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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/28/2025 has been entered.
Response to Amendment
The amendment filed on 10/28/2025 has been entered and made of record. Claims 1, 16 and 21 are amended. Claim 20 is cancelled. Claims 1-19 and 21 are pending.
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 of this title, 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.
Claims 1-6, 8-13, 16-19 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over STEINBERG (US 2020/0038109) in view of Lang (US 2019/0110842) and Cameron et al. (US 2018/0325610), further in view of Farley et al. (US 2021/0307833 A1).
As to Claim 1, STEINBERG teaches A surgical guidance system for computer assisted navigation of spinal surgery, the surgical guidance system operative to:
obtain feedback data provided by distributed networked computers for each of a plurality of prior patients who have undergone spinal surgery, the feedback data characterizing spinal geometric structures of the prior patient, characterizing a surgical procedure performed on the prior patient, characterizing an implant device that was surgically implanted into the prior patient's spine, and characterizing the prior patient's surgical outcome (STEINBERG discloses “accessing a database including data for a plurality of patients, with data for each patient comprising data on any pathology types developed, and additional data on at least one of the associated (a) pre-operative images, (b) post-operative images, (c) clinically relevant quantitative data, or (d) surgical plan” in [0025]; “Furthermore, in the above mentioned step (vii), the one or more sets of data for patients with similar characteristics may comprise a set of data for patients with similar characteristics as those of the above mentioned pre-operative images and surgical plan, and another set of data for patients with similar characteristics as those of the above mentioned post-operative images” in [0040]; “In step 2, clinically relevant quantitative data of the subject is obtained, such as… any pre-operative pathologies… medical history, history of illnesses… and past medical history which may include previous surgeries” in [0058]; “In order to calculate these parameters, coronal and sagittal plane segmentation may be performed on the three-dimensional images, providing an approximate relative position and orientation of each vertebrae, and the coronal, sagittal and long axes of the spine may likewise be identified in the images” in [0060]; multiple pathology classifiers in Fig 2; “In step 7, it is obtained from the database, a set of data having patients with similar pre-operative relationships of vertebral pairs, and a similar surgical plan, to those of the subject” in [0063]. Here, STEINBERG’s patient data can be collected from networked devices);
train a machine learning model based on the feedback data (STEINBERG discloses “Furthermore, the data from the test set may be incorporated into the database, and machine learning algorithms may be applied to the newly expanded database for more accurate prediction of pathology or instrument failure development in future subjects” in [0055]; see also [0026, 0028, 0072]);
obtain pre-operative data from one of the distributed network computers characterizing spinal geometric structures of a candidate patient for planned surgery (STEINBERG discloses “In step 4, relationships are determined between selected pairs of vertebrae in the pre-operative three-dimensional images…The relationships between vertebrae are based on predefined parameters, and may include one or more of measurements between vertebral endplates, shape or volume of vertebra, distance between vertebrae, shape or volume of intervertebral space or characteristics of a disc between a vertebral pair, and degree of rotation or translation of a vertebra… In order to calculate these parameters, coronal and sagittal plane segmentation may be performed on the three-dimensional images, providing an approximate relative position and orientation of each vertebrae, and the coronal, sagittal and long axes of the spine may likewise be identified in the images” in [0060]);
generate a surgical plan for the candidate patient based on processing the pre-operative data through the machine learning model, wherein the surgical plan is initially generated by the machine learning model without user manipulation (STEINBERG discloses “Embodiments of the present disclosure relate to the field of image based pathology prediction for planning spinal surgery, using predictive modeling such as machine learning, deep learning or any statistical method that can be used to predict outcomes” in [0002]; see also [0018, 0020-0028, 0038]. See also above Response to Arguments for the new limitation “wherein the surgical plan is initially generated by the machine learning model without user manipulation”).
