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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “possible identification” in claim 1 is a relative term which renders the claim indefinite. The term “possible” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what signifies a possible identification and whether there is a case where the identification can be incorrect.
Claim 2 recites the limitation “creating a depth map” which renders the claim unclear. It is unclear whether a new depth map is being created or if this is the previous depth map mentioned earlier in claim 1.
Claim 3 recites the limitation “creating a depth map” which renders the claim unclear. It is unclear whether a new depth map is being created or if this is the previous depth map mentioned earlier in claim 1.
Claim 9 recites the limitation “identifying critical regions of the implant; wherein the critical regions are given greater importance when calculating the error” which renders the claim unclear. It is unclear what occurs differently when the critical regions are give greater importance. For the purposes of this examination it is interpreted as surface features being iteratively reorientated to minimize an error.
Claim 11 recites the limitation "the predetermined threshold." In Line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation "the predetermined threshold." In Line 2. There is insufficient antecedent basis for this limitation in the claim.
The term “possible identification” in claim 18 is a relative term which renders the claim indefinite. The term “possible” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what signifies a possible identification and whether there is a case where the identification can be incorrect.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims 1-20 recite a method comprising: positioning arrays of reflective markers on the patient; creating a depth map of the surgical site; localizing surfaces of the implant; providing a possible identification of the implant; calculating an error between the depth map and the identification of the implant; and iteratively reorienting the identification of the implant with respect to the depth map to minimize the error; as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but presumable recitation of generic computer components.
That is, other than presumably reciting “a surgical controller”, and “reflective marker”, nothing in the claim element precludes the step from practically being performed in the mind.
For example “creating a depth map of the surgical site; localizing surfaces of the implant” in the context of this claim encompasses the user conducting a data gathering step. The user could manually also “providing a possible identification of the implant” by reviewing the implant with their own eyes as well as “ calculating an error between the depth map and the identification of the implant; and iteratively reorienting the identification of the implant model with respect to the depth map to minimize the error” by comparing the implant to already known manufacturer implants.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the imitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The judicial exception is not integrated into a practical application. In particular, the claim only recites three additional element – using a surgical controller, and a reflective marker to perform the above noted steps. The surgical controller is recited at a high-level of generality (i.e., as a generic controller performing a generic processing function) such that it amounts no more than mere instructions to apply the exception. The reflective markers are also recited at a high level of generality and are not specifically required to be used with the creation of a depth map. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the determining steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
As for the depending claim(s), they are also rejected under 35 USC 101 at least for the similar
reasons noted above as they are directed to abstract ideas and does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claims are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 6, 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Casas (US20210289188A1) in view of Bonny et al (US20210038315A1; hereinafter referred to as Bonny).
Regarding Claim 1, Casas discloses a method for registering a surgical site containing an implant (“ a real-time surgery method and apparatus for displaying a stereoscopic augmented view of a patient from a static or dynamic viewpoint of the surgeon, which employs real-time three-dimensional surface reconstruction for preoperative and intraoperative image registration.” [Abstract], “The preoperative image may be presented, recorded, or registered (referred to hereinafter collectively and individually as “registered”) over the patient, in real time. Thus, the internal anatomical structures of the patient may be blended with the media recorded by the mounted cameras… tracking may be configured to be added to instruments or implants within the preoperative image” [0013-0014]) comprising:
positioning arrays of reflective markers on the patient (“In embodiments, registration is done with the help of optical markers, e.g. color or reflective markers in certain predefined anatomical landmarks of the patient 118 for the 3D scanner system 110, corresponding to markers of the same size and shape placed on the same predefined anatomical landmarks of the patient 118 during the preoperative 102 or intraoperative imaging 106 (e.g. radiopaque markers for CT or x-ray imaging), or virtual markers placed on the predefined anatomical parts in the graphical representation of the images obtained, in the preoperative or intraoperative setting, through the available user interface means 130, 132. In other embodiments, a combination of rigid and nonrigid registration methods are used.” [0085];
creating a depth map of the surgical site; localizing the surface; calculating an error between the depth map and the surface; and iteratively reorienting the surface with respect to the depth map to minimize the error (“an output of the surface reconstruction 112 is stored in point sets or depth maps. The markerless registration may be completed by applying known methods (e.g. the iterative closest point algorithm, the Curie point depth algorithm, or the scale invariant feature transform algorithm) to the output of volume rendering 104 (e.g. 3D volume image). This markerless registration may be completed by 2D or stereoscopic digital images 108 from previous imaging studies 102 or intraoperative images 106 (e.g. CT scans or MR scans).” [0086], iterative closest point algorithm inherently corrects for error in surface matching).
