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 § 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 12-20 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 pre-AIA the applicant regards as the invention.
Claim 12 recites the "computer system comprising: a memory; and a processor in communication with the memory, wherein the computer system is configured to perform a method for modeling patient anatomy in a current position and pose, including registering bone that is obscured by soft tissue, the method comprising: generating …”. This recitation is unclear because it is unclear whether the system is being claimed or the method. For examination purposes, Examiner of record takes this to be the “computer system for modeling patient anatomy in a current position and pose, including registering bone that is obscured by soft tissue, comprising: a memory; and a processor in communication with the memory storing instructions that, when executed by the processor, cause the computer system to: generate …”.
Claims dependent upon the rejected claims above, but not directly addressed, are also rejected because they inherit the indefiniteness of the claim(s) they respectively depend upon.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5-6, 9, 12-13, 15-16, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Amanatullah (US 20190231433), hereinafter Amanatullah.
Regarding claim 1, Amanatullah teaches a computer-implemented method (Figs. 1A-B and 2-5) for modeling patient anatomy in a current position and pose (“three 1″-diameter steel spheres can be placed at different (X, Y, Z) positions around a patient's left knee when the patient's left knee is imaged in an MRI scanner. When analyzing an MRI scan to generate a surgical plan, the computer system can interpolate real dimensions of the patient's tissues (e.g., general and feature-specific length, width, depth of the tibia, femur, ...)… The computer system can label regions of patient tissues with these dimensions and/or can scale or modify the virtual patient model into alignment with these dimensions extracted from the patient scan data.” [0036]; Figs. 1A-B and 2-5), including registering bone that is obscured by soft tissue (“preserving registration of the virtual patient model—such as including virtual representations of an unresected hard tissue of interest of the patient” [0023]; “in FIGS. 1A and 3, the computer system coarsely registers the virtual patient model to the hard tissue of interest in the surgical field based on patient features detected in optical scans prior to the surgeon incising the patient near the hard tissue of interest and/or prior to exposure of the hard tissue of interest.” [0057]; “map the virtual patient model to real features of the patient's body—detected in optical scan data (e.g., 2D or 3D color images) recorded by an optical sensor facing the surgical field—in order to anticipate locations, dimensions, and contours, etc. of both visible and obscured anatomical features (e.g., a patella, a tibial head, or other sub-dermal tissues) of the patient.” [0059]), the method comprising:
generating a point cloud of the patient anatomy (S120) (“combine the first set of three-dimensional color point clouds into a composite three-dimensional color point cloud depicting hard tissue and soft tissue of the patient in Block S120. Based on the composite three-dimensional color point cloud, the computer system can then detect the hard tissue of interest in Block S132” [0046]; Fig. 1A);
segmenting the point cloud to identify visible bone surface regions of the patient anatomy and visible soft tissue surface regions of the patient anatomy (“extract three-dimensional (“3D”) anatomical features representing this bone surface” [0014]; “point cloud depicting hard tissue and soft tissue of the patient” [0046]; “in order to anticipate locations, dimensions, and contours, etc. of … visible … anatomical features” [0059]; “extract a 3D surface profile of the exposed, unresected hard tissue of interest from optical scans of the surgical field;” [0079]; “these hard tissue of interest data are referenced to soft tissue features” [0094] Fig. 1A);
registering a reference bone model (“the virtual patient model defining … a generic unresected contour of the hard tissue of interest and a virtual representation of a generic target resected contour of the hard tissue of interest;” Claim 5) for the patient to the identified visible bone surface regions (“registering virtual hard tissue features defined in the virtual patient model to the unresected contour of the hard tissue of interest; and detecting the set of intermediate features” Claim 2), wherein the registering provides an initial patient anatomy model (“the virtual patient model” [0058]) having an estimation of a pose of bone portions of the patient anatomy (“predicts a region of the surgical field occupied by the hard tissue of interest based on the orientation of the patient and a human anatomy model.” [0058]; “morphing the virtual representation of the generic unresected contour of the hard tissue of interest in the virtual patient model into conformity with the unresected contour of the hard tissue of interest detected in the initial sequence of optical scans; and morphing the virtual representation of the generic resected contour of the hard tissue of interest in the virtual patient model into conformity with the virtual representation of the generic unresected contour of the hard tissue of interest in the virtual patient model” claim 5);
