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 ("RCE"), 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 April 24, 2026, has been entered.
Status of Claims
Claims 75-120 were previously pending and subject to a Final Office Action having a notification date of December 2, 2025 (“Final Office Action”). Following the Final Office Action, Applicant filed an amendment on April 24, 2026 (“Amendment”), amending claims 75, 81, 89, 94, 95, 97, 104, 106, 108, 114, 115, 117, and 119. The Amendment resulted in an Advisory Action dated April 30, 2026 , indicating non-entry of the Amendment. Applicant then filed the RCE on May 4, 2026, requesting entry of the Amendment. The present non-final Office Action addresses pending claims 75-120 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §112
While these rejections are withdrawn in view of the Amendment, new rejections are presented herein in view of the Amendment.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §103
Applicant’s arguments are moot in view of the new grounds of rejection as necessitated by the Amendment.
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.
Claim 117 is 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.
Claim 117 recites the limitation "the training portion" in lines 5-6. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 119 and 120 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2022/0249168 to Besier et al. ("Besier") in view of U.S. Patent App. Pub. No. 2022/0142709 to Zucker ("Zucker") and U.S. Patent App. Pub. No. 2019/0146458 to Roh et al. ("Roh"):
Regarding claim 119, Besier discloses a method comprising:
receiving, at a computer system, imaging data of a subject under evaluation for surgery (step 641 in Figure 6, items 756, 757 in Figure 7, and [0120]-[0122] illustrates/discloses receiving images of a patient under evaluation for surgery at a server (computing system));
generating, via at least one processor of the computer system using the imaging data, a virtual three-dimensional (3D) biomechanical model of the subject (steps 642, 643 in Figure 6, items 753, 762, 763 in Figure 7, and [0120], [0123]-[0150] illustrate/disclose generating a 3D model of the patient anatomy using an image processing application running on the server which necessarily includes a processor);
executing a virtual surgical procedure, via the at least one processor, of a surgical procedure on the subject using the virtual 3D biomechanical model (step 644 in Figure 6, the item after item 763 in Figure 7, and [0120], [0151]-[0162] illustrate/disclose simulating a fit between an implant and the patient anatomy in the 3D model (executing a "virtual surgical procedure"), resulting in predicted post-operative biomechanical parameters ([0164] discloses determining geometric/functional measurements (predicted post-operative biomechanical parameters) between the implant and bones),
wherein the surgical procedure comprises at least one of:
implanting a physical 3D hardware implant into the subject ([0009]-[0011] discloses implant surgical procedures (physical 3D hardware implant));
cutting a bone of the subject ([0165] discloses bone resection); or
a spinal decompression of the subject, and
wherein execution of the virtual surgical procedure of the surgical procedure on the subject further comprises:
sequentially performing the virtual surgical procedure corresponding to the surgical procedure (step 647 in Figure 6, the arrow going back to "implant fit simulation" in Figure 7, and [0120] illustrate/discuss performing the simulation for a plurality of different implants (sequentially performing the virtual surgical procedure)), with at least one step in individual steps of the virtual surgical procedure performed using a plurality of … surgical approaches, the individual steps occurring at a plurality of potential locations within the virtual 3D biomechanical model of the subject ([0155] discloses how different regions/landmarks can be used as objectives or constraints in the fitting simulation (virtual surgical procedure) depending on the bone and type/brand/size/variant of the implant; accordingly, different implants can be implanted at different regions/landmarks (different locations) resulting in a plurality of surgical approaches at potential locations in the 3D model), resulting in a plurality of virtual surgical procedure results ([0177] discloses simulation results (virtual surgical procedure results)); and
performing, for each virtual surgical procedure result in the plurality of virtual surgical procedure results, a dynamic analysis, resulting in the predicted post-operative biomechanical parameters ([0164] discusses determining for each simulation/virtual surgical procedure the geometric/functional measurements between the implant and bones e.g., range of joint motion which would require a "dynamic analysis"; furthermore, Figures 2, 4, 6, 7, [0120] illustrate/discuss predicting post-operative motion assessments/analyses for each simulation with a different implant (for each virtual surgical procedure result));
…; and
scoring, via the at least one processor using the predicted post-operative biomechanical parameters, potential surgical outcomes of the surgical procedure on the subject ([0164] discloses calculating, based on the geometric/functional measurements between an implant and bones (predicted post-operative biomechanical parameters) during a simulation, a score indicative of the quality of fit such as range of motion, etc. (potential surgical outcome) while [0107]-[0115] discloses how various potential implants can be scored and ranked to facilitate a surgeon's to selection of an appropriate implant) for each of the plurality of potential locations, resulting in a plurality of surgical location prediction scores ([0155] discloses how different regions/landmarks (locations) can be used as objectives or constraints in the fitting simulation (virtual surgical procedure) depending on the bone and type/brand/size/variant of the implant while step 647 in Figure 6, the arrow going back to "implant fit simulation" in Figure 7, and [0120] illustrate/discuss performing the simulation for a plurality of different implants (sequentially performing the virtual surgical procedure); therefore, a plurality of "surgical location prediction scores" for each of the potential locations are determined).
While [0155] discloses how different regions/landmarks can be used as objectives or constraints in the fitting simulation (virtual surgical procedure) depending on the bone and type/brand/size/variant of the implant such that different implants implanted at different regions/landmarks (different locations) result in a plurality of surgical approaches at potential locations in the 3D model, Besier might be silent regarding such surgical approaches being distinct surgical approaches.
Nevertheless, Zucker teaches ([0031]) that it was known in the healthcare informatics art for common spinal surgery (e.g., lumbar interbody fusion) approaches to include anterior (ALIF), oblique/anterior to psoas (OLIF/ATP), lateral/extreme lateral (LLIF/XLIF), posterior (PLIF), and transforaminal (TLIF) ("distinct surgical approaches") to account for the difficulty in accessing the spinal column due to the internal positioning of the spinal column and because no single approach is ideal for each application ([0004]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the plurality of surgical approaches of Besier to be distinct surgical approaches as taught by Zucker to account for the difficulty in accessing the spinal column due to the internal positioning of the spinal column and because no single approach is ideal for each application. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Furthermore, the Besier/Zucker combination might be silent regarding the dynamic analysis specifically including analyzing redistribution of forces and moments to adjacent spinal levels.