Casas does not specifically disclose providing a possible identification of the implant; and reorienting the identification of the implant.
However, in a similar field of endeavor, Bonny discloses a method for registering a surgical site containing an implant (“A method for removing an implant attached to a bone during revision joint replacement surgery includes a library of implant models. A series of surface points are collected on the implant with a digitizer. A best match is computed between the collected surface points and an implant model in the library of implant models to register the position of the implant model to the implant” [Abstract])
Bonny also teaches providing a possible identification of the implant (“determining the implant make/model/size, and registering the manufacturer's 3D model to the segmented implant in the patient, thus providing a transformation matrix between manufacturer's 3D model and the patient's DICOM data. “ [0033]);
And reorienting the identification of the implant (“As used herein, the term “digitizer” refers to a device capable of measuring or designating the location of physical points in three-dimensional space… a non-mechanically tracked digitizer probe (e.g., optically tracked, electromagnetically tracked, acoustically tracked, and equivalents thereof)… the term “digitizing” refers to the collecting, measuring, and/or recording of physical points in space using a digitizer” [0014-0015], “registration of the inventive embodiment for the TKA restoration includes the steps of: digitizing the patient's implant which is rigidly fixed to the patient's operative bone; registering the digitized implant to the nominal 3D model, thus providing the transformation matrix between the digitized implant and the manufacturer's 3D model; and computing the transformation matrix between the digitized implant and the patient's DICOM data, thereby registering the entire patient's operative bone to the set of anatomic landmarks.” [0038]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas as outlined above with providing a possible identification of the implant; and reorienting the identification of the implant as taught by Bonny, because there exists a need for a more effective process to adequately remove the previous implant, any bone cement, and prepare a new cavity for a revision implant without further compromising the structure of the bone [0006].
Regarding Claim 2, Casas discloses that creating a depth map of the surgical site comprises: imaging the surgical suite using a camera in a structured light modality (“For example, a 3D scanner system 110 may include a laser scanner, a time-of-flight 3D laser scanner, a structured-light 3D scanner, hand-held laser scanner, a time-of-flight camera, a depth camera, or a combination of these or other devices.” [0076]).
Regarding Claim 3, Casas discloses that creating a depth map of the surgical site comprises: using a depth sensor selected from a group comprising a time of flight sensors, a LIDAR, a laser scanner and an epipolar scanner (“For example, a 3D scanner system 110 may include a laser scanner, a time-of-flight 3D laser scanner, a structured-light 3D scanner, hand-held laser scanner, a time-of-flight camera, a depth camera, or a combination of these or other devices.” [0076]).
Regarding Claim 4, Casas discloses all limitations noted above except that the identification of the implant is an implant model selected from a library of implant models using a surface matching technique.
However, in a similar field of endeavor, Bonny teaches that the identification of the implant is an implant model selected from a library of implant models using a surface matching technique (“A method for removing an implant attached to a bone during revision joint replacement surgery includes a library of implant models. A series of surface points are collected on the implant with a digitizer. A best match is computed between the collected surface points and an implant model in the library of implant models to register the position of the implant model to the implant.” [0007]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas as outlined above with the identification of the implant is an implant model selected from a library of implant models using a surface matching technique as taught by Bonny, because there exists a need for a more effective process to adequately remove the previous implant, any bone cement, and prepare a new cavity for a revision implant without further compromising the structure of the bone [0006].