STEINBERG doesn’t explicitly teach extended reality to overlay virtual data on the display. The combination of Lang further teaches following limitations:
provide at least a portion of the surgical plan to a display device for visual review by a user, wherein the surgical guidance system includes an extended reality (XR) headset including the display device (Lang discloses “Using a virtual or other interface, the surgeon wearing OHMD 1 11 can execute commands 32, e.g. to display the next predetermined bone cut, e.g. from a virtual surgical plan or an imaging study or intra-operative measurements, which can trigger the OHMD's 11, 12, 13, 14 to project digital holograms of the next surgical step 34 superimposed onto and aligned with the surgical site in a predetermined position and/or orientation” in [0146]), the XR headset including one or more cameras that operate as a gesture sensor by capturing for identification user hand gestures performed within a field of view of the one or more cameras (Lang discloses “For example, one or more cameras integrated or attached to the OHMD can capture the movement of the surgeon's finger(s) in relationship to the touch area; using gesture tracking software, the virtual plane can then be moved by advancing the finger towards the touch area in a desired direction” in [0382]), and
receive a user manipulation of the surgical plan with one or more hand gestures (Lang discloses “The surgeon can move the virtual implant component to place it and/or align and/it or orient in a desired position, location, and/or orientation over the implantation site for a given patient. Since the moving and aligning is performed over the live implantation site of the patient, the surgeon can optimize the implant position, location, and/or orientation…After the surgeon has placed, aligned and/or oriented the virtual implant component superimposed in the desired position and/or orientation over or aligned with the live implantation site, the coordinates of the virtual implant component can be saved, e.g. in a common coordinate system in which the OHMD and the implantation site can also be registered. The saved coordinates of the virtual implant component can, optionally be incorporated in a virtual surgical plan, which can optionally also be registered in the common coordinate system” in [0887].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of STEINBERG with the teaching of Lang so as to modify and/or optimize the position, location, and/or orientation of the virtual implant component with the physical implant component for a desired function in an implantation site, update the virtual surgical plan, and display one or more digital holograms of one or more virtual surgical instruments and/or virtual implant components at the desired position, location and orientation (Lang, [0887]).
STEINBERG and Lang don’t provide detailed description of robot. The combination of Cameron further teaches following limitations:
wherein the surgical guidance system communicates with a surgical robot, the surgical robot including a robot base, a robot arm connected to the robot base, where the robot arm configured to connect to an end effector which guides movement of the surgical tool, and at least one motor operatively connected to move the robot arm relative to the robot base (Lang teaches head movement to control robotic arm holding a surgical instrument along the direction of the surgical instrument in [0155]. Cameron further discloses “FIG. 6 illustrates a medical robot system 600 consistent with some embodiments. Medical robot system 600 may comprise end-effector 602, robot arm 604, guide tube 606, instrument 608, and robot base 610… In an operation, robot base 610 may be configured to be in electronic communication with robot arm 604 and end-effector 602 so that medical robot system 600 may assist a user (for example, a doctor) in operating on the patient 210” in [0070]),
wherein the surgical robot includes at least one controller is connected to the at least one motor and operative to determine a pose of the end effector relative to a planned pose of the end effector based on the surgical plan while guiding movement of the surgical tool along a planned trajectory associated with the surgical plan during implantation of the spinal implant device, and generate navigation information based on comparison of the planned pose and the determined pose of the end effector (Lang discloses “With surgical navigation, a first virtual instrument can be displayed on a computer monitor which is a representation of a physical instrument tracked with navigation markers, e.g. infrared or RF markers, and the position and/or orientation of the first virtual instrument can be compared with the position and/or orientation of a corresponding second virtual instrument generated in a virtual surgical plan” in [0069]; “the accuracy of the physical placement/position and/or orientation of each pedicle screw can be assessed compared to the intended virtual placement/position/and/or orientation in the virtual surgical plan using any of the foregoing techniques. Optionally a coordinate transfer or coordinate correction can be determined based on any deviations between physical and intended virtual placement of the pedicle screw and the pedicle screw can be used for registration of the patient” in [0804]; “The surgeon can move the head forward. This forward motion is captured by an IMU and translated into a forward movement of a robotic arm holding a surgical instrument along the direction of the surgical instrument.” in [0155]; “In FIG. 17D, continuation of intra-operative virtual surgical plan… the surgeon can, for example, advance an awl towards the entry point for a pedicle screw 224. The actual or physical awl can be aligned with a virtual awl 225. Other physical instruments can be aligned with their corresponding virtual instrument or, for example, an intended path or endpoint 226. Consecutive surgical steps can be executed aligning physical with virtual tools, instruments or implants 227….” in [1557]. Cameron further discloses “In some further embodiments, medical robot 102 can be configured to correct the path of the medical instrument 608 if the medical instrument 608 strays from the selected, preplanned trajectory” in [0052]),