augmenting the initial patient anatomy model with soft tissue bodies, each comprising a respective volume and respective surface, based on the estimation of the pose of the bone portions of the patient anatomy (“Upon detecting soft tissue features in this region of the surgical field, the computer system can then coarsely register the virtual patient model to these soft tissue features, such as including orienting the virtual patient model based on the detected orientation of the patient (e.g., to set the longitudinal axis of the virtual patient model parallel to the longitudinal axis of the patient's torso).” [0058]), wherein the augmenting provides a full patient anatomy model having (i) the soft tissue bodies representing soft tissue portions of the patient anatomy and (ii) elements representing the bone portions of the patient anatomy (“dimensionally-accurate contour, surface, and/or volumetric anatomical hard tissue and soft tissue features of the patient's left leg” [0059]); and
registering the full patient anatomy model to the segmented point cloud such that the full patient anatomy model accurately reflects a current position and pose of the patient anatomy (“registering the virtual patient model to the patient based on the constellation of intermediate features…the computer system can implement the virtual patient model as the virtual “ground truth” representation of the patient's anatomy—registered to other hard and/or soft tissue features”. [0018]; “the computer system can map the virtual patient model to real features of the patient's body—detected in optical scan data (e.g., 2D or 3D color images) recorded by an optical sensor facing the surgical field—in order to anticipate locations, dimensions, and contours, etc. of both visible and obscured anatomical features” [0059]).
Regarding claim 2, Amanatullah teaches the method of claim 1, wherein the segmenting also identifies anatomical landmarks (“registered to other hard and/or soft tissue features” [0018]; “For example, in Block S136, the computer system can aggregate a set of intermediate features that includes a constellation of visible skin features on the patient proximal the hard tissue of interest, such as: moles; freckles; bruises; veins; or notes or fiducials applied by medical staff with an ink marker.” [0088]).
Regarding claim 5, Amanatullah teaches the method of claim 1, wherein the augmenting provides the soft tissue bodies such that articulation of femur cartilage (“the patient's cartilage” [0039]) grossly matches an articulating distal surface of femur bone (“a femoral condyle” [0094]) represented in the full patient anatomy model, and a flat surface of tibia cartilage (“the patient's cartilage” [0039]) grossly matches a flat surface of a proximal surface of tibia bone (“a tibial plateau” [0094]) represented in the full patient anatomy model (“a dimensionally-accurate contour, surface, and/or volumetric anatomical hard tissue and soft tissue features of the patient's left leg.” [0033]; “the patient's cartilage structure around the hard tissue of interest;” [0039]) (“the computer system can generate a virtual patient model depicting the patient's left femur and left tibia prior to a left knee replacement.” [0014]; “a femur and a tibia … the computer system can generate a virtual scale representation of the patient's left leg, such as in the form of a virtual patient model that includes a dimensionally-accurate contour, surface, and/or volumetric anatomical hard tissue and soft tissue features of the patient's left leg.” [0033]; “the virtual patient model can include: a first layer containing a 3D representation of the patient's bone structure around the hard tissue of interest; a second layer containing a 3D representation of the patient's cartilage structure around the hard tissue of interest;” [0039]; “inject hard tissue of interest data—depicting the location, orientation, and geometry of a bone surface (e.g., a femoral condyle, a tibial plateau) detected in the fourth image—into the 3D field representation of the patient such that these hard tissue of interest data are referenced to soft tissue features (and/or vice versa) in the 3D field representation of the patient.” [0094]).
