DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Claim(s) 1-2, 4, 6, 23, 31-32, 34, 36, 53, and 63 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Poltaretskyi et al. (Prediction of the pre-morbid 3D anatomy of the proximal humerus based on statistical shape modelling) hereinafter referenced as Poltaretskyi.
Regarding claim 1, Poltaretskyi discloses: A method comprising: obtaining image data of a joint that comprises at least a portion of a humerus; segmenting the image data to identify portions of the image data that correspond to cortical bone; generating a three-dimensional (3D) model based on the portions of the image data that correspond to cortical bone, wherein the 3D model comprises one or more 3D meshes corresponding to surfaces of the portions of the image data that correspond to cortical bone (Poltaretskyi: Figures 1 & 2; Materials and Methods: “The CT scans were manually segmented and humeral 3D surfaces were reconstructed using Amira 5.3.3 software (Zuse Institute Berlin, Berlin, Germany). A second database of CT scans, termed the ‘test database’ (TD), was used to evaluate the accuracy of the SSM’s prediction of proximal humeral anatomy. This database contained 52 CT scans of normal human shoulders, including the entire humerus…and the images were segmented and 3D surfaces were reconstructed using the same software.”; Wherein the CT images comprising the humeral head constitute joint image data, and wherein the 3d surface reconstruction corresponds to a cortical bone 3d model comprising one or more 3d meshes);
identifying, in the one or more 3D meshes, a portion of a 3D mesh that corresponds to a diaphysis (Poltaretskyi: Figure 4; Materials and Methods: “The SSM that was created from the first database was then fitted into the diaphyseal portion (Fig. 4a) of every humerus from the TD by adjusting the anatomical variations,”; Wherein the SSM model is fitted to the diaphysis of the TD humerus models);
determining an estimated pre-morbid shape of the humerus based on a shape of the
portion of the 3D mesh that corresponds to the diaphysis; and generating an output based on the estimated pre-morbid shape of the humerus (Poltaretskyi: Figure 4; Page 926: “Using a SSM, a 3D geometric model was created which corresponded to the average shape of normal humeri, termed ‘training shapes’ (TS) while also containing natural variations derived from a statistical analysis of the cohort. The model can be fitted automatically to the patient’s anatomy, distal to the surgical neck, in order to predict the pre-morbid morphology of the proximal humerus and this predicted pre-morbid shape can be used for pre-operative planning.”;
Section: Materials and Methods: “The SSM that was created from the first database was then fitted into the diaphyseal portion (Fig. 4a) of every humerus from the TD by adjusting the anatomical variations, such that the distance between the SSM and the surface of the diaphyseal portion was minimal (Fig. 4b).”).
Regarding claim 2, Poltaretskyi discloses: The method of claim 1, further comprising: registering the estimated pre-morbid shape of the humerus to a reference point (Poltaretskyi: Section: Materials and Methods: “Two fitting procedures were performed on the TS set to accommodate for the variations in humeral size and shape, scale and shape fitting. Scale fitting brings all humeral lengths to the same size, and shape fitting reduces the variations of shape. The quality of both these fitting operations directly affects the quality of the final SSM. Shape fitting is a complex procedure, for which several algorithms have been proposed. The one that was adopted for this study involved fitting one humerus into another using anatomical landmarks…The SSM that was created from the first database was then fitted into the diaphyseal portion (Fig. 4a) of every humerus from the TD by adjusting the anatomical variations, such that the distance between the SSM and the surface of the diaphyseal portion was minimal (Fig. 4b).”).
Regarding claim 4, Poltaretskyi discloses: The method of claim 1, further comprising:
identifying, in the one or more 3D meshes, a portion of a 3D mesh that corresponds to a
humeral head (Poltaretskyi: Figure 4; Page 928: “The purpose of this study is to test a novel method, a statistical shape model (SSM), to predict pre-morbid proximal humeral morphology in normal humeri. Using a SSM, a 3D geometric model was created which corresponded to the average shape of normal humeri, termed ‘training shapes’ (TS) while also containing natural variations derived from a statistical analysis of the cohort. The model can be fitted automatically to the patient’s anatomy, distal to the surgical neck, in order to predict the pre-morbid morphology of the proximal humerus and this predicted pre-morbid shape can be used for pre-operative planning.”).