wherein the navigation information indicates where the end effector needs to be moved to become aligned with the planned pose so that the surgical tool will be guided by the end effector along the planned trajectory toward the patient (Cameron discloses “In some further embodiments, medical robot 102 can be configured to correct the path of the medical instrument 608 if the medical instrument 608 strays from the selected, preplanned trajectory. In some embodiments, medical robot 102 can be configured to permit stoppage, modification, and/or manual control of the movement of end-effector 112 and/or the medical instrument 608” in [0052]; “FIG. 16 is a block diagram of a method 1100 for navigating and moving the end-effector 1012 (or any other end-effector described herein) of the robot 102 to a desired target trajectory” in [0124]; “The surgical plan that the surgeon creates can then be relayed to a surgical robot, which can then move to this trajectory and hold the trajectory while the surgeon places implants or performs other surgical procedures” in [157]; “end-effector 112 that controls a medical device, instrument, or implant” in [0139]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of STEINBERG and Lang with the teaching of Cameron so as to monitor the tracking devices with a high degree of precision as the instrument is being position and moved by a robot (Cameron, [0003]).
STEINBERG, Land and Cameron don’t teach a dynamic reference array and generating a surgical plan through machine learning model. The combination of Farley further teaches following limitation:
generate a surgical plan for the candidate patient based on processing the pre-operative data through the machine learning model, wherein the surgical plan is initially generated by the machine learning model (Farley discloses “The Operative Patient Care System 320 is designed to utilize patient specific data, surgeon data, healthcare facility data, and historical outcome data to develop an algorithm that suggests or recommends an optimal overall treatment plan for the patient's entire episode of care (preoperative, operative, and postoperative) based on a desired clinical outcome… In addition to utilizing statistical and mathematical models, simulation tools (e.g., LIFEMOD®) can be used to simulate outcomes, alignment, kinematics, etc. based on a preliminary or proposed surgical plan, and reconfigure the preliminary or proposed plan to achieve desired or optimal results according to a patient's profile” in [0125]; “Prior to surgery, the Patient Data 310, 315 and Healthcare Professional Data 325 may be capture… The computing system is configured to generate a case plan for use with a CASS 100” in [0134]; “Historical data sets from the online database are used as inputs to a machine learning model such as, for example, a recurrent neural network (RNN) or other form of artificial neural network… The machine learning model is trained to predict one or more values based on the input data. For the sections that follow, it is assumed that the machine learning model is trained to generate predictor equations” in [0135]; “Any subsequent calculation of the target equation via the RNN will include the data from the previous patient in this manner, allowing for continuous improvement of the system” in [0136]; see also [0137, 0139]);
wherein the XR headset includes a dynamic reference array and wherein the surgical guidance system tracks the dynamic reference array of the XR headset and determines the pose of the dynamic reference array in relation to the target anatomical structure (Cameron discloses a dynamic reference array in Fig 10; attach a dynamic reference base to the patient to track the position of the patient in [0085]. It is well-known that the dynamic reference array can be attached on physician to track the pose of physician in related to the patient. For example, Farley discloses “By tracking fiducial marks associated with that tool or bone structure, or by using other conventional image tracking modalities, a processor may track that tool or bone as it moves through the environment in a three-dimensional model” in [0047]; “In some embodiments, certain markers, such as fiducial marks that identify individuals, important tools, or bones in the theater may include passive or active identifiers that can be picked up by a camera or camera array associated with the tracking system. For example, an IR LED can flash a pattern that conveys a unique identifier to the source of that pattern, providing a dynamic identification mark” in [0048]; “In addition to optical tracking, certain features of objects can be tracked by registering physical properties of the object and associating them with objects that can be tracked, such as fiducial marks fixed to a tool or bone… By optically tracking the position and orientation (pose) of the fiducial mark associated with that bone, a model of that surface can be tracked with an environment through extrapolation” in [0049]; see also HMD in Fig 1 and anatomical landmarks in [0050, 0057].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of STEINBERG, Lang and Cameron with the teaching of Farley so as to generate a surgical plan through machine learning model to improve the chance of successful clinical outcomes, and provide a dynamic identification mark to track the object during the surgery.
