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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 16, 2026 has been entered.
Claim Status
Applicant’s amendment filed on March 16, 2026 is acknowledged. Currently claims 1-20 are pending. Claims 1, 10, and 20 have been amended.
EXAMINER’S NOTE
An examiner’s amendment was proposed to the applicant on March 26, 2026. The examiner’s amendment was not accepted.
If the applicant chooses to amend the independent claims as seen below, the examiner will place the application in condition for allowance. See the proposed examiner’s amended claims 1, 10, and 20 below.
(Currently amended) A method for fibrotic cap identification in blood vessels, the method comprising:
receiving, by one or more processors, one or more input images of a blood vessel;
processing, by the one or more processors, the one or more input images using a machine learning model trained to identify locations of fibrotic caps in blood vessels, wherein the machine learning model is trained using a plurality of training images annotated with one or more locations of one or more fibrotic caps, each fibrotic cap adjacent to a respective pool of lipid;
receiving, by the one or more processors and as output from the machine learning model, one or more output images having segments that are visually annotated representing predicted locations of one or more fibrotic caps, including an initial boundary of one or more fibrotic caps; and
generating, using the one or more processors and from the one or more output images, an updated boundary, which is different than the initial boundary, of a fibrotic cap relative to an adjacent pool of lipid based on radial signal intensities within for a plurality of points in the one or more input images;
wherein generating the updated boundary comprises:
measuring, by the one or more processors, radial signal intensities for a plurality of points along one or more arc-lines enclosing the fibrotic cap;
determining, by the one or more processors, a boundary between the fibrotic cap and the adjacent pool of lipid based on a measured rate of decay of the radial signal intensities for the plurality of points, wherein the calculated rate of decay is within a threshold value of a predetermined rate of decay of radial signal intensity through a fibrotic cap and a pool of lipid.
(Currently amended) A system comprising:
one or more processors configured to:
receive one or more input images of a blood vessel;
process the one or more input images using a machine learning model trained to identify locations of fibrotic caps in blood vessels, wherein the machine learning model is trained using a plurality of training images annotated with locations of one or more fibrotic caps, each fibrotic cap adjacent to a respective pool of lipid;
receive, as output from the machine learning model, one or more output images having segments that are visually annotated representing predicted locations of one or more fibrotic caps, including an initial boundary of one or more of the fibrotic caps; and
generate from the one or more output images, an updated boundary, which is different than the initial boundary, of a fibrotic cap relative to an adjacent pool of lipid based on radial signal intensities for a plurality of points within the one or more input images;
wherein generating the updated boundary comprises:
measuring, by the one or more processors, radial signal intensities for a plurality of points along one or more arc-lines enclosing the fibrotic cap;
determining, by the one or more processors, a boundary between the fibrotic cap and the adjacent pool of lipid based on a measured rate of decay of the radial signal intensities for the plurality of points, wherein the calculated rate of decay is within a threshold value of a predetermined rate of decay of radial signal intensity through a fibrotic cap and a pool of lipid.
(Currently amended) One or more non-transitory computer-readable media storing instructions that when executed by one or more processors causes the one or more processors to perform operations comprising:
receiving one or more input images of a blood vessel;
processing the one or more input images using a machine learning model trained to identify locations of fibrotic caps in blood vessels, wherein the machine learning model is trained using a plurality of training images each annotated with locations of one or more fibrotic caps, each fibrotic cap adjacent to a respective pool of lipid;
receiving, as output from the machine learning model, one or more output images having segments that are visually annotated representing predicted locations of one or more fibrotic caps, including an initial boundary of one or more of the fibrotic caps; and
generating from the one or more output images, an updated boundary, which is different than the initial boundary, of a fibrotic cap relative to an adjacent pool of lipid based on radial signal intensities for a plurality of points within the one or more input images;
wherein generating the updated boundary comprises:
measuring, by the one or more processors, radial signal intensities for a plurality of points along one or more arc-lines enclosing the fibrotic cap;
determining, by the one or more processors, a boundary between the fibrotic cap and the adjacent pool of lipid based on a measured rate of decay of the radial signal intensities for the plurality of points, wherein the calculated rate of decay is within a threshold value of a predetermined rate of decay of radial signal intensity through a fibrotic cap and a pool of lipid.
Response to Arguments and Amendments
Applicant alleges that Choi in view of Chamie fails to teach “an initial boundary of an output image for a fibrotic cap that is updated within output images” as recited in claim 1.
