
Applicant’s arguments filed on May 20, 2026, with respect to the change over time in displacement, have been fully considered but are moot because the arguments rely on newly added and/or amended claim limitations. The examiner has revised the rejections to match the new claim limitations.
In particular, see Chenal ¶¶0033: “by finding a border on each intervening image in sequence between end systole and end diastole. In a given image sequence this may comprise 20-30 image frames … Since the LV is expanding when proceeding from systole to diastole, confidence measures include the displacement of the landmark points in an outward direction from frame to frame”; Min ¶¶0465: “programmed to be in motion or movement in sync with the image acquisition or the patient's heart or respiratory motion … two or more normalization devices 1200 can be affixed to and/or positioned alongside a patient during medical image scanning in order to account for changes in coloration and/or gray scale levels at different depths within the scanner and/or different locations within the scanner”; Min ¶¶0892: “the normalization device may be configured to account for various time-based changes … may provide four-dimensional (positional plus time) calibration tool. This can help to account for changes that occur in time, for example, as caused by patient movement due to respiration, heartbeat, blood flow, etc.”
Applicant's arguments filed on May 20, 2026, with respect to using a model, have been fully considered but they are not persuasive.
Applicant’s Representative submits that the ‘528 and ‘573 patents do not set forth or render obvious the features recited in claim 1.
The examiner respectfully disagrees. The features of claim 1 are recited in See ‘528 claim 1: “A blood vessel wall thickness estimation method comprising: obtaining behavioral information based on a video including a blood vessel wall obtained using four-dimensional angiography, the behavioral information being numerical information about changes over time in positions of a plurality of predetermined points in the blood vessel wall; generating estimation information for estimating a thickness of the blood vessel wall based on the behavioral information obtained in the obtaining; and outputting the estimation information generated in the generating, wherein the estimation information is information in which at least one of the following is visualized: a change in displacement over time; a change in speed over time; a change in acceleration over time; a change in kinetic energy over time; a spring constant obtained from the displacement and the acceleration; or a Fourier coefficient obtained from the change in the displacement over time, and the outputting includes: when the change in the displacement over time is visualized in the estimation information, outputting the thickness of the blood vessel wall estimated based on an indicator of the displacement; when the change in the speed over time is visualized in the estimation information, outputting the thickness of the blood vessel wall estimated based on an indicator of the speed; when the change in the acceleration over time is visualized in the estimation information, outputting the thickness of the blood vessel wall estimated based on an indicator of the acceleration; when the change in the kinetic energy over time is visualized in the estimation information, outputting the thickness of the blood vessel wall estimated based on an indicator of the kinetic energy; when the spring constant is visualized in the estimation information, outputting the thickness of the blood vessel wall estimated based on a magnitude of the spring constant; and when the Fourier coefficient is visualized in the estimation information, outputting the thickness of the blood vessel wall estimated based on an indicator of the Fourier coefficient.” Regarding a trained model, see ‘573 claim 8: “A wall thickness estimation device … generating includes calculating … Exp. (1)”
Claim Rejections - 35 USC § 103
Claim(s) 1-12 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chenal et al. (US 2002/0072671 A1), in view of Min et al. (US 2021/0319558 A1), hereinafter referred to as Chenal and Min, respectively.
