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 .
Response to Amendment
The Amendment filed January 21st, 2026 has been entered. Claims 1-7, 9-14, and 16-19 are pending in the application. Claims 8, 15, and 20 are cancelled. Applicant’s amendments to the Claims 1, 11, and 17 have overcome the rejections previously set forth in the Non-Final Office Action mailed October 21st 2025. Previously cited reference Taylor (US 20170018081 A1) was used for the amended claim limitations.
Response to Arguments
Applicant's arguments filed January 21st 2026 have been fully considered but they are not persuasive.
The Applicant argues that: “As a first point, Taylor is directed towards "using patient-specific contrast distribution of a patient-specific anatomic model to assess severity of a plaque and/or a stenotic lesion" and as part of this analysis teaches "receiving one or more blood flow characteristics of the anatomic model using contrast distribution." See Taylor, 74 and 76.
As such, at best Taylor teaches receiving blood flow characteristics of a model using contrast distribution. This is different than what is claimed. For example, the claims recite receiving "receive an indication of an additional physiological characteristic of the vessel of the patient" and not of a model of the vessel.”
The Examiner respectfully disagrees with the characterization of the teachings of Taylor. Applicant’s citation to Taylor would make clear to one of ordinary skill in the art that while a model is being received, the model is obtained in order to obtain the blood flow characteristics, so that both a model and the characteristics are received. For comparison, in paragraph [0011], Taylor teaches: “calculating, using a processor, one or more blood flow characteristics of blood flow through the patient-specific anatomic model based on the updated physiological and boundary conditions”. That is, while Taylor teaches “receiving blood flow characteristics of a model using contrast distribution”, Taylor simultaneously teaches “receiv[ing] an indication of an additional physiological characteristic of the vessel of the patient.”
The Applicant further argues that: “Taylor further teaches "constructing a hemodynamic model using the received blood flow characteristics at one or more locations of the patient-specific anatomic mode." See Taylor, 77. Thus, at best Taylor teaches to construct a hemodynamic model using blood flow characteristics at locations of an anatomical model.
This is not the same as what is claimed. For example, the claims recite generating a 3D model of the vessel using the images and the physiological characteristic. This is not taught or suggested by Taylor.”
The Examiner respectfully disagrees that Taylor fails to teach “generating a 3D model of the vessel using the images and the physiological characteristic”. Specifically, the Examiner notes that Taylor recites: “Step 304 may include constructing a patient-specific anatomic model from the received one or more patient-specific images. This step may include methods to directly segment the image data and create a patient-specific three-dimensional anatomic model of the patient's arteries” [0042] and that “the hemodynamic model may be created by overlaying the received one or more blood flow characteristics on the received patient-specific anatomic model” [0077] (emphasis added). In other words, the hemodynamic model may be a 3D model that was generated from a three-dimensional anatomy model overlaid with the physiological characteristic, where the three-dimensional anatomy model was obtained from the plurality of images.
For at least the reasons cited above, the Examiner is not convinced that “claim 1 is not obvious in view of the cited art”.
Claim Rejections - 35 USC § 103
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, 3, 4, 5, 6, 7, 11, 12, 13, 14, 17, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo (WO 2022069254 A1; from applicant’s IDS. See attached document for paragraph numbers) in view of Huennekens (US 20060241465 A1) and Sheehan (US 20140275995 A1) and Taylor (US 20170018081 A1).
Regarding claim 1:
Kuo teaches:
An apparatus for a vascular imaging medical device, comprising:
a processor arranged to be coupled to an intravascular imaging device and a fluoroscope device (Kuo: The co-registration system includes a processor circuit configured for communication with a display, an x-ray fluoroscopy imaging device, and an intravascular catheter or guidewire [0007]); and
a memory device coupled to the processor, the memory device comprising instructions, which when executed by the processor cause the apparatus to (Kuo: The intravascular data processing system 144 can execute computer readable instructions stored on a non-transitory tangible computer readable medium [0032]):
receive, from the fluoroscope device, an angiographic image (Kuo: receive the x-ray angiography data from an x-ray angiography device in communication with the processor circuit, [0008]) if a vessel of a patient (Kuo: the x-ray angiography data comprises a first x-ray angiography image of the blood vessel, [0008]);
receive, from the intravascular imaging device, a plurality of images associated with the vessel of the patient (Kuo: receive, from the intravascular catheter or guidewire, intravascular data representative of the blood vessel [0007]); and
generate a three-dimensional (3D) model of a physiology of the vessel from the angiographic image (Kuo: The x-ray angiography images are used to create a three-dimensional model of the vasculature, [0006]).