Regarding claim 6, Amanatullah teaches the method of claim 1, wherein the augmenting comprises deforming elements of the full patient anatomy model, the elements comprising at least the soft tissue bodies (“deform the constellation of soft tissue features (e.g., visible skin features)—in the set of intermediate features—according to a soft tissue gravity model based on the orientation of the hard tissue of interest relative to gravity” [0107]; “the computer system can implement a fixed gravity-based soft tissue deformation model. Alternatively, the computer system can generate a custom soft tissue deformation model to predict deformation of soft tissue around the hard tissue of interest as a function of position and orientation relative to gravity,” [0109]).
Regarding claim 9, Amanatullah teaches the method of claim 6, wherein the deforming emphasizes deforming the soft tissue bodies over deforming the elements representing the bone portions of the patient anatomy (“the computer system can generate a custom soft tissue deformation model to predict deformation of soft tissue around the hard tissue of interest as a function of position and orientation relative to gravity, such as based on changes in 3D skin surface geometry of the patient's soft tissue detected in a sequence of optical scans recorded by the optical sensor before, during, and after incision of the knee and prior to resection of the hard tissue of interest.” [0109]).
Regarding claim 12, Amanatullah teaches computer system (“The computer system” [0036]; Fig. 4) comprising:
a memory (a memory of a computer seen in S110 Fig. 4); and
a processor (S110) (Fig. 4) in communication with the memory, wherein the computer system is configured to perform a method (Figs. 1A-B and 2-5) for modeling patient anatomy in a current position and pose (“three 1″-diameter steel spheres can be placed at different (X, Y, Z) positions around a patient's left knee when the patient's left knee is imaged in an MRI scanner. When analyzing an MRI scan to generate a surgical plan, the computer system can interpolate real dimensions of the patient's tissues (e.g., general and feature-specific length, width, depth of the tibia, femur, ...)… The computer system can label regions of patient tissues with these dimensions and/or can scale or modify the virtual patient model into alignment with these dimensions extracted from the patient scan data.” [0036]; Figs. 1A-B and 2-5), including registering bone that is obscured by soft tissue (“preserving registration of the virtual patient model—such as including virtual representations of an unresected hard tissue of interest of the patient” [0023]; “in FIGS. 1A and 3, the computer system coarsely registers the virtual patient model to the hard tissue of interest in the surgical field based on patient features detected in optical scans prior to the surgeon incising the patient near the hard tissue of interest and/or prior to exposure of the hard tissue of interest.” [0057]; “map the virtual patient model to real features of the patient's body—detected in optical scan data (e.g., 2D or 3D color images) recorded by an optical sensor facing the surgical field—in order to anticipate locations, dimensions, and contours, etc. of both visible and obscured anatomical features (e.g., a patella, a tibial head, or other sub-dermal tissues) of the patient.” [0059]), the method comprising:
generating a point cloud of the patient anatomy (S120) (“combine the first set of three-dimensional color point clouds into a composite three-dimensional color point cloud depicting hard tissue and soft tissue of the patient in Block S120. Based on the composite three-dimensional color point cloud, the computer system can then detect the hard tissue of interest in Block S132” [0046]; Fig. 1A);
segmenting the point cloud to identify visible bone surface regions of the patient anatomy and visible soft tissue surface regions of the patient anatomy (“extract three-dimensional (“3D”) anatomical features representing this bone surface” [0014]; “point cloud depicting hard tissue and soft tissue of the patient” [0046]; “in order to anticipate locations, dimensions, and contours, etc. of … visible … anatomical features” [0059]; “extract a 3D surface profile of the exposed, unresected hard tissue of interest from optical scans of the surgical field;” [0079]; “these hard tissue of interest data are referenced to soft tissue features” [0094] Fig. 1A);