The examiner respectfully disagrees. The examiner continues to interpret the updated boundary to be “plurality of points along one or more arc-lines enclosing the fibrotic cap” based on measured signal intensities wherein “generating the updated boundary can include measuring signal intensities for a plurality of points along one or more arc-lines enclosing the fibrotic cap” (found in para. [0013]-[0014] of Applicant’s specifications). Therefore, utilizing broadest reasonable interpretation, an initial boundary that is updated automatically during the automatic segmentation resulting in list of points in space is considered to be an initial and updated boundary.
Thus, Choi in view of Chamie teaches an updated boundary, which is different than the initial boundary (See Fig. 2A and B, wherein the plurality of points and arc lines depict the boundaries; wherein the boundary depicted in Fig.2A is the initial boundary and the boundary in Fig.2B is the updated boundary), of a fibrotic cap relative to an adjacent pool of lipid based on signal intensities (Chamie – [OCT and Fibrous Cap: Identification] “On OCT, fibrous tissue appears as homogenous, signal-rich regions; fibrocalcific or calcific tissue appear as well-delineated, signal-poor regions with sharp borders; and lipids are depicted as signal-poor regions with diffuse borders”). See Fig.1A) (Chamie - [OCT and Fibrous Cap: Identification] the combination of a signal-rich band overlying a signal-poor region produces a unique, distinct signature, which can be easily identified on OCT images; wherein signal intensities are the signal-rich bands that produce a unique, distinct signature).
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Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5-7, 10-11, 14-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable of Choi et al., US 9,679,374 B2, (hereinafter “Choi”) in view of Chamie et al., “Optical Coherence Tomography and Fibrous Cap Characterization”, (hereinafter “Chamie”).
Regarding claim 1, Choi teaches a method for fibrotic cap identification in blood vessels, the method comprising:
receiving, by one or more processors (platform may also include a central processing unit (CPU) 620, in the form of one or more processors, for executing program instructions [Col.21, lines 61-64]), one or more input images of a blood vessel (the present disclosure describes certain principles and embodiments for using patients' cardiac imaging to: (1) derive a patient-specific geometric model (in which Choi defines a geometric model as patient imaging data) of the coronary vessels [Col.4, lines 42-45]);
processing, by the one or more processors, the one or more input images using a machine learning model trained(training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and the predictive features; wherein a machine learning model is a machine learning algorithm [Col. 2, lines 22-25]), wherein the machine learning model is trained using a plurality of training images (Method 300 completes the training mode by inputting into a learning system 310 both the feature vectors 304 formed from the plurality of patients' imaging data and physiologic and/or hemodynamic information [Col.6, lines 40-43])
receiving, by the one or more processors and as output from the machine learning model (training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and the predictive features; wherein a machine learning model is a machine learning algorithm [Col. 2, lines 22-25]), one or more output images (Method 350 may then include operating the machine learning system 310 on the feature vectors generated for the patient to obtain an output 316 of the estimates of the presence or onset of plaque at each of a plurality of points in the patient's geometric model, and translating the output into useable information 318 about the location, onset, and/or change of plaque in the patient 318 [Col.7, lines 2-9]) having segments that are visually annotated (image then may be segmented manually or automatically to identify voxels belonging to the aorta and the lumen of the coronary arteries. Given a 3D image of coronary vasculature, any of the many available methods may be used for extracting a patient-specific model of cardiovascular geometry. Inaccuracies in the geometry extracted automatically may be corrected by a human observer who compares the extracted geometry with the images and makes corrections as needed [Col.12, lines 27-32])
generating, using the one or more processors and from the one or more output images, (Method 350 may include generating a feature vector 314 for each of a plurality of points of the patient's geometric model, based on one or more elements of the received physiologic and/or hemodynamic information [Col.6, lines 66-Col.7, lines 1-2]).
Choi does not specifically disclose the following elements: 1) identify locations of fibrotic caps in blood vessels. 2) images are annotated with one or more locations of one or more fibrotic caps, each fibrotic cap adjacent to a respective pool of lipid. 3) predicted locations of one or more fibrotic caps, including the boundary of one or more fibrotic caps. 4) an updated boundary, which is different than the initial boundary, of a fibrotic cap relative to an adjacent pool of lipid based on radial signal intensities.