Kuo fails to teach:
receive, from the intravascular imaging device, a plurality of images associated with the vessel of the patient, the plurality of images comprising multidimensional and multivariate images; and
generate a three-dimensional (3D) model of a physiology of the vessel from the angiographic image and the plurality of images.
Huennekens teaches:
a processor arranged to be coupled to an intravascular imaging device and a fluoroscope device (Huennekens: A co-registration processor 30 receives IVUS image data from the catheter image processor 26 via line 32 and radiological image data from the radiological image processor 18 via line 34 [0041]; see Note 1B); and
receive, from the intravascular imaging device (Huennekens: a imaging probe (e.g., an IVUS transducer probe) [0036]), a plurality of images associated with the vessel of the patient (Huennekens: intravascular images [0036]), the plurality of images comprising multidimensional and multivariate images (see Note 1A); and
generate a three-dimensional (3D) model of a physiology of the vessel from the plurality of images (Huennekens: IVUS probes can be configured to render a variety of two and three-dimensional images [0014]).
Note 1A: Huennekens teaches: “The second manner of intravascular imaging comprises imaging the vessel itself using a catheter-mounted intravascular probe. Intravascular imaging of blood vessels provides a variety of information about the vessel including: the cross-section of the lumen, the thickness of deposits on a vessel wall, the diameter of the non-diseased portion of a vessel, the length of diseased sections, and the makeup of the atherosclerotic plaque on the wall of the vessel. [0009]. That is, using a probe, intravascular images may be produced that comprise multiple variables, such as deposit thicknesses, diameters, and more.
Huennekens further teaches: “Catheter-mounted probes, and in particular, IVUS probes can be configured to render a variety of two and three-dimensional images,” [0014]. That is, probes such as the catheter-mounted intravascular probe taught by Huennekens above may produce images in multiple different dimensions.
Therefore, it is reasonable to conclude that the intravascular images taught by Huennekens are multivariate and multidimensional. The Examiner would also like to draw comparison between Huennekens’ intravascular images and the IVUS images taught in [0059] of the specification of the present application.
Note 1B: In Note 1A, it was shown that the intravascular imaging utilizes a catheter-mounted probe. Huennekens in [0041] recites that IVUS (intravascular ultrasound) images may be obtained by a catheter image processor. Therefore, it is reasonable to conclude that the intravascular imaging device of the present application is analogous to the catheter-mounted probe taught by Huennekens.
Huennekens teaches: “Radiological image data acquired by the angiography/fluoroscopy c-arm 14 passes to an angiography/fluoroscopy processor 18 via transmission cable 16. The angiography/fluoroscopy processor 18 converts the received radiological image data received via the cable 16 into angiographic/fluoroscopic image data,” [0037]. That is, Huennekens teaches a angiography/fluoroscopy c-arm that is analogous to the fluoroscope device of the present application.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Huennekens with Kuo. Receiving, from the intravascular imaging device, a plurality of images associated with the vessel of the patient, the plurality of images comprising multidimensional and multivariate images, as in Huennekens, would benefit the Kuo teachings by enabling visualizations of data in the intravascular images that may not be apparent in the angiographic images: “the ability to view the EEL, and calculate its dimensions, allows an IVUS image to render a more reliable determination than angiography,” Huennekens, [0013].
Kuo in view of Huennekens still fails to teach:
generate a three-dimensional (3D) model of a physiology of the vessel from the angiographic image and the plurality of images.
Sheehan teaches:
generate a three-dimensional (3D) model of a physiology of the vessel from the angiographic image and the plurality of images (Sheehan: For step 168, a two- or three-dimensional vasculature map of the imaged vessel is displayed incorporating imaging data from both the IVUS imaging data and from the angiographic imaging data [0046]).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Sheehan with Kuo in view of Huennekens. Generating a three-dimensional (3D) model of a physiology of the vessel from the angiographic image and the plurality of images, as in Sheehan, would benefit the Kuo in view of Huennekens teachings by enabling the user to “obtain a more complete vasculature map and more realistic representation of the vessel under examination.” Sheehan, [0046].
Kuo in view of Huennekens and Sheehan fails to teach:
receive an indication of an additional physiological characteristic of the vessel of the patient; and
generate the 3D model of the physiology of the vessel from the angiographic image, the plurality of images, and the additional physiological characteristic of the vessel, wherein the additional physiological characteristic of the vessel comprises pressure or flow.