Regarding claim 6, Poltaretskyi discloses: The method of claim 4, wherein identifying the portion of the 3D mesh that corresponds to the diaphysis comprises determining in the one or more 3D meshes, a vertex that is farthest from the portion of the 3D mesh that corresponds to the humeral head (Poltaretskyi: Figure 3b: “Reconstructions of diaphyseal segments that mimic clinical situations…b) proximal humerus with missing epiphysis and metaphysis (surgical neck fracture)”; Section: Materials and Methods: “The second segment (Fig. 3b) is missing the metaphysis and models the anatomy typically seen with proximal humeral fractures involving the surgical neck, the sequelae of fractures or revisions.”; Wherein the segment in Fig. 3b constitutes the segment consisting of the diaphysis, and wherein the identification of the portions of the 3D model corresponding to the humeral head and diaphysis, and thus the edges of the diaphysis and humeral head segments within the model, comprises the determining of the farthest vertex corresponding to the humeral head.).
Regarding claim 23, Poltaretskyi discloses: The method of claim 1, wherein generating the output comprises: aligning the image data of the joint to an image of the estimated pre-morbid shape of the humerus; generating a composite image that shows a portion of the image data of the joint and a portion of the image of the estimated pre-morbid shape of the humerus (Poltaretskyi: Section: Discussion: “Secondly, the predicted pre-morbid morphology can be used intra-operatively using an augmented reality system. In this scenario, the pre-morbid humeral shape and the plane of the osteotomy can be projected onto the field of surgery using augmented reality glasses.”; Wherein the projection of the pre-morbid humeral shape onto the field of surgery containing the humeral joint using AR glasses constitutes the generation of the composite image.).
As per claim(s) 31, arguments made in rejecting claim(s) 1 are analogous. In addition, Section: Materials and Methods and Section: Discussion of Poltaretskyi disclose the usage of 3D reconstruction software and the implementation of preoperative virtual surgical planning software, thus implying the usage of “memory configured to store image data of a joint that comprises at least a portion of a humerus; and processing circuitry.”
As per claim(s) 32, arguments made in rejecting claim(s) 2 are analogous.
As per claim(s) 34, arguments made in rejecting claim(s) 4 are analogous.
As per claim(s) 36, arguments made in rejecting claim(s) 6 are analogous.
As per claim(s) 53, arguments made in rejecting claim(s) 23 are analogous.
As per claim(s) 63, arguments made in rejecting claim(s) 1 are analogous. In addition, Section: Materials and Methods and Section: Discussion of Poltaretskyi disclose the usage of 3D reconstruction software and the implementation of preoperative virtual surgical planning software, thus implying the usage of a “non-transitory computer-readable storage medium.”
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 and 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poltaretskyi, and further in view of Mayya et al. (Mesh correspondence improvement using Regional Affine Registration: Application to Statistical Shape Model of the scapula) hereinafter referenced as Mayya.
Regarding claim 3, Poltaretskyi discloses: The method of claim 2.
Poltaretskyi does not disclose expressly: further comprising: identifying in the 3D model, a 3D mesh that corresponds to a scapula; identifying the reference point based on the 3D mesh that corresponds to the scapula.
Thus, Poltaretskyi does not disclose expressly the registration of the estimated pre-morbid shape of the humerus to a reference point identified based on an identified 3D mesh corresponding to a scapula.
Mayya discloses: a method for registering a 3D Statistical Shape model of a human scapula to its corresponding counterpart scapula model (Mayya: Abstract: “We introduce the Regional Affine Registration (RAR) that is based on object segmentation where each region is affinely registered to its counterpart...As an application, we integrate RAR in the construction of an SSM of the human scapula…The RAR method proved to initiate better the nonrigid registration which gave more accurate correspondence among synthetic and real database shapes. This was also reflected in the SSM validation tests.”;
3. Scapulae subpopulation: “CT scans are segmented semi-automatically to label the cortical bone of the scapula using Amira 5.3.3 software (Zuse Institute Berlin, Berlin, Germany). Once labels are ready for each slice of the CT volume, the software employs marching cubes [18] to extract the triangulated surface. The algorithm proceeds through the volume, forming an imaginary cube with 8 neighbor locations at a time, then determining the polygons needed to represent the part of the label that passes through this cube. The polygons are then fused into the desired surface.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the human scapula based Regional Affine Registration method taught by Mayya for the fitting of the SSM and TD humerus models disclosed by Poltaretskyi. The suggestion/motivation for doing so would have been “The RAR method proved to initiate better the nonrigid registration which gave more accurate correspondence among synthetic and real database shapes.” (Mayya: Abstract). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Poltaretskyi with Mayya to obtain the invention as specified in claim 3.