However, Chamie teaches 1) identify locations of fibrotic caps in blood vessels (c, 3-D rendering reconstruction with fly-through of the coronary vessel [Figure 2]). 2) images are annotated with one or more locations of one or more fibrotic caps, each fibrotic cap adjacent to a respective pool of lipid (see Fig. 1A below). 3) predicted locations of one or more fibrotic caps, including the boundary of one or more fibrotic caps (The fibrous cap is segmented in all frames containing a lipid-rich plaque, allowing for real-time volumetric assessment [Figure 2]. See Fig. 2B below; wherein the boundary is the plurality of points). 4) an updated boundary, which is different than the initial boundary (See Fig. 2A and B, wherein the plurality of points and arc lines depict the boundaries; wherein the boundary depicted in Fig.2A is the initial boundary and the boundary in Fig.2B is the updated boundary), of a fibrotic cap relative to an adjacent pool of lipid based on radial signal intensities (Chamie – [OCT and Fibrous Cap: Identification] “On OCT, fibrous tissue appears as homogenous, signal-rich regions; fibrocalcific or calcific tissue appear as well-delineated, signal-poor regions with sharp borders; and lipids are depicted as signal-poor regions with diffuse borders”). See Fig.1A) (Chamie - [OCT and Fibrous Cap: Identification] the combination of a signal-rich band overlying a signal-poor region produces a unique, distinct signature, which can be easily identified on OCT images; wherein radial signal intensities are the signal-rich bands that produce a unique, distinct signature).
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It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Choi with the above elements. Chamie teaches that acute coronary syndrome is commonly associated with atherosclerotic plaque rupture which is often caused by inflammation, thinning, and disruption of fibrotic caps. The motivation would be to use the method, taught by Choi, to identify the predicted location of fibrotic caps and measure their thickness as an indicator of potential cap inflammation, thinning, and disruption, taught by Chamie. Therefore, it would have been obvious to combine Choi with Chamie to obtain the method specified in claim 1.
Regarding claim 2, Choi in view of Chamie teaches the method as claimed in claim 1, wherein the one or more input images (Choi - the present disclosure describes certain principles and embodiments for using patients' cardiac imaging to: (1) derive a patient-specific geometric model of the coronary vessels [Col. 4, lines 42-45]) are further annotated with segments corresponding to locations of at least one of calcium, a lumen in the blood vessel, or media (Choi - image then may be segmented manually or automatically to identify voxels belonging to the aorta and the lumen of the coronary arteries [Col. 12, lines 27-29]).
The motivation for combining Choi and Chamie is the same motivation as used for claim 1 above.
Regarding claim 5, Choi in view of Chamie teaches the method of claim 1, wherein the plurality of points are along one or more arc-lines enclosing the fibrotic cap (Chamie - see image labeled ‘B’ below) (Choi - identifying, for each of a plurality of points in the geometric models, features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals [Col.2, lines 19-22]).
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The motivation for combining Choi and Chamie is the same motivation as used for claim 1 above.
Regarding claim 6, Choi in view of Chamie teaches the method of claim 1, wherein receiving the one or more output images (Choi - an output 316 of the estimates of the presence or onset of plaque at each of a plurality of points in the patient's geometric model [Col.7, lines 5-7]) comprises receiving, for each input image (Choi - a patient-specific geometric model of the coronary vessels [Col.4, lines 44-45]), a respective visually annotated segment of the input image representing a predicted location (Choi - image then may be segmented manually or automatically to identify voxels belonging to the aorta and the lumen of the coronary arteries. Given a 3D image of coronary vasculature, any of the many available methods may be used for extracting a patient-specific model of cardiovascular geometry. Inaccuracies in the geometry extracted automatically may be corrected by a human observer who compares the extracted geometry with the images and makes corrections as needed [Col. 12, lines 27-32]) for a fibrotic cap (Chamie - The present article provides an overview of the potential role of OCT in identifying and characterizing fibrous cap morphology, thickness, and inflammation in human coronary plaques [Abstract]).
The motivation for combining Choi and Chamie is the same motivation as used for claim 1 above.