Taylor teaches:
receive an indication of an additional physiological characteristic of the vessel of the patient (Taylor: Step 604 may include receiving one or more blood flow characteristics of the anatomic model using contrast distribution [0076]); and
generate the 3D model (Taylor: The hemodynamic model may be created by overlaying the received one or more blood flow characteristics on the received patient-specific anatomic model [0077]; see Note 8C) of the physiology of the vessel from the angiographic image (Taylor: The image data may be obtained from two-dimensional scans (e.g., coronary angiography, biplane angiography, etc.) [0056]; Taylor: directly segment the image data and create a patient-specific three-dimensional anatomic model of the patient's arteries [0057]; see Note 8A), the plurality of images (Taylor: receiving one or more patient-specific images of at least a portion of a patient's vasculature [0011]; Taylor: Step 304 may include constructing a patient-specific anatomic model from the received one or more patient-specific images. [0057]; see Note 8B), and the additional physiological characteristic of the vessel, wherein the additional physiological characteristic of the vessel comprises pressure or flow (Taylor: Blood flow velocity and pressure fields may be computed for the entire three-dimensional model, [0027]).
Note 8A: In [0056] and [0057] cited above, Taylor teaches that “image data” may comprise angiographic scans, and that the image data may be used to create a three-dimensional model.
Note 8B: In [0011] and [0057] cited above, Taylor teaches that one or more images of vasculature may be received, and that those images may be used to create a three-dimensional anatomic model.
Note 8C: The Examiner interprets the hemodynamic model taught by Taylor to be a 3D model because Taylor teaches “overlaying” the blood flow characteristics onto the 3D anatomic model.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Taylor with Kuo in view of Huennekens and Sheehan. Receiving an indication of an additional physiological characteristic of the vessel of the patient; and generating the 3D model of the physiology of the vessel from the angiographic image, the plurality of images, and the additional physiological characteristic of the vessel, wherein the additional physiological characteristic of the vessel comprises pressure or flow, as in Taylor, would benefit the Kuo in view of Huennekens and Sheehan teachings by enabling realistic flow predictions: “This coupling between three-dimensional models and reduced order models enable the solution of realistic coronary artery flow and pressure waveforms,” Taylor, [0005].
Regarding claim 2:
Kuo in view of Huennekens, Sheehan, and Taylor teaches:
The apparatus of claim 1 (as shown above), the instructions when executed by the processor further cause the apparatus to:
generate a graphical information element comprising an indication of the 3D model (Kuo: Fig. 10 is a diagrammatic view of a graphical user interface 1000 displaying intravascular data 1010 co-registered to an angiography-based roadmap image 880 [0091]; see Note 2A); and cause the graphical information element to be displayed on a display coupled to the apparatus (Kuo: In some aspects, the processor circuit is configured to output, to the display, the 3D model and the visual representation of the intravascular data overlaid on the 3D model, [0008]).
Note 2A: Kuo teaches: “It is noted that the roadmap image 880 is not an image directly acquired by an imaging device or system. Rather, it is a computer generated, two- dimensional projection of the 3D model,” [0080]. Therefore, the roadmap image is analogous to an indication of the 3D model.
Regarding claim 3:
Kuo in view of Huennekens, Sheehan, and Taylor teaches:
The apparatus of claim 1 (as shown above), the instructions when executed by the processor further cause the apparatus to co-register the angiographic image and the plurality of images (Kuo: intravascular data may be co-registered with a angiographic roadmap image obtained at a different angle than fluoroscopic images of an intravascular device movement through the vessel [0001]; see Note 3A).
Note 3A: The intravascular data is interpreted to be analogous to the plurality of images, as shown in the claim mapping for claim 1 of the present application above.
Regarding claim 4:
Kuo in view of Huennekens, Sheehan, and Taylor teaches:
The apparatus of claim 3 (as shown above), the instructions when executed by the processor further cause the apparatus to:
identify a centerline of the vessel between start point and the end point (Kuo: In some embodiments, the system 100 or a user of the system 100 may identify or mark a series of points within each angiography image 605 and 610 to define centerlines of depicted vessels including major vessels and branching vessels [0063]).