As per claim(s) 33, arguments made in rejecting claim(s) 3 are analogous.
Claim(s) 5 and 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poltaretskyi, and further in view of Chabanas et al. (US-20130083984-A1) hereinafter referenced as Chabanas.
Regarding claim 5, Poltaretskyi discloses: The method of claim 4.
Poltaretskyi does not disclose expressly: wherein identifying the portion of the 3D mesh that corresponds to the humeral head comprises: determining normal vectors for vertices of the one or more 3D meshes; determining a most common point of intersection for the normal vectors for the vertices of the one or more 3D meshes; and identifying vertices with normal vectors intersecting the most common point of intersection as being vertices that belong to the portion of the 3D mesh that corresponds to the humeral head.
Chabanas discloses: determining normal vectors for vertices of the one or more 3D meshes; determining a most common point of intersection for the normal vectors for the vertices of the one or more 3D meshes; and identifying vertices with normal vectors intersecting the most common point of intersection as being vertices that belong to the portion of the 3D mesh that corresponds to a bone head (Chabanas: 0013: “The invention provides a method for automatically determining, on a bone comprising a head portion contiguous to a neck portion, parameters for characterizing a bump deformation on the head-neck junction of the bone from acquired 3D medical image, the method comprising the following steps: i) constructing a 3D surface model of the bone from acquired 3D medical image; ii) fitting a sphere on the spherical portion of the head of the bone; iii) determining a neck axis characterizing the neck portion of the bone; iv) determining from the fitted sphere and the neck axis, a clock face referential on the head of the bone rotating around the neck axis; v) determining a 3D curve on the 3D surface model characterizing the head-neck junction of the bone”;
0014: “The step of determining the clock face referential is advantageously performed by an automatic computation comprising of the following steps: i) determining a 12 o'clock superior coronal hemi-plane passing through the neck axis of the bone; ii) determining on the head of the bone the 12 o'clock index at the location of the intersection of the coronal hemi-plane and the 3D surface model of the bone, at the most superior portion of the head of the bone; iii) determining on the head of the bone the successive clock indices by rotating the coronal hemi-plane around the neck axis for each hour, the current hour index being determined at the location of the intersection of the current rotated hemi-plane and the 3D surface model of the bone.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the method for determining the head of a bone taught by Chabanas for the identification of the 3D mesh that corresponds to the humeral head disclosed by Poltaretskyi. The suggestion/motivation for doing so would have been “Usually those characterization measurements are performed manually by a radiologist, which takes time and efforts and is prone to human errors or inaccurate measurements, and potentially misleading the choice of surgical treatment. Our method provides then a fast and more reliable process to perform these measurements during the analysis of the pathology.” (Chabanas: 0076). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Poltaretskyi with Chabanas to obtain the invention as specified in claim 5.
As per claim(s) 35, arguments made in rejecting claim(s) 5 are analogous.
Claim(s) 7-14, 24-25, 37-44, and 54-55 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poltaretskyi, and further in view of Sabczynski et al. (US2021077191A1) hereinafter referenced as Sabczynski.
Regarding claim 7, Poltaretskyi discloses: The method of claim 4.
Poltaretskyi does not disclose expressly: further comprising: identifying, in the 3D model, 3D meshes corresponding to unknown fragments, wherein the 3D meshes corresponding to the unknown fragments comprise 3D meshes that are not the 3D mesh that corresponds to the diaphysis or the 3D mesh that corresponds to the humeral head.
Thus, Poltaretskyi does not disclose expressly the identification of 3d meshes corresponding to unknown fragments, wherein the 3D meshes corresponding to the unknown fragments are determined as 3D meshes separate from the segments corresponding to the diaphysis and the humeral head.