Nevertheless, Roh teaches ([0127]-[0133]) that it was known in the healthcare informatics art to provide surgical assistance to a spinal surgeon via building a virtual 3D model of a patient's spine based on images of the patient (the virtual model including e.g., virtual vertebrae per [0130]), virtually insert/implant implants into the spine in the virtual 3D model, and analyze mechanical interaction between a patient's vertebrae and loading of implants to generate output usable to implant configurations/trajectories/locations to optimize axial/shear loads (forces) and moments to manage stresses between adjacent vertebrae (adjacent spinal levels). Such axial/shear loads (forces) and moments to adjacent spinal levels would need to be calculated/analyzed for each implant configuration/location/etc. (redistribution of forces and moments to adjacent spinal levels) in order to optimize such axial/shear loads (forces) and moments to manage stresses between adjacent vertebrae. This arrangement advantageously provides assistance to a surgeon in screw/implant placement during a spinal procedure ([0007]) via providing patient-specific surgical information, surgical plans, technology recommendations (e.g., implant/instrument recommendations), etc. ([0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the dynamic analysis of the Besier/Zucker combination to include analyzing redistribution of forces and moments to adjacent spinal levels similar to as taught by Roh to advantageously provide assistance to a surgeon in screw/implant placement during a spinal procedure via providing patient-specific surgical information, surgical plans, technology recommendations, etc. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 120, the Besier/Zucker/Roh combination discloses the method of claim 119, further including wherein the plurality of distinct surgical approaches comprises at least an anterior approach, a posterior approach, or a lateral approach (Zucker discloses ([0031]) common spinal surgery (e.g., lumbar interbody fusion) approaches to include anterior (ALIF), oblique/anterior to psoas (OLIF/ATP), lateral/extreme lateral (LLIF/XLIF), posterior (PLIF), and transforaminal (TLIF) ("distinct surgical approaches") to account for the difficulty in accessing the spinal column due to the internal positioning of the spinal column and because no single approach is ideal for each application ([0004]); again, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the plurality of surgical approaches of Besier to be distinct surgical approaches as taught by Zucker to account for the difficulty in accessing the spinal column due to the internal positioning of the spinal column and because no single approach is ideal for each application. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claims 75, 80-83, 85, 89, 94-100, 102, 106-111, 113-116, and 118 are rejected under 35 U.S.C. 103 as being unpatentable over NPL "Preoperative Planning Simulator for Spinal Deformity Surgeries" to Aubin et al. ("Aubin") in view of U.S. Patent App. Pub. No. 2022/0249168 to Besier et al. ("Besier"), U.S. Patent App. Pub. No. 2021/0169576 to Yoshinaka et al. ("Yoshinaka"), and U.S. Patent App. Pub. No. 2019/0146458 to Roh et al. ("Roh"):
Regarding claim 75, Aubin discloses a method comprising:
receiving, at a computer system, imaging data of a subject under evaluation for surgery (the bottom half of the left column on page 2144 and the bottom of the left column to the top of the right column on page 2145 discloses how a spine surgery simulator including software obtains preoperative radiographs of a patient's spine, where software would be implemented on a computer system);
generating, via at least one processor of the computer system using the imaging data, a virtual three-dimensional (3D) biomechanical model of the subject (the bottom half of the left column on page 2144 and the bottom of the left column to the top of the right column on page 2145 discloses how the simulator generates a 3D reconstructed model of the patient's spine (virtual 3D biomechanical model of the subject) using the acquired radiographs/images; the above-mentioned computer system necessarily includes a processor that executes the software);
executing a virtual surgical procedure, via the at least one processor, of a surgical procedure on the subject using the virtual 3D biomechanical model (the top half of the right column on page 2145 discloses performing surgical maneuvers on the 3D model which is a "virtual surgical procedure"), resulting in predicted post-operative biomechanical parameters (the bottom half of the right column on page 2145, the left column on page 2146, and the top of the left column on page 2151 disclose generating geometry and reaction forces, etc. for each action in the simulation (predicted post-operative biomechanical parameters)), wherein the surgical procedure comprises at least one of:
implanting a physical 3D hardware implant into the subject (the right column on page 2145 discloses inserting implants while the end of the left column on page 2151 discloses how the simulator allows surgeons to simulate surgical corrections before actual surgery; accordingly, the implant insertion simulation corresponds to implanting a physical 3D hardware implant into the patient/subject);
cutting a bone of the subject; or
a spinal decompression of the subject (the bottom of the right column on page 2145 discloses distraction which is a type of decompression), and
wherein execution of the virtual surgical procedure of the surgical procedure on the subject further comprises:
sequentially performing the virtual surgical procedure corresponding to the surgical procedure at a plurality of potential locations within the virtual 3D biomechanical model of the subject (page 2145 discusses using the simulator to simulate the installation of implants into the patient's 3D model at various locations; the left and right columns of page 2151 discuss how surgeons can use the simulator to analyze/compare different preoperative surgical strategies and obtain a report regarding the details including implant type and position at each level (plurality of potential locations within the virtual 3D biomechanical model); also, Table 2 on page 2150 discloses various implants at various locations; still further, the middle of the right column on page 2145 discloses how surgeons can adjust locations/orientations); each virtual procedure/adjustment performed at a potential location after a previous virtual procedure/adjustment is performed at a respective potential location is a sequentially performed virtual procedure), resulting in a plurality of virtual surgical procedure results (the left and right columns on page 2147 and the left column on page 2050 disclose simulation results; for instance, Figure 3 illustrates simulation results; furthermore, every simulation has some result/outcome/conclusion); and
performing, for each virtual surgical procedure result in the plurality of virtual surgical procedure results, a dynamic analysis, resulting in the predicted post-operative biomechanical parameters (the bottom half of the right column on page 2145, the left column on page 2146, and the top of the left column on page 2151 disclose generating geometry and reaction forces, etc. for each simulation (performing, for each virtual/simulation result, a dynamic analysis resulting in the predicted post-operative biomechanical parameters));
…
…
While Aubin discloses (right column of page 2151) how the simulator provides a means of examining possible outcomes of different instrumentation strategies before planning the surgery, Aubin might be silent regarding specifically
training a machine learning algorithm using historical data of individuals which previously underwent the surgical procedure, the historical data comprising:
historical pre-operative data of the individuals:
historical post-operative data of the individuals; and
historical surgical success outcome of the individuals,
wherein the historical surgical success outcome comprises both successes and failures of the surgical procedure; and
executing, via the at least one processor using the predicted post-operative biomechanical parameters as input, the machine learning algorithm,
…, and
wherein the machine learning algorithm outputs potential surgical outcomes of the surgical procedure on the subject for each of the plurality of potential locations…
