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 .
Claim Objections
Claim 1, 8, 15 and 18 are objected to because of the following informalities:
Claim 1 line 14, limitation "a machine learning (ML) model" should read "the machine learning (ML) model".
Claim 8 line 9 – 10, limitation "the vessel and/or lumen border segmentations" should read "the vessel and/or lumen border segmentations of each of the second plurality of cardiac arteries".
Claim 8 line 14 – 15, limitation "the vessel and/or lumen border segmentations or each of the second plurality of cardiac arteries" should read "the vessel and/or lumen border segmentations of each of the second plurality of cardiac arteries".
Claim 15 line 3, limitation "generating" should read "generate".
Claim 15 line 5, limitation "generating" should read "generate".
Claim 15 line 7, limitation "solving" should read "solve".
Claim 18 line 11, limitation "a machine learning (ML) model" should read "the machine learning (ML) model".
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1 – 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fonte et al. (US 2014/0073976 A1; published on 03/13/2014) (hereinafter "Fonte").
Regarding claim 1, Fonte discloses a computing system to train a machine learning (ML) model to infer pressure information for a cardiac artery from border segmentations of the cardiac artery ("… a patient-specific geometric model of a patient's blood vessels, and combining this geometry with the patient-specific physiological information and boundary conditions … disclosed systems and methods involve two phases: first, a training phase in which a machine learning system is trained to predict one or more blood flow characteristics …" [0007]; "... cause the computer to perform a method ..." [0010]), comprising:
a processor ("… a processor …" [0009]); and
memory comprising instructions executable by the processor, which instructions when executed cause the computing system ("… a data storage device storing instructions for estimating individual-specific blood flow characteristics; and a processor configured to execute the instructions to perform a method …" [0009]) to:
receive vessel and/or lumen border segmentations for a plurality of cardiac arteries ("The resulting CT images may then be segmented manually or automatically to identify voxels belonging to the aorta and to the lumen of the coronary arteries." [0031]);
generate models of the plurality of cardiac arteries from the vessel and/or lumen border segmentations ("Once all relevant voxels are identified, the geometric model can be derived (e.g., using marching cubes)." [0031]);
derive pressure information for each of the plurality of cardiac arteries ("For every point in the patient-specific geometric model for which there is a ... estimated or simulated value of the blood flow characteristics, server systems 106 may create a feature vector for that point that contains a numerical description of physiological or phenotypic parameters of the patient and a description of the local geometry. Specifically the feature vector may contain: (i) systolic and diastolic blood pressures ..." [0034]; "FFR=(P−ΔP)/P where P is the aortic pressure and ΔP is the change in pressure from the aorta to the location of interest." [0060]) based in part on solving a system of equations defining the pressure information using numerical analysis applied to the models of the plurality of cardiac arteries ("Using some or all of the information above, a network of flow resistance may be created. Pressure drop may be estimated by relating the amount of blood flow to the resistance to blood flow using any of a variety of analytical models, such as Poiseuille's equation, energy loss models, etc." [0059]); and
add the vessel and/or lumen border segmentations and the associated derived pressure information to ground truth data for training a machine learning (ML) model ("Training method 202 may involve acquiring, for each of a plurality of individuals, e.g., in digital format: (a) a patient-specific geometric model, (b) one or more measured or estimated physiological parameters, and (c) values of blood flow characteristics." [0022]).
Regarding claim 2, Fonte discloses all claim limitations, as applied in claim 1, and further discloses wherein when executed the instructions further cause the computing system to:
generate three-dimensional (3D) volumes for each of the cardiac arteries from the vessel and/or lumen border segmentations ("The resulting CT images may then be segmented manually or automatically to identify voxels belonging to the aorta and to the lumen of the coronary arteries. Once all relevant voxels are identified, the geometric model can be derived (e.g., using marching cubes)." [0031]);
generate volume meshes for each of the cardiac arteries from the 3D volumes, wherein each volume mesh comprises a plurality of discrete elements ("… and computing cross-sectional area at each centerline point and map it to corresponding surface and volume mesh points." [0037]); and
solve, for each of the volume meshes, the system of equations for each one of the plurality of discrete elements of the volume mesh ("Server systems 106 may interpolate FFR along the centerline using FFRi, project FFR values to 3D surface mesh node, and vary αi, βi, γi and obtain new sets of FFR estimation as necessary for training …" [0043]).