Regarding Claim 6, Casas discloses further comprising: orienting the surface relative to the depth map using an iterative-closest-point (JCP) algorithm (“an output of the surface reconstruction 112 is stored in point sets or depth maps. The markerless registration may be completed by applying known methods (e.g. the iterative closest point algorithm)” [0086]).
Casas does not specifically disclose orienting the implant.
However, in a similar field of endeavor, Bonny teaches orienting the implant (“A method for removing an implant attached to a bone during revision joint replacement surgery includes a library of implant models. A series of surface points are collected on the implant with a digitizer. A best match is computed between the collected surface points and an implant model in the library of implant models to register the position of the implant model to the implant.” [0007]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas as outlined above with orienting the implant as taught by Bonny, because there exists a need for a more effective process to adequately remove the previous implant, any bone cement, and prepare a new cavity for a revision implant without further compromising the structure of the bone [0006].
Regarding Claim 9, Casas discloses calculating the error between the depth map and the identification of the implant (“an output of the surface reconstruction 112 is stored in point sets or depth maps. The markerless registration may be completed by applying known methods (e.g. the iterative closest point algorithm)” [0086]).
Casas does not specifically disclose further comprising: identifying critical regions of the implant; wherein the critical regions are given greater importance.
However, in a similar field of endeavor, Bonny teaches further comprising: identifying critical regions of the implant; wherein the critical regions are given greater importance (“A method for removing an implant attached to a bone during revision joint replacement surgery includes a library of implant models. A series of surface points are collected on the implant with a digitizer. A best match is computed between the collected surface points and an implant model in the library of implant models to register the position of the implant model to the implant.” [0007]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas as outlined above with further comprising: identifying critical regions of the implant; wherein the critical regions are given greater importance as taught by Bonny, because there exists a need for a more effective process to adequately remove the previous implant, any bone cement, and prepare a new cavity for a revision implant without further compromising the structure of the bone [0006].
Regarding Claim 13, Casas discloses all limitations noted above except that the library contains various models and sizes of implants from various manufacturers.
However, in a similar field of endeavor, Bonny teaches that the library contains various models and sizes of implants from various manufacturers (“A method for removing an implant attached to a bone during revision joint replacement surgery includes a library of implant models. A series of surface points are collected on the implant with a digitizer. A best match is computed between the collected surface points and an implant model in the library of implant models to register the position of the implant model to the implant.” [0007], “determining the implant make/model/size, and registering the manufacturer's 3D model to the segmented implant in the patient” [0037]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas as outlined above with the library contains various models and sizes of implants from various manufacturers as taught by Bonny, because there exists a need for a more effective process to adequately remove the previous implant, any bone cement, and prepare a new cavity for a revision implant without further compromising the structure of the bone [0006].
Regarding Claim 15, Bonny discloses that the camera is a stereoscopic camera (“the 3D scanner system 110 is composed of a dedicated stereoscopic camera system” [0081]).
Regarding Claim 16, Bonny discloses that the surfaces of the implant are localized using a machine learning model (“computer means 100 may receive a dense 3D point cloud provided by the 3D scanning process, that represents the surface of the target portion of the patient 118 by a point cloud construction algorithm” [0077]).
Regarding Claim 17, Bonny discloses all limitations noted above except that the surfaces of the implant are localized using a navigated probe.