Regarding claim 1, Chenal teaches a wall thickness estimation method comprising:
obtaining behavioral information that is based on a video in which an organ wall or a blood vessel wall is captured, the behavioral information being numerical information about changes over time in a position of each of a plurality of predetermined points in the organ wall or the blood vessel wall (Chenal ¶¶0031: “Once these three major landmarks of the LV have been located, one of a number of predetermined standard shapes for the LV is fitted to the three landmarks and the endocardial wall … Such measurements are made along paths orthogonal to the shape and extending from points along the shape.”; Chenal ¶¶0044: “The walls of blood vessels such as the carotid artery can similarly be traced by identifying the center line of the vessel, then extending straight line shapes out from opposite sides of the center line to fit small line segments to the endothelial wall”; Chenal ¶¶0045: “FIG. 11 illustrates a technique for assessing regional wall motion using automated border detection. The drawing of FIG. 11 represents an ultrasound display in which the continuous motion of the endocardium or myocardium is shown over several complete heart cycles”; Chenal ¶¶0046: “This is overcome by tracking the anatomy from a baseline of control points over the heart cycle, as by speckle tracking each local point of the heart wall along the ABD trace from frame to frame”; Chenal ¶¶0058: “a video processor”);
generating estimation information using a model trained to take as an input an image indicating a physical parameter based on the behavioral information obtained in the obtaining and output an index indicating a thickness at each of the plurality of predetermined points in the organ wall or the blood vessel wall, the estimation information being information visualizing the thickness (Chenal Abstract: “automatically tracing a tissue border in an ultrasonic image”; Chenal ¶¶0004: “a technique for automatically delineating the border or boundary of an object in an ultrasound image … locates key landmarks of the object in the image, then fits one of a plurality of predefined standard shapes to the key landmarks and the object”; Chenal ¶¶0050: “Automatically drawn cardiac borders may also be used to define the myocardial area in contrast-enhanced images or loops … Automatically drawn cardiac borders and perfusion information presented simultaneously in an image or loop is a powerful combination since the clinician can assess wall motion, thickening, and perfusion simultaneously. Given that the borders are known, the thickness of the myocardial walls between the endocardial and epicardial edges can be determined on a segment-by-segment basis as shown in FIG. 12”); and
outputting the estimation information generated in the generating (Chenal ¶¶0050 discussed above; Chenal Figs. 11, 12 & 14),
wherein the physical parameter is a parameter about change over time in displacement of each of the plurality of predetermined points (Chenal ¶¶0033: “by finding a border on each intervening image in sequence between end systole and end diastole. In a given image sequence this may comprise 20-30 image frames … Since the LV is expanding when proceeding from systole to diastole, confidence measures include the displacement of the landmark points in an outward direction from frame to frame”).
However, Chenal does not appear to explicitly teach using four-dimensional angiography.
Pertaining to the same field of endeavor, Min teaches using four-dimensional angiography (Min ¶¶1191: “the system can be configured to visualize plaque in 2D, 3D, and/or 4D”; ¶¶01241: “undergoing coronary CT angiography”).
Min also teaches using a trained model and detecting changes in displacement over time (Min ¶¶0465: “programmed to be in motion or movement in sync with the image acquisition or the patient's heart or respiratory motion … two or more normalization devices 1200 can be affixed to and/or positioned alongside a patient during medical image scanning in order to account for changes in coloration and/or gray scale levels at different depths within the scanner and/or different locations within the scanner”; Min ¶¶0892: “the normalization device may be configured to account for various time-based changes … may provide four-dimensional (positional plus time) calibration tool. This can help to account for changes that occur in time, for example, as caused by patient movement due to respiration, heartbeat, blood flow, etc.”).
Chenal and Min are considered to be analogous art because they are directed to medical image processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for automated border detection in diagnostic images (as taught by Chenal) to use 4D angiography (as taught by Min) because the combination enables continued personalized treatment for a subject with atherosclerotic cardiovascular disease (ASCVD) risk (Min ¶¶1633).
Regarding claim 2, Chenal, in view of Min, teaches the wall thickness estimation method according to claim 1, further comprising:
training the model using one or more datasets as training data, each of the datasets being constituted by a combination of (i) the image indicating the physical parameter based on the behavioral information at each predetermined point among the plurality of predetermined points and (ii) the index indicating the thickness at the predetermined point (Min ¶¶0187: “one or more AI and/or ML algorithms can be trained using a Convolutional Neural Network (CNN) on a set of medical images on which arteries or coronary arteries have been identified, thereby allowing the AI and/or ML algorithm automatically identify arteries or coronary arteries directly from a medical image. In some embodiments, the arteries or coronary arteries are identified by size and/or location”; Min ¶¶0231: “parameters associated with the left ventricle can include size, mass, volume, shape, eccentricity, surface area, thickness, and/or the like. Similarly, in some embodiments, parameters associated with the right ventricle can include size, mass, volume, shape, eccentricity, surface area, thickness, and/or the like. In some embodiments, parameters associated with the left atrium can include size, mass, volume, shape, eccentricity, surface area, thickness, pulmonary vein angulation, atrial appendage morphology, and/or the like. In some embodiments, parameters associated with the right atrium can include size, mass, volume, shape, eccentricity, surface area, thickness, and/or the like”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for automated border detection in diagnostic images (as taught by Chenal) to use a trained model (as taught by Min) because the combination can be configured to automatically and/or dynamically identify from raw medical images the presence and/or parameters of vessels, coronary arteries, and/or plaque (Min ¶¶0175).