Regarding claim 3, Fonte discloses all claim limitations, as applied in claim 1, and further discloses wherein the pressure information is a pressure curve defining pressure ratios along a portion of the length of the cardiac artery ("FFR=(P−ΔP)/P where P is the aortic pressure and ΔP is the change in pressure from the aorta to the location of interest." [0060]).
Regarding claim 4, Fonte discloses all claim limitations, as applied in claim 3, and further discloses wherein the pressure curve defines distal pressure (Pd) over proximal pressure (Pa) along the portion of the length of the cardiac artery ("FFR=(P−ΔP)/P where P is the aortic pressure and ΔP is the change in pressure from the aorta to the location of interest." [0060]; here the aorta is interpreted as the proximal point and the location of interest is interpreted as the distal point).
Regarding claim 5, Fonte discloses all claim limitations, as applied in claim 1, and further discloses wherein the vessel and/or lumen border segmentations comprises both vessel and lumen border segmentations ("The resulting CT images may then be segmented manually or automatically to identify voxels belonging to the aorta and to the lumen of the coronary arteries." [0031]).
Regarding claim 6, Fonte discloses all claim limitations, as applied in claim 1, and further discloses wherein when executed the instructions further cause the computing system to:
receive image data associated with each of the cardiac arteries ("This model may be derived by performing a cardiac CT imaging study of the patient during the end diastole phase of the cardiac cycle." [0031]); and
generate the vessel and/or lumen border segmentations from the image data ("The resulting CT images may then be segmented manually or automatically to identify voxels belonging to the aorta and to the lumen of the coronary arteries." [0031]).
Regarding claim 7, Fonte discloses all claim limitations, as applied in claim 6, and further discloses wherein when executed the instructions further cause the computing system to apply an image processing algorithm to the image data to identify borders of the vessel and/or lumen of the cardiac arteries ("The resulting CT images may then be segmented ... automatically to identify voxels belonging to the aorta and to the lumen of the coronary arteries." [0031]).
Regarding claim 8, Fonte discloses all claim limitations, as applied in claim 6, and further discloses wherein the plurality of cardiac arteries are a first plurality of cardiac arteries, wherein the image data comprises image data of a first image modality ("The anatomic data may consist of imaging data (ie CT) …" [0046]), and wherein when executed the instructions further cause the computing system to:
receive second image data associated with each of a second plurality of cardiac arteries, the second image data comprising image data of a second image modality different than the first image modality ("… or measurements and anatomic representation already obtained from imaging data (quantitative angiography, vessel segmentations from third party software, vascular diameters, etc)." [0046]; angiography is different from CT);
generate vessel and/or lumen border segmentations for each of the second plurality of cardiac arteries from the second image data ("… or measurements and anatomic representation already obtained from imaging data (quantitative angiography, vessel segmentations from third party software, vascular diameters, etc)." [0046]; "Information about the following coronary anatomy, including but not limited to the following features derived from imaging data ..." [0048]);
generate models of the second plurality of cardiac arteries from the vessel and/or lumen border segmentations ("… and anatomic representation already obtained from imaging data (quantitative angiography, vessel segmentations from third party software, vascular diameters, etc)." [0046]);
derive pressure information for each of the second plurality of cardiac arteries based in part on solving the system of equations defining the pressure information using numerical analysis applied to the models of the second plurality of cardiac arteries ("Estimates of ischemia (blood flow, FFR, etc) may be generated for a specific location in a vessel …" [0047]: "the algorithm to estimate FFR from patient anatomy consists of deriving an analytical model based on fundamentals of physiology and physics, for example analytical fluid dynamics equations and morphometry scaling laws." [0048]); and
add the vessel and/or lumen border segmentations or each of the second plurality of cardiac arteries and the associated derived pressure information to the ground truth data ("Information about the following coronary anatomy … serves as an input …" [0048]; see detailed information in [0049] - [0062], which includes vessel/lumen geometry and pressure information).