Kuo fails to teach:
identify a start point of a pull-back operation associated with the plurality of images on the vessel represented in the angiographic image;
identify an end point of the pull-back operation associated with the plurality of images on the vessel represented in the angiographic image; and
Huennekens teaches:
identify a start point of a pull-back operation associated with the plurality of images on the vessel represented in the angiographic image (Huennekens: the initial location of a radiopaque marker [0056]; see Note 4A);
identify an end point of the pull-back operation associated with the plurality of images on the vessel represented in the angiographic image (Huennekens: the cursor can be placed by the system at a distance from the initial location along the calculated path 550/650 that represents the product of the pullback rate and the time period [0056]; see Note 4A); and
identify a centerline of the vessel between start point and the end point (Huennekens: As seen in FIGS. 5 and 6, the calculated path 550/650 is shown as a curve that matches the tortuosity of a vessel through which the probe 22 passes--represented by a center line through the displayed vessel [0057]).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Huennekens with Kuo. Identifying a start point of a pull-back operation associated with the plurality of images on the vessel represented in the angiographic image and identifying an end point of the pull-back operation associated with the plurality of images on the vessel represented in the angiographic image, as in Huennekens, would benefit the Kuo teachings by enabling the data collected by the imaging system to be related to the path taken through a vessel: “FIGS. 4-7 includes a "slider" control that allows an operator to track through a series of stored frames representing sequentially acquired data along a traversed path within a vessel,” Huennekens, [0059].
Note 4A: Huennekens teaches: “The marker artifact 520/620 is superimposed on the angiogram image at a location calculated from non-visual position data (e.g., pull-back distance, spatial position sensors, angular orientation sensors, etc.). For example, if the initial location of a radiopaque marker within the enhanced radiological image 510/610 is known and the catheter is pulled by an automatic pullback system at a specific rate for a known amount of time, the cursor can be placed by the system at a distance from the initial location along the calculated path 550/650 that represents the product of the pullback rate and the time period,” [0056].
In summary, a ”marker artifact” is identified at a “initial location of a radiopaque marker”, and then based on pull back distance, a second point can be identified “at a distance from the initial location along the calculated path 550/650 that represents the product of the pullback rate and the time period”. It is reasonable to name the second point an “end point” of the pull-back operation, as it is determined by the time and rate of the pullback. It follows that it is also reasonable to name the initial point a “start point” for the pullback, as it is the point from which the end point is displaced.
Regarding claim 5:
Kuo in view of Huennekens, Sheehan, and Taylor teaches:
The apparatus of claim 4 (as shown above), the instructions when executed by the processor further cause the apparatus to:
identify a plurality of side branches of the vessel (Huennekens: Turning to FIG. 2, the angiography/fluoroscopy processor 18 captures an angiographic "roadmap" image 200 in a desired projection (patient/vessel orientation) and magnification. [...] Thus, side branches such as side branch 210 and other vasculature landmarks can be displayed and seen clearly [0045]; see Note 5A) on the angiographic image and in the plurality of images (Kuo: the IVUS image 830 and its corresponding location along the path 840 may be projected onto the angiography-based model 700 as shown by the arrow 868, such that the point 841 is also identified along the vessel [0082]; see Note 5B); and
match a one of the plurality of side branches identified on the angiographic image with a one of the plurality of side branches identified in the plurality of images (Kuo: The IVUS image 830 may be an image acquired by the intravascular device 820 at a position within the vasculature and within the fluoroscopy image 810 as shown by the circle 825 [0072]; see also Note 5B).
Note 5A: In [0045] cited above, Huennekens teaches that side branches may be identified (“such as side branch 210”) in a intravascular image.
Kuo also teaches: “Fig. 6A is a diagrammatic view of an x-ray angiography image 605 of vessels 630 of the heart 620,” [0054] and that “obtaining intravascular data 630 and fluoroscopy images 810 may be performed at various times in relation to one another” [0078]. That is, Kuo teaches that vessels 630 may be “obtained” or identified at various times in relation to one another.
Note 5B: In other words, Kuo teaches in [0082] cited above that the IVUS (intravascular ultrasound) image and angiography-based image may have positions that are matched along a vessel.
Regarding claim 6:
Kuo in view of Huennekens, Sheehan, and Taylor teaches:
The apparatus of claim 5 (as shown above), the instructions when executed by the processor further cause the apparatus to
map frames of the plurality of images with locations along the centerline of the vessel on the angiographic image (Huennekens: As the user drags and drops the cursor along the path, the co-registration processor 30 acquires and presents corresponding co-registered images [0059]; see Note 6A).
Note 6A: Huennekens teaches: “a user interface associated with the displayed images provided in FIGS. 4-7 includes a "slider" control that allows an operator to track through a series of stored frames representing sequentially acquired data along a traversed path within a vessel,” [0059] and that “A user selects and drags the cursor along a path similar to the calculated path 750. As the user drags and drops the cursor along the path, the co-registration processor 30 acquires and presents corresponding co-registered images. [0059]”.