Regarding claim 3, Chenal, in view of Min, teaches the wall thickness estimation method according to claim 2,
wherein in the training, the model is trained using machine learning (Min ¶¶0187 discussed above).
Regarding claim 4, Chenal, in view of Min, teaches the wall thickness estimation method according to claim 1,
wherein the estimation information is image information visualizing the thickness (Chenal Figs. 10, 12 & 14; Min ¶¶0353: “This system can display vessels in multi-planar formats, cross-sectional views, 3D coronary artery tree view, axial, sagittal, and coronal views based on a set of computerized tomography (CT) images, e.g., generated by a CT scan of a patient's vessels”).
Regarding claim 5, Chenal, in view of Min, teaches the wall thickness estimation method according to claim 1,
wherein the blood vessel wall is a wall of an arterial aneurysm or a varicose vein (Min ¶¶0985: “Vascular morphology—e.g., lumen volume, vessel volume, arterial remodeling, anomaly, aneurysm, bridging, dissection, etc.”; Min ¶¶1117: “the system can be configured to generate an assessment of other artery consequences, such as for example carotid (stroke), lower extremity (claudication, critical limb ischemia, amputation), aorta (dissection, aneurysm), renal artery (hypertension), cerebral artery (aneurysm, rupture), and/or the like”).
Regarding claim 6, Chenal, in view of Min, teaches the wall thickness estimation method according to claim 1,
wherein the blood vessel wall is a wall of a cerebral aneurysm (Min ¶¶1117 discussed above).
Regarding claim 7, Chenal, in view of Min, teaches the wall thickness estimation method according to claim 1,
wherein the blood vessel wall is a blood vessel wall of an artery or a vein (Min ¶¶0985 & ¶¶1117 discussed above).
Regarding claim 8, Chenal, in view of Min, teaches a non-transitory computer-readable recording medium having recorded thereon a computer program for causing a computer to execute the wall thickness estimation method according to claim 1 (Chenal Fig. 16).
Regarding claim 9, Chenal teaches a training method comprising:
obtaining behavioral information that is based on a video in which an organ wall or a blood vessel wall is captured, the behavioral information being numerical information about changes over time in a position of each of a plurality of predetermined points in the organ wall or the blood vessel wall, wherein the physical parameter is a parameter about change over time in displacement of each of the plurality of predetermined points (Chenal ¶¶0031, ¶¶0033, ¶¶0044-¶¶0046 & ¶¶0058 discussed above).
Chenal further teaches using a combination of (i) the image indicating a physical parameter based on the behavioral information at each predetermined point among the plurality of predetermined points, the behavioral information being the behavioral information obtained in the obtaining, and (ii) an index indicating a thickness at the predetermined point among the plurality of predetermined points (Chenal Figs. 11, 12, 14, & ¶¶0050).
However, Chenal does not appear to explicitly teach training a model using training data.
Pertaining to the same field of endeavor, Min teaches training a model using, as training data, one or more datasets constituted by a combination of (i) the image indicating a physical parameter based on the behavioral information at each predetermined point among the plurality of predetermined points, the behavioral information being the behavioral information obtained in the obtaining, and (ii) an index indicating a thickness at the predetermined point among the plurality of predetermined points (Min ¶¶0187 & ¶¶0231 discussed above).
In addition, Min also teaches tracking displacement over time (Min ¶¶0465 & ¶¶0892 discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for automated border detection in diagnostic images (as taught by Chenal) to train a model (as taught by Min) because the combination can be configured to automatically and/or dynamically identify from raw medical images the presence and/or parameters of vessels, coronary arteries, and/or plaque (Min ¶¶0175).
Regarding claim 10, Chenal, in view of Min, teaches the training method according to claim 9,
wherein the video is obtained using four-dimensional angiography or a two-dimensional video capturing device (Chenal ¶¶0058 & Min ¶¶1191, ¶¶01241 discussed above).
