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 .
Specification
The disclosure is objected to because of the following informalities: The first paragraph of the specification should be updated for the status of the parent case (“abandoned”). [Note: MPEP 608.01(p).I.A.: Abandoned applications less than 20 years old can be incorporated by reference to the same extent as copending applications]
Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1)
Claims 1-11 are drawn to a method and Claims 12-20 are drawn to a machine, thus the Claims belong in one of the four statutory categories. All dependent Claims meet this step.
Step 2a, prong one)
The claims recite “obtaining an anatomical mesh of … a part of the anatomical structure; subdividing the anatomical mesh into one … other mesh[es] of the anatomical structure that [is]… more granular than the anatomical mesh, wherein the … other mesh[es] comprise a plurality of spatial map elements; generating velocity values by interpolating local scalar values for each of the plurality of spatial map elements that form the… other mesh[es], wherein the interpolating is based on a slowness vector; tracing a path of velocity vectors on the … other meshes based on the interpolating of each of the plurality of spatial map elements; and projecting the path on the anatomical mesh to generate … [a] visualization of the anatomical structure“, which is directed to an ineligible concept, namely an abstract idea. In terms of abstract ideas, these limitations would cover a mental process performed in the human mind or by mere use of pen and paper, such as solving motion mechanics problems on a grid or mesh with other shapes and/or via finite element methods etc. This abstract idea also belongs to the grouping of mathematical calculations, since it covers solving motion mechanics problems on a grid or mesh with other shapes and/or via finite element methods etc. The abstract idea also covers merely drawing vectors on a mesh.
Step 2a, prong two)
This judicial exception is not integrated into a practical application because: the claims do not recite an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Claim 1 adds nothing more, except a nominal and indefinite (see 112 section below) label of “enhanced visualization”. There is nothing clear as to what in the claim is an enhancement, compared to what, and in what technology or technological field. Claim 12 adds the generic “system”, “a memory”, and “one …processor(s) …communicatively coupled to the memory”. Clearly, these are generally linking the use of the exception to a generic technological environment or field of use, and merely use a computer to perform the ineligible concept. None of these limitations add to the claims: a) improvements to the functioning of a computer, or to any other technology or technical field, b) use of the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, c) a particular machine or a transformation, or d) use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The independent claims do not even provide an output that is perceptible by a user, something which is indicative of the generality of the scope of the claims and the lack of integration to anything practical.
Dependent Claims 2-11, 13-20, are completely drawn to the abstract idea itself, as they only add details of the shape of the mesh, and the types of calculations and parameters involved.
Thus, no claims meet the requirements of step 2a, prong two.
Step 2b)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of all the reasons explained in Step 2a, prong two, and in addition, all of those additional elements individually or in combination are well-understood, routine and conventional activity in the field. The method of claim 1 offers no additional structural elements. The generic “system”, “a memory”, and “one …processor(s) …communicatively coupled to the memory”, as recited in the independent claims, are all well-understood, routine, conventional activity in the field. These are recited with a high level of generality, that merely append the exception to the bare minimum of any type of monitoring in the art. This conclusion is further supported by the subject matter rejections below. As noted in step 2a), dependent Claims 2-11, 13-20, are completely drawn to the abstract idea itself, as they only add details of the shape of the mesh, and the types of calculations and parameters involved. Thus, no claim meets the requirements of Step 2b. Claims 1-20 are ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1 and 12, the term “enhanced” in “enhanced visualization” is relevant and subjective. What is this enhancement relative to?
Regarding Claims 1 and 12, the terms “local scalar values” is unclear and incomplete. Scalar values of what?
Regarding Claims 6 and 17, the terms “a center value” are unclear and incomplete. A center of what? A value of what?
Regarding Claims 8-9, it is unclear whether the passive limitations of “is taken” and “are used” refer to steps, intended uses, intended field of use, or other optional limitations. An undisputable method claim has clauses that are designated by a present participle and separated with a comma (or a semicolon that includes a comma). See Credle v. Bond, 25 F.3d 1566, 1572 (Fed. Cir. 1994). Also see Ex parte Erlich, 3 USPQ2d 1011 (Bd. Pat. App. & Inter. 1986), requiring active positive steps for a method to be definite.
Regarding Claims 9 and 20, “the velocity values” lack antecedent basis.
Claim Rejections - 35 USC § 102
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by NPL by Coveney et al., Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity, 4/24/20 (Applicant submitted in parent case 17/115,199, 6/6/22, 21 pages).