However, in a similar field of endeavor, Bonny teaches that the library contains various models and sizes of implants from various manufacturers (“As used herein, the term “digitizer” refers to a device capable of measuring or designating the location of physical points in three-dimensional space. By way of example but not limitation, the “digitizer” may be: a “mechanical digitizer” having passive links and joints, such as the high-resolution electro-mechanical sensor arm described in U.S. Pat. No. 6,033,415 (which U.S. patent is hereby incorporated herein by reference); a non-mechanically tracked digitizer probe (e.g., optically tracked, electromagnetically tracked, acoustically tracked, and equivalents thereof)” [0014]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas as outlined above with the library contains various models and sizes of implants from various manufacturers as taught by Bonny, because there exists a need for a more effective process to adequately remove the previous implant, any bone cement, and prepare a new cavity for a revision implant without further compromising the structure of the bone [0006].
Regarding Claim 18, Casas discloses A system for registering a surgical site containing an implant (“ a real-time surgery method and apparatus for displaying a stereoscopic augmented view of a patient from a static or dynamic viewpoint of the surgeon, which employs real-time three-dimensional surface reconstruction for preoperative and intraoperative image registration.” [Abstract], “The preoperative image may be presented, recorded, or registered (referred to hereinafter collectively and individually as “registered”) over the patient, in real time. Thus, the internal anatomical structures of the patient may be blended with the media recorded by the mounted cameras… tracking may be configured to be added to instruments or implants within the preoperative image” [0013-0014]) comprises:
a surgical computer; and software, executing on the surgical computer and causing the surgical computer to perform the functions of (“the 3D scanner system 110 is composed of time-of-flight cameras and/or other motion tracking devices, which are used for gesture recognition by software means, allowing for interaction of the surgeon 128 or other users with the computer 100 during surgery through the real-time user interface means 132, without touching anything in the operating room. Alternatively or in addition to gesture recognition, voice commands are used to avoid touching the computer 100 or other devices included in this invention and controlled by computer means 100.” [0082]):
registering arrays of reflective markers on the patient (“In embodiments, registration is done with the help of optical markers, e.g. color or reflective markers in certain predefined anatomical landmarks of the patient 118 for the 3D scanner system 110, corresponding to markers of the same size and shape placed on the same predefined anatomical landmarks of the patient 118 during the preoperative 102 or intraoperative imaging 106 (e.g. radiopaque markers for CT or x-ray imaging), or virtual markers placed on the predefined anatomical parts in the graphical representation of the images obtained, in the preoperative or intraoperative setting, through the available user interface means 130, 132. In other embodiments, a combination of rigid and nonrigid registration methods are used.” [0085];
creating a depth map of the surgical site; localizing the surface; calculating an error between the depth map and the surface; and iteratively reorienting the surface with respect to the depth map to minimize the error (“an output of the surface reconstruction 112 is stored in point sets or depth maps. The markerless registration may be completed by applying known methods (e.g. the iterative closest point algorithm, the Curie point depth algorithm, or the scale invariant feature transform algorithm) to the output of volume rendering 104 (e.g. 3D volume image). This markerless registration may be completed by 2D or stereoscopic digital images 108 from previous imaging studies 102 or intraoperative images 106 (e.g. CT scans or MR scans).” [0086], iterative closest point algorithm inherently corrects for error in surface matching).
Casas does not specifically disclose a library of surgical implants; providing a possible identification of the implant; and reorienting the identification of the implant.
However, in a similar field of endeavor, Bonny discloses a library of surgical implants (“A method for removing an implant attached to a bone during revision joint replacement surgery includes a library of implant models. A series of surface points are collected on the implant with a digitizer. A best match is computed between the collected surface points and an implant model in the library of implant models to register the position of the implant model to the implant” [Abstract])
Bonny also teaches providing a possible identification of the implant (“determining the implant make/model/size, and registering the manufacturer's 3D model to the segmented implant in the patient, thus providing a transformation matrix between manufacturer's 3D model and the patient's DICOM data. “ [0033]);
And reorienting the identification of the implant (“As used herein, the term “digitizer” refers to a device capable of measuring or designating the location of physical points in three-dimensional space… a non-mechanically tracked digitizer probe (e.g., optically tracked, electromagnetically tracked, acoustically tracked, and equivalents thereof)… the term “digitizing” refers to the collecting, measuring, and/or recording of physical points in space using a digitizer” [0014-0015], “registration of the inventive embodiment for the TKA restoration includes the steps of: digitizing the patient's implant which is rigidly fixed to the patient's operative bone; registering the digitized implant to the nominal 3D model, thus providing the transformation matrix between the digitized implant and the manufacturer's 3D model; and computing the transformation matrix between the digitized implant and the patient's DICOM data, thereby registering the entire patient's operative bone to the set of anatomic landmarks.” [0038]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas as outlined above with a library of surgical implants; providing a possible identification of the implant; and reorienting the identification of the implant as taught by Bonny, because there exists a need for a more effective process to adequately remove the previous implant, any bone cement, and prepare a new cavity for a revision implant without further compromising the structure of the bone [0006].
