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 . This non-final office action on merits is in response to the Patent Application filed on 10/11/2025.
Status of claims
Claims 1-16 are pending and considered below. This application is a 371 of PCT/KR2023/004023 filed on 03/27/2023, which claims the benefit of KR Application Number KR10-2022-0046544 filed on 04/14/2022.
Information Disclosure Statement
The information disclosure statements (IDSs) filed on 10/11/2024 and 01/14/2025 have been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Subject Matter Free of Prior Art
Claims 8-10, and 12-14 includes subject matter that is free of the prior art of record.
Claim 8 includes subject matter that is free of the prior art of record. The cited prior art fails to expressly teach or suggest, either alone or in combination, the specific mesh simplification technique recited in claim 8, including selecting uniform vertices from a plurality of vertices by applying a Poisson disk sampling algorithm, simplifying the selected uniform vertices by applying an edge collapse algorithm, and configuring the mesh with uniform vertices while preserving the mesh surface through application of a re-tiling algorithm.
The closest prior art of record includes Madabhushi et al. (U.S. Patent Publication 2021/0093281 A1) and Salomie (U.S. Patent Publication 2003/0052875 A1) . Madabhushi teaches the use of cardiac imaging data, segmentation, feature extraction, and machine-learning models for predicting atrial fibrillation recurrence. However, Madabhushi fails to teach or suggest selecting uniform vertices using a Poisson disk sampling algorithm, simplifying a mesh through an edge collapse algorithm, or preserving a mesh surface through a re-tiling operation. Salomie teaches polygon meshes, triangulated surfaces, mesh generation, interpolation techniques, and general mesh processing operations. However, Salomie fails to teach or suggest the specific mesh simplification process of selecting uniform vertices through Poisson disk sampling, performing edge-collapse simplification on the selected vertices, and preserving the mesh surface through application of a re-tiling algorithm.
Accordingly, the prior art of record fails to expressly teach or suggest the specific mesh simplification process recited in claim 8, including the combination of Poisson disk sampling, edge-collapse simplification, and re-tiling for mesh surface preservation. Therefore, claim 8 contains subject matter that is free of the prior art of record.
Claim 9 includes subject matter that is free of the prior art of record. The cited prior art fails to expressly teach or suggest, either alone or in combination, the specific mesh simplification control technique recited in claim 9, including performing mesh simplification under a control condition in which a reference geometric shape criterion based on Hausdorff distance is less than 2 and a structural similarity criterion (SSIM) is less than 0.7.
The closest prior art of record includes Madabhushi and Salomie. Madabhushi teaches the use of cardiac imaging data, segmentation, feature extraction, and machine-learning models for predicting atrial fibrillation recurrence. However, Madabhushi fails to teach or suggest performing mesh simplification using control conditions based on Hausdorff distance and structural similarity metrics. Salomie teaches polygon meshes, triangulated surfaces, mesh generation, interpolation techniques, and mesh-processing operations. However, Salomie fails to teach or suggest controlling mesh simplification using a reference geometric shape criterion based on Hausdorff distance and a structural similarity criterion based on SSIM, including the specific threshold values recited in claim 9.
Accordingly, the prior art of record fails to expressly teach or suggest the specific mesh simplification control process recited in claim 9, including the combination of a Hausdorff-distance-based geometric fidelity criterion and an SSIM-based structural similarity criterion having the claimed threshold values. Therefore, claim 9 contains subject matter that is free of the prior art of record.
Claim 10 includes subject matter that is free of the prior art of record. The cited prior art fails to expressly teach or suggest, either alone or in combination, a target data structure that, following mesh simplification preprocessing, comprises a three-dimensional polygon mesh structure including 2,048 vertices, 6,144 edges, and 4,096 faces. While the applied references generally teach mesh generation, mesh processing, mesh simplification, remeshing, and multi-resolution mesh representations, none of the references disclose or suggest reducing or standardizing a mesh to the specific claimed configuration of vertices, edges, and faces.
The closest prior art of record includes Madabhushi and Salomie. Madabhushi teaches predicting atrial fibrillation recurrence using cardiac imaging data and machine-learning techniques but does not teach or suggest a simplified mesh having the claimed numbers of vertices, edges, and faces. Salomie teaches mesh generation, smoothing, simplification, remeshing, and multi-resolution mesh representations; however, Salomie does not teach or suggest a three-dimensional polygon mesh structure including exactly 2,048 vertices, 6,144 edges, and 4,096 faces following preprocessing. Accordingly, the specific mesh configuration recited in Claim 10 is not taught or suggested by the prior art of record.
Claim 12 includes subject matter that is free of the prior art of record. The cited prior art fails to expressly teach or suggest, either alone or in combination, the specific processing architecture recited in claim 12, including: (i) calculating edge-based features from preprocessed target data, (ii) calculating a first output value by inputting the extracted edge-based features into a prediction model and applying mesh convolution and mesh pooling, and (iii) calculating and outputting a second output value representing a prediction value of recurrence of atrial fibrillation by applying soft voting to the first output value.
The closest prior art of record includes Madabhushi and Salomie. Madabhushi teaches predicting atrial fibrillation recurrence using extracted radiomic and fractal features and machine-learning classifiers. However, Madabhushi fails to teach or suggest calculating edge-based features from a polygon mesh, applying mesh convolution and mesh pooling operations to the extracted edge-based features, or generating a final prediction value using soft voting. Salomie teaches polygon meshes, vertices, edges, triangulated surfaces, mesh generation, and mesh-processing operations. However, Salomie fails to teach or suggest extracting edge-based features for input into a prediction model, applying mesh convolution and mesh pooling to generate a first output value, or applying soft voting to generate a second output value.
Accordingly, the prior art of record fails to expressly teach or suggest the specific combination of edge-based feature extraction, mesh convolution, mesh pooling, and soft-voting prediction operations recited in claim 12. Therefore, claim 12 contains subject matter that is free of the prior art of record.
Claim 13 includes subject matter that is free of the prior art of record. The cited prior art fails to expressly teach or suggest, either alone or in combination, the specific edge-based feature extraction technique recited in claim 13, including calculating a diameter at all edges of a geometric topological mesh structure and calculating the diameter by estimating collision points from each edge to an opposite surface through ray casting.