Regarding Claim 1, Coveney discloses a method for generating an enhanced visualization of an anatomical structure, the method comprising:
obtaining an anatomical mesh of a part of the anatomical structure (e.g. page 1, last line: “the left atrium, which is modeled as a 2D manifold in 3D space represented as a triangle mesh”);
subdividing the anatomical mesh into one other mesh of the anatomical structure that is more granular than the anatomical mesh, wherein the one other mesh comprises a plurality of spatial map elements [e.g. page 3, par. 1: “triangulated mesh of the left atrium, obtained using the respective mapping systems…and the triangulation was smoothed (using …element sub-division)”; Figure 1, and page 5, section 2.5, par. 3, “Right: subdivision of a mesh triangle face into 9 triangles by addition of vertices, including a vertex at the mesh face centroid” and page 5, last par.: “calculated for this finer mesh”];
generating velocity values by interpolating local scalar values for each of the plurality of spatial map elements that form the one other mesh (e.g. page 3, lines 3-5; page 6, par. 1; sections 2.7, 3.1 and 3.2, Table 1, Figure 2: LAT and CV interpolation at the vertices and centroids of each triangle; e.g. section 2.7 discloses “For each interpolation mesh vertex, we record LAT from the matching simulation vertex. We calculate CV for the simulation in two ways. Firstly, we calculate CV element-wise at each interpolation centroid from LAT at the face vertices (‘element’ method):” and “Secondly, since the CV field is heterogeneous and CV often varies on a length-scale smaller than the distance between observations, we also calculate CV taking account of a larger area (‘wave’ method). For the 20 vertices nearest to where CV is to be calculated, the vertex coordinates are projected into 2D such that the geodesic distances are optimally conserved. A least-squares linear fit to LAT is used to calculate a gradient corresponding to plane wave propagation [8]. We do this for 1000 random centroids locations, optimized to be well spaced over the mesh using a maximin distance criterion over 10 000 designs. Note that 20 vertices corresponds to at least two ‘rings’ of vertices around a centroid.”), wherein the interpolating is based on a slowness vector (e.g. abstract: “The gradient of LAT maps can be used to calculate conduction velocity (CV)”; Section 2.6: “CV is the inverse of the LAT gradient”; The LAT gradient is a vector as disclosed in section 2.7 and equation 11, which also shows that CV and LAT gradient are inversely related; As Applicant also informs us in ¶ 71 and equation 2, slowness is the inverse of conduction velocity, thus the LAT gradient, which is used to calculate the CV vector, is a slowness vector and since it is used to calculate the CV vector, it is also used in the interpolation)
tracing a path of velocity vectors on the one other mesh based on the interpolating of each of the plurality of spatial map elements and projecting the path on the anatomical mesh to generate the enhanced visualization of the anatomical structure [e.g. abstract: “Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds“; Fig. 2 and Fig. 7 show CV vectors which form traces and are projected on the meshes. This includes “tracing”, ie. “delineating or sketching” (See Merriam-Webster, definition of verb “trace/tracing”), the paths of said vectors; Also see: section 6: “conduction velocity maps”, section 3.2, first paragraph, section 2.7: wave propagation and collision; Alse see Annotated Fig. 3 and 7, below, which clearly show CV vector tracing, as CV vectors form traces on the map, each vector colored based on LAT. The entirety of those figures are projections of vectors on the meshes of the manifold of the heart. As a side note, the spheres in the figures represent LATs].
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Figure 1 Annotated Fig. 3
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Figure 2 Annotated Fig. 7
Regarding Claim 2, Coveney teaches the method of claim 1, wherein the plurality of spatial map elements comprise a plurality of sub-triangles (e.g. Fig. 1, right: subdivision of mesh triangle face into smaller triangles by addition of vertices).
Regarding Claim 3, Coveney teaches the method of claim 1, wherein the local scalar values comprise local activation time values (as discussed in Claim 1 for LAT values).
Regarding Claim 4, Coveney teaches the method of claim 2, wherein the anatomical mesh comprises one triangle and the plurality of sub-triangles are in the one triangle (e.g. section 2.5 and Fig. 1, right: the initial triangles of the mesh as subdivided into smaller triangles within the initial mesh).
Regarding Claim 5, Coveney teaches the method of claim 2, wherein each of the plurality of sub-triangles constitute a single plane for the anatomical mesh (as seen in Fig. 1, right, noting that the claim does not require each single plane to be different from the other single planes).
Regarding Claim 6, Coveney teaches the method of claim 2, wherein the local scalar values comprise local activation time values, and wherein the local activation time values are determined at vertices of the plurality of sub-triangles using a center value [e.g. section 2.5: “For each original mesh face, there are now 10 vertices which lie in the plane of this face. To calculate the gradients of the eigenfunctions at a face centroid, we first rotate these (finer mesh) vertex coordinates into a 2D frame, and then fit a cubic polynomial to the vertex eigenfunction values and 2D coordinates using least-squares regression. We then evaluate the gradient of the eigenfunctions at the centroid using the derivative of the polynomial expression, and rotate this (vector) gradient back into the original 3D coordinate frame. This allows us to calculate the eigenfunction gradients at the centroids, even when the eigenfunctions vary nonlinearly across a face. For simplicity, we retain the eigenfunction values at the mesh vertices and face centroids of the original mesh only (i.e. we discard the vertices from subdividing the face edges), which allows us to assign LAT observations and make LAT predictions at both vertices and centroids.”].