Calculated path 750 is a centerline, as shown in Figure 7 of Huennekens. The centerline is part of an angiographic image, as Huennekens teaches: “FIG. 2 depicts an illustrative angiogram image;” [0020]. Figure 7 of Huennekens contains a near identical figure shown in Figure 2, except with a centerline and marker identified. Therefore, Huennekens teaches that when the user selects a position along path 750 in the angiographic image, a co-registered image corresponding to the location along the centerline of the vessel will be presented. Presenting the coregistered image (which includes the plurality of images, see Note 6B) based on a position along the path requires that the co-registered image is mapped to the position along said path.
Note 6B: Huennekens teaches: “The co-registration processor 30 renders a co-registration image including both radiological and IVUS image frames,” [0041]. The IVUS (intravascular ultrasound) image frames are interpreted to be analogous to the plurality of images consistent with the mapping in claim 1.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Huennekens with Kuo. Mapping frames of the plurality of images with locations along the centerline of the vessel on the angiographic image, as in Huennekens, would benefit the Kuo teachings by enabling a user to easily view the relevant images pertaining to a location in the vessel: “the operator creates a reference mark 760 at one or more points on a calculated path 750. The reference mark 760 serves a variety of potential uses, […] In yet other embodiments, the reference mark 760 is used to highlight a particular point of interest […] A bookmark is placed within a series of cross-sectional images associated with the IVUS image 700 portion of the display 701. The bookmark allows quick access to a particular archived image frame corresponding to the reference mark,” Huennekens, [0058].
Regarding claim 7:
Kuo in view of Huennekens, Sheehan, and Taylor teaches:
The apparatus of claims 1 (as shown above), the instructions when executed by the processor further cause the apparatus to
generate assessments of the vessel, wherein the assessments comprise a diameter of the vessel (Kuo: The ILD 912 may provide a visual representation (e.g., numerical/alphanumerical, graphical, symbolic, etc.) of relative diameters of the imaged vessel at all positions along the imaged vessel [0086]), an area of the vessel, or a diameter and area of the vessel and
wherein the assessments comprise a diameter of the lumen (Kuo: the system 144 may process received intravascular data to calculate metrics relating to the medium surrounding the device 146 such as, but not limited to, the diameter of a body lumen [0026]), an area of the lumen, or a diameter and area of the lumen.
Regarding claim 11:
Claim 11 is substantially similar to claim 1, and is therefore rejected for similar reasons. Claim 11 contains the following notable differences:
Claim 11 claims a computer-readable storage device instead of an apparatus. Kuo teaches a computer-readable storage device: “The intravascular data processing system 144 can execute computer readable instructions stored on a non-transitory tangible computer readable medium” [0032].
Regarding claim 12:
Claim 12 is substantially similar to claim 2, and is therefore rejected for similar reasons. Claim 12 contains the following notable differences:
Claim 12 claims a computer-readable storage device instead of an apparatus. Kuo teaches a computer-readable storage device: “The intravascular data processing system 144 can execute computer readable instructions stored on a non-transitory tangible computer readable medium” [0032].
Regarding claim 13:
Kuo in view of Huennekens, Sheehan, and Taylor teaches:
The computer-readable storage device of claim 11 (as shown above), the instructions when executed by the processor further cause the computing device to:
identify a start point of a pull-back operation associated with the plurality of images on the vessel represented in the angiographic image (Huennekens: the initial location of a radiopaque marker [0056]; see Note 4A above);
identify an end point of the pull-back operation associated with the plurality of images on the vessel represented in the angiographic image (Huennekens: the cursor can be placed by the system at a distance from the initial location along the calculated path 550/650 that represents the product of the pullback rate and the time period [0056]; see Note 4A above);
identify a centerline of the vessel between start point and the end point (Kuo: In some embodiments, the system 100 or a user of the system 100 may identify or mark a series of points within each angiography image 605 and 610 to define centerlines of depicted vessels including major vessels and branching vessels [0063]);
identify a plurality of side branches of the vessel (Huennekens: Turning to FIG. 2, the angiography/fluoroscopy processor 18 captures an angiographic "roadmap" image 200 in a desired projection (patient/vessel orientation) and magnification. [...] Thus, side branches such as side branch 210 and other vasculature landmarks can be displayed and seen clearly [0045]; see Note 5A above) on the angiographic image and in the plurality of images (Kuo: the IVUS image 830 and its corresponding location along the path 840 may be projected onto the angiography-based model 700 as shown by the arrow 868, such that the point 841 is also identified along the vessel [0082]; see Note 5B above); and
match a one of the plurality of side branches identified on the angiographic image with a one of the plurality of side branches identified in the plurality of images (Kuo: The IVUS image 830 may be an image acquired by the intravascular device 820 at a position within the vasculature and within the fluoroscopy image 810 as shown by the circle 825 [0072]; see also Note 5B above); and
map frames of the plurality of images with locations along the centerline of the vessel on the angiographic image (Huennekens: As the user drags and drops the cursor along the path, the co-registration processor 30 acquires and presents corresponding co-registered images [0059]; see Note 6A above).