Nevertheless, Besier teaches that it was known in the healthcare informatics and machine learning art for an ML model to learn (i.e., be trained) from pre and post-operative data (which would necessarily be associated with a patient that underwent a surgical procedure and which is "historical" data as it already existed at the time it was used to train the ML model) ([0095]-[0099]) and surgical outcomes (surgical success outcomes) ([0078]) and to input predicted post-operative assessment data (which relates to predicted post-operative function of the anatomical structure for one or more implants per [0014] ("predicted post-operative biomechanical parameters")) into the ML model as part of determining (outputting) implant selection and placement ([0086]). For instance, the ML model can automatically/iteratively determine whether each of a plurality of implants results in ranges of motion (potential surgical outcomes) that fall within one or more threshold parameters ([0082]). Training and executing an ML model to generate surgical outcome predictions in this manner advantageously processes data automatically with minimal input from surgeons/medical professionals ([0078]) to assist with predictive functions of the system ([0086]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have trained a machine learning algorithm using historical data of an individual which previously underwent the surgical procedure, the historical data comprising: historical pre-operative data of the individual, historical post-operative data of the individual, and historical surgical success outcome of the individual; and executed, via the at least one processor using the predicted post-operative biomechanical parameters as input, the machine learning algorithm to output potential surgical outcomes of the surgical procedure on the subject in system of Aubin as taught by Besier to advantageously process data automatically with minimal input from surgeons/medical professionals to assist with predictive functions of the system. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id. As Aubin already discloses performing the virtual surgical procedure at a plurality of potential locations as discussed above, then the potential surgical outcomes of the surgical procedure on the subject outputted by the machine learning algorithm per the above combination with Besier are "for each of the plurality of potential locations."
Furthermore, the Aubin/Besier combination appears to be silent regarding the historical data used to train the ML algorithm being of [a plurality of] individuals and the historical surgical success outcome of the individuals including both successes and failures of the surgical procedure.
Nevertheless, Yoshinaka teaches ([0068]-[0075]) that it was known in the healthcare informatics and machine learning art to train an ML model of a surgical planning system with prior surgical case outcomes (associated with various patients per [0056]) including both successful surgical outcomes and unsuccessful surgical outcomes (failures) to provide a recommended surgical plan as output to advantageously maximize positive outcomes under the circumstances ([0068]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have trained the ML algorithm of the Aubin/Besier combination with historical data of a plurality of individuals and for the historical surgical success outcome of the individuals to include both successes and failures of the surgical procedure similar to as taught by Yoshinaka to advantageously maximize positive outcomes under the circumstances. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Finally, the Aubin/Besier/Yoshinaka combination might be silent regarding the dynamic analysis specifically including analyzing redistribution of forces and moments to adjacent spinal levels and the machine learning algorithm determining a correlation between (1) at least one of pre-operative and post-operative biomechanical parameters and (2) success or failure of the surgical procedure, such that the output potential surgical outcomes are based on the correlation.
Nevertheless, Roh teaches ([0127]-[0133]) that it was known in the healthcare informatics art to provide surgical assistance to a spinal surgeon via building a virtual 3D model of a patient's spine based on images of the patient (the virtual model including e.g., virtual vertebrae per [0130]), virtually insert/implant implants into the spine in the virtual 3D model, and analyze mechanical interaction between a patient's vertebrae and loading of implants to generate output usable to implant configurations/trajectories/locations to optimize axial/shear loads (forces) and moments to manage stresses between adjacent vertebrae (adjacent spinal levels). Such axial/shear loads (forces) and moments to adjacent spinal levels would need to be calculated/analyzed for each implant configuration/location/etc. (redistribution of forces and moments to adjacent spinal levels) in order to optimize such axial/shear loads (forces) and moments to manage stresses between adjacent vertebrae. Roh also teaches ([0135]-[0136]) that it was known in the healthcare informatics and machine learning art to train an ML model with "training items" including input and corresponding scored results similar to when in use such as pre-operative MRI scans (converted into a format suitable for input into the ML model such as measuring vertebrae features in the images (e.g., "biomechanical parameters") per [0105]-[0106]) and surgical results (e.g., positive/success, negative/failure). As part of generating such results based on the input "biomechanical parameters," the ML model would necessarily determine a correlation/association/link between the biomechanical parameters and success/positive result or failure/negative result of the implantation/surgical procedure as that is how ML models function (i.e., based on particular inputs, the ML model determines corresponding/correlated outputs). This arrangement advantageously provides an efficient mechanism for providing assistance to a surgeon in screw placement during a spinal procedure and patient-specific surgical systems ([0007]) via providing patient-specific surgical information, surgical plans, technology recommendations (e.g., implant/instrument recommendations), etc. ([0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the dynamic analysis of the Aubin/Besier/Yoshinaka combination to include analyzing redistribution of forces and moments to adjacent spinal levels and for the machine learning algorithm to determine a correlation between (1) at least one of pre-operative and post-operative biomechanical parameters and (2) success or failure of the surgical procedure such that the output potential surgical outcomes are based on the correlation similar to as taught by Roh to advantageously provide assistance to a surgeon in screw/implant placement during a spinal procedure via providing patient-specific surgical information, surgical plans, technology recommendations, etc. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 80, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, further including wherein the surgical procedure comprises spinal decompression of the subject (the bottom of the right column on page 2145 of Aubin discloses spinal distraction which is a type of spinal decompression), the execution of the virtual surgical procedure further comprises:
sequentially performing spinal decompression at the plurality of potential locations within the virtual 3D biomechanical model (page 2145 of Aubin discusses using the simulator to simulate the installation of implants into the patient's 3D model at various locations including spinal distraction/decompression; the left and right columns of page 2151 discuss how surgeons can use the simulator to analyze/compare different preoperative surgical strategies and obtain a report regarding the details including implant type and position at each level (plurality of potential locations within the virtual 3D biomechanical model); also, Table 2 on page 2150 discloses various implants at various locations for performing distraction; still further, the middle of the right column on page 2145 discloses how surgeons can adjust locations/orientations); each virtual procedure/adjustment/distraction/decompression performed at a potential location after a previous virtual procedure/adjustment/distraction/decompression is performed at a respective potential location is a sequentially performed spinal distraction/decompression), resulting in a plurality of virtual surgical procedure results (the left and right columns on page 2147 and the left column on page 2050 disclose simulation results; for instance, Figure 3 illustrates simulation results; furthermore, every simulation has some result/outcome/conclusion).