Regarding Claim 7, Coveney teaches the method of claim 6, the method further comprises determining a velocity to project to the vertices (e.g. section 2.6, page 6, last paragraph and section 2.7; also note that “to project” is not a step).
Regarding Claim 8, Coveney teaches the method of claim 6, wherein the vertices belong to more than one of the one triangle, and a mean value is taken for the local activation time values (e.g. page 6, par. 1 and section 2.7; also note that “is taken” is not a step).
Regarding Claim 9, Coveney teaches the method of claim 6, wherein the local activation time values at the vertices are used to compute the velocity values for each of the plurality of sub-triangles [e.g. section 2.5: “For each original mesh face, there are now 10 vertices which lie in the plane of this face. To calculate the gradients of the eigenfunctions at a face centroid, we first rotate these (finer mesh) vertex coordinates into a 2D frame, and then fit a cubic polynomial to the vertex eigenfunction values and 2D coordinates using least-squares regression. We then evaluate the gradient of the eigenfunctions at the centroid using the derivative of the polynomial expression, and rotate this (vector) gradient back into the original 3D coordinate frame. This allows us to calculate the eigenfunction gradients at the centroids, even when the eigenfunctions vary nonlinearly across a face. For simplicity, we retain the eigenfunction values at the mesh vertices and face centroids of the original mesh only (i.e. we discard the vertices from subdividing the face edges), which allows us to assign LAT observations and make LAT predictions at both vertices and centroids.”].
Regarding Claim 10, Coveney teaches the method of claim 2, wherein the local scalar values comprise local activation time values, and wherein a center sub-triangle of the plurality of sub-triangles preserves an original local activation time and the velocity vector (e.g. page 6, par. 1).
Regarding Claim 11, Coveney teaches the method of claim 1, wherein the anatomical structure comprises a heart, and the at least part of the anatomical structure comprises a chamber (e.g. page 3, line 2: “triangulated mesh of the left atrium”).
Regarding Claim 12, Coveney discloses a system for generating an enhanced visualization of an anatomical structure, the system comprising:
a memory and a processor communicatively coupled to the memory (e.g. page 3, line 2: “triangulated mesh of the left atrium, obtained using the respective mapping systems”; also section 1 of Supplementary Material: PentaRay mapping system), wherein the processor is collectively configured to:
obtain an anatomical mesh of a part of the anatomical structure (e.g. page 1, last line: “the left atrium, which is modeled as a 2D manifold in 3D space represented as a triangle mesh”);
subdivide the anatomical mesh into one other mesh of the anatomical structure that is more granular than the anatomical mesh, wherein the one other mesh comprises a plurality of spatial map elements [e.g. page 3, par. 1: “triangulated mesh of the left atrium, obtained using the respective mapping systems…and the triangulation was smoothed (using …element sub-division)”; Figure 1, and page 5, section 2.5, par. 3, “Right: subdivision of a mesh triangle face into 9 triangles by addition of vertices, including a vertex at the mesh face centroid” and page 5, last par.: “calculated for this finer mesh”];
generate velocity values by interpolating local scalar values for each of the plurality of spatial map elements that form the other mesh (e.g. page 3, lines 3-5; page 6, par. 1; sections 2.7, 3.1 and 3.2, Table 1, Figure 2: LAT and CV interpolation at the vertices and centroids of each triangle; e.g. section 2.7 discloses “For each interpolation mesh vertex, we record LAT from the matching simulation vertex. We calculate CV for the simulation in two ways. Firstly, we calculate CV element-wise at each interpolation centroid from LAT at the face vertices (‘element’ method):” and “Secondly, since the CV field is heterogeneous and CV often varies on a length-scale smaller than the distance between observations, we also calculate CV taking account of a larger area (‘wave’ method). For the 20 vertices nearest to where CV is to be calculated, the vertex coordinates are projected into 2D such that the geodesic distances are optimally conserved. A least-squares linear fit to LAT is used to calculate a gradient corresponding to plane wave propagation [8]. We do this for 1000 random centroids locations, optimized to be well spaced over the mesh using a maximin distance criterion over 10 000 designs. Note that 20 vertices corresponds to at least two ‘rings’ of vertices around a centroid.”), wherein the interpolating is based on a slowness vector (e.g. abstract: “The gradient of LAT maps can be used to calculate conduction velocity (CV)”; Section 2.6: “CV is the inverse of the LAT gradient”; The LAT gradient is a vector as disclosed in section 2.7 and equation 11, which also shows that CV and LAT gradient are inversely related; As Applicant also informs us in ¶ 71 and equation 2, slowness is the inverse of conduction velocity, thus the LAT gradient, which is used to calculate the CV vector, is a slowness vector and since it is used to calculate the CV vector, it is also used in the interpolation);
trace a path of velocity vectors on the one other mesh based on the interpolating of each of the plurality of spatial map elements; and project the path on the anatomical mesh to generate the enhanced visualization of the anatomical structure [e.g. abstract: “Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds“; Fig. 2 and Fig. 7 show CV vectors which form traces and are projected on the meshes. This includes “tracing”, ie. “delineating or sketching” (See Merriam-Webster, definition of verb “trace/tracing”), the paths of said vectors; Also see: section 6: “conduction velocity maps”, section 3.2, first paragraph, section 2.7: wave propagation and collision; Alse see Annotated Fig. 3 and 7, below, which clearly show CV vector tracing, as CV vectors form traces on the map, each vector colored based on LAT. The entirety of those figures are projections of vectors on the meshes of the manifold of the heart].
