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 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.
Claims 78-86, 88-97 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Neumann (US 10733910 B2).
Regarding claims 88, 78, 97,
Neumann teaches:
A non-transitory computer readable medium that includes software that uses artificial intelligence to model a heart of an individual, (Neumann Abstract “A patient-specific multi-physics computational heart model is generated based on the patient-specific anatomical model by personalizing parameters of a cardiac electrophysiology model, a cardiac biomechanics model, and a cardiac hemodynamics model based on medical image data and clinical measurements of the patient … parameters can be personalized using a machine-learning based statistical model” Note: Neumann teaches that the modelling of a heart can be performed with a machine learning/AI model.) wherein the software causes a processor to:(a) receiving or obtaining, with an input software module, an image of the heart of the individual; (Neumann Col. 4 Line 50 “The medical image data can be 3D medical images including a cardiac region of the patient. The medical images may be a dynamic sequence of medical images acquired over at least one complete heart cycle”) (b) segmenting, with a segmenting software module, the image of the heart, thereby generating at least one image segment; (Neumann Col. 5 Line 17 “The LV and RV models can be detected in any preoperative images (e.g., US or cardiac MR) that cover the entirety of both cardiac ventricles. The LV and RV models can be extracted by segmenting the left endocardium, right endocardium, epicardium, and left and right outflow tract using a marginal space-learning based machine learning method.”) (c) applying, using an analysis software module, one or more differential equations to the at least one image segment, thereby generating at least one analysis result; (Neumann Col. 7 Line 41 “the cardiac hemodynamics model is personalized based on pressure measurements and dynamic medical images of the patient … Arterial pressures are calculated using a 3-element Windkessel (WK) model … The hemodynamics personalization estimates the WK parameters … The hemodynamics personalization relies on the arterial pressure measured during cardiac catheterization and a blood pool volume curve derived from 4D medical image data (e.g., MRI). The blood pool volume curve can be generated based on the patient-specific anatomical model by estimating the blood pool volume at each time point based on the volume of the segmented ventricles in a corresponding frame of the 4D cardiac image data” Col. 8 Line 59 “When blood flows into the arteries ( Φar ( t ) > 0 ) during ejection, the 3-element Windkessel model can be expressed as:
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” Note: Neumann teaches that segmented images of the left and right ventricles (LV and RV) are obtained, it is also stated that those segmented ventricle images are used together to form another piece of data describing the imaging over time, a blood pool curve. This piece of data made up of segmented images is directly input to a Windkessel model, a known cardiovascular system model which leverages differential equations. A specific differential equation is shown above. This teaches that segmented images have a differential equation applied to them as part of an analysis to produce an analysis result, in this case the Windkessel outputs like arterial pressure.) and(d) generating, using a modeling software module, the model of the heart of the individual using the analysis result of (c). (Neumann Col. 7 Line 41 cited previously teaches the analysis result, arterial pressure, is obtained to develop a cardiac hemodynamics model, a model of the heart that specifically models blood flow, pressure, and volume.)
Regarding claims 79,
The method of claim 78, wherein the analysis result comprises at least one functional feature, at least one electrophysiological feature, or any combination thereof features. (Neumann Col. 7 Line 3 “a patient-specific multi-physics computational heart model is generated for the patient by personalizing parameters of the electrophysiology model, the biomechanics model, and the hemodynamics model. In an advantageous implementation, 17 total parameters are personalized: 5 each for Windkessel models of both arteries for the hemodynamics model; myocardial, left (LV) and right (RV) ventricular diffusivity, and time during which the ion channels are closed for the electrophysiology (EP) model; and Young's modulus and LV and RV myocyte contraction for tissue biomechanics.” Note: In Col. 7 Line 41 cited previously the use of the Windkessel (WK) model to produce the described analysis results was taught, the arterial pressure being an example of a functional feature. Neumann also describes the functional features like contractions for tissue biomechanics are found from the same WK model. Neumann also describes the same WK model also outputs the time ion channels are closed, taught directly to be used in an electrophysiology model.)