The closest prior art of record includes Madabhushi and Salomie. Madabhushi teaches the use of cardiac imaging data, shape-based features, texture-based features, and machine-learning models for predicting atrial fibrillation recurrence. However, Madabhushi fails to teach or suggest calculating edge-based diameter features for a mesh or estimating collision points from edges to an opposite surface using ray casting. Salomie teaches polygon meshes, vertices, edges, triangulated surfaces, interpolation techniques, mesh generation, and mesh-processing operations. However, Salomie fails to teach or suggest determining edge diameters by estimating collision points between rays projected from mesh edges and an opposite surface of the mesh.
Accordingly, the prior art of record fails to expressly teach or suggest the specific technique of calculating a diameter at all edges as an edge-based feature through ray-casting-based estimation of collision points from each edge to an opposite surface, as recited in claim 13. Therefore, claim 13 contains subject matter that is free of the prior art of record.
Claim 14 includes subject matter that is free of the prior art of record. The cited prior art fails to expressly teach or suggest, either alone or in combination, the specific edge-based feature extraction technique recited in claim 14, including calculating mapping information at mesh edges by estimating a center of gravity of two triangles sharing an edge, estimating a distance between the center of gravity and a center point of the edge, and applying a linear interpolation method to determine mapping information associated with the edge.
The closest prior art of record includes Madabhushi and Salomie. Madabhushi teaches the use of cardiac imaging data, anatomical characteristics, electrophysiological characteristics, radiomic features, and machine-learning models for predicting atrial fibrillation recurrence. However, Madabhushi fails to teach or suggest calculating edge-based mapping information using geometric relationships between adjacent triangles and a shared edge. Salomie teaches polygon meshes, triangulated surfaces, vertices, edges, and interpolation techniques used in mesh generation and surface construction. However, Salomie fails to teach or suggest the specific calculation of mapping information at edges through estimation of a center of gravity of two triangles adjacent to an edge, determination of a distance between the center of gravity and the edge center point, and application of linear interpolation based on those values.
Accordingly, the prior art of record fails to expressly teach or suggest the specific edge-based feature extraction process recited in claim 14, including the combination of centroid estimation, edge-center distance calculation, and linear interpolation for determining mapping information at mesh edges. Therefore, claim 14 contains subject matter that is free of the prior art of record.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1-14 are drawn to a method and claim 15 is drawn to an apparatus. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
The claimed invention is also directed to nonstatutory subject matter. Claim 16 does not fall within at least one of the four categories of patent eligible subject matter because the claim(s) are directed to a computer code per se.
Step 2A Prong One
Claim 15 recites the limitations of (B) an operation of performing preprocessing on the generated target data; and (C) an operation of calculating a prediction value of recurrence of atrial fibrillation of the patient, and learning the prediction value, wherein the target data is defined by a plurality of vertices and edges and adopts a three-dimensional polygon mesh including a plurality of triangular faces, i.e., minimum units of faces configured of vertices and edges, as a basic structure. These limitations, as drafted, are processes that, under their broadest reasonable interpretations, cover performance of the limitations in the mind or by using a pen and paper. Even when considering the “into a prediction model” language, the claim encompasses a user analyzing the generated target data, evaluating characteristics represented by the vertices, edges, and triangle faces of the three dimensional polygon mesh, and determining or predicting a likelihood of recurrence of atrial fibrillation based on that analysis in their mind or by using a pen and paper. The nominal recitation of into a prediction model does not take the claim limitations out of the mental processes grouping. Thus, the claim recites a mental process which is an abstract idea.
Independent claim 15 recites identical or nearly identical steps with respect to claims 1 and 16 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Under Step 2A Prong Two
The claimed limitations, as per claim 15, include:
one or more processors; a network interface; a memory that loads a computer program executed by the processor; and a storage that stores a large amount of network data and the computer program, wherein the computer program executes, by the one or more processors,
(A) an operation of generating target data including any one or more among geometric topological data, anatomical data, and electrophysiological data of a patient's atrium;
(B) an operation of performing preprocessing on the generated target data; and
(C) an operation of inputting the preprocessed target data into a prediction model, calculating and outputting a prediction value of recurrence of atrial fibrillation of the patient, and learning the prediction value, wherein the target data is defined by a plurality of vertices and edges and adopts a three-dimensional polygon mesh including a plurality of triangular faces, i.e., minimum units of faces configured of vertices and edges, as a basic structure.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claim 15 is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of analyzing patient atrial data and predicting a recurrence of atrial fibrillation based on that analysis in a computer environment. The claimed computer components (i.e., one or more processors; a network interface; a memory that loads a computer program executed by the processor; and a storage that stores a large amount of network data and the computer program, wherein the computer program executes, by the one or more processors; into a prediction model) are recited at a high level of generality and are merely invoked as tools to perform an existing process of analyzing information and evaluating the information to determine a predicted recurrence of atrial fibrillation. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application.
The judicial exception expressed in claim 15 is not integrated into a practical application. The claim recites the additional elements of (A) an operation of generating target data including any one or more among geometric topological data, anatomical data, and electrophysiological data of a patient's atrium; (C) an operation of inputting the preprocessed target data; and outputting a prediction value of recurrence of atrial fibrillation of the patient. These limitations are recited at a high level of generality (i.e., as a general means of gathering patient information and presenting the results of an analysis), and amounts to merely data gathering and displaying a result, which are forms of insignificant extra-solution activities. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Therefore, under step 2A, the claim is directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claim 15 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of analyzing patient atrial data and predicting a recurrence of atrial fibrillation based on that analysis in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
For claim 15, under step 2B, the additional elements of (A) an operation of generating target data including any one or more among geometric topological data, anatomical data, and electrophysiological data of a patient's atrium; (C) an operation of inputting the preprocessed target data; and outputting a prediction value of recurrence of atrial fibrillation of the patient have been evaluated. The apparatus comprising one or more processors performs a general function of receiving patient data for subsequent analysis and prediction, which represents a well-understood, routine, and conventional activity in the field of computer implemented medical data processing and clinical decision support systems. The specification discloses that the processor is used in its ordinary capacity as a data input device and does not describe any improvement to the computer itself or to the functioning of the overall computer system (see [58] and [62]). Also noted in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), merely collecting information for analysis without a technological improvement does not add significantly more to an abstract idea. The use of the apparatus is no more than gathering information before performing analysis and predicting a recurrence of atrial fibrillation, and displaying a result, and does not integrate the abstract idea into a practical application. Therefore, the claim does not recite an inventive concept and is not patent eligible.