Regarding Claim 13, Coveney teaches the system of claim 12, wherein the plurality of spatial map elements comprise a plurality of sub-triangles (e.g. Fig. 1, right: subdivision of mesh triangle face into smaller triangles by addition of vertices).
Regarding Claim 14, Coveney teaches the system of claim 12, wherein the local scalar values comprise local activation time values (as discussed in Claim 12).
Regarding Claim 15, Coveney teaches the system of claim 13, wherein the anatomical mesh comprises at least one triangle and the plurality of sub-triangles are in the at least one triangle (e.g. section 2.5 and Fig. 1, right: the initial triangles of the mesh as subdivided into smaller triangles within the initial mesh).
Regarding Claim 16, Coveney teaches the system of claim 13, wherein each of the plurality of sub-triangles constitute a single plane for the anatomical mesh (as seen in Fig. 1, right, noting that the claim does not require each single plane to be different from the other single planes).
Regarding Claim 17, Coveney teaches the system of claim 13, wherein the local scalar values comprise local activation time values, and wherein the local activation time values are determined at vertices of the plurality of sub-triangles using a center value [e.g. section 2.5: “For each original mesh face, there are now 10 vertices which lie in the plane of this face. To calculate the gradients of the eigenfunctions at a face centroid, we first rotate these (finer mesh) vertex coordinates into a 2D frame, and then fit a cubic polynomial to the vertex eigenfunction values and 2D coordinates using least-squares regression. We then evaluate the gradient of the eigenfunctions at the centroid using the derivative of the polynomial expression, and rotate this (vector) gradient back into the original 3D coordinate frame. This allows us to calculate the eigenfunction gradients at the centroids, even when the eigenfunctions vary nonlinearly across a face. For simplicity, we retain the eigenfunction values at the mesh vertices and face centroids of the original mesh only (i.e. we discard the vertices from subdividing the face edges), which allows us to assign LAT observations and make LAT predictions at both vertices and centroids.”].
Regarding Claim 18, Coveney teaches the system of claim 17, wherein the one or more processors are further collectively configured to: determine a velocity to project to the vertices (e.g. section 2.6, page 6, last paragraph and section 2.7; also note that “to project” is not a step).
Regarding Claim 19, Coveney teaches the system of claim 17, wherein the vertices belong to more than one of the at least one triangle, and a mean value is taken for the local activation time values (e.g. page 6, par. 1 and section 2.7; also note that “is taken” is not a step).
Regarding Claim 20, Coveney teaches the system of claim 17, wherein the local activation time values at the vertices are used to compute the velocity values for each of the plurality of sub-triangles [e.g. section 2.5: “For each original mesh face, there are now 10 vertices which lie in the plane of this face. To calculate the gradients of the eigenfunctions at a face centroid, we first rotate these (finer mesh) vertex coordinates into a 2D frame, and then fit a cubic polynomial to the vertex eigenfunction values and 2D coordinates using least-squares regression. We then evaluate the gradient of the eigenfunctions at the centroid using the derivative of the polynomial expression, and rotate this (vector) gradient back into the original 3D coordinate frame. This allows us to calculate the eigenfunction gradients at the centroids, even when the eigenfunctions vary nonlinearly across a face. For simplicity, we retain the eigenfunction values at the mesh vertices and face centroids of the original mesh only (i.e. we discard the vertices from subdividing the face edges), which allows us to assign LAT observations and make LAT predictions at both vertices and centroids.”].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2020/0113465 by Cohen teaches tracing and projecting LAT based CV vectors (e.g. abstract, Fig. 5, 12)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANOLIS Y PAHAKIS whose telephone number is (571)272-7179. The examiner can normally be reached M-F 9-5, EST.
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/MANOLIS PAHAKIS/Examiner, Art Unit 3796
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