Regarding claim 7, Choi in view of Chamie teaches the method of claim 1, wherein the method further comprises receiving, by the one or more processors (Choi - The platform may also include a central processing unit (CPU) 620, in the form of one or more processors, for executing program instructions [Col. 21, lines 61-64]) and for each of the one or more output images (Choi - an output 316 of the estimates of the presence or onset of plaque at each of a plurality of points in the patient's geometric model [Col.7, lines 5-7]), one or more measures of thickness for each fibrotic cap whose location is predicted in the output image (Chamie - A color classification scheme is constructed in a 3-D–rendered image, mapping the entire extent of the fibrous cap in different regions according to its thickness in red (<65 μm), green (65–150 μm), or blue (>150 μm) (Fig. 2). Hence, minimal, maximal, and mean values of the fibrous cap thickness can be determined along with the quantification of area and volume of the whole cap. The 3-D display enables a comprehensive visualization of the longitudinal and circumferential distribution of the fibrous cap, and identification of “hotspots” of thin regions [OCT and Fibrous Cap: 3-D Assessment]).
The motivation for combining Choi and Chamie is the same motivation as used for claim 1 above.
Regarding claim 10, the claim recites similar limitations to claim 1 but in the form of a system. Therefore, claim 10 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 11, the claim recites similar limitations to claim 2 but in the form of a system. Therefore, claim 11 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 14, the claim recites similar limitations to claim 5 but in the form of a system. Therefore, claim 14 recites similar limitations to claim 5 and is rejected for similar rationale and reasoning (see the analysis for claim 5 above).
Regarding claim 15, the claim recites similar limitations to claim 6 but in the form of a system. Therefore, claim 15 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 16, the claim recites similar limitations to claim 7 but in the form of a system. Therefore, claim 16 recites similar limitations to claim 7 and is rejected for similar rationale and reasoning (see the analysis for claim 7 above).
Regarding claim 20, the claim recites similar limitations to claim 1 but in the form of one or more non-transitory computer-readable media that when executed by one or more processors causes the one or more processors to perform the method of claim 1. Therefore, claim 20 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable of Choi et al., US 9,679,374 B2, (hereinafter “Choi”) in view of in view of Chamie et al., “Optical Coherence Tomography and Fibrous Cap Characterization”, (hereinafter “Chamie”), and further in view of Van der Meer et al., “Localized Measurement of Optical Attenuation Coefficients of Atherosclerotic Plaque Constituents by Quantitative Optical Coherence Tomography”, (hereinafter “Van der Meer”).
Regarding claim 8, Choi in view of Chamie teaches the method of claim 1, wherein generating the updated boundary comprises: measuring, by the one or more processors (Choi - platform may also include a central processing unit (CPU) 620, in the form of one or more processors, for executing program instructions [Col. 21, lines 61-64]), signal intensities for a plurality of points along one or more arc-lines enclosing the fibrotic cap (Chamie - see image below);
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and determining, by the one or more processors Chamie - The combination of a signal-rich band overlying a signal-poor region produces a unique, distinct signature, which can be easily identified on OCT images [OCT and Fibrous Cap: Identification]).
Choi in view of Chamie does not specifically teach a comparison of a measured rate of decay of the signal intensities for the plurality of points and a predetermined rate of decay of signal intensity.
However, Van der Meer teaches a comparison of a measured rate of decay of the signal intensities for the plurality of points and a predetermined rate of decay of signal intensity (During OCT imaging, the OCT signal is measured as a function of depth in the tissue. When OCT is assumed to detect only light that has been scattered once, the decay of the OCT signal with depth simply follows the Lambert-Beer law, i.e., an exponential decay in which the decay constant is the attenuation coefficient [Data Analysis]).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Choi in view of Chamie, to measure a rate of decay of the signal intensities to better identify the fibrotic caps of lipid. Van der Meer teaches that using the optical attenuation coefficient for signal intensities enables further differentiation between plaque constituents on a quantitative basis. The motivation would be to use a predetermined rate of decay in comparison to a measured rate of decay, as taught by Van der Meer, to enhance boundary detection of the fibrotic caps of lipid, taught by Choi in view of Chamie. Therefore, it would have been obvious to combine Van der Meer with Choi in view of Chamie, to obtain the method specified in claim 8.
Regarding claim 9, Choi in view of Chamie teaches the method of claim 8, wherein determining the boundary between the fibrotic cap and the adjacent pool of lipid comprises identifying a point of the plurality of points (Chamie - see image below) having a measured signal intensity (Chamie - The combination of a signal-rich band overlying a signal-poor region produces a unique, distinct signature, which can be easily identified on OCT images [OCT and Fibrous Cap: Identification])
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Choi in view of Chamie does not specifically teach a measured signal intensity that is proportional within a predetermined threshold to a peak signal intensity of the plurality of points.