Regarding Claim 19, Bonny discloses further comprising: a stereoscopic camera in a structured light modality; wherein the surgical computer performs the further function of: imaging the surgical suite using the stereoscopic camera to create the depth map. (“For example, a 3D scanner system 110 may include a laser scanner, a time-of-flight 3D laser scanner, a structured-light 3D scanner, hand-held laser scanner, a time-of-flight camera, a depth camera, or a combination of these or other devices.” [0076], “the 3D scanner system 110 is composed of a dedicated stereoscopic camera system” [0081]).
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Casas in view of Bonny as applied to Claim 1 above, and further in view of Qiu et al (B. Qiu et al., “Automatic segmentation of mandible from conventional methods to deep learning—a review,” Journal of Personalized Medicine, vol. 11, no. 7, p. 629, Jul. 2021; hereinafter referred to as Qiu).
Regarding Claim 7, Casas discloses that the error between the orientation of the surface and the depth map is calculated (“an output of the surface reconstruction 112 is stored in point sets or depth maps. The markerless registration may be completed by applying known methods (e.g. the iterative closest point algorithm, the Curie point depth algorithm, or the scale invariant feature transform algorithm) to the output of volume rendering 104 (e.g. 3D volume image). This markerless registration may be completed by 2D or stereoscopic digital images 108 from previous imaging studies 102 or intraoperative images 106 (e.g. CT scans or MR scans).” [0086], iterative closest point algorithm inherently corrects for error in surface matching).
Casas does not specifically disclose orienting the implant; and calculating the error using a Hausdorff distance.
However, Bonny teaches orienting the implant (“As used herein, the term “digitizer” refers to a device capable of measuring or designating the location of physical points in three-dimensional space… a non-mechanically tracked digitizer probe (e.g., optically tracked, electromagnetically tracked, acoustically tracked, and equivalents thereof)… the term “digitizing” refers to the collecting, measuring, and/or recording of physical points in space using a digitizer” [0014-0015], “registration of the inventive embodiment for the TKA restoration includes the steps of: digitizing the patient's implant which is rigidly fixed to the patient's operative bone; registering the digitized implant to the nominal 3D model, thus providing the transformation matrix between the digitized implant and the manufacturer's 3D model; and computing the transformation matrix between the digitized implant and the patient's DICOM data, thereby registering the entire patient's operative bone to the set of anatomic landmarks.” [0038]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas as outlined above with orienting the implant as taught by Bonny, because there exists a need for a more effective process to adequately remove the previous implant, any bone cement, and prepare a new cavity for a revision implant without further compromising the structure of the bone [0006].