Claims 3-6, and 9-11 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 2, 7-8, and 12-14 recite the additional elements of by applying a marching cubes algorithm (claim 2), by applying a Laplacian smoothing algorithm (claim 7), by applying a Poisson disk sampling algorithm (claim 8), by applying an edge collapse algorithm (claim 8), by inputting the preprocessed target data into the prediction model (claim 12), by inputting the extracted edge-based features into the prediction model (claim 12), outputting the prediction value of recurrence of atrial fibrillation of the patient (claim 12), inputting the preprocessed target data into the prediction model (claim 13), inputting the preprocessed target data into the prediction model (claim 14). However, these additional elements amount to implementing an abstract idea on a generic computing device, or mere data gathering or displaying a result (i.e., an insignificant extra-solution activities). As such, these additional elements, when considered individually or in combination with the prior devices, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
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 1-3, 6, 11, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi et al. (U.S. Patent Publication 2021/0093281 A1), referred to hereinafter as Madabhushi, in view of Salomie (U.S. Patent Publication 2003/0052875 A1), referred to hereinafter as Salomie.
Regarding claim 1, Madabhushi teaches a method of predicting recurrence of atrial fibrillation through an apparatus including a processor and a memory, the method comprising the steps of (Madabhushi [0005] “FIG. 2 illustrates a flow diagram of an example method/set of operations that can be performed by one or more processors to construct a machine learning classifier to generate a prediction of recurrence or non-recurrence of atrial fibrillation post-ablation based at least in part on one or more radiomic fractal-based features, according to various aspects discussed herein.”):
(a) generating target data including any one or more among geometric topological data, anatomical data, and electrophysiological data of a patient's atrium (Madabhushi [0030] “The set of operations 100 can comprise, at 110, accessing a cardiac CT scan of a patient with AF. In various embodiments and in the example use case discussed below, the CT scan can be obtained via a system and/or apparatus implementing the set of operations 100, or can be obtained from a separate medical imaging system (e.g., a CT system/apparatus). Additionally, the CT scan can be accessed contemporaneously with or at any point prior to performing the set of operations 100.”, and Madabhushi [0031] “The set of operations 100 can further comprise, at 120, generating a binary mask of at least a portion of the cardiac CT scan. In various embodiments, the portion of the cardiac CT scan can be based on one or more of the models discussed herein (e.g., left atrium wall, left atrium body and ostia of pulmonary veins, left atrium lumen and full structure of pulmonary veins, etc.).”);
(c) inputting the preprocessed target data into a prediction model, calculating and outputting a prediction value of recurrence of atrial fibrillation of the patient, and learning the prediction value (Madabhushi [0042] “The following discussion provides example embodiments in connection with a first example use case involving training, validation, and testing of machine learning models to predict recurrence of atrial fibrillation based at least in part on fractal features.”, Madabhushi [0043] “Background: Left atrium (LA) remodeling may increase likelihood of recurrent atrial fibrillation (AF) after catheter ablation. The first example use case hypothesized that computerized morphologic analysis of the LA and pulmonary veins (PVs) via fractal measurements of shape and texture features of the LA myocardial wall could predict AF recurrence after ablation.”, Madabhushi [0044] “Methods: Pre-ablation contrast CT scans were collected for 203 patients who underwent AF ablation. The LA body, PVs, and LA myocardial tissue were segmented using a semi-automated region growing method. 28 fractal-based shape and texture-based features were extracted from resulting segments. The top 5 features most associated with post-ablation recurrence were identified using feature selection and subsequently evaluated with a Random Forest classifier. Feature selection and classifier construction were performed on a discovery cohort (D1) of 137 patients; classifiers were subsequently validated on an independent set (D2) of 66 patients. Dedicated classifiers to capture the fractal and morphologic properties of LA body (CLA), PVs (CPV), and LA myocardial (CLAM) tissue were constructed, as well as a model (CAII) capturing properties of all segmented compartments. Fractal-based models were also compared against a model employing machine estimation of LA volume. To assess the effect of clinical parameters, such as AF type and catheter technique, a clinical model (Cclin) was also compared against CAII.”);
Madabhushi fails to explicitly teach (b) performing preprocessing on the generated target data; and wherein the target data is defined by a plurality of vertices and edges and adopts a three-dimensional polygon mesh including a plurality of triangular faces, i.e., minimum units of faces configured of vertices and edges, as a basic structure.
Salomie teaches (b) performing preprocessing on the generated target data (Salomie [0390] “For digital images, direct extraction of iso-contours by implicit segmentation (e.g. using thresholding) is sometimes very difficult, if not impossible (e.g. due to noisy data). For the TriScan algorithm of the present invention, it is presumed that the segmentation has been done in a pre-processing step and that the input to the algorithm is a binary (only black and white), or a labeled (containing only a small set of discrete values) 3D image data set consisting of isotropic voxels, (i.e. all edges of the voxels have the same length). Otherwise, some interpolation within the data can be done in an extra pre-processing step to achieve this isotropy.”);
wherein the target data is defined by a plurality of vertices and edges and adopts a three-dimensional polygon mesh including a plurality of triangular faces, i.e., minimum units of faces configured of vertices and edges, as a basic structure (Salomie [0049] “Tiling means using slice chords to triangulate the strip lying between contours of two adjacent slices into tiling triangles 5, as shown in FIG. 4. A slice chord 6 connects a vertex 7 of a given contour 8 to a vertex 7′ of the contour in an adjacent slice 8′. Each tiling triangle 5 consists of exactly two slice chords 6 and one contour segment.” Salomie [0050] “An example with dissimilar contours 9, 10 located on two adjacent slices is shown in FIG. 5(a). FIG. 5(b) shows a tiling in which all vertices of the top contour 9 tile to vertices of the bottom contour 10. In the vertical cross section shown in FIG. 5(c), which is a cross section passing through the point P of the solid in FIG. 5(b) of a reconstruction as in FIG. 5(b), it is noticed that the scalar data (value) along the vertical line L flips (from outside the surface to inside, or vice versa) twice between two adjacent slices because the surface is intersected twice by L. This is an unlikely topology, especially when the distance between the two slices is small. Another tiling possibility is shown in FIG. 5(d), with the vertical cross section displayed in FIG. 5(e), in which the vertices of the dissimilar portion of the contour tile to the medial axis 11, 11′ of that dissimilar portion, resulting in a highly likely topology.”);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the atrial fibrillation recurrence prediction system of Madabhushi with the three dimensional triangulated surface representation techniques taught by Salomie. Madabhushi teaches generating patient specific cardiac data from CT scans, segmenting left atrial structures, extracting morphological features therefrom, and applying machine learning classifiers to predict recurrence of atrial fibrillation following ablation. However, Madabhushi does not describe representing the segmented atrial structures as a three dimensional polygon mesh defined by vertices, edges, and triangular faces. Salomie teaches generating three dimensional surface reconstructions from medical image data using vertices, connecting edges, and tiling triangles to form a triangulated polygonal representation of anatomical structures.