Regarding claim 11, Chenal, in view of Min, teaches a model construction method comprising:
obtaining the estimation information generated in the generating according to claim 1 (Chenal ¶¶0050 discussed above); and
constructing a blood vessel model including the blood vessel wall according to claim 1, the blood vessel model being constructed based on the thickness visualized by the estimation information obtained in the obtaining of the estimation information to cause the blood vessel wall included in the blood vessel model to exhibit a different form according to the thickness (Min Fig. 6; Min ¶¶0197: “the system is configured to generate a quantized color mapping based on the analyzed and/or determined parameters … the system is configured to generate a visualization of the analyzed medical image by generating a quantized color mapping of calcified plaque, non-calcified plaque, good plaque, bad plaque, stable plaque, and/or unstable plaque as determined using any of the analytical techniques described herein … the quantified color mapping can also include arteries and/or epicardial fat, which can also be determined by the system, for example by utilizing one or more AI and/or ML algorithms”; Min ¶¶0212: “the system can be configured assign different colors to each of the different regions associated with different matters”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for automated border detection in diagnostic images (as taught by Chenal) to visualize differently based on thickness (as taught by Min) because the combination allows the physicians to quickly identify regions of interest, e.g., non-calcified plaque, good plaque, bad plaque, stable plaque, and/or unstable plaque, etc. (Min ¶¶0197).
Regarding claim 12, Chenal, in view of Min, teaches the model construction method according to claim 11,
wherein in the constructing of the blood vessel model, the blood vessel model is constructed such that the blood vessel wall exhibits a different color according to the thickness (Min ¶¶0197 & ¶¶0212 discussed above).
Regarding claim 15, Chenal, in view of Min, further teaches a wall thickness estimation device comprising an obtainer, a generator, and an outputter that perform the method described in claim 1 (Chenal Fig. 16; Min Fig. 19E). Therefore, claim 15 is rejected using the same rationale as applied to claim 1 discussed above.
Regarding claim 16, Chenal, in view of Min, further teaches a wall thickness estimation system comprising:
the wall thickness estimation device according to claim 15 (Chenal Fig. 16; Min Fig. 19E; refer to the rejection of claims 1 and 15 discussed above);
a video information processing device that obtains the video, generates the behavioral information, and outputs the behavioral information to the obtainer (Chenal ¶¶0031, ¶¶0044-¶¶0046, ¶¶0050, & ¶¶0058 discussed above); and
a display that displays the estimation information output by the outputter (Chenal Figs. 10 & 16; Min Fig. 6).
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chenal et al. (US 2002/0072671 A1), in view of Min et al. (US 2021/0319558 A1), and further in view of Cornelissen et al. (“CT Imaging of Intracranial Vessels,” 2015, In: Trivedi, R., Saba, L., Suri, J. (eds) 3D Imaging Technologies in Atherosclerosis. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7618-5_4), hereinafter referred to as Chenal, Min, and Cornelissen, respectively
Regarding claim 13, Chenal, in view of Min, teaches the model construction method according to claim 12,
wherein the blood vessel wall included in the blood vessel model constructed in the constructing of the blood vessel model is a wall of a cerebral aneurysm (Min ¶¶1117 discussed above).
However, Chenal, in view of Min, does not appear to explicitly teach constructing a brain model into which the blood vessel model constructed in the constructing of the blood vessel model is incorporated.
Pertaining to the same field of endeavor, Cornelissen teaches constructing a brain model into which the blood vessel model constructed in the constructing of the blood vessel model is incorporated (Cornelissen pg. 94: “Anatomy of the Intracranial Vessels”; Cornelissen Figs. 1, 8, 10-12, 15, 25, 30; Cornelissen pg. 109: “In the acute setting a non-contrast-enhanced CT (NECT) of the brain can reveal hemorrhage”).
Chenal, in view of Min, and Cornelissen are considered to be analogous art because they are directed to medical image processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for automated border detection in diagnostic images using machine learning (as taught by Chenal, in view of Min) to construct a brain model with blood vessels (as taught by Cornelissen) because the combination allows the physicians to locate hemorrhage (Cornelissen pg. 109).
Regarding claim 14, Chenal, in view of Min and Cornelissen, teaches the model construction method according to claim 13, further comprising:
constructing a skull model for containing the brain model constructed in the constructing of the brain model (Cornelissen Figs. 2, 5, 15, 25).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for automated border detection in diagnostic images using machine learning (as taught by Chenal, in view of Min) to construct a skull model with blood vessels (as taught by Cornelissen) because the combination allows the physicians to locate regions of interest within the cranial cavity (Cornelissen pg. 101, Fig. 15).
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.
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Claims 1-12 and 15-16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8 of U.S. Patent No. US 12,171,528 B2 and claims 1-11 of US 12,274,573 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the patents and the application are directed to estimating the thickness of a blood vessel or organ wall, e.g., vein, artery, etc. The patents and the application use behavioral information (i.e., changes in position over time) by tracking predetermined points over time and visualize the estimation by displaying the results. The patents also use a device/machine for automated estimation using e.g., expression (1).