registering a reference bone model (“the virtual patient model defining … a generic unresected contour of the hard tissue of interest and a virtual representation of a generic target resected contour of the hard tissue of interest;” Claim 5) for the patient to the identified visible bone surface regions (“registering virtual hard tissue features defined in the virtual patient model to the unresected contour of the hard tissue of interest; and detecting the set of intermediate features” Claim 2), wherein the registering provides an initial patient anatomy model (“the virtual patient model” [0058]) having an estimation of a pose of bone portions of the patient anatomy (“predicts a region of the surgical field occupied by the hard tissue of interest based on the orientation of the patient and a human anatomy model.” [0058]; “morphing the virtual representation of the generic unresected contour of the hard tissue of interest in the virtual patient model into conformity with the unresected contour of the hard tissue of interest detected in the initial sequence of optical scans; and morphing the virtual representation of the generic resected contour of the hard tissue of interest in the virtual patient model into conformity with the virtual representation of the generic unresected contour of the hard tissue of interest in the virtual patient model” claim 5);
augmenting the initial patient anatomy model with soft tissue bodies, each comprising a respective volume and respective surface, based on the estimation of the pose of the bone portions of the patient anatomy (“Upon detecting soft tissue features in this region of the surgical field, the computer system can then coarsely register the virtual patient model to these soft tissue features, such as including orienting the virtual patient model based on the detected orientation of the patient (e.g., to set the longitudinal axis of the virtual patient model parallel to the longitudinal axis of the patient's torso).” [0058]), wherein the augmenting provides a full patient anatomy model having (i) the soft tissue bodies representing soft tissue portions of the patient anatomy and (ii) elements representing the bone portions of the patient anatomy (“dimensionally-accurate contour, surface, and/or volumetric anatomical hard tissue and soft tissue features of the patient's left leg” [0059]); and
registering the full patient anatomy model to the segmented point cloud such that the full patient anatomy model accurately reflects a current position and pose of the patient anatomy (“registering the virtual patient model to the patient based on the constellation of intermediate features…the computer system can implement the virtual patient model as the virtual “ground truth” representation of the patient's anatomy—registered to other hard and/or soft tissue features”. [0018]; “the computer system can map the virtual patient model to real features of the patient's body—detected in optical scan data (e.g., 2D or 3D color images) recorded by an optical sensor facing the surgical field—in order to anticipate locations, dimensions, and contours, etc. of both visible and obscured anatomical features” [0059]).
Regarding claim 13, Amanatullah teaches the computer system of claim 12, wherein the segmenting also identifies anatomical landmarks (“registered to other hard and/or soft tissue features” [0018]; “For example, in Block S136, the computer system can aggregate a set of intermediate features that includes a constellation of visible skin features on the patient proximal the hard tissue of interest, such as: moles; freckles; bruises; veins; or notes or fiducials applied by medical staff with an ink marker.” [0088]).
Regarding claim 15, Amanatullah teaches the computer system of claim 12, wherein the augmenting provides the soft tissue bodies such that articulation of femur cartilage (“the patient's cartilage” [0039]) grossly matches an articulating distal surface of femur bone (“a femoral condyle” [0094]) represented in the full patient anatomy model, and a flat surface of tibia cartilage (“the patient's cartilage” [0039]) grossly matches a flat surface of a proximal surface of tibia bone (“a tibial plateau” [0094]) represented in the full patient anatomy model (“a dimensionally-accurate contour, surface, and/or volumetric anatomical hard tissue and soft tissue features of the patient's left leg.” [0033]; “the patient's cartilage structure around the hard tissue of interest;” [0039]) (“the computer system can generate a virtual patient model depicting the patient's left femur and left tibia prior to a left knee replacement.” [0014]; “a femur and a tibia … the computer system can generate a virtual scale representation of the patient's left leg, such as in the form of a virtual patient model that includes a dimensionally-accurate contour, surface, and/or volumetric anatomical hard tissue and soft tissue features of the patient's left leg.” [0033]; “the virtual patient model can include: a first layer containing a 3D representation of the patient's bone structure around the hard tissue of interest; a second layer containing a 3D representation of the patient's cartilage structure around the hard tissue of interest;” [0039]; “inject hard tissue of interest data—depicting the location, orientation, and geometry of a bone surface (e.g., a femoral condyle, a tibial plateau) detected in the fourth image—into the 3D field representation of the patient such that these hard tissue of interest data are referenced to soft tissue features (and/or vice versa) in the 3D field representation of the patient.” [0094]).