As to Claim 2, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 1, wherein the machine learning model is operative to:
process the pre-operative data to output the surgical plan identifying type and dimension sizing of a spinal implant device proposed for surgical implantation in the spine of the candidate patient (Farley discloses “Implants can be superimposed onto the bone in the display 125, and implant colors can change or correspond to different types or sizes of implants” in [0096]; “the surgical parameters may include… implant type, size and position” in [0117]; “Moreover, aspects such as implant type and dimension, patient demographics, etc. can further be used to enhance the overall dataset. Once the dataset has been established, it may be used to train a machine learning model (e.g., RNN) to make predictions of how surgery will proceed based on the current state of the CASS 100.” in [0142].)
As to Claim 3, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 2, wherein the machine learning model is further operative to process the pre-operative data to output the surgical plan with further identification of an estimated level of surgical outcome success predicted for the candidate patient from surgical implantation of the spinal implant device in the spine of the candidate patient, wherein the estimated level of surgical outcome success indicates a most likely patient reported outcome measure or spinal deformity correction measurement that will be obtained by surgical implantation of the spinal implant device in the spine of the candidate patient (STEINBERG discloses “determining an acceptable surgical plan using image based pathology or instrument failure prediction” in [0013]; “For example, patients 35-40 years old with a pre-operative deformity X have an average probability of 8% of developing pathology P” in [0070]. Farley also discloses “Historical data sets from the online database are used as inputs to a machine learning model such as, for example, a recurrent neural network (RNN) or other form of artificial neural network… The machine learning model is trained to predict one or more values based on the input data. For the sections that follow, it is assumed that the machine learning model is trained to generate predictor equations. These predictor equations may be optimized to determine the optimal size, position, and orientation of the implants to achieve the best outcome or satisfaction level.” in [0135], see also [0125, 0130, 0174-0175].)
As to Claim 4, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 2, wherein the machine learning model is further operative to process the pre-operative data to output the surgical plan with further identification of a planned pose for implantation of the spinal implant device in the spine of the candidate patient (STEINBERG discloses “The selected surgical plan may include the level of instrumentation, instrumentation properties such as screw system, diameter, position in the vertebra, interbody size, angle, location in the intervertebral disc space and the like” in [0077]. Farley further discloses “Based on information provided about the patient's hip joint and the planned implant position and orientation after broach tracking is completed, the surgeon can make modifications or adjustments to the surgical plan” in [0058]; “and the display 125 can show the implant's position and orientation as the surgeon manipulates the leg and hip. The CASS 100 can provide the surgeon with the option of re-planning and re-doing the reaming and implant impaction by preparing a new surgical plan if the surgeon is not satisfied with the original implant position and orientation.” in [0060], see also [0133, 0135].)