Sabczynski discloses: identifying, in the 3D model, 3D meshes corresponding to unknown fragments, wherein the 3D meshes corresponding to the unknown fragments comprise 3D meshes (Sabczynski: 0012: “In particular, a 3D image of a broken bone of a subject may be acquired. The model may be a 3D mesh model. The model may be divided into portions that correspond to the fragments of the broken bone. The portions may be fitted to corresponding fragments of the broken bone in the 3D image… a transformation may be determined that anatomically aligns the fragments of the broken bone with the corresponding portions of the undivided model of the unbroken bone.”) that are not the 3D mesh that corresponds to undeformed segments of the unbroken bone (Sabczynski: 0045: “Generally, the acquired model comprises an indication of the shape of the broken bone in its unbroken form. For example, the acquired model may be shaped according to a generic shape of the unbroken bone…For example, the model may be shaped according to an average (or mean) shape of one or more corresponding unbroken bones of other subjects.”;
0047: “In some embodiments, the acquired model of the corresponding unbroken bone can comprise a mesh. Thus, in these embodiments, the acquired mesh may be a mesh that is derived from one or more corresponding unbroken bones of other subjects, medical literature, medical research, and/or a drawing by a medical professional. The mesh can comprise a plurality of segments.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the algorithms for reconstructing a broken bone based on a model of an unbroken bone taught by Sabczynski by utilizing the SSM and TD models disclosed by Poltaretskyi to reconstruct unaligned TD model fragments based on the unbroken SSM model. The suggestion/motivation for doing so would have been “deformations of the model according to the at least one parameter are allowed when the corresponding portions of the model of the unbroken bone are fitted to the fragments of the broken bone. This can ensure that the model that is used to determine the transformation accurately reflects (for example, the shape) of the bone in the image. This provides a more accurate fit, compared to assuming a fixed model, and thus the determined transformation that anatomically aligns the fragments of broken bone is more reliable.” (Sabczynski: 0022). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Poltaretskyi with Sabczynski to obtain the invention as specified in claim 7.
Regarding claim 8, Poltaretskyi in view of Sabczynski discloses: The method of claim 7, further comprising: determining an allowed region for the 3D meshes corresponding to the unknown fragments based on the estimated pre-morbid shape of the humerus, the 3D mesh that corresponds to the diaphysis, and the 3D mesh that corresponds to the humeral head (Sabczynski: 0011: “The method comprises acquiring an image of a broken bone of a subject, wherein the bone is broken into two or more fragments. The method also comprises acquiring a model of a corresponding unbroken bone and at least one parameter defining one or more deformations to the model that are permitted when fitting portions of the model of the unbroken bone to corresponding fragments of the broken bone, fitting portions of the model of the unbroken bone to corresponding fragments of the broken bone based on the at least one parameter, and determining a transformation that anatomically aligns the fragments of the broken bone with the corresponding portions of the model.”)
(Poltaretskyi: Figure 3: “Reconstructions of diaphyseal segments that mimic clinical situations: a) proxius with missing epiphysis (osteoarthritis); b) proximal humerus with missing epiphysis and metaphysis (surgical neck fracture); c) and d) humerus with missing epiphysis, metaphysis and proximal diaphysis (proximal humeral fracture with diaphyseal extension).”; Section: Materials and Methods: “The first segment (Fig. 3a) is missing the epiphysis and models the pathological anatomy typically seen with primary osteoarthritis. The second segment (Fig. 3b) is missing the metaphysis and models the anatomy typically seen with proximal humeral fractures involving the surgical neck, the sequelae of fractures or revisions.”; Wherein the TD models incorporate diaphysis, and humeral head segments as disclosed by Poltaretskyi, and unaligned broken bone fragments as disclosed by Sabczynski).
Regarding claim 9, Poltaretskyi in view of Sabczynski discloses: The method of claim 8, further comprising: determining, in the allowed region, locations for the 3D meshes corresponding to the unknown fragments (Sabczynski: 0011: “The method comprises acquiring an image of a broken bone of a subject, wherein the bone is broken into two or more fragments. The method also comprises acquiring a model of a corresponding unbroken bone and at least one parameter defining one or more deformations to the model that are permitted when fitting portions of the model of the unbroken bone to corresponding fragments of the broken bone, fitting portions of the model of the unbroken bone to corresponding fragments of the broken bone based on the at least one parameter, and determining a transformation that anatomically aligns the fragments of the broken bone with the corresponding portions of the model.”).
Regarding claim 10, Poltaretskyi in view of Sabczynski discloses: The method of claim 8.
Poltaretskyi in view of Sabczynski does not disclose expressly: further comprising: determining a minimization value based on distances between a boundary of the allowed region and the 3D meshes corresponding to the unknown fragments.