The Examiner notes that claim 13 is a combination of claims 4, 5, and 6 modified for a computer-readable storage device. For the motivations to combine Kuo, Huennekens, Sheehan, and Taylor, please see the respective claim limitations in the aforementioned claims.
Regarding claim 14:
Claim 14 is substantially similar to claim 7, and is therefore rejected for similar reasons. Claim 14 contains the following notable differences:
Claim 14 claims a computer-readable storage device instead of an apparatus. Kuo teaches a computer-readable storage device: “The intravascular data processing system 144 can execute computer readable instructions stored on a non-transitory tangible computer readable medium” [0032].
Regarding claim 17:
Claim 17 is substantially similar to claim 1, and is therefore rejected for similar reasons. Claim 17 contains the following notable differences:
Claim 17 claims a computer-implemented method instead of an apparatus. Kuo in view of Huennekens, Sheehan, and Taylor teaches the apparatus of claim 1, and therefore teaches the corresponding method.
Regarding claim 18:
Claim 18 is substantially similar to claim 2, and is therefore rejected for similar reasons. Claim 18 contains the following notable differences:
Claim 18 claims a computer-implemented method instead of an apparatus. Kuo in view of Huennekens, Sheehan, and Taylor teaches the apparatus of claim 2, and therefore teaches the corresponding method.
Regarding claim 19:
Kuo in view of Huennekens and Sheehan teaches:
The computer-implemented method of claim 17 (as shown above), comprising:
identifying a start point of a pull-back operation associated with the plurality of images on the vessel represented in the angiographic image (Huennekens: the initial location of a radiopaque marker [0056]; see Note 4A above);
identifying an end point of the pull-back operation associated with the plurality of images on the vessel represented in the angiographic image (Huennekens: the cursor can be placed by the system at a distance from the initial location along the calculated path 550/650 that represents the product of the pullback rate and the time period [0056]; see Note 4A above);
identifying a centerline of the vessel between start point and the end point (Kuo: In some embodiments, the system 100 or a user of the system 100 may identify or mark a series of points within each angiography image 605 and 610 to define centerlines of depicted vessels including major vessels and branching vessels [0063]);
identifying a plurality of side branches of the vessel (Huennekens: Turning to FIG. 2, the angiography/fluoroscopy processor 18 captures an angiographic "roadmap" image 200 in a desired projection (patient/vessel orientation) and magnification. [...] Thus, side branches such as side branch 210 and other vasculature landmarks can be displayed and seen clearly [0045]; see Note 5A above) on the angiographic image and in the plurality of images (Kuo: the IVUS image 830 and its corresponding location along the path 840 may be projected onto the angiography-based model 700 as shown by the arrow 868, such that the point 841 is also identified along the vessel [0082]; see Note 5B above); and
matching a one of the plurality of side branches identified on the angiographic image with a one of the plurality of side branches identified in the plurality of images (Kuo: The IVUS image 830 may be an image acquired by the intravascular device 820 at a position within the vasculature and within the fluoroscopy image 810 as shown by the circle 825 [0072]; see also Note 5B above); and
mapping frames of the plurality of images with locations along the centerline of the vessel on the angiographic image (Huennekens: As the user drags and drops the cursor along the path, the co-registration processor 30 acquires and presents corresponding co-registered images [0059]; see Note 6A above).
The Examiner notes that claim 19 is a combination of claims 4, 5, and 6 modified for a computer-implemented method. For the motivations to combine Kuo, Huennekens, Sheehan, and Taylor, please see the respective claim limitations in the aforementioned claims.
Claims 9, 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo (WO 2022069254 A1; from applicant’s IDS) in view of Huennekens (US 20060241465 A1), Sheehan (US 20140275995 A1), Taylor (US 20170018081 A1), and Itu (WO 2021014181 A1).