As to Claim 5, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 4, further operative to:
determine a planned trajectory for implantation of the spinal implant device to the planned pose in the spine of the candidate patient identified by the surgical plan (Lang discloses “In some embodiments, the surgical plan is used to derive one or more of a location, position, orientation, alignment, trajectory, plane, start point, or end point for one or more surgical instruments. In some embodiments, the one or more of a location, orientation, or alignment or coordinates of bone removal are used to derive one or more of a location, position, orientation, alignment, trajectory, plane, start point, or end point for one or more surgical instruments. In some embodiments, the one or more optical head mounted displays visualize the one or more of a location, position, orientation, alignment, trajectory, plane, start point, or end point for one or more surgical instruments projected onto and registered with the physical joint” in C13L30-43. Farley further discloses “Based on information provided about the patient's hip joint and the planned implant position and orientation after broach tracking is completed, the surgeon can make modifications or adjustments to the surgical plan” in [0058]; “and the display 125 can show the implant's position and orientation as the surgeon manipulates the leg and hip. The CASS 100 can provide the surgeon with the option of re-planning and re-doing the reaming and implant impaction by preparing a new surgical plan if the surgeon is not satisfied with the original implant position and orientation.” in [0060], see also [0133, 0135]);
obtain from a camera tracking system a present pose of a surgical tool being used to implant the spinal implant device in the spine of the candidate patient (STEINBERG discloses “In step 21, an instrumental spinal surgical plan is selected for a subject, including the instrument geometry and placement, for pathology and instrument failure prediction analysis” in [0077]. Cameron discloses “The cameras 200, 326 may track the location of instrument 608 based on the position and orientation of tracking array 612 and markers 804” in [0076]; see also [0071, 0074]);
generate navigation information based on comparison of the present pose of the surgical tool and the planned trajectory, wherein the navigation information indicates how the surgical tool needs to be posed to be aligned with the planned trajectory; and provide at least first part of the navigation information to a display device (Cameron discloses “In some further embodiments, medical robot 102 can be configured to correct the path of the medical instrument 608 if the medical instrument 608 strays from the selected, preplanned trajectory” in [0052]; “This calculated position is compared to the actual position of the marker 1018 as recorded from the tracking system. If the values agree, it can be assured that the robot 102 is in a known location” in [0127]; “The camera 200, 326 or other tracking devices may track end-effector 602 as it moves to different positions and viewing angles by following the movement of tracking markers 702… This display 110, 304 may allow a user to ensure that end-effector 602 is in a desirable position in relation to robot arm 604, robot base 610, the patient 210, and/or the user” in [0073]; “The surgical plan that the surgeon creates can then be relayed to a surgical robot, which can then move to this trajectory and hold the trajectory while the surgeon places implants or performs other surgical procedures” in [0157].)
As to Claim 6, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 5, further operative to:
provide at least second part of the navigation information to a surgical robot to control movement of a robot arm having an end effector which guides movement of the surgical tool, wherein the at least second part of the navigation information indicates where the end effector needs to be moved so the surgical tool will be guided by the end effector along the planned trajectory for implantation of the spinal implant device to the planned pose of the spinal implant device in the spine of the candidate patient (Cameron discloses “The camera 200, 326 or other tracking devices may track end-effector 602 as it moves to different positions and viewing angles by following the movement of tracking markers 702… This display 110, 304 may allow a user to ensure that end-effector 602 is in a desirable position in relation to robot arm 604, robot base 610, the patient 210, and/or the user” in [0073]; “The surgical plan that the surgeon creates can then be relayed to a surgical robot, which can then move to this trajectory and hold the trajectory while the surgeon places implants or performs other surgical procedures” in [0157]; “In some further embodiments, medical robot 102 can be configured to correct the path of the medical instrument 608 if the medical instrument 608 strays from the selected, preplanned trajectory” in [0052]; “FIG. 16 is a block diagram of a method 1100 for navigating and moving the end-effector 1012 (or any other end-effector described herein) of the robot 102 to a desired target trajectory” in [0124]).
As to Claim 8, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 2, further operative to:
process the surgical plan to obtain a three-dimensional model of the spinal implant device and provide a graphical representation of the three-dimensional model for display though the display device within the extended reality (XR) headset as an overlay on the candidate patient (Lang discloses “In some embodiments, the optical head mount display uses a computer graphics viewing pipeline that consists of the following steps to display 3D objects or 2D objects positioned in 3D space or other computer-generated objects and models… The different objects to be displayed by the OHMD computer graphics system (for instance virtual anatomical models, virtual models of instruments, geometric and surgical references and guides) are initially all defined in their own independent model coordinate system” in C21L54-66; “Virtual 3D models of surgical instruments and devices components can be generated which can include their predetermined position, location, rotation, orientation, alignment and/or direction 243. The virtual 3D models can be registered, for example in relationship to the OHMD and the patient 244. The virtual 3D models can be registered relative to the live patient data 245” in C159L49-56.)