Regarding claim 81, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, further including wherein the machine learning algorithm outputs the potential surgical outcomes based at least in part on comparing the predicted post-operative biomechanical parameters against known results of other subjects having undergone the surgical procedure ([0086] of Besier discloses how the ML model performs/outputs the post-operative function assessment (potential surgical outcomes) based on the predicted post-operative assessment (predicted post-operative biomechanical parameters) and post-operative data obtained from previous patients (other subjects having undergone the procedure); similar to as discussed above, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the ML algorithm to output the potential surgical outcomes based at least in part on comparing the predicted post-operative biomechanical parameters against known results of other subjects having undergone the surgical procedure as taught by Besier to advantageously process data automatically with minimal input from surgeons/medical professionals to assist with predictive functions of the system. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Regarding claim 82, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, further including wherein the surgical procedure further comprises one of a set of possible surgical interventions including artificial intervertebral disc replacement, spinal fusion, laminectomy, or spinal deformity correction (the Title and Conclusion of Aubin on pages 2143 and 2151 discloses correction of spinal deformities/scoliosis).
Regarding claim 83, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, further including wherein performing of the dynamic analysis for each of the plurality of potential locations further comprises analyzing one or more of forces, moments, range of motion, stress analysis, ligament strength, and vertebral strength of at least some spinal segments (the bottom half of the right column on page 2145, the left column on page 2146, and the top of the left column on page 2151 of Aubin disclose generating geometry and reaction forces between the implants and vertebrae (spinal segments)).
Regarding claim 85, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, further including wherein the virtual 3D biomechanical model of the subject can represent the spine of the subject both at rest and in positions of motion (the right column of page 2144 of Aubin discloses degrees of freedom, displacement, flexibility, and rotation of vertebrae/implants of the 3D biomechanical model of the patient/subject; the top right column of page 2145 discloses how the 3D model can be uploaded ready to be used to simulate surgical maneuvers; also, the left column of page 2146 of Aubin discloses simulating maneuvers including vertebral rotation (movement of spine); alternatively, when the surgical maneuvers are not being simulated, then the 3D model represents the spine of the subject at rest).
Regarding claim 89, Aubin discloses a system (the bottom half of the left column on page 2144 and the bottom of the left column to the top of the right column on page 2145 discloses a spine surgery simulator including software which would be implemented on a computer system) comprising:
at least one processor (a computer system includes a processor); and
a non-transitory computer-readable storage media having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations (a computer system includes instructions stored on memory and executable by the processor).
The remaining limitations of claim 89 are disclosed by the Aubin/Besier/Yoshinaka/Roh combination as discussed above in relation to claim 75.
Claim 94 is rejected in view of the Aubin/Besier/Yoshinaka/Roh combination as discussed above in relation to claim 75.