Sabczynski further discloses: determining a minimization value based on distances between a boundary of the allowed region and the 3D meshes corresponding to the unknown fragments (Sabczynski: 0049: “For example, in some embodiments, the at least one parameter can define an upper limit on the extent to which the position of a portion of the model is deformable (or adjustable). Alternatively or in addition, the at least one parameter can define an upper limit on the extent to which the position of a portion of the model is deformable (or adjustable) with respect to another portion of the model. Alternatively or in addition, the at least one parameter can define one or more directions in which the model is deformable ( or adjustable). Alternatively or in addition, the at least one parameter can define one or more dimensions within which the model is deformable (or adjustable). In embodiments where the at least one parameter defines an upper limit on the extent, the extent to which the position of a portion of the model is deformable ( or adjustable) may be in a certain direction and/or dimension.”; Wherein the parameter defining the upper limit of deformation for a portion of the unbroken model to a fragment constitutes the minimization value based on distances between the allowed region and the unknown fragments.).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate the deformation upper limit parameter further taught by Sabczynski into the method of pre-morbid shape estimation disclosed by Poltaretskyi in view of Sabczynski. The suggestion/motivation for doing so would have been “The at least one parameter thus ensures that any deformation (or adjustment) that is made to the acquired model in the fitting process, which will be described later, is consistent with the one or more corresponding unbroken bones and is thus reasonable” (Sabczynski: 0051). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Poltaretskyi in view of Sabczynski with the further teaching of Sabczynski to obtain the invention as specified in claim 10.
Regarding claim 11, Poltaretskyi in view of Sabczynski discloses: The method of claim 8.
Poltaretskyi in view of Sabczynski does not disclose expressly: further comprising: determining a minimization value based on a percentage of the allowed region covered by the 3D meshes corresponding to the unknown fragments.
Sabczynski further discloses: determining a minimization value based on a percentage of the allowed region covered by the 3D meshes corresponding to the unknown fragments (Sabczynski: 0055: “In some embodiments, the fitting of portions of the model of the unbroken bone to corresponding fragments of the broken bone based on the at least one parameter at block 206 of FIG. 2 can comprise adjusting the model according to the at least one parameter to fit the portions of the model of the unbroken bone to the corresponding fragments of the broken bone in the image. For example, in some embodiments, for one or more of the portions of the model of the unbroken bone, the fitting process may comprise fitting a plurality of different deformations of the portion of the model (which, for example, correspond to different bone shapes) to the corresponding fragment of the broken bone in the image, determining which deformation (for example, which bone shape) provides the optimal fit for the portion of the model to the corresponding fragment of the broken bone in the image, and selecting the optimally fitting deformed model for use in the rest of the method. In this way, a deformed version of the model is used that most accurately reflects the real shape of the bone of the subject and therefore the anatomical alignment of the fragments is improved.”;
Wherein the optimization of the model deformation, such that the fragments are optimally fit, constitutes a minimization value based on a percentage of the allowed region covered by the unknown fragments).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate the known technique of optimizing the deformation, such that model fits the fragments, further taught by Sabczynski into the method of pre-morbid shape estimation disclosed by Poltaretskyi in view of Sabczynski. The suggestion/motivation for doing so would have been “a deformed version of the model is used that most accurately reflects the real shape of the bone of the subject and therefore the anatomical alignment of the fragments is improved.” (Sabczynski: 0055). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Poltaretskyi in view of Sabczynski with the further teaching of Sabczynski to obtain the invention as specified in claim 11.
Regarding claim 12, Poltaretskyi in view of Sabczynski discloses: The method of claim 11.
Poltaretskyi in view of Sabczynski does not disclose expressly: further comprising: determining the minimization value further based on distances between respective 3D meshes corresponding to the unknown fragments.
Sabczynski further discloses: further comprising: determining the minimization value further based on distances between respective 3D meshes corresponding to the unknown fragments (Sabczynski: 0049: “in some embodiments, the at least one parameter can define an upper limit on the extent to which the position of a portion of the model is deformable (or adjustable). Alternatively or in addition, the at least one parameter can define an upper limit on the extent to which the position of a portion of the model is deformable (or adjustable) with respect to another portion of the model.”;
0054: “In some embodiments, the fitting of portions of the model of the unbroken bone to corresponding fragments of the broken bone at block 206 can comprise dividing ( or segmenting) the model of the unbroken bone into the portions that correspond to the fragments of the broken bone in the image. In some embodiments, this may be performed by fitting a portion of the model of the unbroken bone to a corresponding fragment of the broken bone in the image and then dividing ( or segmenting) the model at the point ( or points) at which the fragment is broken from at least one other fragment. This leaves the portion of the model of the unbroken bone fitted to the corresponding fragment of the broken bone and one or more other portions of the model of the unbroken bone, which can then be fitted to the other fragments of the bone. Thus, for one or more of the fragments of the broken bone, a surface of the fragment that has broken from (for example, broken away from) at least one other fragment may be identified and the model of the unbroken bone may be divided ( or segmented) along a corresponding surface in the model.”;
Wherein the parameter defining the upper limit of deformation for a portion of the unbroken model to a fragment with respect to another portion of the model constitutes the minimization value based on distances between the unknown fragments.).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate the deformation upper limit parameter further taught by Sabczynski into the method of pre-morbid shape estimation disclosed by Poltaretskyi in view of Sabczynski. The suggestion/motivation for doing so would have been “The at least one parameter thus ensures that any deformation (or adjustment) that is made to the acquired model in the fitting process, which will be described later, is consistent with the one or more corresponding unbroken bones and is thus reasonable” (Sabczynski: 0051). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Poltaretskyi in view of Sabczynski with the further teaching of Sabczynski to obtain the invention as specified in claim 12.