Regarding claim 16, Amanatullah teaches the computer system of claim 12, wherein the augmenting comprises deforming elements of the full patient anatomy model, the elements comprising at least the soft tissue bodies (“deform the constellation of soft tissue features (e.g., visible skin features)—in the set of intermediate features—according to a soft tissue gravity model based on the orientation of the hard tissue of interest relative to gravity” [0107]; “the computer system can implement a fixed gravity-based soft tissue deformation model. Alternatively, the computer system can generate a custom soft tissue deformation model to predict deformation of soft tissue around the hard tissue of interest as a function of position and orientation relative to gravity,” [0109]).
Regarding claim 18, Amanatullah teaches the computer system of claim 17, wherein the deforming emphasizes deforming the soft tissue bodies over deforming the elements representing the bone portions of the patient anatomy (“the computer system can generate a custom soft tissue deformation model to predict deformation of soft tissue around the hard tissue of interest as a function of position and orientation relative to gravity, such as based on changes in 3D skin surface geometry of the patient's soft tissue detected in a sequence of optical scans recorded by the optical sensor before, during, and after incision of the knee and prior to resection of the hard tissue of interest.” [0109]).
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 3 is rejected under 35 U.S.C. 103 as being unpatentable over Amanatullah as applied to claim 2, and further in view of Mahfouz (US 20120265496), hereinafter, Mahfouz.
Regarding claim 3, Amanatullah teaches the method of claim 2.
While teaching articulating cartilage surfaces (“the virtual patient model can include: … a second layer containing a 3D representation of the patient's cartilage structure around the hard tissue of interest;” [0039]), Amanatullah does not teach that the landmarks comprise most distal points of articulating cartilage surfaces.
However, in the orthopaedic devices field of endeavor, Mahfouz discloses orthopaedic implants and custom cutting jigs, which is analogous art. Mahfouz teaches that the landmarks comprise most distal points of articulating cartilage surfaces (“the medial and lateral profiles 410, 412 are generated with respect to a plane having within it three points: … the most distal point … of each condyle with cartilage in place.” [0230]).
Therefore, based on Mahfouz’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Amanatullah to employ the landmarks that comprise most distal points of articulating cartilage surfaces, as taught by Mahfouz, in order to improve fabrication of customized orthopaedic implants.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Amanatullah as applied to claims 1 and 12, and further in view of Sofka et al (US 20140093153), hereinafter, Sofka.
Regarding claim 4, Amanatullah teaches the method of claim 1.
Amanatullah does not teach receiving user-provided indications of one or more of soft tissue or bone regions, wherein the segmenting uses the user-provided indications.
However, in the orthopaedic devices and systems field of endeavor, Sofka discloses a method and system for bone segmentation and landmark detection for joint replacement surgery, which is analogous art. Sofka teaches receiving user-provided indications of one or more of soft tissue or bone regions, wherein the segmenting uses the user-provided indications (“At step 512, the bone segmentation is refined. In particular, an interactive refinement method is performed to refine the bone segmentation based on user inputs. To interactively correct any segmentation errors, a user, via the user interface, can place marks (referred to herein as seeds) inside or outside the bone structure of interest by using brushes or strokes to indicate a few pixels belonging to the foreground or background of the target bone structure.” [0041]).
Therefore, based on Sofka’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Amanatullah to receive user-provided indications of one or more of soft tissue or bone regions, wherein the segmenting uses the user-provided indications, as taught by Sofka, in order to improve fabrication of customized orthopaedic implants.
Regarding claim 14, Amanatullah teaches the computer system of claim 12.
Amanatullah does not teach receiving user-provided indications of one or more of soft tissue or bone regions, wherein the segmenting uses the user-provided indications.