Regarding claim 95, Aubin discloses a method comprising:
receiving, at a computer system, pre-operative imaging data of a subject under evaluation for surgery (the bottom half of the left column on page 2144 and the bottom of the left column to the top of the right column on page 2145 discloses how a spine surgery simulator including software obtains preoperative radiographs of a patient's spine, where software would be implemented on a computer system);
generating, via at least one processor of the computer system using the pre-operative imaging data, a virtual three-dimensional (3D) biomechanical model of the subject (the bottom half of the left column on page 2144 and the bottom of the left column to the top of the right column on page 2145 discloses how the simulator generates a 3D reconstructed model of the patient's spine (virtual 3D biomechanical model of the subject) using the acquired radiographs/images; the above-mentioned computer system necessarily includes a processor that executes the software);
executing a virtual surgical procedure, via the at least one processor, of a planned surgical procedure on the subject using the virtual 3D biomechanical model (the top half of the right column on page 2145 discloses performing surgical maneuvers on the 3D model), resulting in predicted post-operative biomechanical parameters (the bottom half of the right column on page 2145, the left column on page 2146, and the top of the left column on page 2151 disclose generating geometry and reaction forces, etc. for each action in the simulation (predicted post-operative biomechanical parameters),
wherein execution of the virtual surgical procedure further comprises:
sequentially inserting each of a plurality of potential virtual 3D implants into the virtual 3D biomechanical model of the subject (page 2145 discusses using the simulator to simulate the installation of implants into the patient's 3D; the left and right columns of page 2151 discuss how surgeons can use the simulator to analyze/compare different preoperative surgical strategies (e.g., different implant types, configurations, etc.) and obtain a report regarding the details including implant type and position at each level; also, Table 2 on page 2150 discloses various implants; each virtual procedure/implant performed after a previous virtual procedure/implant is a sequentially inserted virtual implant), the plurality of potential virtual 3D implants corresponding to a plurality of potential physical 3D implants (the right column on page 2145 discloses inserting implants while the end of the left column on page 2151 discloses how the simulator allows surgeons to simulate surgical corrections before actual surgery; accordingly, the potential virtual 3D implants correspond to respective potential physical 3D implants); and
performing, for each potential virtual 3D implant in the plurality of potential virtual 3D implants, a dynamic analysis, resulting in the predicted post-operative biomechanical parameters for each of the plurality of potential physical 3D implants (the bottom half of the right column on page 2145, the left column on page 2146, and the top of the left column on page 2151 disclose generating geometry and reaction forces, Cobb angles, vertebral rotation, pullout forces, etc. (predicted post-operative biomechanical parameters generated via a dynamic analysis));
…
…
While Aubin discloses (right column of page 2151) how the simulator provides a means of examining possible outcomes of different instrumentation strategies before planning the surgery, Aubin might be silent regarding specifically
training a machine learning algorithm using historical data of individuals which previously underwent the surgical procedure, the historical data comprising:
historical pre-operative data of the individuals:
historical post-operative data of the individuals; and
historical surgical success outcome of the individuals,
wherein the historical surgical success outcome comprises both successes and failures of the surgical procedure; and
executing, via the at least one processor using the predicted post-operative biomechanical parameters as input, the machine learning algorithm,
…, and
wherein the machine learning algorithm outputs potential surgical outcomes of the surgical procedure of the subject for each of the plurality of potential virtual 3D implants…
Nevertheless, Besier teaches that it was known in the healthcare informatics and machine learning art for an ML model to learn (i.e., be trained) from pre and post-operative data (which would necessarily be associated with a patient that underwent a surgical procedure and which is "historical" data as it already existed at the time it was used to train the ML model) ([0095]-[0099]) and surgical outcomes (surgical success outcomes) ([0078]) and to input predicted post-operative assessment data (which relates to predicted post-operative function of the anatomical structure for one or more implants per [0014]("predicted post-operative biomechanical parameters")) into the ML model as part of determining (outputting) implant selection and placement ([0086]). For instance, the ML model can automatically/iteratively determine whether each of a plurality of implants results in ranges of motion (potential surgical outcomes) that fall within one or more threshold parameters ([0082]). Training and executing an ML model to generate surgical outcome predictions in this manner advantageously processes data automatically with minimal input from surgeons/medical professionals ([0078]) to assist with predictive functions of the system ([0086]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have trained a machine learning algorithm using historical data of an individual which previously underwent the surgical procedure, the historical data comprising: historical pre-operative data of the individual, historical post-operative data of the individual, and historical surgical success outcome of the individual; and executed, via the at least one processor using the predicted post-operative biomechanical parameters as input, the machine learning algorithm to output potential surgical outcomes of the surgical procedure on the subject for each of a plurality of potential virtual 3D implants in system of Aubin as taught by Besier to advantageously process data automatically with minimal input from surgeons/medical professionals to assist with predictive functions of the system. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Furthermore, the Aubin/Besier combination appears to be silent regarding the historical data used to train the ML algorithm being of [a plurality of] individuals and the historical surgical success outcome of the individuals including both successes and failures of the surgical procedure.
Nevertheless, Yoshinaka teaches ([0068]-[0075]) that it was known in the healthcare informatics and machine learning art to train an ML model of a surgical planning system with prior surgical case outcomes (associated with various patients per [0056]) including both successful surgical outcomes and unsuccessful surgical outcomes (failures) to provide a recommended surgical plan as output to advantageously maximize positive outcomes under the circumstances ([0068]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have trained the ML algorithm of the Aubin/Besier combination with historical data of a plurality of individuals and for the historical surgical success outcome of the individuals to include both successes and failures of the surgical procedure similar to as taught by Yoshinaka to advantageously maximize positive outcomes under the circumstances. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Finally, the Aubin/Besier/Yoshinaka combination might be silent regarding the dynamic analysis specifically including analyzing redistribution of forces and moments to adjacent spinal levels and the machine learning algorithm determining a correlation between (1) at least one of pre-operative and post-operative biomechanical parameters and (2) success or failure of the surgical procedure, such that the output potential surgical outcomes are based on the correlation.