Regarding claim 9, Fonte discloses all claim limitations, as applied in claim 8, and further discloses wherein the first image modality is coronary computed tomography angiography (CCTA) ("This model may be derived by performing a cardiac CT imaging study of the patient during the end diastole phase of the cardiac cycle." [0031]; "The anatomic data may consist of imaging data (ie CT) …" [0046]).
Regarding claim 10, Fonte discloses all claim limitations, as applied in claim 8, and further discloses wherein the second image modality is angiographic ("… or measurements and anatomic representation already obtained from imaging data (quantitative angiography ..." [0046]).
Regarding claim 11, Fonte discloses all claim limitations, as applied in claim 1, and further discloses wherein when executed the instructions further cause the computing system to train the ML model with the ground truth data ("… disclosed systems and methods involve two phases: first, a training phase in which a machine learning system is trained to predict one or more blood flow characteristics …" [0007]).
Regarding claim 12, Fonte discloses all claim limitations, as applied in claim 1, and further discloses wherein the system of equations is the Navier-Stokes equations ("Pressure drop may be estimated by relating the amount of blood flow to the resistance to blood flow using any of a variety of analytical models, such as Poiseuille's equation, energy loss models, etc." [0059]; Poiseuille equation is a derivation from Navier-Stokes equations, to describe the motion of viscous fluids).
Regarding claim 13, Fonte discloses all claim limitations, as applied in claim 1, and further discloses wherein the plurality of cardiac arteries are a first plurality of cardiac arteries ("The anatomic data may consist of imaging data (ie CT) …" [0046]), and wherein when executed the instructions further cause the computing system to:
receive vessel and/or lumen border segmentations for a second plurality of cardiac arteries ("Training method 202 may involve acquiring, for each of a plurality of individuals, e.g., in digital format: (a) a patient-specific geometric model …" [0022]);
receive pressure information associated with each of the second plurality of cardiac arteries ("(b) one or more measured ... physiological parameters …" [0022]; "… the measured FFR may be calculated by traditional catheterized methods …" [0018]), wherein the pressure information associated with each of the second plurality of cardiac arteries is based on pressure measured with an intravascular pressure measurement device ("… the measured FFR may be calculated by traditional catheterized methods …" [0018]); and
add the vessel and/or lumen border segmentations and the pressure information for the second plurality of cardiac arteries to the ground truth data ("Training method 202 may involve acquiring … (a) a patient-specific geometric model, (b) one or more measured or estimated physiological parameters, and (c) values of blood flow characteristics." [0022]).
Regarding claim 14, Fonte discloses a non-transitory computer-readable storage device, comprising instructions that when executed by a processor of a computing system cause the computing system ("… a data storage device storing instructions for estimating individual-specific blood flow characteristics; and a processor configured to execute the instructions to perform a method …" [0009]) to:
receive vessel and/or lumen border segmentations for a plurality of cardiac arteries ("The resulting CT images may then be segmented manually or automatically to identify voxels belonging to the aorta and to the lumen of the coronary arteries." [0031]);
generate models of the plurality of cardiac arteries from the vessel and/or lumen border segmentations ("Once all relevant voxels are identified, the geometric model can be derived (e.g., using marching cubes)." [0031]);
derive pressure information for each of the plurality of cardiac arteries ("For every point in the patient-specific geometric model for which there is a ... estimated or simulated value of the blood flow characteristics, server systems 106 may create a feature vector for that point that contains a numerical description of physiological or phenotypic parameters of the patient and a description of the local geometry. Specifically the feature vector may contain: (i) systolic and diastolic blood pressures ..." [0034]; "FFR=(P−ΔP)/P where P is the aortic pressure and ΔP is the change in pressure from the aorta to the location of interest." [0060]) based in part on solving a system of equations defining the pressure information using numerical analysis applied to the models of the plurality of cardiac arteries ("Using some or all of the information above, a network of flow resistance may be created. Pressure drop may be estimated by relating the amount of blood flow to the resistance to blood flow using any of a variety of analytical models, such as Poiseuille's equation, energy loss models, etc." [0059]); and
add the vessel and/or lumen border segmentations and the associated derived pressure information to ground truth data for training a machine learning (ML) model ("Training method 202 may involve acquiring, for each of a plurality of individuals, e.g., in digital format: (a) a patient-specific geometric model, (b) one or more measured or estimated physiological parameters, and (c) values of blood flow characteristics." [0022]).