One of ordinary skill in the art would have been motivated to incorporate Salomie's triangulated mesh representation into Madabhushi's CT atrial analysis framework because triangulated polygon meshes were a well-known and predictable technique for representing complex anatomical structures extracted from medical images. Doing this would have provided a standardized geometric representation of the segmented atrial anatomy, facilitating subsequent feature extraction, processing, and computational analysis while preserving the anatomical shape of the underlying cardiac structures. The combination applies a known data representation technique to a known cardiac image analysis system and would have yielded the predictable result of providing a machine readable geometric model suitable for further processing and prediction.
Furthermore, one of ordinary skill in the art would have recognized that preprocessing operations on image-derived anatomical data, such as segmentation, interpolation, and preparation of reconstructed surfaces, were routinely performed before feature extraction and machine learning analysis. Incorporating Salomie's preprocessing and triangulated reconstruction techniques into Madabhushi's recurrence prediction workflow would have been no more than the predictable use of prior-art elements according to their established functions to improve the representation and processing of atrial anatomical data for recurrence prediction. Therefore, the combination of Madabhushi and Salomie would have rendered the subject matter of claim 1 obvious.
Regarding claim 2, Madabhushi and Salomie teach the invention in claim 1, as discussed above, and further teach wherein the geometric topological data of the patient's atrium is data generated as a three-dimensional polygon mesh structure by applying a marching cubes algorithm while structures adjacent to the heart are excluded from a computed tomography (CT) image of the patient's heart and only segmented shapes of the atrium are extracted (Madabhushi [0133] “FIGS. 16-19 illustrate example images, charts, and plots in connection with the framework of the third example use case. Referring to FIG. 16, illustrated are example CT scans of an AF+ patient (top) and an AF− patient (bottom), in connection with various aspects discussed herein. Referring to FIG. 17, illustrated are example binary images of the CT scans of FIG. 9 based (AF+ in the left column and AF− in the right column) on two segmentation masks, the left atrial lumen model (top row) and PV model (bottom row), in connection with various aspects discussed herein. The lumen model consisted of the left atrium body and the ostia of the PVs. The PV model consisted of the left atrium body and the full structure of PVs visible on CT. Referring to FIG. 18, illustrated are charts showing 3D fractal analysis of lumen models for AF+ (top) and AF− (bottom), in connection with various aspects discussed herein. Fractal analysis was used to quantify morphological variations within each lumen and PV model. 3D fractal features were extracted from 3D binary images using a spectral density function to characterize self-similarity and heterogeneity of the models. Referring to FIG. 19, illustrated are boxplots of 3D fractal feature for the lumen model (left) and the PV model (right), in connection with various aspects discussed herein. Fractal features were compared between the AF+ and AF− groups.”, and
Salomie [0060] “The current standard technique for extracting iso-surfaces (Σ) from a 3D grid of data is the Marching-Cubes algorithm (MC), revealed in the patents U.S. Pat. No. 4,710,876 and U.S. Pat. No. 4,729,098, and the related paper W. E. Lorensen and H. E. Cline, “Marching Cubes: A high resolution 3D surface construction algorithm”, in M. C. Stone, editor, Computer Graphics (SIGGRAPH '87 Proceedings), volume 21, pages 163-169, 1987.”, Salomie [0075] “Some new concepts are introduced. A polygon is a connected set of edges that forms an iso-surface. The ordering of a polygon is the path taken by the vertices describing it (v 0→v1→v2→. . . →v0). Two neighboring polygons are coherently ordered if the path of an edge shared by the two is opposite. A surface is coherently connected if all edges occur twice in opposite directions (two complementary edges) and if no polygon touches another polygon except at the common edge.”, Salomie [0076] “The algorithm guarantees that the obtained polygons are coherently ordered and connected, with no polygon occurring more than once, and that each surface is complete, that is, no holes due to surface generation errors occur. The generated Σ consists of either the minimum number of (non-planar) polygons with 3 to 12 vertices or a minimum number of triangles.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the atrial fibrillation recurrence prediction method of Madabhushi with the three dimensional surface reconstruction techniques of Salomie such that the segmented atrial structures obtained from CT images are represented as a three dimensional polygon mesh generated using a Marching Cubes algorithm. Madabhushi teaches obtaining CT images of atrial fibrillation patients, segmenting left atrial structures from the CT images, and utilizing the segmented anatomical data for predicting atrial fibrillation recurrence, while Salomie teaches that the well known Marching Cubes algorithm is a standard technique for extracting three dimensional isosurfaces from CT data and generating polygonal and triangular surface representations defined by vertices and edges. One of ordinary skill in the art would have been motivated to apply Salomie’s Marching Cubes-based surface generation techniques to Madabhushi’s segmented atrial CT data because doing so would provide a known and predictable method of converting segmented anatomical structures into a standardized three dimensional geometric representation suitable for computational analysis and machine learning processing. The combination would have merely involved the predictable use of known image processing and surface-reconstruction techniques according to their established functions to improve the representation of segmented atrial anatomy for recurrence prediction.