Nevertheless, Roh teaches ([0127]-[0133]) that it was known in the healthcare informatics art to provide surgical assistance to a spinal surgeon via building a virtual 3D model of a patient's spine based on images of the patient (the virtual model including e.g., virtual vertebrae per [0130]), virtually insert/implant implants into the spine in the virtual 3D model, and analyze mechanical interaction between a patient's vertebrae and loading of implants to generate output usable to implant configurations/trajectories/locations to optimize axial/shear loads (forces) and moments to manage stresses between adjacent vertebrae (adjacent spinal levels). Such axial/shear loads (forces) and moments to adjacent spinal levels would need to be calculated/analyzed for each implant configuration/location/etc. (redistribution of forces and moments to adjacent spinal levels) in order to optimize such axial/shear loads (forces) and moments to manage stresses between adjacent vertebrae. Roh also teaches ([0135]-[0136]) that it was known in the healthcare informatics and machine learning art to train an ML model with "training items" including input and corresponding scored results similar to when in use such as pre-operative MRI scans (converted into a format suitable for input into the ML model such as measuring vertebrae features in the images (e.g., "biomechanical parameters") per [0105]-[0106]) and surgical results (e.g., positive/success, negative/failure) based on various screw/implant configurations. As part of generating such results based on the input "biomechanical parameters," the ML model would necessarily determine a correlation/association/link between the biomechanical parameters and success/positive result or failure/negative result of the implantation/surgical procedure as that is how ML models function (i.e., based on particular inputs, the ML model determines corresponding/correlated outputs). This arrangement advantageously provides an efficient mechanism for providing assistance to a surgeon in screw placement during a spinal procedure and patient-specific surgical systems ([0007]) via providing patient-specific surgical information, surgical plans, technology recommendations (e.g., implant/instrument recommendations), etc. ([0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the dynamic analysis of the Aubin/Besier/Yoshinaka combination to include analyzing redistribution of forces and moments to adjacent spinal levels and for the machine learning algorithm to determine a correlation between (1) at least one of pre-operative and post-operative biomechanical parameters and (2) success or failure of the surgical procedure such that the output potential surgical outcomes are based on the correlation similar to as taught by Roh to advantageously provide assistance to a surgeon in screw/implant placement during a spinal procedure via providing patient-specific surgical information, surgical plans, technology recommendations, etc. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 96, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 95, further including selecting at least one of the plurality of potential physical 3D implants for use in the planned surgical procedure based at least in part on the plurality of potential surgical outcomes (page 2151 of Aubin discloses using the predictions to facilitate preoperative planning of a surgical strategy which would include selecting a surgical strategy for an actual surgical procedure while [0082] of Besier discloses selecting an implant when a predicted range of motion (potential surgical outcome) falls within a threshold; similar to as discussed above, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have selected at least one of the plurality of potential physical 3D implants of the Aubin/Besier/Yoshinaka combination for use in the surgical procedure based at least in part on the potential surgical outcomes as taught by Besier to advantageously facilitate a surgeon's selection of an appropriate implant and surgical procedure to improve patient surgical outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Claims 97, 98, 100, and 102 are rejected in view of the Aubin/Besier/Yoshinaka/Roh combination as respectively discussed above in relation to claims 81, 82, 83, and 85.
Regarding claim 99, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 95, further including wherein the planned surgical procedure comprises insertion of one or more hardware implants (page 2145 of Aubin disclose insertion of implants).
Regarding claim 106, Aubin discloses a system (the bottom half of the left column on page 2144 and the bottom of the left column to the top of the right column on page 2145 discloses a spine surgery simulator including software which would be implemented on a computer system) comprising:
at least one processor (a computer system includes a processor); and
a non-transitory computer-readable storage media having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations (a computer system includes instructions stored on memory and executable by the processor).
The remaining limitations of claim 106 are disclosed by the Aubin/Besier/Yoshinaka/Roh combination as discussed above in relation to claim 95.
Claim 107 is rejected in view of the Aubin/Besier/Yoshinaka/Roh combination as discussed above in relation to claim 96.
Claims 108, 109, 111, and 113 are rejected in view of the Aubin/Besier/Yoshinaka/Roh combination as respectively discussed above in relation to claims 81, 82, 83, and 85.
Claim 110 is rejected in view of the Aubin/Besier/Yoshinaka/Roh combination as discussed above in relation to claim 99.
Claim 114 is rejected in view of the Aubin/Besier/Yoshinaka/Roh combination as discussed above in relation to claim 106.
Regarding claim 115, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, further including wherein the dynamic analysis performed on each virtual surgical procedure result in the plurality of virtual surgical procedure results further comprises:
embedding at least one of virtual surgical intervention or at least one virtual implant in the virtual 3D biomechanical model (the right column on page 2145 of Aubin discloses inserting implants while the end of the left column on page 2151 discloses how the simulator allows surgeons to simulate surgical corrections before actual surgery; accordingly, the implant insertion simulation corresponds to embedding a virtual implant into the virtual 3D model).
Regarding claim 116, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 115, further including wherein the potential surgical outcomes further comprises a plurality of surgical location prediction scores ([0164] of Besier discloses calculating scores indicative of the quality of fit such as range of motion, etc. (potential surgical outcome) to advantageously facilitate a surgeon's to selection of an appropriate implant ([0107]-[0115]); therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the potential surgical outcomes of the Aubin/Besier/Yoshinaka/Roh combination to further include a plurality of surgical location prediction scores as taught by Besier to advantageously facilitate a surgeon's selection of an appropriate implant. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Regarding claim 118, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 116, further including
updating the machine learning algorithm based on the predicted post-operative biomechanical parameters, resulting in an updated machine learning algorithm, wherein the updated machine learning algorithm is used in future evaluations (Figure 2 and [0078] of Besier illustrates/discloses feeding predicted post-operative assessments back to the ML model to update the model for future modeling/processing; therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have updated the machine learning algorithm based on the predicted post-operative biomechanical parameters, resulting in an updated machine learning algorithm, wherein the updated machine learning algorithm is used in future evaluations, in the system of the Aubin/Besier/Yoshinaka/Roh combination as taught by Besier to advantageously improve the accuracy of future evaluations; A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Claims 76-79 and 90-93 are rejected under 35 U.S.C. 103 as being unpatentable over NPL "Preoperative Planning Simulator for Spinal Deformity Surgeries" to Aubin et al. ("Aubin") in view of U.S. Patent App. Pub. No. 2022/0249168 to Besier et al. ("Besier"), U.S. Patent App. Pub. No. 2021/0169576 to Yoshinaka et al. ("Yoshinaka"), and U.S. Patent App. Pub. No. 2019/0146458 to Roh et al. ("Roh"), and further in view of U.S. Patent App. Pub. No. 2021/0307833 to Farley et al. ("Farley"):
Regarding claim 76, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, further including…, the execution of the virtual surgical procedure further comprises:
sequentially inserting into the plurality of potential locations each of a plurality of potential virtual 3D implants into the virtual 3D biomechanical model (page 2145 of Aubin discusses using the simulator to simulate the installation of implants into the patient's 3D model at various locations; the left and right columns of page 2151 discuss how surgeons can use the simulator to analyze/compare different preoperative surgical strategies (e.g., different implant types, configurations, etc.) and obtain a report regarding the details including implant type and position at each level (plurality of potential locations within the virtual 3D biomechanical model); also, Table 2 on page 2150 discloses various implants at various locations; still further, the middle of the right column on page 2145 discloses how surgeons can adjust locations/orientations); each virtual procedure/adjustment performed after a previous virtual procedure/adjustment is a sequentially performed virtual procedure), the plurality of potential virtual 3D implants corresponding to a plurality of potential physical 3D implants (the right column on page 2145 of Aubin discloses inserting implants while the end of the left column on page 2151 discloses how the simulator allows surgeons to simulate surgical corrections before actual surgery; accordingly, the potential virtual 3D implants correspond to respective potential physical 3D implants); and
performing, for each potential virtual 3D implant in the plurality of potential virtual 3D implants, an additional dynamic analysis, resulting in additional predicted post-operative biomechanical parameters for each of the plurality of potential physical 3D implants (the bottom half of the right column on page 2145, the left column on page 2146, and the top of the left column on page 2151 of Aubin disclose generating geometry and reaction forces, Cobb angles, vertebral rotation, pullout forces, etc. (some of which are the "initial" predicted post-operative biomechanical parameters and others of which are "additional" predicted post-operative biomechanical parameters that are generated via an "additional" dynamic analysis); additionally or alternatively, the middle of the right column on page 2145 discloses how each time a user defines an action during the simulation, the resulting geometry and reaction forces are generated and displayed ("additional" predicted post-operative biomechanical parameters that are generated via an "additional" dynamic analysis).