However, Van der Meer teaches a measured signal intensity that is proportional within a predetermined threshold to a peak signal intensity of the plurality of points (see image below).
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The motivation for combining Van der Meer with Choi in view of Chamie would be to use a predetermined rate of decay in comparison to a measured rate of decay, as taught by Van der Meer, to enhance boundary detection of the fibrotic caps of lipid, taught by Choi in view of Chamie. Therefore, it would have been obvious to combine Van der Meer with Choi in view of Chamie, to obtain the method specified in claim 9.
Claims 3-4, 12-13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable of Choi et al., US 9,679,374 B2, (hereinafter “Choi”) in view of in view of Chamie et al., “Optical Coherence Tomography and Fibrous Cap Characterization”, (hereinafter “Chamie”) and Ebbini et al., US 2016/0143617 A1, (hereinafter “Ebbini”), and further in view of Bouma et al., US 2021/0113101 A1, (hereinafter “Bouma”).
Regarding claim 3, Choi in view of Chamie, teaches the method of claim 2, wherein the one or more input images (using patients' cardiac imaging to: (1) derive a patient-specific geometric model of the coronary vessels [Col. 4, lines 42-45]) comprise annotated segments representing one or more regions of media (image then may be segmented manually or automatically to identify voxels belonging to the aorta and the lumen of the coronary arteries [Col. 12, lines 27-29]);
and wherein the method further comprises: estimating, by the one or more processors (the platform may also include a central processing unit (CPU) 620, in the form of one or more processors, for executing program instructions [Col. 21, lines 61-64]), (image then may be segmented manually or automatically to identify voxels belonging to the aorta and the lumen of the coronary arteries [Col. 12, lines 27-29]) in the one or more input images and one or more ground-truth annotations of regions of media (***ground-truth annotations are defined as training data. See specifications.) (Given the image of a new patient, training data of local disease progression may then be used to predict the change in disease at each location [Col. 16, lines 25-27]) in the one or more input images;
and in response, flagging (Figure 5A, step 510), by the one or more processors, the one or more output images corresponding to the one or more input images
Choi in view of Chamie does not specifically disclose that the one or more input images are images received from an imaging probe during a pullback of the imaging probe in the blood vessel.
However, Bouma teaches the one or more input images are images received from an imaging probe during a pullback of the imaging probe in the blood vessel (the probe 154 may be inserted into a vessel such as a blood vessel through which blood is flowing such that the direction of blood flow is away from the end of the probe. As the released fluid moves past the probe, the probe is controlled to collect cross-sectional image data during time periods, e.g. 30 msec or less [0047]).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Choi in view of Chamie, to utilize an imaging probe, disclosed by Bouma. An imaging probe can be coupled to an imaging system enabling, by a processor, the imaging system to collect data in the blood vessel from the probe, taught by Bouma. The motivation would be to use the imaging probe, taught by Bouma, in the method disclosed by Choi in view of Chamie, to acquire cross-sectional images of blood vessels to characterize and locate fibrotic caps. Therefore, it would have been obvious to combine Bouma with Choi in view of Chamie, to obtain the method specified in claim 3.
Choi in view of Chamie in further view of Bouma does not specifically disclose the one or more processors estimate the average signal-to-noise ratio (SNR) of the one or more input images based on comparisons and that the output images are flagged when determined that the average SNR falls below a predetermined threshold.
However, Ebbini teaches the one or more processors estimate the average signal-to-noise ratio (SNR) (One or more embodiments of methods or systems described herein may include one or more of the following features or processes: 1) real-time implementation of an adaptive algorithm to optimize the imaging performance (e.g., based on specified signal to noise ratio (SNR) and/or contrast ratio (CR) values for a given control-point selection [0013])of the one or more input images based on comparisons and that the output images are flagged when determined that the average SNR falls below a predetermined threshold (If the SNR is not higher than the threshold (block 96), then spectral matching between the transmit waveforms and the echo components is emphasized [0102]).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Choi in view of Chamie, in further view of Bouma, to calculate a signal to noise ratio to be used as a marker for categorizing output images, as taught by Ebbini. Chamie teaches that the interface between the fibrous tissue and lipidic necrotic core produces a high superficial backscattering signal, in which the combination of a signal-rich band overlying a signal-poor region produces a unique, distinct signature. The motivation would be to modify the method disclosed by Choi in view of Chamie, in further view of Bouma, to calculate and use a signal to noise ratio as a mechanism to identify fibrotic caps more accurately. Therefore, it would have been obvious to combine Ebbini with Choi in view of Chamie, in further view of Bouma, to obtain the method specified in claim 3.