Regarding claim 13, Poltaretskyi in view of Sabczynski discloses: The method of claim 8, further comprising: performing multiple transformations on the 3D meshes corresponding to the unknown fragments (Sabczynski: 0011: “Therefore, according to a first aspect, there is provided a computer-implemented method for determining a transformation for anatomically aligning fragments of a broken bone. The method comprises acquiring an image of a broken bone of a subject, wherein the bone is broken into two or more fragments. The method also comprises acquiring a model of a corresponding unbroken bone and at least one parameter defining one or more deformations to the model that are permitted when fitting portions of the model of the unbroken bone to corresponding fragments of the broken bone…and determining a transformation that anatomically aligns the fragments of the broken bone with the corresponding portions of the model.”;
0063: “a transformation that anatomically aligns the fragments of the broken bone with the corresponding portions of the model is determined...The transformation that is determined to anatomically align the fragments of the broken bone with the corresponding portions of the model may comprise a translation of the fragments of the broken bone, a rotation of the of the broken bone, or a combination of a translation of the fragments of the broken bone and a rotation of the of the broken bone. The transformation may thus be any affine transformation, which can include any one or more of translation, rotation, scaling, shearing, or any other affine transformation, or any combination of affine transformations.”).
Poltaretskyi in view of Sabczynski does not disclose expressly: determining, for each of the multiple transformations, a minimization value based on one or more of a percentage of the allowed regions covered by the 3D meshes corresponding to the unknown fragments, distances between a boundary of the allowed region and the 3D meshes corresponding to the unknown fragments, or distances between respective 3D meshes corresponding to the unknown fragments; and selecting a fragment reduction based on the minimization values for the multiple transformations.
Sabczynski further discloses: determining a minimization value based on distances between a boundary of the allowed region and the 3D meshes corresponding to the unknown fragments (Sabczynski: 0049: “For example, in some embodiments, the at least one parameter can define an upper limit on the extent to which the position of a portion of the model is deformable (or adjustable). Alternatively or in addition, the at least one parameter can define an upper limit on the extent to which the position of a portion of the model is deformable (or adjustable) with respect to another portion of the model. Alternatively or in addition, the at least one parameter can define one or more directions in which the model is deformable ( or adjustable). Alternatively or in addition, the at least one parameter can define one or more dimensions within which the model is deformable (or adjustable). In embodiments where the at least one parameter defines an upper limit on the extent, the extent to which the position of a portion of the model is deformable ( or adjustable) may be in a certain direction and/or dimension.”; Wherein the parameter defining the upper limit of deformation for a portion of the unbroken model to a fragment constitutes the minimization value based on distances between the allowed region and the unknown fragments.); and selecting a fragment reduction based on the minimization values for the multiple transformations (Sabczynski: 0040-0041: “FIG. 2 illustrates a computer-implemented method 200 for determining a transformation for anatomically aligning fragments of a broken bone...The method also comprises fitting portions of the model of the unbroken bone to corresponding fragments of the broken bone based on the at least one parameter ( at block 206 of FIG. 2) and determining a transformation that anatomically aligns the fragments of the broken bone with the corresponding portions of the model (at block 208 of FIG. 2).”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate the deformation upper limit parameter further taught by Sabczynski into the method of pre-morbid shape estimation disclosed by Poltaretskyi in view of Sabczynski. The suggestion/motivation for doing so would have been “The at least one parameter thus ensures that any deformation (or adjustment) that is made to the acquired model in the fitting process, which will be described later, is consistent with the one or more corresponding unbroken bones and is thus reasonable” (Sabczynski: 0051). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Poltaretskyi in view of Sabczynski with the further teaching of Sabczynski to obtain the invention as specified in claim 13.