Bonny does not specifically teach calculating the error using a Hausdorff distance
However, in a similar field of endeavor, Qiu teaches segmentation methods for anatomical structures and implants placed on the patient [Abstract]
Qiu also teaches calculating the error using a Hausdorff distance (“In addition to the differences in the used dataset, the results are evaluated and presented in different ways in the reviewed papers. Moreover, there are no standard metrics for the segmentation evaluation, so different evaluation metrics are used to report the segmentation performance. For segmentation, the evaluation metrics are mainly divided into three categories: overlap-based metrics, distance-based metrics, and volume-based metrics… The most commonly used overlap-based metrics include the Dice similarity coefficient (Dice), Sensitivity (Sen), false positive volume fraction (FPVF) [35], false negative volume fraction (FNVF) [35], etc. To measure the contour difference between automatic and manual segmentation, the most commonly used metrics are distance-based metrics. In the context of mandibular segmentation, the following distance-based metrics have been frequently used: average symmetric surface distance (ASD), Hausdorff distance (HD), 95th-percentile Hausdorff distance (95HD), mean square error (MSE) [36], and root mean square error (RMSE) [36].” [3.3. Evaluation Metrics] .
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas in view of Bonny as outlined above with calculating the error using a Hausdorff distance as taught by Qiu, because In some medical image segmentation tasks, the volume of the object is also very important for treatment planning, and the metric based on volume is helpful to evaluate the performance of the segmentation method. Volume overlap error and volume error are the common indices to evaluate the results of mandibular segmentation [3.3. Evaluation Metrics].
Regarding Claim 8, Casas discloses that the error between the orientation of the surface and the depth map is calculated (“an output of the surface reconstruction 112 is stored in point sets or depth maps. The markerless registration may be completed by applying known methods (e.g. the iterative closest point algorithm, the Curie point depth algorithm, or the scale invariant feature transform algorithm) to the output of volume rendering 104 (e.g. 3D volume image). This markerless registration may be completed by 2D or stereoscopic digital images 108 from previous imaging studies 102 or intraoperative images 106 (e.g. CT scans or MR scans).” [0086], iterative closest point algorithm inherently corrects for error in surface matching).
Casas does not specifically disclose orienting the implant; and calculating the error using a dice coefficient.
However, Bonny teaches orienting the implant (“As used herein, the term “digitizer” refers to a device capable of measuring or designating the location of physical points in three-dimensional space… a non-mechanically tracked digitizer probe (e.g., optically tracked, electromagnetically tracked, acoustically tracked, and equivalents thereof)… the term “digitizing” refers to the collecting, measuring, and/or recording of physical points in space using a digitizer” [0014-0015], “registration of the inventive embodiment for the TKA restoration includes the steps of: digitizing the patient's implant which is rigidly fixed to the patient's operative bone; registering the digitized implant to the nominal 3D model, thus providing the transformation matrix between the digitized implant and the manufacturer's 3D model; and computing the transformation matrix between the digitized implant and the patient's DICOM data, thereby registering the entire patient's operative bone to the set of anatomic landmarks.” [0038]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas as outlined above with orienting the implant as taught by Bonny, because there exists a need for a more effective process to adequately remove the previous implant, any bone cement, and prepare a new cavity for a revision implant without further compromising the structure of the bone [0006].
Bonny does not specifically teach calculating the error using a dice coefficient.
However, in a similar field of endeavor, Qiu teaches calculating the error using a dice coefficient (“In addition to the differences in the used dataset, the results are evaluated and presented in different ways in the reviewed papers. Moreover, there are no standard metrics for the segmentation evaluation, so different evaluation metrics are used to report the segmentation performance. For segmentation, the evaluation metrics are mainly divided into three categories: overlap-based metrics, distance-based metrics, and volume-based metrics… The most commonly used overlap-based metrics include the Dice similarity coefficient (Dice), Sensitivity (Sen), false positive volume fraction (FPVF) [35], false negative volume fraction (FNVF) [35], etc. To measure the contour difference between automatic and manual segmentation, the most commonly used metrics are distance-based metrics. In the context of mandibular segmentation, the following distance-based metrics have been frequently used: average symmetric surface distance (ASD), Hausdorff distance (HD), 95th-percentile Hausdorff distance (95HD), mean square error (MSE) [36], and root mean square error (RMSE) [36].” [3.3. Evaluation Metrics] .