However, the Aubin/Besier/Yoshinaka/Roh combination appears to be silent regarding the sequential virtual 3D implant insertion and additional dynamic analysis occurring when the surgical procedure comprises cutting a bone of the subject.
Nevertheless, Farley teaches ([0093]-[0095], [0099]) that it was known in the healthcare informatics art to simulate surgical procedures via performing bone cuts on a 3D model of a patient; installing implants; and generating information regarding bone spacing, joint tension/rotation/alignment, functional parameters, etc. to advantageously improve the ability of the simulation to simulate an actual surgical procedure and convey important information to surgeons to optimize implant placement.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the sequential virtual 3D implant insertion and additional dynamic analysis of the Aubin/Besier/Yoshinaka/Roh combination to occur when the surgical procedure comprises cutting a bone of the subject as taught by Farley to advantageously improve the ability of the simulation to simulate an actual surgical procedure and convey important information to surgeons to optimize implant placement. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 77, the Aubin/Besier/Yoshinaka/Roh/Farley combination discloses the method of claim 76, further including wherein the additional predicted post-operative biomechanical parameters are provided as further inputs to the machine learning algorithm ([0086] of Besier discloses inputting predicted post-operative assessment data (which relates to predicted post-operative function of the anatomical structure for one or more implants per [0014]("predicted post-operative biomechanical parameters")) into the ML model as part of determining (outputting) implant selection and placement to advantageously assist with predictive functions of the system ([0086]); therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have provided the additional predicted post-operative biomechanical parameters as further inputs to the machine learning algorithm as taught by Besier to advantageously assist with predictive functions of the system thereby improving surgical outcomes for patients. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Regarding claim 78, the Aubin/Besier/Yoshinaka/Roh/Farley combination discloses the method of claim 76, further including:
selecting at least one of the plurality of potential physical 3D implants for use in the surgical procedure based at least in part on the potential surgical outcomes (page 2151 of Aubin discloses using the predictions to facilitate preoperative planning of a surgical strategy which would include selecting a surgical strategy for an actual surgical procedure while [0082] of Besier discloses selecting an implant when a predicted range of motion (potential surgical outcome) falls within a threshold; similar to as discussed above, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have selected at least one of the plurality of potential physical 3D implants of the Aubin/Besier/Yoshinaka/Roh/Farley combination for use in the surgical procedure based at least in part on the potential surgical outcomes as taught by Besier to advantageously facilitate a surgeon's selection of an appropriate implant and surgical procedure to improve patient surgical outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Regarding claim 79, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, further including wherein the surgical procedure comprises implanting the physical 3D hardware implant into the subject (the right column on page 2145 of Aubin discloses inserting implants while the end of the left column on page 2151 discloses how the simulator allows surgeons to simulate surgical corrections before actual surgery; accordingly, the implant insertion simulation corresponds to implanting a physical 3D hardware implant into the patient/subject).
However, the Aubin/Besier/Yoshinaka/Roh combination appears to be silent regarding the execution of the virtual surgical procedure further including sequentially cutting the bone at the plurality of potential locations within the virtual 3D biomechanical model, resulting in the plurality of virtual surgical procedure results.
Nevertheless, Farley teaches ([0093]-[0095], [0099]) that it was known in the healthcare informatics art to simulate surgical procedures via performing bone cuts on a 3D model of a patient (e.g., where some cuts can be performed before other bone cuts per [0097] (sequential bone cuts)) and installing implants to see how changes to a surgical plan impact the procedure and final position and orientation of implants installed on bone (virtual surgical procedure results) to advantageously improve the ability of the simulation to simulate an actual surgical procedure and convey important information to surgeons to optimize implant placement.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the simulation of the Aubin/Besier/Yoshinaka/Roh combination to include sequentially cutting the bone at the plurality of potential locations within the virtual 3D biomechanical model, resulting in the plurality of virtual surgical procedure results as taught by Farley to advantageously improve the ability of the simulation to simulate an actual surgical procedure and convey important information to surgeons to optimize implant placement. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claims 90-93 are rejected in view of the Aubin/Besier/Yoshinaka/Roh/Farley combination as respectively discussed above in relation to claims 76-79.