Regarding claim 9:
Kuo in view of Huennekens, Sheehan, and Taylor teaches:
The apparatus of claim 1 (as shown above), the instructions when executed by the processor further cause the apparatus to
Kuo in view of Huennekens, Sheehan, and Taylor fails to teach:
generate an inference of the 3D model of the physiologic of the vessel from a machine learning (ML) model based in part on applying the angiographic image and the plurality of images as inputs to the ML model.
Itu teaches:
generate an inference of the 3D model of the physiologic of the vessel from a machine learning (ML) model based in part on applying the angiographic image and the plurality of images as inputs to the ML model (Itu: the first modality is an intravascular imaging modality, the second modality is x-ray angiography, and the one or more first medical images and the one or more second medical images are of a same patient. The artificial intelligence model may be trained by performing a three- dimensional reconstruction of the vessel from the one or more second medical images, performing a co-registration between the one or more first medical images … [0008]; see Note 9A).
Note 9A: In [0008] cited above, Itu teaches that a 3D reconstruction will be performed (inference of the 3D model of the physiologic of the vessel) via an artificial intelligence model (machine learning (ML) model) based in part on training from angiography (angiographic image) and intravascular imaging (plurality of images).
As best understood by the examiner, training the machine learning model by “performing a three- dimensional reconstruction of the vessel from the one or more second medical images, [and] performing a co-registration between the one or more first medical images” requires inputting the images into the model so that the reconstruction and co-registration can be performed.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Itu with Kuo in view of Huennekens, Sheehan and Taylor. Generating an inference of the 3D model of the physiologic of the vessel from a machine learning (ML) model based in part on applying the angiographic image and the plurality of images as inputs to the ML model, as in Itu, would benefit the Kuo in view of Huennekens, Sheehan, and Taylor teachings by enabling the system to learn details and features of the vessels that may improve 3D model generation.
Regarding claim 10:
Kuo in view of Huennekens, Sheehan, Taylor, and Itu teaches:
The apparatus of claim 9 (as shown above), wherein the ML model is trained based in part on a supervised learning training algorithm (Itu: In one example, the calcified portions of the vessel may be detected using an artificial intelligence model trained based on annotated training data. [0038]; see Note 10A)
Note 10A: Itu teaches that the model may be trained based on annotated training data, which indicates a supervised learning method.
Kuo in view of Huennekens, Sheehan and Itu fails to teach:
with expected outputs of the ML model derived based on a computation fluid dynamics (CFD) model,
wherein the CFD model takes an angiographic image and a plurality of images as input and generates a 3D vessel physiology model as output.
Taylor teaches:
with expected outputs of the ML model derived based on a computation fluid dynamics (CFD) model (Taylor: Step 208 may be performed using computational fluid dynamics (CFD) and/or by using a trained system (e.g., machine learning algorithm) [0033]),
generate the 3D model of the physiology of the vessel from the angiographic image (Taylor: The image data may be obtained from two-dimensional scans (e.g., coronary angiography, biplane angiography, etc.) [0056]; Taylor: directly segment the image data and create a patient-specific three-dimensional anatomic model of the patient's arteries [0057]; see Note 8A above), the plurality of images (Taylor: receiving one or more patient-specific images of at least a portion of a patient's vasculature [0011]; Taylor: Step 304 may include constructing a patient-specific anatomic model from the received one or more patient-specific images. [0057]),
wherein the CFD model (see Note 10B) takes an angiographic image (Taylor: The image data may be obtained from two-dimensional scans (e.g., coronary angiography, biplane angiography, etc.) [0056]; Taylor: directly segment the image data and create a patient-specific three-dimensional anatomic model of the patient's arteries [0057]; see Note 8A above), and a plurality of images as input (Taylor: receiving one or more patient-specific images of at least a portion of a patient's vasculature [0011]; Taylor: Step 304 may include constructing a patient-specific anatomic model from the received one or more patient-specific images. [0057]; see Note 8B above) and generates a 3D vessel physiology model as output (Taylor: outputting […] the patient-specific anatomic model to an electronic storage medium or display [0012]).