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas in view of Bonny as outlined above with calculating the error using a dice coefficient as taught by Qiu, because In some medical image segmentation tasks, the volume of the object is also very important for treatment planning, and the metric based on volume is helpful to evaluate the performance of the segmentation method. Volume overlap error and volume error are the common indices to evaluate the results of mandibular segmentation [3.3. Evaluation Metrics].
Claims 5, 10-12, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Casas in view of Bonny as applied to Claim 1 & 18 above, and further in view of Modrow et al (US20140005685A1; hereinafter referred to as Modrow)
Regarding Claim 5, Casas in view of Bonny discloses all limitations noted above except that during creation of the depth map, a confidence metric is provided that characterizes a likelihood of a match between the depth map and an implant model in the library.
However, in a similar field of endeavor, Modrow teaches a method for preparing the reconstruction of a damaged bone structure using an implant [Abstract].
Modrow also teaches during creation of the depth map, a confidence metric is provided that characterizes a likelihood of a match between the depth map and an implant model in the library (“The third step of the method involves selecting an implant and providing a shape dataset which represents the shape of the implant, wherein “selecting” means in particular that the computer automatically picks out one of the available implants.” [0009] “In a fourth step, the selected implant is positioned in order to determine an implant position… Automatic implant positioning is in particular a step of virtually positioning the implant and is preferably performed on the structure dataset and the shape dataset by the computer.” [0010], “In a fifth step, a determination is made as to whether or not the selected implant is suitable, and the method returns to the third step if the implant is determined to not be suitable. The determination is preferably made automatically by the computer, for example by comparing the target structure with the positioned shape dataset or a combination of the structure dataset and the positioned shape dataset. The implant is suitable if the shape of the implant matches the shape of the target structure or a part of the target structure to a predetermined level of accuracy.” [0011]
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas in view of Bonny as outlined above with during creation of the depth map, a confidence metric is provided that characterizes a likelihood of a match between the depth map and an implant model in the library as taught by Modrow, because of improving and simplifying preparation of the reconstruction, in particular finding a suitable implant for the affected bone structure [0002].
Regarding Claim 10, Casas in view of Bonny discloses all limitations noted above except that the identification of the implant is considered a positive match to the actual implant if the minimized error falls below a predetermined threshold.
However, in a similar field of endeavor, Modrow teaches a method for preparing the reconstruction of a damaged bone structure using an implant [Abstract].
Modrow also teaches that the identification of the implant is considered a positive match to the actual implant if the minimized error falls below a predetermined threshold (“The third step of the method involves selecting an implant and providing a shape dataset which represents the shape of the implant, wherein “selecting” means in particular that the computer automatically picks out one of the available implants.” [0009] “In a fourth step, the selected implant is positioned in order to determine an implant position… Automatic implant positioning is in particular a step of virtually positioning the implant and is preferably performed on the structure dataset and the shape dataset by the computer.” [0010], “In a fifth step, a determination is made as to whether or not the selected implant is suitable, and the method returns to the third step if the implant is determined to not be suitable. The determination is preferably made automatically by the computer, for example by comparing the target structure with the positioned shape dataset or a combination of the structure dataset and the positioned shape dataset. The implant is suitable if the shape of the implant matches the shape of the target structure or a part of the target structure to a predetermined level of accuracy.” [0011]
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas in view of Bonny as outlined above with the identification of the implant is considered a positive match to the actual implant if the minimized error falls below a predetermined threshold as taught by Modrow, because of improving and simplifying preparation of the reconstruction, in particular finding a suitable implant for the affected bone structure [0002].
Regarding Claim 11, Casas in view of Bonny discloses all limitations noted above except that the identification of the implant is considered a negative match to the actual implant if the minimize error is above the predetermined threshold.
However, in a similar field of endeavor, Modrow teaches a method for preparing the reconstruction of a damaged bone structure using an implant [Abstract].