Regarding claim 3, Madabhushi and Salomie teach the invention in claim 2, as discussed above, and further teach wherein the anatomical data of the patient's atrium is data that maps information on atrial wall thickness at all points included in the patient's atrium expressed as a three-dimensional polygon mesh, which is the geometric topological data of the patient's atrium (Madabhushi [0048] “AF induces morphological changes of the left atrium (LA), which can manifest as changes in atrial volume, shape, the atrial wall, and PVs. Remodeling of these structural changes after AF ablation has been detected using computed tomography (CT). Several models have been proposed to predict AF recurrence after ablation using clinical features, such as age, hypertension, and persistence of AF and LA scarring. Further investigation into the different LA morphologies associated with AF recurrence support the use of imaging in identifying patients for ablation. Several investigators have reported that LA geometry has an important role in assessing AF incidence and recurrence using various cardiac imaging modalities. Cardiac CT can assess atrial wall thickness and PV and LA anatomy before catheter ablation procedures for AF. One group demonstrated that shape features of LA can be useful for predicting AF recurrence from screening CT scans, and a second group reported that enlarged PVs by magnetic resonance imaging (MRI) predicted AF recurrence after ablation. A third group demonstrated that changes in myocardial structure in the LA are an important part of this pathological remodeling process.” , and Madabhushi [0080] “Experiment 3 explored the effect of clinical and procedural variables on likelihood of recurrence and investigated the role of radiomic features as a function of AF type and catheter technique. Clinically features previously predictive of recurrence are persistent AF and hypertension; here clinical models perform similarly to previous clinical predictive models of AF recurrence. Radiomic features were not strongly correlated with catheter type, suggesting that cardiac morphology may be an independent inherent predictor of recurrence rather than an artifact of procedure-specific anatomic challenges. The thickness of the atrium may have an important role in the non-invasive assessment of atrial structure. In combination with atrial tissue characterization, a comprehensive assessment of the atrial dimensions may allow prediction of atrial electrophysiological behavior. Another group found that heterogeneity in the left atrial wall thickness contributes to AF recurrence after catheter ablation. It was found that the 3D fractal dimension assesses heterogeneity in the left atrial wall that characterizes texture variations. The results suggest that the shape-based features of CLA and CPV and texture-based feature of atrial wall were most significant in predicting AF recurrence. Recent studies have been shown that the LA wall thickness in patients with persistent AF is lower than that of patients with paroxysmal AF; based on fractal analysis, the AF type is highly correlated with 3D texture features of atrial wall as shown in FIG. 9 at 920-930. The fractal feature based model was found to significantly outperform the model based off LA volume in predicting AF recurrence.”
Salomie [0075] “Some new concepts are introduced. A polygon is a connected set of edges that forms an iso-surface. The ordering of a polygon is the path taken by the vertices describing it (v 0→v1→v2→. . . →v0). Two neighboring polygons are coherently ordered if the path of an edge shared by the two is opposite. A surface is coherently connected if all edges occur twice in opposite directions (two complementary edges) and if no polygon touches another polygon except at the common edge.”, Salomie [0076] “The algorithm guarantees that the obtained polygons are coherently ordered and connected, with no polygon occurring more than once, and that each surface is complete, that is, no holes due to surface generation errors occur. The generated Σ consists of either the minimum number of (non-planar) polygons with 3 to 12 vertices or a minimum number of triangles.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the atrial fibrillation recurrence prediction method of Madabhushi with the three dimensional polygonal surface representation techniques of Salomie such that atrial wall thickness information is represented in association with a three-dimensional polygon mesh of the patient's atrium. Madabhushi teaches that cardiac CT imaging can assess atrial wall thickness and that atrial wall characteristics, morphology, and structural remodeling are important predictors of atrial fibrillation recurrence, while Salomie teaches representing anatomical structures as polygonal surfaces composed of connected edges and triangles. One of ordinary skill in the art would have been motivated to utilize Salomie's polygon mesh representation for the atrial anatomical information of Madabhushi because polygon meshes were a well known and predictable method for representing and analyzing anatomical structures derived from medical imaging data. Doing this would have allowed wall thickness information obtained from CT imaging to be associated with corresponding locations on the reconstructed atrial structure, thereby facilitating computational analysis of spatial anatomical characteristics relevant to recurrence prediction and yielding the predictable result of a three dimensional atrial model containing anatomical wall-thickness information for use in the prediction process.
Regarding claim 6, Madabhushi and Salomie teach the invention in claim 1, as discussed above, and further teach wherein step (b) includes the steps of: (b-1) performing mesh smoothing, which is first preprocessing, on the generated target data (b-2) performing mesh simplification, which is second preprocessing, on the target data on which the mesh smoothing has been performed; and (b-3) performing generation of N three-dimensional polygon- derived meshes (N is a positive integer), which is third preprocessing, on the target data on which the mesh simplification has been performed, and merging the generated mesh with the target data (Salomie [0011] “A number of mesh editing and post-processing techniques can be applied, such as: simplification, smoothing, intersection or union of meshes, removal of certain parts, etc.”)., Salomie [0620] “Another example of a multi-resolution MeshGrid is illustrated for two objects (FIG. 86 and FIG. 87) originally represented as single-resolution IndexedFaceSet models that have been remeshed and represented in the MeshGrid format. The object from FIG. 86, a 3D eight model, has been converted to a multi-resolution MeshGrid with 4 mesh resolutions, i.e. a hierarchical connectivity-wireframe consisting of 4 resolution levels and the corresponding reference-grid. The original object represented as an IndexedFaceSet is shown FIG. 86( a), while FIGS. 86(b)-(e) illustrate increasing resolutions of the MeshGrid representations of the object. The first row of images display the object as a wireframe, while the second row of images display the same object as a shaded surface of its polygonal representation obtained from the MeshGrid representation. In a similar way, the bone model from FIG. 87(a) represented as an IndexedFaceSet, has been converted to a multi-resolution MeshGrid, as shown for increasing mesh resolution levels in FIGS. 87(b)-(e). The images display the shaded surface of the polygonal representation of the MeshGrid object.”, Salomie [0621] “A multi-resolution MeshGrid surface description obtained from a single-resolution quadrilateral mesh is illustrated in FIG. 84. The different mesh resolutions are displayed as a shaded surface of the polygonal representation with the outline of the connectivity-wireframe, corresponding to that resolution level, drawn on top of the shaded surface (see FIGS. 84(a)-(c)).”, Salomie [0622] “Notice that both the multi-resolution connectivity-wireframe obtained with the TriScan method of the present invention, and the multi-resolution MeshGrid representation of a connectivity-wireframe allow, in different resolutions of the same object, topological changes, visible especially when displaying the polygonal representation of the connectivity-wireframe. As illustrated in FIG. 93 for increasing resolutions of the eight model, the lowest mesh resolution level (FIG. 93( a)) does not have any holes. They only appear at the second resolution level (FIG. 93(b)), and continue to exist for the higher resolution levels. The first row of images display the different resolutions as a wireframe, while the second row of images show the shaded polygonal surface.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to preprocess the mesh atrial data of Madabhushi using the mesh editing and multi resolution mesh generation techniques taught by Salomie prior to performing recurrence prediction. Salomie expressly teaches applying mesh smoothing and mesh simplification as known mesh editing and post-processing operations and further teaches converting a mesh into multiple remeshed polygonal representations having different resolution levels and topological characteristics through a multi resolution MeshGrid representation. One of ordinary skill in the art would have been motivated to apply such preprocessing techniques to the atrial mesh data used in Madabhushi because smoothing and simplification reduce irregularities and complexity in mesh representations, while generating multiple derived mesh representations of the same object exposes an analytical model to different geometric and topological representations of the underlying anatomy. This would have improved the robustness and consistency of subsequent computational analysis while preserving the anatomical information represented by the mesh. The modification involves applying known mesh processing techniques to known medical image mesh data for their established purpose of improving mesh quality and providing multiple usable mesh representations, yielding the predictable result of smoothed, simplified, and multiple derived polygon meshes that may be utilized together with the original mesh data during predictive analysis.