Alternatively, if the claims were to further recite that the trained model is a machine learning model, claims 1-12 and 15-16 are unpatentable over claims 1-8 of U.S. Patent No. US 12,171,528 B2, in view of Min et al. (US 2021/0319558 A1), and claims 1-11 of US 12,274,573 B2, in view of Min et al. (US 2021/0319558 A1), hereinafter referred to as the patents and Min, respectively.
Although the claims at issue are not identical, they are not patentably distinct from each other because the patents and the application are directed to estimating the thickness of a blood vessel or organ wall, e.g., vein, artery, etc. The patents and the application use behavioral information (i.e., changes in position over time) by tracking predetermined points over time and visualize the estimation by displaying the results.
Regarding the use of training data for training a model, Min teaches that it was known to use a machine learning algorithm (Min ¶¶0187: “one or more AI and/or ML algorithms can be trained using a Convolutional Neural Network (CNN) on a set of medical images on which arteries or coronary arteries have been identified, thereby allowing the AI and/or ML algorithm automatically identify arteries or coronary arteries directly from a medical image. In some embodiments, the arteries or coronary arteries are identified by size and/or location”; Min ¶¶0231: “parameters associated with the left ventricle can include size, mass, volume, shape, eccentricity, surface area, thickness, and/or the like. Similarly, in some embodiments, parameters associated with the right ventricle can include size, mass, volume, shape, eccentricity, surface area, thickness, and/or the like. In some embodiments, parameters associated with the left atrium can include size, mass, volume, shape, eccentricity, surface area, thickness, pulmonary vein angulation, atrial appendage morphology, and/or the like. In some embodiments, parameters associated with the right atrium can include size, mass, volume, shape, eccentricity, surface area, thickness, and/or the like”).
The patents and Min are considered to be analogous art because they are directed to medical image processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the blood vessel wall thickness estimation method and system (as taught by the patents) to use a machine learning model (as taught by Cornelissen) because the combination allows automated identification (Min ¶¶0187).
Claims 13-14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8 of U.S. Patent No. US 12,171,528 B2 in view of Cornelissen et al. (“CT Imaging of Intracranial Vessels,” 2015, 3D Imaging Technologies in Atherosclerosis), and claims 1-11 of US 12,274,573 B2, in view of Cornelissen et al. (“CT Imaging of Intracranial Vessels,” 2015, 3D Imaging Technologies in Atherosclerosis), hereinafter referred to as the patents and Cornelissen, respectively.
Regarding claim 13, both patents teach the model construction method according to claim 12 (patents claim 8).
However, the patents do not appear to teach that the blood vessel wall included in the blood vessel model constructed in the constructing of the blood vessel model is a wall of a cerebral aneurysm and constructing a brain model into which the blood vessel model constructed in the constructing of the blood vessel model is incorporated.
Pertaining to the same field of endeavor, Cornelissen teaches the blood vessel wall included in the blood vessel model constructed in the constructing of the blood vessel model is a wall of a cerebral aneurysm constructing a brain model into which the blood vessel model constructed in the constructing of the blood vessel model is incorporated (Cornelissen pg. 94: “Anatomy of the Intracranial Vessels”; Cornelissen Figs. 1, 8, 10-12, 15, 25, 30; Cornelissen pg. 109: “In the acute setting a non-contrast-enhanced CT (NECT) of the brain can reveal hemorrhage”).
The patents and Cornelissen are considered to be analogous art because they are directed to medical image processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the blood vessel wall thickness estimation method and system (as taught by the patents) to construct a brain model with blood vessels (as taught by Cornelissen) because the combination allows the physicians to locate hemorrhage (Cornelissen pg. 109).
Regarding claim 14, the patents, in view of Cornelissen, teaches the model construction method according to claim 13, further comprising:
constructing a skull model for containing the brain model constructed in the constructing of the brain model (Cornelissen Figs. 2, 5, 15, 25).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the blood vessel wall thickness estimation method and system (as taught by the patents) to construct a skull model with blood vessels (as taught by Cornelissen) because the combination allows the physicians to locate regions of interest within the cranial cavity (Cornelissen pg. 101, Fig. 15).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Soo Shin/Primary Examiner, Art Unit 2667
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