Regarding claim 15, Fonte discloses all claim limitations, as applied in claim 14, and further discloses wherein when executed the instructions further cause the computing system to:
generating three-dimensional (3D) volumes for each of the cardiac arteries from the vessel and/or lumen border segmentations ("The resulting CT images may then be segmented manually or automatically to identify voxels belonging to the aorta and to the lumen of the coronary arteries. Once all relevant voxels are identified, the geometric model can be derived (e.g., using marching cubes)." [0031]);
generating volume meshes for each of the cardiac arteries from the 3D volumes, wherein each volume mesh comprises a plurality of discrete elements ("… and computing cross-sectional area at each centerline point and map it to corresponding surface and volume mesh points." [0037]); and
solving, for each of the volume meshes, the system of equations for each one of the plurality of discrete elements of the volume mesh ("Server systems 106 may interpolate FFR along the centerline using FFRi, project FFR values to 3D surface mesh node, and vary αi, βi, γi and obtain new sets of FFR estimation as necessary for training …" [0043]).
Regarding claim 16, Fonte discloses all claim limitations, as applied in claim 15, and further discloses wherein the pressure information is a pressure curve defining pressure ratios along a portion of the length of the cardiac artery ("FFR=(P−ΔP)/P where P is the aortic pressure and ΔP is the change in pressure from the aorta to the location of interest." [0060]).
Regarding claim 17, Fonte discloses all claim limitations, as applied in claim 16, and further discloses wherein the pressure curve defines distal pressure (Pd) over proximal pressure (Pa) along the portion of the length of the cardiac artery ("FFR=(P−ΔP)/P where P is the aortic pressure and ΔP is the change in pressure from the aorta to the location of interest." [0060]; here the aorta is interpreted as the proximal point and the location of interest is interpreted as the distal point), and
wherein the vessel and/or lumen border segmentations comprises both vessel and lumen border segmentations ("The resulting CT images may then be segmented manually or automatically to identify voxels belonging to the aorta and to the lumen of the coronary arteries." [0031]).
Regarding claim 18, Fonte discloses a method for forming ground truth data to train a machine learning (ML) model to infer pressure information for a cardiac artery from border segmentations of the cardiac artery ("… a patient-specific geometric model of a patient's blood vessels, and combining this geometry with the patient-specific physiological information and boundary conditions … disclosed systems and methods involve two phases: first, a training phase in which a machine learning system is trained to predict one or more blood flow characteristics …" [0007]; "... cause the computer to perform a method ..." [0010]), comprising:
receiving vessel and/or lumen border segmentations for a plurality of cardiac arteries ("The resulting CT images may then be segmented manually or automatically to identify voxels belonging to the aorta and to the lumen of the coronary arteries." [0031]);
generating models of the plurality of cardiac arteries from the vessel and/or lumen border segmentations ("Once all relevant voxels are identified, the geometric model can be derived (e.g., using marching cubes)." [0031]);
deriving pressure information for each of the plurality of cardiac arteries ("For every point in the patient-specific geometric model for which there is a ... estimated or simulated value of the blood flow characteristics, server systems 106 may create a feature vector for that point that contains a numerical description of physiological or phenotypic parameters of the patient and a description of the local geometry. Specifically the feature vector may contain: (i) systolic and diastolic blood pressures ..." [0034]; "FFR=(P−ΔP)/P where P is the aortic pressure and ΔP is the change in pressure from the aorta to the location of interest." [0060]) based in part on solving a system of equations defining the pressure information using numerical analysis applied to the models of the plurality of cardiac arteries ("Using some or all of the information above, a network of flow resistance may be created. Pressure drop may be estimated by relating the amount of blood flow to the resistance to blood flow using any of a variety of analytical models, such as Poiseuille's equation, energy loss models, etc." [0059]); and
adding the vessel and/or lumen border segmentations and the associated derived pressure information to ground truth data for training a machine learning (ML) model ("Training method 202 may involve acquiring, for each of a plurality of individuals, e.g., in digital format: (a) a patient-specific geometric model, (b) one or more measured or estimated physiological parameters, and (c) values of blood flow characteristics." [0022]).