Regarding claim 12, the claim recites similar limitations to claim 3 but in the form of a system. Therefore, claim 12 recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above).
Regarding claim 4, Choi and Chamie, in view of Ebbini, discloses the method of claim 3. Further, Bouma teaches the method of claim 3, wherein the imaging probe is an optical coherence tomography (OCT) imaging probe, an intravascular ultrasound (IVUS) imaging probe, a near-infrared spectroscopy (NIRS) imaging probe, an OCT-NIRS imaging probe, or a micro- OCT (pOCT) imaging probe (an ultrasound (e.g. intravascular ultrasound, or IVUS) probe (e.g. associated with a fluid delivery system as in FIG. 1A) may be inserted into a vessel and used to track clearance of a fluid from the vessel [0057]).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by either Choi, Chamie, or Ebbini, to utilize an intravascular ultrasound (IVUS) imaging probe, disclosed by Bouma. Chamie teaches that intravascular ultrasound (IVUS) provides real-time tomographic cross-sectional images of the coronary vessel wall, providing valuable insights into quantification and composition of coronary plaques. The motivation would have been to use the IVUS probe, taught by Bouma, as an imaging modality in the method disclosed by Choi, Chamie, or Ebbini, to acquire cross-sectional images of blood vessels to characterize and locate fibrotic caps. Therefore, it would have been obvious to combine Bouma with Choi,Chamie, and Ebbini to obtain the method specified in claim 4.
Regarding claim 17, Choi teaches the system of claim 11, wherein the system further comprises (The platform may also include a central processing unit (CPU) 620, in the form of one or more processors, for executing program instructions [Col. 21, lines 61-64]); and to receive the one or more input images of the blood vessel (the present disclosure describes certain principles and embodiments for using patients' cardiac imaging to: (1) derive a patient-specific geometric model of the coronary vessels [Col. 4, lines 42-45]), the one or more processors are further configured to receive image data corresponding to the one or more input images (a processor configured to execute the instructions to perform a method including the steps of: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for at least part of the individual's vascular system [Col.2 lines 35-40])
Choi does not specifically disclose an imaging probe that is communicatively connected to the one or more processors and that the one or more input images are acquired from the imaging probe while the imaging prove is inside the blood vessel.
However, Bouma teaches an imaging probe that is communicatively connected to the one or more processors (the probe being optically coupled to an imaging system; collecting, using a processor, data from the imaging system based on the release of the differential-contrast fluid into the vessel [0017]) and that the one or more input images are acquired from the imaging probe while the imaging prove is inside the blood vessel (the probe may be inserted into a vessel such as a blood vessel [0043]).
The motivation for combining Choi in view of Chamie, in further view of Ebbini, and Bouma is the same motivation as used for claim 4 above.
Regarding claim 13, the claim recites similar limitations to claim 4 but in the form of a system. Therefore, claim 13 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above).
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable of Choi et al., US 9,679,374 B2, (hereinafter “Choi”), Chamie et al., “Optical Coherence Tomography and Fibrous Cap Characterization”, (hereinafter “Chamie”), Ebbini et al., US 2016/0143617 A1, (hereinafter “Ebbini”), and Bouma et al., US 2021/0113101 A1, (hereinafter “Bouma”), and further in view of Van der Meer et al., “Localized Measurement of Optical Attenuation Coefficients of Atherosclerotic Plaque Constituents by Quantitative Optical Coherence Tomography”, (hereinafter “Van der Meer”).
Regarding claims 18-19, Choi and Chamie, further in view of Van der Meer teaches the limitations recited in claims 18-19 as set forth in claims 8-9 but in the form of a system. Therefore, claims 18-19 are rejected for similar rationale and reasoning (see the analysis for claims 8-9 above), and are not repeated herewith.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA PEARSON whose telephone number is (703)-756-5786. The examiner can normally be reached Monday - Friday 9:00 - 5:00.
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
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