However, in the orthopaedic devices and systems field of endeavor, Sofka discloses a method and system for bone segmentation and landmark detection for joint replacement surgery, which is analogous art. Sofka teaches receiving user-provided indications of one or more of soft tissue or bone regions, wherein the segmenting uses the user-provided indications (“At step 512, the bone segmentation is refined. In particular, an interactive refinement method is performed to refine the bone segmentation based on user inputs. To interactively correct any segmentation errors, a user, via the user interface, can place marks (referred to herein as seeds) inside or outside the bone structure of interest by using brushes or strokes to indicate a few pixels belonging to the foreground or background of the target bone structure.” [0041]).
Therefore, based on Sofka’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Amanatullah to receive user-provided indications of one or more of soft tissue or bone regions, wherein the segmenting uses the user-provided indications, as taught by Sofka, in order to improve fabrication of customized orthopaedic implants.
Claims 7-8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Amanatullah as applied to claims 6 and 16, and further in view of Matsumura (JP 5890358), hereinafter, Matsumura.
Regarding claim 7, Amanatullah teaches the method of claim 6.
While teaching soft tissue thickness or depth in one or more soft tissue regions of the patient anatomy (“the computer system can access scan data recorded by a multispectral camera in the operating room and distinguish different hard and soft tissues in the surgical field based on different multispectral signatures of these tissues; the computer system can then project boundaries of different tissues identified in these multispectral data onto a concurrent depth image to isolate and extract 3D geometries of these different hard and soft tissues from the depth image.” [0052]), Amanatullah does not teach that the deforming uses assumed constraints on properties of the soft tissue bodies, including constraints as to one or more of soft tissue thickness or depth in one or more soft tissue regions of the patient anatomy.
However, in the orthopaedic devices and systems field of endeavor, Matsumura discloses a method and system for bone segmentation and landmark detection for joint replacement surgery, which is analogous art. Matsumura teaches that the deforming uses assumed constraints on properties of the soft tissue bodies, including constraints as to one or more of soft tissue thickness or depth in one or more soft tissue regions of the patient anatomy (“As shown in FIG. 5, the condition setting unit 180, based on the tomographic image (B mode image) 40 displayed on the display unit 10, it sets the conditions of the measurement site (shape and boundary conditions). For example, as shown in FIG. 5, the distance of deformable soft tissue Obe by compression (from fat to pectoralis major muscle) (thickness) of about 30 [mm], hardly hard tissue deformation by compression (rib) of a bottom (supporting surface) to support the soft tissue, are constrained (fixed) in the xyz direction.”; 2nd complete para., p. 6).
Therefore, based on Matsumura’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Amanatullah to employ the deforming that uses assumed constraints on properties of the soft tissue bodies, including constraints as to one or more of soft tissue thickness or depth in one or more soft tissue regions of the patient anatomy, as taught by Matsumura, in order to improve outcomes of joint replacement surgeries.
Regarding claim 8, Amanatullah modified by Matsumura teaches the method of claim 7.
Amanatullah does not teach that the assumed constraints are based on prior known anatomical approximation of the soft tissue thickness.
However, in the elastic strain measurements inside a subject field of endeavor, Matsumura discloses ultrasonic image pickup apparatus and ultrasonic image display method, which is analogous art. Matsumura teaches that the assumed constraints are based on prior known anatomical approximation of the soft tissue thickness (“(thickness) of about 30 [mm]”).
Therefore, based on Matsumura’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Amanatullah to employ the assumed constraints that are based on prior known anatomical approximation of the soft tissue thickness, as taught by Matsumura, in order to improve outcomes of joint replacement surgeries.
Regarding claim 17, Amanatullah teaches the computer system of claim 16.
While teaching soft tissue thickness or depth in one or more soft tissue regions of the patient anatomy (“the computer system can access scan data recorded by a multispectral camera in the operating room and distinguish different hard and soft tissues in the surgical field based on different multispectral signatures of these tissues; the computer system can then project boundaries of different tissues identified in these multispectral data onto a concurrent depth image to isolate and extract 3D geometries of these different hard and soft tissues from the depth image.” [0052]), Amanatullah does not teach that the deforming uses assumed constraints on properties of the soft tissue bodies, including constraints as to one or more of soft tissue thickness or depth in one or more soft tissue regions of the patient anatomy.