Regarding claims 80, 89,
The method of claim 79, wherein the functional feature comprises a ventricular gauge pressure of a right and left ventricle. (Neumann Col. 8 Line 10 “In order to personalize the cardiac hemodynamics model, a cardiac cycle is interactively selected from the pressure trace. The arterial and ventricular pressure is low-pass filtered, resulting in a smoothed pressure curve. The blood pool volume curve is also low-pass filtered. Next, the pressure curve is automatically adjusted to match the heart rate at the 4D medical image data acquisition so that the pressure curve will be synchronized with the arterial inflow estimate obtained from the medical image data.” Note: Neumann teaches that the ventricular pressure is also a functional feature obtained from its analysis results. As previously taught, the cardiac hemodynamics model takes LV and RV segmented image data to produce its outputs, this informs us that the described ventricular pressure output is for the left and right ventricles.)
Regarding claims 81, 90,
The method of claim 79, wherein the functional feature comprises a wall thickness of a chamber of the heart. (Neumann Col. 5 Line 1 “At step 204, a patient-specific anatomical model of the heart is generated from on the medical image data of the patient. The patient-specific anatomical model can include all of the cardiac chambers or a subset of the cardiac chambers. According to an advantageous implementation, the patient-specific anatomical model can include the left ventricle (LV) and the right ventricle (RV) … The method of FIG. 3 can be used to implement step 204 of FIG. 2. At step 302, anatomical models of the LV and RV are extracted from the medical images. In an advantageous embodiment, the LV and RV anatomical models show patient-specific heart morphology and dynamics, and are calculated automatically from MRI or ultrasound images. The LV and RV models can be detected in any preoperative images (e.g., US or cardiac MR) that cover the entirety of both cardiac ventricles. The LV and RV models can be extracted by segmenting … Obtained triangulations (meshes) are automatically labeled according to the anatomy they represent for subsequent processing.” Col. 7 Line 35 “the patient-specific anatomical model is generated using robust machine learning and mesh processing based on a clinical 3D image of the heart and a rule-based fiber architecture …” Note: Neumann teaches that a 3D model of the heart based including fiber architecture is created. To create the 3D model, segmented images of the heart are taken and a mesh is reconstructed from them including the cardiac chambers. As the rendering of the chambers is 3D with a high level of detail specifying fiber architecture this teaches that the width, along with all other 3D chamber measurements, are found.)
Regarding claims 82,
Neumann teaches:
The method of claim 79, wherein the functional feature comprises a wall motion of at least a portion of the heart. (Neumann Col. 10 Line 57 “the cardiac biomechanics model is personalized. The EP signal is coupled with myocardial tissue mechanics through models of active and passive tissue behavior to compute realistic cardiac motion. Accordingly, the dynamics equation Mü+C{dot over (u)}+Ku=f.sub.a+f.sub.p+f.sub.b must be solved (e.g., using finite element methods)” Note: Neumann teaches that the heart wall, specifically the myocardial tissue, has motion calculated for it.)
Regarding claims 83, 91,
The method of claim 79, wherein the electrophysiological feature comprises an electrical property of a tissue of the heart, and wherein the electrical property of the tissue of the heart comprises an activation map, voltage map, or any combination thereof. (Neumann Col. 21 Line 14 “personalizing parameters of the cardiac electrophysiology model by estimating parameters including tissue diffusivity parameters and action potential duration based on at least one of a clinical ECG signal” Col. 9 Line 51 “According to an advantageous implementation, the Mitchell-Schaeffer (MS) phenomenological model, which has parameters closely related to the shape of the action potential, is used as the cardiac EP model and is solved using LBM-EP, a near real-time solver for patient-specific cardiac EP based on an efficient GPU implementation of the Lattice-Boltzmann method … The main free parameters that need to be personalized in order to generate realistic EP for the patient include tissue diffusivity c, which determines the speed of electrical wave propagation throughout the heart, and the time during which ion channels are closed ô.sub.cl” Note: Neumann teaches that for an electrophysiology model the action potential of the tissue is found, teaching a model/mapping of activation. Neumann also teaches that wave propagation through the heart is calculated from tissue diffusivity and ion channel time information.)
Regarding claims 84, 92,
Neumann teaches:
The method of claim 79, wherein the electrical property comprises at least one current vector. (Neumann Col. 10 Line 20 “procedures which run an EP simulation on a patient-specific anatomical model using the provided parameters and then calculate the named ECG feature (QT, QRS, and electrical axis (EA), respectively) by mapping the simulated potentials to the torso, as described above. Automatic methods are used to derive the duration of the QRS and QT complex (Δ.sub.QRS and Δ.sub.QT, respectively), and electrical axis (á)” Note: Neumann teaches that as part of its electrophysiology simulation from its electrocardiogram images the electrical axis is found, referring to an axis made from all electrical/current vectors teaching that the used electrical properties comprise at least one current vector.)