Regarding claim 11, Madabhushi and Salomie teach the invention in claim 6, as discussed above, and further teach wherein in generation of derived meshes at step (b-3), which is third preprocessing, N is 5, and the three-dimensional polygon-derived meshes are three-dimensional polygon meshes having a shape the same as that of the target data on which the mesh simplification has been performed, and are meshes having edges to which the vertices are connected are different (Salomie [0620] “Another example of a multi-resolution MeshGrid is illustrated for two objects (FIG. 86 and FIG. 87) originally represented as single-resolution IndexedFaceSet models that have been remeshed and represented in the MeshGrid format. The object from FIG. 86, a 3D eight model, has been converted to a multi-resolution MeshGrid with 4 mesh resolutions, i.e. a hierarchical connectivity-wireframe consisting of 4 resolution levels and the corresponding reference-grid. The original object represented as an IndexedFaceSet is shown FIG. 86( a), while FIGS. 86(b)-(e) illustrate increasing resolutions of the MeshGrid representations of the object. The first row of images display the object as a wireframe, while the second row of images display the same object as a shaded surface of its polygonal representation obtained from the MeshGrid representation. In a similar way, the bone model from FIG. 87(a) represented as an IndexedFaceSet, has been converted to a multi-resolution MeshGrid, as shown for increasing mesh resolution levels in FIGS. 87(b)-(e). The images display the shaded surface of the polygonal representation of the MeshGrid object.”, Salomie [0621] “A multi-resolution MeshGrid surface description obtained from a single-resolution quadrilateral mesh is illustrated in FIG. 84. The different mesh resolutions are displayed as a shaded surface of the polygonal representation with the outline of the connectivity-wireframe, corresponding to that resolution level, drawn on top of the shaded surface (see FIGS. 84(a)-(c)).”, Salomie [0622] “Notice that both the multi-resolution connectivity-wireframe obtained with the TriScan method of the present invention, and the multi-resolution MeshGrid representation of a connectivity-wireframe allow, in different resolutions of the same object, topological changes, visible especially when displaying the polygonal representation of the connectivity-wireframe. As illustrated in FIG. 93 for increasing resolutions of the eight model, the lowest mesh resolution level (FIG. 93( a)) does not have any holes. They only appear at the second resolution level (FIG. 93(b)), and continue to exist for the higher resolution levels. The first row of images display the different resolutions as a wireframe, while the second row of images show the shaded polygonal surface.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to generate multiple derived polygon mesh representations from a simplified target mesh, wherein the derived meshes maintain the same underlying anatomical shape while exhibiting different edge connectivity and topological characteristics, as taught by Salomie. Specifically, Salomie teaches converting a mesh into a multi-resolution MeshGrid representation having multiple mesh resolutions, generating different mesh representations from the same underlying object, and allowing topological changes between representations while preserving the geometry of the object. One of ordinary skill in the art would have recognized that generating multiple mesh representations of the same anatomical structure provides additional topological variations for analysis while maintaining the underlying geometric information. It would therefore have been obvious to utilize multiple derived meshes together with the original mesh data in order to improve robustness of subsequent mesh-based processing and machine learning analysis by exposing the analytical model to alternative valid mesh topologies representing the same anatomy. Furthermore, selecting a particular number of derived meshes, such as five, would have been an obvious matter of design choice involving a balance between computational efficiency and topological diversity, since Salomie teaches generating multiple mesh representations of the same object and recognizes the utility of representing an object at different mesh resolutions and topological configurations.
Claims 15 and 16 are analogous to claim 1, thus claims 15 and 16 are similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi et al. (U.S. Patent Publication 2021/0093281 A1), referred to hereinafter as Madabhushi, in view of Salomie (U.S. Patent Publication 2003/0052875 A1), referred to hereinafter as Salomie, further in view of Bar-Tal et al. (AU Publication No. AU2019232870A1), referred to hereinafter as Bar-Tal.
Regarding claim 4, Madabhushi and Salomie teach the invention in claim 2, as discussed above, and further teach wherein the electrophysiological data of the patient's atrium; and expressed as a three-dimensional polygon mesh, which is the geometric topological data of the patient's atrium (Madabhushi [0080] “Experiment 3 explored the effect of clinical and procedural variables on likelihood of recurrence and investigated the role of radiomic features as a function of AF type and catheter technique. Clinically features previously predictive of recurrence are persistent AF and hypertension; here clinical models perform similarly to previous clinical predictive models of AF recurrence. Radiomic features were not strongly correlated with catheter type, suggesting that cardiac morphology may be an independent inherent predictor of recurrence rather than an artifact of procedure-specific anatomic challenges. The thickness of the atrium may have an important role in the non-invasive assessment of atrial structure. In combination with atrial tissue characterization, a comprehensive assessment of the atrial dimensions may allow prediction of atrial electrophysiological behavior. Another group found that heterogeneity in the left atrial wall thickness contributes to AF recurrence after catheter ablation. It was found that the 3D fractal dimension assesses heterogeneity in the left atrial wall that characterizes texture variations. The results suggest that the shape-based features of CLA and CPV and texture-based feature of atrial wall were most significant in predicting AF recurrence. Recent studies have been shown that the LA wall thickness in patients with persistent AF is lower than that of patients with paroxysmal AF; based on fractal analysis, the AF type is highly correlated with 3D texture features of atrial wall as shown in FIG. 9 at 920-930. The fractal feature based model was found to significantly outperform the model based off LA volume in predicting AF recurrence.”, and
Salomie [0075] “Some new concepts are introduced. A polygon is a connected set of edges that forms an iso-surface. The ordering of a polygon is the path taken by the vertices describing it (v 0→v1→v2→. . . →v0). Two neighboring polygons are coherently ordered if the path of an edge shared by the two is opposite. A surface is coherently connected if all edges occur twice in opposite directions (two complementary edges) and if no polygon touches another polygon except at the common edge.”, Salomie [0076] “The algorithm guarantees that the obtained polygons are coherently ordered and connected, with no polygon occurring more than once, and that each surface is complete, that is, no holes due to surface generation errors occur. The generated Σ consists of either the minimum number of (non-planar) polygons with 3 to 12 vertices or a minimum number of triangles.”).