Modrow also teaches that the identification of the implant is considered a negative match to the actual implant if the minimize error is above the predetermined threshold (“The third step of the method involves selecting an implant and providing a shape dataset which represents the shape of the implant, wherein “selecting” means in particular that the computer automatically picks out one of the available implants.” [0009] “In a fourth step, the selected implant is positioned in order to determine an implant position… Automatic implant positioning is in particular a step of virtually positioning the implant and is preferably performed on the structure dataset and the shape dataset by the computer.” [0010], “In a fifth step, a determination is made as to whether or not the selected implant is suitable, and the method returns to the third step if the implant is determined to not be suitable. The determination is preferably made automatically by the computer, for example by comparing the target structure with the positioned shape dataset or a combination of the structure dataset and the positioned shape dataset. The implant is suitable if the shape of the implant matches the shape of the target structure or a part of the target structure to a predetermined level of accuracy.” [0011]
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas in view of Bonny as outlined above with the identification of the implant is considered a negative match to the actual implant if the minimize error is above the predetermined threshold as taught by Modrow, because of improving and simplifying preparation of the reconstruction, in particular finding a suitable implant for the affected bone structure [0002].
Regarding Claim 12, Casas in view of Bonny discloses all limitations noted above except further comprising: providing a new identification of the implant if the minimized error falls above the predetermined threshold.
However, in a similar field of endeavor, Modrow teaches a method for preparing the reconstruction of a damaged bone structure using an implant [Abstract].
Modrow also teaches providing a new identification of the implant if the minimized error falls above the predetermined threshold (“The third step of the method involves selecting an implant and providing a shape dataset which represents the shape of the implant, wherein “selecting” means in particular that the computer automatically picks out one of the available implants.” [0009] “In a fourth step, the selected implant is positioned in order to determine an implant position… Automatic implant positioning is in particular a step of virtually positioning the implant and is preferably performed on the structure dataset and the shape dataset by the computer.” [0010], “In a fifth step, a determination is made as to whether or not the selected implant is suitable, and the method returns to the third step if the implant is determined to not be suitable. The determination is preferably made automatically by the computer, for example by comparing the target structure with the positioned shape dataset or a combination of the structure dataset and the positioned shape dataset. The implant is suitable if the shape of the implant matches the shape of the target structure or a part of the target structure to a predetermined level of accuracy.” [0011]
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas in view of Bonny as outlined above with providing a new identification of the implant if the minimized error falls above the predetermined threshold as taught by Modrow, because of improving and simplifying preparation of the reconstruction, in particular finding a suitable implant for the affected bone structure [0002].
Regarding Claim 20, Casas in view of Bonny discloses all limitations noted above except the surgical computer performs the further function of: providing a new identification of the implant if the minimized error falls above the predetermined threshold.
However, in a similar field of endeavor, Modrow teaches a method for preparing the reconstruction of a damaged bone structure using an implant [Abstract].
Modrow also teaches providing a new identification of the implant if the minimized error falls above the predetermined threshold (“The third step of the method involves selecting an implant and providing a shape dataset which represents the shape of the implant, wherein “selecting” means in particular that the computer automatically picks out one of the available implants.” [0009] “In a fourth step, the selected implant is positioned in order to determine an implant position… Automatic implant positioning is in particular a step of virtually positioning the implant and is preferably performed on the structure dataset and the shape dataset by the computer.” [0010], “In a fifth step, a determination is made as to whether or not the selected implant is suitable, and the method returns to the third step if the implant is determined to not be suitable. The determination is preferably made automatically by the computer, for example by comparing the target structure with the positioned shape dataset or a combination of the structure dataset and the positioned shape dataset. The implant is suitable if the shape of the implant matches the shape of the target structure or a part of the target structure to a predetermined level of accuracy.” [0011]
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Casas in view of Bonny as outlined above with providing a new identification of the implant if the minimized error falls above the predetermined threshold as taught by Modrow, because of improving and simplifying preparation of the reconstruction, in particular finding a suitable implant for the affected bone structure [0002].
Conclusion
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/Steven Maldonado/
Patent Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797