Regarding claim 19, Fonte discloses all claim limitations, as applied in claim 18, and further discloses wherein when executed the instructions further cause the computing system to:
receiving image data associated with each of the cardiac arteries ("This model may be derived by performing a cardiac CT imaging study of the patient during the end diastole phase of the cardiac cycle." [0031]); and
generating the vessel and/or lumen border segmentations from the image data ("The resulting CT images may then be segmented manually or automatically to identify voxels belonging to the aorta and to the lumen of the coronary arteries." [0031]).
Regarding claim 20, Fonte discloses all claim limitations, as applied in claim 19, and further discloses wherein the plurality of cardiac arteries are a first plurality of cardiac arteries, wherein the image data comprises image data of a first image modality ("The anatomic data may consist of imaging data (ie CT) …" [0046]), the method further comprising:
receiving second image data associated with each of a second plurality of cardiac arteries, the second image data comprising image data of a second image modality different than the first image modality ("… or measurements and anatomic representation already obtained from imaging data (quantitative angiography, vessel segmentations from third party software, vascular diameters, etc)." [0046]; angiography is different from CT);
generating vessel and/or lumen border segmentations for each of the second plurality of cardiac arteries from the second image data ("… or measurements and anatomic representation already obtained from imaging data (quantitative angiography, vessel segmentations from third party software, vascular diameters, etc)." [0046]; "Information about the following coronary anatomy, including but not limited to the following features derived from imaging data ..." [0048]);
generating models of the second plurality of cardiac arteries from the vessel and/or lumen border segmentations ("… and anatomic representation already obtained from imaging data (quantitative angiography, vessel segmentations from third party software, vascular diameters, etc)." [0046]);
deriving pressure information for each of the second plurality of cardiac arteries based in part on solving the system of equations defining the pressure information using numerical analysis applied to the models of the second plurality of cardiac arteries ("Estimates of ischemia (blood flow, FFR, etc) may be generated for a specific location in a vessel …" [0047]: "the algorithm to estimate FFR from patient anatomy consists of deriving an analytical model based on fundamentals of physiology and physics, for example analytical fluid dynamics equations and morphometry scaling laws." [0048]); and
adding the vessel and/or lumen border segmentations or each of the second plurality of cardiac arteries and the associated derived pressure information to the ground truth data ("Information about the following coronary anatomy … serves as an input …" [0048]; see detailed information in [0049] - [0062], which includes vessel/lumen geometry and pressure information).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bouwman et al. (US 2025/0090032 A1; priority date on 06/15/2018) teach method and apparatus for generating 3D model of coronary arteries and deriving blood characteristics including pressure information at each location (e.g. FFR).
Kim et al. (Patient-Specific Modeling of Blood Flow and Pressure in Human Coronary Arteries; published on 063/18/2010) teach a 3D finite-element model of blood flow and vessel wall dynamics for coronary vascular network. Pressure information is derived at each element in the model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAO SHENG whose telephone number is (571)272-8059. The examiner can normally be reached Monday to Friday, 8:30 am to 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne M. Kozak can be reached at (571) 270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHAO SHENG/ Primary Examiner, Art Unit 3797
Prosecution Insights