As to Claim 9, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 1, wherein the feedback data used to train the machine learning model and which characterizes the prior patient's surgical outcome, includes post-operative feedback data characterizing a patient reported outcome measure or a spinal deformity correction measurement (STEINBERG discloses “Furthermore, in the above mentioned step (vii), the one or more sets of data for patients with similar characteristics may comprise a set of data for patients with similar characteristics as those of the above mentioned pre-operative images and surgical plan, and another set of data for patients with similar characteristics as those of the above mentioned post-operative images” in [0040]. Farley further discloses “The Operative Patient Care System 320 is designed to utilize patient specific data, surgeon data, healthcare facility data, and historical outcome data to develop an algorithm that suggests or recommends an optimal overall treatment plan for the patient's entire episode of care (preoperative, operative, and postoperative) based on a desired clinical outcome… In addition to utilizing statistical and mathematical models, simulation tools (e.g., LIFEMOD®) can be used to simulate outcomes, alignment, kinematics, etc. based on a preliminary or proposed surgical plan, and reconfigure the preliminary or proposed plan to achieve desired or optimal results according to a patient's profile” in [0125]; “Historical data sets from the online database are used as inputs to a machine learning model such as, for example, a recurrent neural network (RNN) or other form of artificial neural network… The machine learning model is trained to predict one or more values based on the input data. For the sections that follow, it is assumed that the machine learning model is trained to generate predictor equations” in [0135]; “Any subsequent calculation of the target equation via the RNN will include the data from the previous patient in this manner, allowing for continuous improvement of the system” in [0136]; see also Fig 4B and [0130, 0174-0175].)
As to Claim 10, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 1, wherein the feedback data used to train the machine learning model characterizes a spinal surgery procedure type performed on the prior patient and a type and dimension sizing of a spinal implant device that was implanted in the prior patient (Lang discloses “Implant size and desired polyethylene thickness can be factored into the virtual surgical plan” in C60L63-64; “one or more implant components, including type of implant used and/or implant size” in C70L1-3; “selected component size (match against virtual surgical plan and/or templating and/or sizing)” in C70L24-26. Farley also discloses “Implants can be superimposed onto the bone in the display 125, and implant colors can change or correspond to different types or sizes of implants” in [0096]; “the type of surgical procedure being performed” in [0097]; “in some embodiments, the Surgical Computer 150 may use data from past surgeries, machine learning models, etc. to help guide the surgical procedure” in [0109]; “Moreover, optimization may be performed using historical data, such as data generated during past surgeries involving, for example, the same surgeon, past patients with physical characteristics similar to the current patient, or the like.” in [0116]; “It is noted that the system has access to historical data from previous patients undergoing treatment, including implant size, placement, and orientation as generated by a computer-assisted, patient-specific knee instrument (PSKI) selection system, or automatically by the CASS 100 itself” in [0134]; “Any subsequent calculation of the target equation via the RNN will include the data from the previous patient in this manner, allowing for continuous improvement of the system” in [0136].)
As to Claim 11, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 10, wherein the feedback data used to train the machine learning model further characterizes a volume of bone graft used with the spinal implant device when implanted in the prior patient (STEINBERG discloses “In step 4, relationships are determined between selected pairs of vertebrae in the pre-operative three-dimensional images…and may include one or more of measurements between vertebral endplates, shape or volume of vertebra, distance between vertebrae, shape or volume of intervertebral space or characteristics of a disc between a vertebral pair…” in [0060]; “using predictive modeling such as machine learning, deep learning or any other statistical method that can be used to predict outcomes, operating on large databases of previously obtained data for similar procedures” in [0016]. Here, STEINBERG teaches using machine learning to characterize a shape or volume of one anatomic space. There is no disclosed criticality to the characterization of a volume of bone graft, but for a specific spinal surgical procedure. It is rendered obvious as a design choice (see MPEP 2144.04) that would have no impact on the function or results of the claimed invention.)