Madabhushi and Salomie fail to explicitly teach includes first electrophysiological data that maps information on local activation time at all points included in the patient's atrium.
Bar-Tal teaches includes first electrophysiological data that maps information on local activation time at all points included in the patient's atrium (Bar-Tal, pages 4-5, “Another embodiment of the present invention provides a method including receiving an input mesh representation of a cardiac chamber, a set of measured locations on a wall tissue of the cardiac chamber, and a respective set of local activation times (LATs) measured at the locations. The input mesh is re-meshed into a regular mesh including regularized polygons. The set of measured locations and respective LATs is data fitted to the regularized polygons. Respective LAT values are iteratively calculated for the regularized polygons, so as to obtain a cyclic EP activation wave solution over the regular mesh that take account of reentry of an EP wave. An electroanatomical map including the cyclic EP activation wave overlaid on the regular mesh is presented.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the atrial fibrillation recurrence prediction method of Madabhushi to include the local activation time mapping techniques taught by Bar-Tal and to represent the resulting electrophysiological information using the polygonal mesh structures taught by Salomie. Madabhushi teaches predicting atrial fibrillation recurrence using patient-specific atrial data derived from cardiac imaging, while Bar-Tal teaches receiving a mesh representation of a cardiac chamber, associating measured local activation times (LATs) with locations on the chamber wall, fitting the LAT data to polygons of a mesh, and generating an electroanatomical activation map over the mesh. Salomie further teaches representing anatomical structures as polygonal surfaces composed of connected edges, vertices, and triangles. One of ordinary skill in the art would have been motivated to incorporate Bar-Tal's mesh LAT mapping into Madabhushi's atrial analysis framework because local activation time is a well known electrophysiological characteristic of atrial tissue and provides clinically relevant information regarding atrial electrical behavior. Combining these teachings would have predictably resulted in electrophysiological data comprising local activation time information mapped to locations of a three-dimensional polygon mesh representation of the patient's atrium, thereby improving the characterization of atrial structure and function for recurrence prediction.
Regarding claim 5, Madabhushi and Salomie teach the invention in claim 2, as discussed above, and further teach expressed as a three-dimensional polygon mesh, which is the geometric topological data of the patient's atrium; and on fibrosis (Madabhushi [0133] “FIGS. 16-19 illustrate example images, charts, and plots in connection with the framework of the third example use case. Referring to FIG. 16, illustrated are example CT scans of an AF+ patient (top) and an AF− patient (bottom), in connection with various aspects discussed herein. Referring to FIG. 17, illustrated are example binary images of the CT scans of FIG. 9 based (AF+ in the left column and AF− in the right column) on two segmentation masks, the left atrial lumen model (top row) and PV model (bottom row), in connection with various aspects discussed herein. The lumen model consisted of the left atrium body and the ostia of the PVs. The PV model consisted of the left atrium body and the full structure of PVs visible on CT. Referring to FIG. 18, illustrated are charts showing 3D fractal analysis of lumen models for AF+ (top) and AF− (bottom), in connection with various aspects discussed herein. Fractal analysis was used to quantify morphological variations within each lumen and PV model. 3D fractal features were extracted from 3D binary images using a spectral density function to characterize self-similarity and heterogeneity of the models. Referring to FIG. 19, illustrated are boxplots of 3D fractal feature for the lumen model (left) and the PV model (right), in connection with various aspects discussed herein. Fractal features were compared between the AF+ and AF− groups.”, and Madabhushi [0082] “The first example use case had a limited sample size with a single center design and retrospective data assessment. Even though independent validation cases were used, all the scans came from a single site, without multisite validation explicitly establishing the generalizability of the approach. While an evaluation of the sensitivity of the approach to segmentation performance was done, the first example use case did not explicitly address or evaluate the sensitivity of the approach to different CT slice thicknesses, reconstruction kernels and scanners. LA fibrosis by late gadolinium enhancement has also been shown to be important factors in arrhythmia and patient stratification by CMR algorithms, which cannot be used on CT scans, though it is possible that textural differences on fractal CT analyses could be detecting similar substrates. Despite these conditions, the first example use case presented a new fractal-based approach that appears to provide independent prognostic value based on shape and textural measurements of LA, PVs, and LA wall in predicting AF recurrence.”, and
Salomie [0075] “Some new concepts are introduced. A polygon is a connected set of edges that forms an iso-surface. The ordering of a polygon is the path taken by the vertices describing it (v 0→v1→v2→. . . →v0). Two neighboring polygons are coherently ordered if the path of an edge shared by the two is opposite. A surface is coherently connected if all edges occur twice in opposite directions (two complementary edges) and if no polygon touches another polygon except at the common edge.”, Salomie [0076] “The algorithm guarantees that the obtained polygons are coherently ordered and connected, with no polygon occurring more than once, and that each surface is complete, that is, no holes due to surface generation errors occur. The generated Σ consists of either the minimum number of (non-planar) polygons with 3 to 12 vertices or a minimum number of triangles.”).
Madabhushi and Salomie fail to explicitly teach wherein the electrophysiological data of the patient's atrium is generated using data that maps voltage information at all points included in the patient's atrium; and as well as the first electrophysiological data, and includes second electrophysiological data that maps information at all points included in the patient's atrium.