Note 10B: Taylor teaches: “In one embodiment, the physiological conditions may be measured, obtained, or derived from the patient-specific anatomic model using computational fluid dynamics,” [0032]. That is, the one or more images and the anatomic model (angiographic image) may be used as input for computational fluid dynamics.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Taylor with Kuo in view of Huennekens, Sheehan, and Itu. Having expected outputs of the ML model derived based on a computation fluid dynamics (CFD) model, and having the CFD model take an angiographic image and a plurality of images as input and generate a 3D vessel physiology model as output, as in Taylor, would benefit the Kuo in view of Huennekens, Sheehan, and Itu teachings by enabling realistic flow predictions: “This coupling between three-dimensional models and reduced order models enable the solution of realistic coronary artery flow and pressure waveforms,” Taylor, [0005].
Regarding claim 16:
Kuo in view of Huennekens, Sheehan, and Taylor teaches:
The computer-readable storage device of claim 11 (as shown above), the instructions when executed by the processor further cause the computing device to
Kuo in view of Huennekens, Sheehan, and Taylor fails to teach:
generate an inference of the 3D model of the physiologic of the vessel from a machine learning (ML) model based in part on applying the angiographic image and the plurality of images as inputs to the ML model.
Itu teaches:
generate an inference of the 3D model of the physiologic of the vessel from a machine learning (ML) model based in part on applying the angiographic image and the plurality of images as inputs to the ML model (Itu: the first modality is an intravascular imaging modality, the second modality is x-ray angiography, and the one or more first medical images and the one or more second medical images are of a same patient. The artificial intelligence model may be trained by performing a three- dimensional reconstruction of the vessel from the one or more second medical images, performing a co-registration between the one or more first medical images … [0008]; see Note 9A above).
wherein the ML model is trained based in part on a supervised learning training algorithm (Itu: In one example, the calcified portions of the vessel may be detected using an artificial intelligence model trained based on annotated training data. [0038]; see Note 10A)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Itu with Kuo in view of Huennekens, Sheehan, and Taylor. Generating an inference of the 3D model of the physiologic of the vessel from a machine learning (ML) model based in part on applying the angiographic image and the plurality of images as inputs to the ML model, as in Itu, would benefit the Kuo in view of Huennekens, Sheehan, and Taylor teachings by enabling the system to learn details and features of the vessels that may improve 3D model generation.
Kuo in view of Huennekens, Sheehan, and Itu still fails to teach:
with expected outputs of the ML model derived based on a computation fluid dynamics (CFD) model,
wherein the CFD model takes an angiographic image and a plurality of images as input and generates a 3D vessel physiology model as output.
Taylor teaches:
with expected outputs of the ML model derived based on a computation fluid dynamics (CFD) model (Taylor: Step 208 may be performed using computational fluid dynamics (CFD) and/or by using a trained system (e.g., machine learning algorithm) [0033]),
generate the 3D model of the physiology of the vessel from the angiographic image (Taylor: The image data may be obtained from two-dimensional scans (e.g., coronary angiography, biplane angiography, etc.) [0056]; Taylor: directly segment the image data and create a patient-specific three-dimensional anatomic model of the patient's arteries [0057]; see Note 8A), the plurality of images (Taylor: receiving one or more patient-specific images of at least a portion of a patient's vasculature [0011]; Taylor: Step 304 may include constructing a patient-specific anatomic model from the received one or more patient-specific images. [0057]),
wherein the CFD model (see Note 10B above) takes an angiographic image (Taylor: The image data may be obtained from two-dimensional scans (e.g., coronary angiography, biplane angiography, etc.) [0056]; Taylor: directly segment the image data and create a patient-specific three-dimensional anatomic model of the patient's arteries [0057]; see Note 8A above), and a plurality of images as input (Taylor: receiving one or more patient-specific images of at least a portion of a patient's vasculature [0011]; Taylor: Step 304 may include constructing a patient-specific anatomic model from the received one or more patient-specific images. [0057]; see Note 8B above) and generates a 3D vessel physiology model as output (Taylor: outputting […] the patient-specific anatomic model to an electronic storage medium or display [0012]).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Taylor with Kuo in view of Huennekens, Sheehan, and Itu. Having expected outputs of the ML model derived based on a computation fluid dynamics (CFD) model, and having the CFD model take an angiographic image and a plurality of images as input and generate a 3D vessel physiology model as output, as in Taylor, would benefit the Kuo in view of Huennekens, Sheehan, and Itu teachings by enabling realistic flow predictions: “This coupling between three-dimensional models and reduced order models enable the solution of realistic coronary artery flow and pressure waveforms,” Taylor, [0005].
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/VINCENT ALEXANDER PROVIDENCE/Examiner, Art Unit 2617 /KING Y POON/Supervisory Patent Examiner, Art Unit 2617
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