However, in the orthopaedic devices and systems field of endeavor, Matsumura discloses a method and system for bone segmentation and landmark detection for joint replacement surgery, which is analogous art. Matsumura teaches that the deforming uses assumed constraints on properties of the soft tissue bodies, including constraints as to one or more of soft tissue thickness or depth in one or more soft tissue regions of the patient anatomy (“As shown in FIG. 5, the condition setting unit 180, based on the tomographic image (B mode image) 40 displayed on the display unit 10, it sets the conditions of the measurement site (shape and boundary conditions). For example, as shown in FIG. 5, the distance of deformable soft tissue Obe by compression (from fat to pectoralis major muscle) (thickness) of about 30 [mm], hardly hard tissue deformation by compression (rib) of a bottom (supporting surface) to support the soft tissue, are constrained (fixed) in the xyz direction.”; 2nd complete para., p. 6).
Therefore, based on Matsumura’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Amanatullah to employ the deforming that uses assumed constraints on properties of the soft tissue bodies, including constraints as to one or more of soft tissue thickness or depth in one or more soft tissue regions of the patient anatomy, as taught by Matsumura, in order to improve outcomes of joint replacement surgeries.
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Amanatullah as applied to claims 6 and 16, and further in view of Kaeseberg et al (US20220087750), hereinafter, Kaeseberg.
Regarding claim 10, Amanatullah teaches the method of claim 6.
Amanatullah teaches the method of claim 6, wherein the deforming uses (i) first constraints on deforming the soft tissue bodies anatomy (“the computer system can generate a custom soft tissue deformation model to predict deformation of soft tissue around the hard tissue of interest as a function of position and orientation relative to gravity, such as based on changes in 3D skin surface geometry of the patient's soft tissue detected in a sequence of optical scans recorded by the optical sensor before, during, and after incision of the knee and prior to resection of the hard tissue of interest.” [0109]) and (ii) second constraints on deforming the elements representing the bone portions of the patient anatomy (“apply this deformation to other virtual hard tissue of interest representations in the virtual patient model … the virtual patient model can include multiple layers of representations of various steps of the surgery, as described above; and the computer system can deform each of these layers into alignment with the actual unresected contour of the hard tissue of interest detected in the surgical field.” [0081]).
Amanatullah does not teach that the first constraints and second constraints provided as weights for the deforming.
However, in the registration methods field of endeavor, Kaeseberg discloses a technique for guiding acquisition of one more registration points on a patient's body, which is analogous art. Kaeseberg teaches that the first constraints and second constraints provided as weights for the deforming (“Different acquired registration points may be given different weights when using the acquired registration points for (e.g., determining, updating or improving) the registration. The priority value of the at least one image surface point may be at least one of correlated with, based on, proportional and equal to the weight of the acquired corresponding registration point to be used for the registration.” [0062] “As described herein, the individual surface points in the different regions 101, 103 and 105 have different priorities because the priority values may be determined by weighting the assigned or preliminary priority values with at least one of the first weighting factor (e.g., based on a distance between the skin surface and the bone surface) and the second weighting factor (e.g., based on an elasticity of body tissue underneath the skin or body surface).” [0122]).
Therefore, based on Kaeseberg’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Amanatullah to employ the first constraints and second constraints that are provided as weights for the deforming, as taught by Kaeseberg, in order to update or improve registration by adjusting deformation based on tissue elasticity.
Regarding claim 19, Amanatullah teaches the computer system of claim 16.
Amanatullah teaches the method of claim 6, wherein the deforming uses (i) first constraints on deforming the soft tissue bodies anatomy (“the computer system can generate a custom soft tissue deformation model to predict deformation of soft tissue around the hard tissue of interest as a function of position and orientation relative to gravity, such as based on changes in 3D skin surface geometry of the patient's soft tissue detected in a sequence of optical scans recorded by the optical sensor before, during, and after incision of the knee and prior to resection of the hard tissue of interest.” [0109]) and (ii) second constraints on deforming the elements representing the bone portions of the patient anatomy (“apply this deformation to other virtual hard tissue of interest representations in the virtual patient model … the virtual patient model can include multiple layers of representations of various steps of the surgery, as described above; and the computer system can deform each of these layers into alignment with the actual unresected contour of the hard tissue of interest detected in the surgical field.” [0081]).