Bar-Tal teaches wherein the electrophysiological data of the patient's atrium is generated using data that maps voltage information at all points included in the patient's atrium; and as well as the first electrophysiological data, and includes second electrophysiological data that maps information at all points included in the patient's atrium (Bar-Tal, pages 4-5, “Another embodiment of the present invention provides a method including receiving an input mesh representation of a cardiac chamber, a set of measured locations on a wall tissue of the cardiac chamber, and a respective set of local activation times (LATs) measured at the locations. The input mesh is re-meshed into a regular mesh including regularized polygons. The set of measured locations and respective LATs is data fitted to the regularized polygons. Respective LAT values are iteratively calculated for the regularized polygons, so as to obtain a cyclic EP activation wave solution over the regular mesh that take account of reentry of an EP wave. An electroanatomical map including the cyclic EP activation wave overlaid on the regular mesh is presented.”, and Bar-Tal page 13, “System 20 may be controlled by a system processor 40, comprising a processing unit 42 communicating with a memory 44. In some embodiments, a memory 44, which is included in system processor 40, stores an LAT and/or voltage map 62 of at least part of wall tissue of heart 34 of patient 26. Processor 40 is typically mounted in a console 46, which comprises operating controls 38, typically including a pointing device 39 such as a mouse or trackball, that physician 28 uses to interact with the processor.”, and
Bar-Tal, page 3. “An embodiment of the present invention provides a method including receiving an input mesh representation of a cardiac chamber, a set of measured locations on a wall tissue of the cardiac chamber, and a respective set of local activation times (LATs) measured at the locations. The input mesh is re-meshed into a regular mesh including regularized polygons. The set of measured locations and respective LATs is data fitted to the regularized polygons. Respective LAT values, and respective probabilities that the wall tissue includes scar tissue, are iteratively calculated for the regularized polygons, so as to obtain an electrophysiological (EP) activation wave over the regular mesh that indicates scar tissue. An electroanatomical map overlaid on the regular mesh, the map including the EP activation wave and the scar tissue, is presented.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the atrial fibrillation recurrence prediction method of Madabhushi to incorporate the mesh-based electrophysiological mapping techniques of Bar-Tal and the polygonal mesh representations taught by Salomie. Madabhushi teaches predicting atrial fibrillation recurrence using patient-specific atrial characteristics and further recognizes that fibrosis and other atrial tissue characteristics are important factors in arrhythmia assessment and patient stratification. Bar-Tal teaches receiving a mesh representation of a cardiac chamber, fitting local activation time (LAT) data to polygons of the mesh, calculating probabilities that wall tissue includes scar tissue, and generating an electroanatomical map overlaid on the mesh that includes both activation-wave and scar-tissue information, while also teaching storage and use of voltage maps. Salomie teaches representing anatomical structures using connected polygonal surfaces composed of vertices, edges, and triangles. One of ordinary skill in the art would have been motivated to combine these teachings because voltage information, local activation time information, and fibrosis or scar information were well known electrophysiological indicators of atrial substrate associated with atrial fibrillation and recurrence risk, and representing such information on a common polygonal mesh would have provided a standardized and computationally efficient framework for analyzing spatial relationships within the atrium. The combination would have merely involved applying known electrophysiological mapping techniques to a known polygon-mesh representation for their established purposes, yielding the predictable result of electrophysiological data including voltage, local activation time, and fibrosis/scar information mapped to a three-dimensional atrial mesh for use in predicting atrial fibrillation recurrence.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi et al. (U.S. Patent Publication 2021/0093281 A1), referred to hereinafter as Madabhushi, in view of Salomie (U.S. Patent Publication 2003/0052875 A1), referred to hereinafter as Salomie, further in view of Taubin (U.S. Patent Publication 2004/0075659 A1), referred to hereinafter as Taubin. Regarding claim 7, Madabhushi and Salomie teach the invention in claim 6, as discussed above.
Madabhushi and Salomie fail to explicitly teach wherein the mesh smoothing, which is first preprocessing performed at step (b-1), is performed by applying a Laplacian smoothing algorithm, and an iterative operation for the Laplacian Smoothing is performed three times.
Taubin teaches wherein the mesh smoothing, which is first preprocessing performed at step (b-1), is performed by applying a Laplacian smoothing algorithm, and an iterative operation for the Laplacian Smoothing is performed three times ((Taubin [0010] “Algorithms with linear time and space complexity are desirable to operate on large data sets, particularly for applications such as surface design and polygon mesh editing, where interactive rates are a primary concern. The simplest polygon mesh smoothing algorithm that satisfies these linear complexity requirements is Laplacian smoothing. Most mesh smoothing algorithms derive from Laplacian smoothing. Laplacian smoothing is a simple and widely used algorithm for denoising polygon mesh signals. It was first introduced to improve the quality of finite element meshes used in engineering applications to perform numerical simulations of physical phenomena. In this context the vertex positions signal of a polygon mesh is two-dimensional, and the polygon mesh connectivity has boundary mesh vertices as well as internal mesh vertices. Vertex positions corresponding to boundary mesh vertices are constrained not to move, but vertex positions corresponding to internal mesh vertices are simultaneously moved to the corresponding output vertex positions computed by a Laplacian smoothing operator. And then the process is iterated a number of times.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to perform mesh smoothing on the generated target data using a Laplacian smoothing algorithm, as taught by Taubin, prior to subsequent mesh processing operations. Taubin expressly teaches that Laplacian smoothing is a widely used technique for denoising polygon meshes and further teaches repeatedly applying a Laplacian smoothing operator, with the smoothing process being iterated multiple times to improve mesh quality. One of ordinary skill in the art would have recognized that applying Laplacian smoothing to a cardiac mesh representation before further analysis would reduce noise and improve the quality and consistency of the mesh data. Although Taubin does not disclose performing the smoothing operation exactly three times, the number of smoothing iterations is a variable that would have been routinely optimized to achieve a desired balance between smoothing effectiveness, preservation of geometric detail, and computational efficiency. Therefore, selecting three iterations of the Laplacian smoothing operation would have been an obvious matter of routine optimization and design choice.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Trayanova et al. (International Patent Publication No WO2016077154A1) teaches generating a personalized atrial fibrillation ablation treatment plan by analyzing cardiac images to identify fibrotic atrial tissue, evaluating its spatial distribution, and selecting ablation targets based on the fibrosis pattern.
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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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