As to Claim 12, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 10, wherein the feedback data used to train the machine learning model further characterizes at least one of:
deviation between a planned level of spinal correction for the prior patient planned during a pre-operative stage and an achieved level of spinal correction for the prior patient measured during an intra-operative stage or post-operative stage;
deviation between a planned surgical procedure that was planned during a pre- operative stage and a used surgical procedure that was performed on the prior patient during an intra-operative stage;
deviation between a type and dimension sizing of a spinal implant device that was planned during a pre-operative stage and a used type and dimension sizing of a spinal implant device that was implanted into the prior patient during an intra-operative stage;
deviation between a planned pose of a spinal implant device for fixation into the spine of the prior patient planned during a pre-operative stage and a used pose of the spinal implant device following fixation into the spine of the prior patient during an intra-operative stage; and
deviation between a planned insertion trajectory for implantation of the spinal implant device into the spine of the prior patient planned during a pre-operative stage and a used trajectory that the spinal implant device was moved along when implanted into the spine of the prior patient during an intra-operative stage (Farley discloses “In some embodiments, the Surgical Computer 150 provides the Effector Platform 105 with instructions defining how to react when a component of the Effector Platform 105 deviates from a surgical plan” in [0089]; “Thus, by comparing the measured position to a position originally specified by the Surgical Computer 150 (see FIG. 2B), the Surgical Computer can identify deviations that take place during surgery” in [0101]; “The surgeon may choose to alter the surgical case plan at any time prior to or during the procedure. If the surgeon elects to deviate from the surgical case plan, the altered size, position, and/ or orientation of the component(s) is locked, and the global optimization is refreshed based on the new size, position, and/or orientation of the component (s) (using the techniques previously described) to find the new ideal position of the other component(s) and the corresponding resections needed to be performed to achieve the newly optimized size, position and/or orientation of the component(s)... If the resections made during the procedure deviate from the surgical plan, the subsequent placement of additional components may be optimized by the processor taking into account the actual resections that have already been made” in [0138], see also [0139-0140, 0149].)
As to Claim 13, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 1, further operative to:
form subsets of the feedback data having similarities that satisfy a defined rule (STEINBERG discloses “obtaining sets of data from the database for patients with similar characteristics to the subject” in Abstract);
within each of the subsets, identify correlations among at least some values of the feedback data; and train the machine learning model based on the correlations identified for each of the subsets (STEINBERG discloses “In step 7, it is obtained from the database, a set of data having patients with similar pre-operative relationships of vertebral pairs, and a similar surgical plan, to those of the subject. Artificial intelligence may be employed to determine a first risk of one or more pathology types for the subject based on correlations of the patients in this set to developed pathology types” in [0063]; “In step 8, it is obtained from the database, a set of data having patients with similar post-operative relationships of vertebral pairs to the vertebral relationships in the predicted post-operative images of the subject. Artificial intelligence may be employed to determine a second risk of one or more pathology types for the subject based on correlations of the patients in this set to developed pathology types” in [0064]).
Claim 16 recites similar limitations as claims 1, 2 & 5.
As to Claim 17, STEINBERG in view of Lang, Cameron and Farley teaches The surgical system of Claim 16, wherein the at least one controller is operative to generate a graphical representation of the navigation information that is provided to the display device of the XR headset to guide operator movement of the surgical tool along the planned trajectory (Lang discloses “Overlaying or superimposing, for example, a true 3D, e.g. stereoscopic 3D, view of the anatomy from the pre- or intra-operative imaging study and/or virtual surgical plan of the patient using the OHMD display onto the same anatomic structures and/or virtual surgical plan displayed in 2D or pseudo 3D… can be beneficial for the surgeon as he or she executes surgical plans or plans next surgical plans during a procedure” in C138L64-C139L5. Farley also discloses “For example, in FIG. 1 the Surgeon 111 is wearing an AR HMD 155 that may, for example, overlay pre-operative image data on the patient or provide surgical planning suggestions. Various example uses of the AR HMD 155 in surgical procedures are detailed in the sections that follow” in [0052]; “The robotic arm 105A and/or end effector 105B can be used to guide the impactor to impact the trial and final implants into the acetabulum in accordance with the surgical plan. The CASS 100 can cause the position and orientation of the trial and final implants vis-a-vis the bone to be displayed to inform the surgeon as to how the trial and final implant' s orientation and position compare to the surgical plan, and the display 125 can show the implant's position and orientation as the surgeon manipulates the leg and hip” in [0060].)
Claim 18 recites similar limitations as claims 1.