Regarding claims 85, 95,
Neumann teaches:
The method of claim 78, wherein the image of the heart is an echocardiogram image. (Neumann Col. 3 Line 13 “The anatomical model 102 is a model of patient-specific heart morphology obtained from volumetric image data 110 (e.g., MRI, CT, DynaCT, 3D ultrasound)” Note: Neumann teaches the ability to obtain echocardiogram images, also known as ultrasound images.)
Regarding claims 86, 96,
The method of claim 85, wherein the echocardiogram comprises a 3D echocardiogram. (Neuman Col. 3 Line 13, cited previously, teaches the use of a 3D ultrasound/echocardiogram.)
Regarding claim 93,
Neumann teaches:
The non-transitory computer readable medium of claim 88, wherein the image of the heart comprises an MRI image, and wherein the MRI comprises CINE MRI, MRI based techniques, or any combination thereof. (Neumann Col. 3 Line 13 “The anatomical model 102 is a model of patient-specific heart morphology obtained from volumetric image data 110 (e.g., MRI, CT, DynaCT, 3D ultrasound) ” Col. 4 Line 20 “The cardiac biomechanics model 106 can be personalized based on dynamic image data 116, such as 4D MRI, CT, or ultrasound of a patient.” Note: Neumann teaches that its imaging consists of normal MRI and 4D MRI images. The CINE MRI in the claims refers to a type of MRI that can record its images over time (video), another name for this is a 4D MRI.)
Regarding claim 94,
The non-transitory computer readable medium of claim 93, wherein the MRI based techniques comprise DENSE, tag-MR, SPAMM, or any combination thereof. (Neumann Col. 6 Line 9 “For example, scar locations and extent can be segmented in delayed-enhancement MR images. The scar information is mapped onto the bi-ventricular myocardium mesh by tagging the tetrahedral elements that lie within the segmented scar regions. This spatial information is important to simulate the electrical wave around scars” Note: Neumann teaches tag-MR/SPAMM technique when the myocardium is tagged in the MR images taken.)
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.
Claim 87 is rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 10733910 B2) in view of Taylor (Patient-specific Modeling of Cardiovascular Mechanics).
Regarding claim 87,
The method of claim 78, comprising analysis results of (c) longitudinally over a period of time. (Neumann Col. 7 Line 41, cited previously, teaches analysis results that span a period of time)
Neumann does not however teach the use of attempting to estimate growth for the heart, doing so is done in Taylor which teaches generating a growth remodeling parameter model with analysis results longitudinally over a period of time. (Taylor Predicting Long-term outcomes using FSG “Clearly, then, to understand hemodynamic-induced changes in vascular geometry, structure, and function, we must quantify the complexities of the flow field and the distribution of stress on and within the arterial wall on a patient-specific basis. Motivated by these and similar observations, Humphrey, Taylor and colleagues (122) have brought together for the first time a Fluid-Solid-Growth (FSG) computational framework for solving the full 3-D hemodynamics problem in patient-specific geometries and the wall mechanics G&R problem … This framework is general enough to enable new data on stress-mediated growth and remodeling processes” Note: Here Taylor clearly teaches that growth remodeling can be facilitated by parameters/values output by a Fluid-Solid-Growth framework that computes on the heart’s structure, vascular geometry, flow fields, etc…)
It would have been obvious to a person having ordinary skill in the art before the effective filing
date of the claimed invention to combine Neumann with Taylor where generating a growth remodeling parameter model leverages the analysis results of (c) over a period of time.
There are several reasons that would motivate one to do so, Neumann has already been shown to learn more from mapping the heart over time to understand its change and movement, as this is already done the idea to model the heart’s potential change in growth over time to gain more insight would have been obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN GREGORY HAKALA whose telephone number is (571)272-7863. The examiner can normally be reached 8:00am-5:00pm.
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/ALAN GREGORY HAKALA/Examiner, Art Unit 2617
/KING Y POON/ Supervisory Patent Examiner, Art Unit 2617