Regarding claim 14, Poltaretskyi discloses: The method of claim 1.
Poltaretskyi does not disclose expressly: further comprising: determining a number of unknown fragments present in the image data, wherein the unknown fragments are represented by 3D meshes that are not the 3D mesh that corresponds to the diaphysis or a 3D mesh that corresponds to a humeral head.
Thus, Poltaretskyi does not disclose expressly the identification of 3d meshes corresponding to unknown fragments, wherein the 3D meshes corresponding to the unknown fragments are determined as 3D meshes separate from the segments corresponding to the diaphysis and the humeral head.
Sabczynski discloses: identifying, in the 3D model, 3D meshes corresponding to unknown fragments, wherein the 3D meshes corresponding to the unknown fragments comprise 3D meshes (Sabczynski: 0012: “In particular, a 3D image of a broken bone of a subject may be acquired. The model may be a 3D mesh model. The model may be divided into portions that correspond to the fragments of the broken bone. The portions may be fitted to corresponding fragments of the broken bone in the 3D image… a transformation may be determined that anatomically aligns the fragments of the broken bone with the corresponding portions of the undivided model of the unbroken bone.”) that are not the 3D mesh that corresponds to undeformed segments of the unbroken bone (Sabczynski: 0045: “Generally, the acquired model comprises an indication of the shape of the broken bone in its unbroken form. For example, the acquired model may be shaped according to a generic shape of the unbroken bone…For example, the model may be shaped according to an average (or mean) shape of one or more corresponding unbroken bones of other subjects.”;
0047: “In some embodiments, the acquired model of the corresponding unbroken bone can comprise a mesh. Thus, in these embodiments, the acquired mesh may be a mesh that is derived from one or more corresponding unbroken bones of other subjects, medical literature, medical research, and/or a drawing by a medical professional. The mesh can comprise a plurality of segments.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the algorithms for reconstructing a broken bone based on a model of an unbroken bone taught by Sabczynski by utilizing the SSM and TD models disclosed by Poltaretskyi to reconstruct unaligned TD model fragments based on the unbroken SSM model. The suggestion/motivation for doing so would have been “deformations of the model according to the at least one parameter are allowed when the corresponding portions of the model of the unbroken bone are fitted to the fragments of the broken bone. This can ensure that the model that is used to determine the transformation accurately reflects (for example, the shape) of the bone in the image. This provides a more accurate fit, compared to assuming a fixed model, and thus the determined transformation that anatomically aligns the fragments of broken bone is more reliable.” (Sabczynski: 0022). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Poltaretskyi with Sabczynski to obtain the invention as specified in claim 14.
Regarding claim 24, Poltaretskyi discloses: The method of claim 23.
Poltaretskyi does not disclose expressly: wherein the composite image further shows a visual representation of one or more unknown fragments, wherein the one or more unknown fragments correspond to 3D meshes that are not the 3D mesh that corresponds to the diaphysis or a 3D mesh that corresponds to a humeral head.
Sabczynski discloses: a visual representation of one or more unknown fragments, wherein the one or more unknown fragments correspond to 3D meshes that are not the 3D mesh that correspond to undeformed segments of the unbroken bone (Sabczynski: 0045: “Generally, the acquired model comprises an indication of the shape of the broken bone in its unbroken form. For example, the acquired model may be shaped according to a generic shape of the unbroken bone…For example, the model may be shaped according to an average (or mean) shape of one or more corresponding unbroken bones of other subjects.”;
0047: “In some embodiments, the acquired model of the corresponding unbroken bone can comprise a mesh. Thus, in these embodiments, the acquired mesh may be a mesh that is derived from one or more corresponding unbroken bones of other subjects, medical literature, medical research, and/or a drawing by a medical professional. The mesh can comprise a plurality of segments.”;
0071: “the method may further comprise outputting the determined transformation. More specifically, the processor 102 of the apparatus 100 may output the determined transformation. For example, in some embodiments, the processor 102 may control a user interface 104 to output ( or render, display, or provide) the determined transformation that anatomically aligns the fragments of the broken bone with the corresponding portions of the model and/or may control a memory 106 to store the determined transformation that anatomically aligns the fragments of the broken bone with the corresponding portions of the model. In some embodiments, the processor 102 may control a user interface 104 to output ( or render, display, or provide) a virtual reconstruction (or reassembly) of the broken bone that shows the determined transformation being used to rearrange ( or re-assemble) the fragments of the broken bone to arrive at a healthy bone in unbroken form. In this way, the determined transformation is provided in an accessible form such that it can be used to plan or guide a medical procedure (such as surgery) to realign the broken bone into an unbroken form.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the algorithms for reconstructing and displaying a broken bone based on a model of an unbroken bone taught by Sabczynski by utilizing the SSM and TD models disclosed by Poltaretskyi to reconstruct unaligned TD model fragments based on the unbroken SSM model. The suggestion/motivation for doing so would have been “deformations of the model according to the at least one parameter are allowed when the corresponding portions of the model of the unbroken bone are fitted to the fragments of the broken bone. This can ensure that the model that is used to determine the transformation accurately reflects (for example, the shape) of the bone in the image. This provides a more accurate fit, compared to assuming a fixed model, and thus the determined transformation that anatomically aligns the fragments of broken bone is more reliable.” (Sabczynski: 0022). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Poltaretskyi with Sabczynski to obtain the invention as specified in claim 24.