As to Claim 19, STEINBERG in view of Lang, Cameron and Farley teaches The surgical system of Claim 18, wherein the at least one controller is further operative to control movement of the at least one motor based on the navigation information to reposition the end effector so the determined pose of the end-effector becomes aligned with the planned pose (Lang, Fig 2 & 7. Cameron, [0052, 0069-0070]).
Claim 21 recites similar limitations as claims 1. For example, Lang discloses a virtual surgical plan including intended trajectory of surgical instrument in [0804], see also Fig 17D; using deep learning or other artificial intelligence methods for registration of virtual data with live data in [0203].
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over STEINBERG in view of Lang and Cameron, further in view of Farley and Ulrich, JR. et al. (US 2019/0247200).
As to Claim 7, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 2. The combination of Ulrich further teaches wherein the machine learning model is further operative to
process the pre-operative data to output the surgical plan with further identification of a portion of the patient's spinal disc that is to be removed to allow insertion of a spacer type of the spinal implant device (STEINBERG discloses “a method for determining if an instrumental spinal surgical plan acceptable for a subject, by predicting the likelihood that the subject will develop one or more pathology types or instrumental failures, such as screw pull-out, rod bending or breakage, implant loosening or the like, after the surgical plan has been performed on the subject” in [0076]. Here, the surgical plan can be an insertion of an implant device into a spinal disc space. For example, Ulrich discloses “identification of a spinal disc in need of repair or replacement, performance of at least a partial discectomy to create a disc space, and selection of the appropriate size of implant for the disc space” in [0021]; “During the method of using the system 700, the caretaker will first identify or diagnose a spinal disc in need of repair or replacement… The caretaker then assesses or gauges the appropriate size of implant 301 to insert into the disc space” in [0148].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of STEINBERG, Lang, Cameron and Farley with the teaching of Ulrich so as to generate a surgical plan of replacing a portion of spinal disc with an implant device.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over STEINBERG in view of Lang and Cameron, further in view of Farley and Cosgrove et al. (US 2020/0057933).
As to Claim 14, STEINBERG in view of Lang, Cameron and Farley teaches The surgical guidance system of Claim 1. The combination of Cosgrove further teaches following limitations:
wherein the machine learning model comprises:
a neural network component including an input layer having input nodes, a sequence of hidden layers each having a plurality of combining nodes, and an output layer having output nodes (STEINBERG discloses “Embodiments of the present disclosure relate to the field of image based pathology prediction for planning spinal surgery, using predictive modeling such as machine learning, deep learning or any statistical method that can be used to predict outcomes” in [0002]; see also [0018, 0020-0028]; artificial intelligence such as machine learning, deep learning, and neural networks in [0038]. Cosgrove further discloses “The neural network circuit has an input layer having input nodes, a sequence of hidden layers each having a plurality of combining nodes, and an output layer having an output node” in [0003]); and at least one processing circuit operative to provide different entries of the pre-operative data to different ones of the input nodes of the neural network model, and to generate the surgical plan based on output of output nodes of the neural network component (STEINBERG discloses “exemplary methods of the present disclosure for machine learning, and prediction for a subject based thereon. The method commences with assembly of a training set of data for a processor-based system for preprocessing and learning. The training set includes data on a plurality of patients, and data for each patient may include, inter alia, pre-operative three-dimensional images, postoperative three-dimensional images, surgical technique used, clinically relevant quantitative data” in [0052]. See also Fig 5 of Cosgrove as shown below:
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).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of STEINBERG, Lang, Cameron and Farley with the teaching of Cosgrove so as to explain data flow diagram of a neural network circuit.
As to Claim 15, STEINBERG in view of Lang, Cameron, Farley and Cosgrove teaches The surgical guidance system of Claim 14, further comprising a feedback training component operative to:
adapt weights and/or firing thresholds that are used by the combining nodes of the neural network component based on values of the feedback data (Cosgrove discloses “in one embodiment, the training module 220 operates to use the forecasted performance metrics from the metric forecasting module 210 and the measured performance metrics 200 to adapt the weights and/or firing thresholds are used by input nodes and which may also be used by nodes of the neural network hidden layers” in [0027].)
Conclusion
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/WEIMING HE/
Primary Examiner, Art Unit 2611