Amanatullah does not teach that the first constraints and second constraints provided as weights for the deforming.
However, in the registration methods field of endeavor, Kaeseberg discloses a technique for guiding acquisition of one more registration points on a patient's body, which is analogous art. Kaeseberg teaches that the first constraints and second constraints provided as weights for the deforming (“Different acquired registration points may be given different weights when using the acquired registration points for (e.g., determining, updating or improving) the registration. The priority value of the at least one image surface point may be at least one of correlated with, based on, proportional and equal to the weight of the acquired corresponding registration point to be used for the registration.” [0062] “As described herein, the individual surface points in the different regions 101, 103 and 105 have different priorities because the priority values may be determined by weighting the assigned or preliminary priority values with at least one of the first weighting factor (e.g., based on a distance between the skin surface and the bone surface) and the second weighting factor (e.g., based on an elasticity of body tissue underneath the skin or body surface).” [0122]).
Therefore, based on Kaeseberg’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Amanatullah to employ the first constraints and second constraints that are provided as weights for the deforming, as taught by Kaeseberg, in order to update or improve registration by adjusting deformation based on tissue elasticity.
Claims 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Amanatullah as applied to claims 1 and 12, and further in view of Teuli`ere et al (Using multiple hypothesis in model-based tracking, 2010 IEEE International Conference on Robotics and Automation, Anchorage Convention District; p. 4559 - 4565; May 3-8, 2010), hereinafter, Teuli`ere.
Regarding claim 11, Amanatullah teaches the method of claim 1.
Amanatullah does not teach that the registering the full patient anatomy model to the segmented point cloud uses a multi-hypothesis registration method.
However, in the registration methods field of endeavor, Teuli`ere discloses using multiple hypothesis in model-based tracking, which is analogous art. Teuli`ere teaches that the registering the full patient anatomy model to the segmented point cloud uses a multi-hypothesis registration method (“This paper presents a new approach allowing to retrieve multiple hypothesis on the camera pose from multiple low-level hypothesis. These hypothesis are integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution.” Abstract; “Fig. 1. In classic edge based tracking, the model is projected into the image plane and points are sampled on the projected edges. A search is performed along the normal (top). When multiple strong edges are close in the image, ambiguities can occur when searching along the normal (bottom). Multiple low level hypothesis can be considered” p. 4560).
Therefore, based on Teuli`ere’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Amanatullah to employ the registering the full patient anatomy model to the segmented point cloud that uses a multi-hypothesis registration method, as taught by Teuli`ere, in order to improve model-based registration methods.
Regarding claim 20, Amanatullah teaches the computer system of claim 12.
Amanatullah does not teach that the registering the full patient anatomy model to the segmented point cloud uses a multi-hypothesis registration method.
However, in the registration methods field of endeavor, Teuli`ere discloses using multiple hypothesis in model-based tracking, which is analogous art. Teuli`ere teaches that the registering the full patient anatomy model to the segmented point cloud uses a multi-hypothesis registration method (“This paper presents a new approach allowing to retrieve multiple hypothesis on the camera pose from multiple low-level hypothesis. These hypothesis are integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution.” Abstract; “Fig. 1. In classic edge based tracking, the model is projected into the image plane and points are sampled on the projected edges. A search is performed along the normal (top). When multiple strong edges are close in the image, ambiguities can occur when searching along the normal (bottom). Multiple low level hypothesis can be considered” p. 4560).
Therefore, based on Teuli`ere’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Amanatullah to employ the registering the full patient anatomy model to the segmented point cloud that uses a multi-hypothesis registration method, as taught by Teuli`ere, in order to improve model-based registration methods.
Conclusion
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/ALEXEI BYKHOVSKI/
Primary Examiner, Art Unit 3798
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