Regarding claim 25, Poltaretskyi discloses: The method of claim 23.
Poltaretskyi does not disclose expressly: wherein the composite image further shows an
annotation identifying a position to move one or more unknown fragments, wherein the one or more unknown fragments correspond to 3D meshes that are not the 3D mesh that corresponds to the diaphysis or a 3D mesh that corresponds to a humeral head.
Sabczynski discloses: the outputting of a visual representation identifying a position to move one or more unknown fragments (Sabczynski: 0071: “the processor 102 of the apparatus 100 may output the determined transformation. For example, in some embodiments, the processor 102 may control a user interface 104 to output ( or render, display, or provide) the determined transformation that anatomically aligns the fragments of the broken bone with the corresponding portions of the model and/or may control a memory 106 to store the determined transformation that anatomically aligns the fragments of the broken bone with the corresponding portions of the model.”), wherein the one or more unknown fragments correspond to 3D meshes that are not the 3D mesh that corresponds to undeformed segments of the unbroken bone (Sabczynski: 0045: “Generally, the acquired model comprises an indication of the shape of the broken bone in its unbroken form. For example, the acquired model may be shaped according to a generic shape of the unbroken bone…For example, the model may be shaped according to an average (or mean) shape of one or more corresponding unbroken bones of other subjects.”;
0047: “In some embodiments, the acquired model of the corresponding unbroken bone can comprise a mesh. Thus, in these embodiments, the acquired mesh may be a mesh that is derived from one or more corresponding unbroken bones of other subjects, medical literature, medical research, and/or a drawing by a medical professional. The mesh can comprise a plurality of segments.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the algorithms for reconstructing and displaying a broken bone based on a model of an unbroken bone taught by Sabczynski by utilizing the SSM and TD models disclosed by Poltaretskyi to reconstruct unaligned TD model fragments based on the unbroken SSM model. The suggestion/motivation for doing so would have been “deformations of the model according to the at least one parameter are allowed when the corresponding portions of the model of the unbroken bone are fitted to the fragments of the broken bone. This can ensure that the model that is used to determine the transformation accurately reflects (for example, the shape) of the bone in the image. This provides a more accurate fit, compared to assuming a fixed model, and thus the determined transformation that anatomically aligns the fragments of broken bone is more reliable.” (Sabczynski: 0022). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Poltaretskyi with Sabczynski to obtain the invention as specified in claim 25.
As per claim(s) 37, arguments made in rejecting claim(s) 7 are analogous.
As per claim(s) 38, arguments made in rejecting claim(s) 8 are analogous.
As per claim(s) 39, arguments made in rejecting claim(s) 9 are analogous.
As per claim(s) 40, arguments made in rejecting claim(s) 10 are analogous.
As per claim(s) 41, arguments made in rejecting claim(s) 11 are analogous.
As per claim(s) 42, arguments made in rejecting claim(s) 12 are analogous.
As per claim(s) 43, arguments made in rejecting claim(s) 13 are analogous.
As per claim(s) 44, arguments made in rejecting claim(s) 14 are analogous.
As per claim(s) 54, arguments made in rejecting claim(s) 24 are analogous.
As per claim(s) 55, arguments made in rejecting claim(s) 25 are analogous.
Conclusion
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/ANTHONY J RODRIGUEZ/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672