Claims 84, 101, and 112 are rejected under 35 U.S.C. 103 as being unpatentable over NPL "Preoperative Planning Simulator for Spinal Deformity Surgeries" to Aubin et al. ("Aubin") in view of U.S. Patent App. Pub. No. 2022/0249168 to Besier et al. ("Besier"), U.S. Patent App. Pub. No. 2021/0169576 to Yoshinaka et al. ("Yoshinaka"), and U.S. Patent App. Pub. No. 2019/0146458 to Roh et al. ("Roh"), and further in view of U.S. Patent App. Pub. No. 2022/0157463 to McKinnon ("McKinnon"):
Regarding claim 84, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, but appears to be silent regarding wherein the predicted post-operative biomechanical parameters comprise a degree of deterioration of at least one of at least one vertebra, at least one intravertebral disc, at least one ligament, or at least one muscle.
Nevertheless, McKinnon teaches ([0035], [0038], [0042], [0047], [0050], [0059]) that it was known in the healthcare informatics art to receive image and anthropometric data of a patient, generate a musculoskeletal simulation (biomechanical model), perform virtual procedures on the patient using the biomechanical simulation/model (simulate a planned surgical procedure using the generated virtual biomechanical model of the subject), and estimate/predict biomechanical data (such as joint loading data, gait mechanics data, force magnitudes in joints, etc. as well as joint damage index per [0052]-[0053] and Figure 3 (degree of deterioration of joints)) (derived post-operative biomechanical parameters). The derived biomechanical data can advantageously be utilized to provide a holistic, whole-body assessment of the patient’s biomechanical profile which can in turn be used to assess therapies that would be most efficacious for the patient ([0042]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the predicted post-operative biomechanical parameters to include a degree of deterioration of at least one joint (which, in the case of Aubin, is at least one vertebra) in the system of the Aubin/Besier/Yoshinaka/Roh combination as taught by McKinnon to advantageously provide a holistic, whole-body assessment of the patient’s biomechanical profile which can in turn be used to assess therapies that would be most efficacious for the patient. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claims 101 and 112 are rejected in view of the Aubin/Besier/Yoshinaka/Roh/McKinnon combination as discussed above in relation to claim 84.
Claims 86 and 103 are rejected under 35 U.S.C. 103 as being unpatentable over NPL "Preoperative Planning Simulator for Spinal Deformity Surgeries" to Aubin et al. ("Aubin") in view of U.S. Patent App. Pub. No. 2022/0249168 to Besier et al. ("Besier"), U.S. Patent App. Pub. No. 2021/0169576 to Yoshinaka et al. ("Yoshinaka"), and U.S. Patent App. Pub. No. 2019/0146458 to Roh et al. ("Roh"), and further in view of U.S. Patent App. Pub. No. 2013/0344079 to Adler-Abramovich et al. ("Adler-Abramovich"):
Regarding claim 86, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, but appears to be silent regarding assessing, via the at least one processor using the virtual 3D biomechanical model, a current clinical orthopedic pathology of the subject and disease progression in absence of the surgical procedure, resulting in a non-surgical assessment.
Nevertheless, Adler-Abramovich teaches ([0085]) that it was known in the healthcare informatics art to classify a pathology, determine a severity of the pathology, and monitor pathology progression both with and without treatment which would advantageously facilitate medical decision-making by medical professionals resulting in improved patient outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have assessed, via the at least one processor using the virtual 3D biomechanical model, a current clinical pathology of the subject (orthopedic in the case of Aubin/Besier/Yoshinaka/Roh) and disease progression in absence of the treatment (surgical procedure in the case of Aubin/Besier/Yoshinaka/Roh), resulting in a non-surgical assessment similar to as taught by Adler-Abramovich to advantageously facilitate medical decision-making by medical professionals resulting in improved patient outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim 103 is rejected in view of the Aubin/Besier/Yoshinaka/Roh/Adler-Abramovich combination as discussed above in relation to claim 86.
Claims 87 and 104 are rejected under 35 U.S.C. 103 as being unpatentable over NPL "Preoperative Planning Simulator for Spinal Deformity Surgeries" to Aubin et al. ("Aubin") in view of U.S. Patent App. Pub. No. 2022/0249168 to Besier et al. ("Besier"), U.S. Patent App. Pub. No. 2021/0169576 to Yoshinaka et al. ("Yoshinaka"), and U.S. Patent App. Pub. No. 2019/0146458 to Roh et al. ("Roh"), and further in view of U.S. Patent App. Pub. No. 2010/0191100 to Anderson et al. ("Anderson"):
Regarding claim 87, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, further including wherein the potential surgical outcomes enables a health practitioner to select at least one surgical procedure ([0079] of Besier how a surgeon can make selections based on post-operative outcome assessments determined by the model, [0086] of Besier notes how predicted post-operative assessments can facilitate determination of implant selection and placement, [0111]-[0115] of Besier discusses how a surgeon can approve/finalize implant selection and the surgical plan based on predicted post-operative function; and Figure 10 and [0175]-[0178] of Besier illustrate/discuss how users can select implants/approve surgical plans based on post implantation function assessment based on the simulation; therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the potential surgical outcomes to enable a health practitioner to select at least one surgical procedure in the system of the Aubin/Besier/Yoshinaka/Roh combination similar to as taught by Besier to advantageously allow health practitioners to make more informed decisions therefore improving patient outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Furthermore, while the dialog window in Figure 1 and the middle of the right column on page 2145 of Aubin discloses implants from a catalog which would presumably correspond to "available" surgical procedures and implants, the Aubin/Besier/Yoshinaka/Roh combination might be silent regarding the selected surgical procedure specifically being from currently available surgical procedures and implants.
Nevertheless, Anderson teaches that it was known in the healthcare informatics art to use modeling/simulation to predict a resultant motion sequence and generate a statistical summary (predicted post-operative biomechanical parameters) of a treatment plan for a current patient that includes implantation of an implant ([0078]-[0079]) and to allow a surgeon to select a procedure from currently available procedures and implants (Figure 12 and [0084]) which would advantageously facilitate surgeon decision-making leading to expedited patient outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the selected surgical procedure of the Aubin/Besier/Yoshinaka/Roh combination to specifically be from currently available surgical procedures and implants as taught by Anderson to advantageously facilitate surgeon decision-making leading to expedited patient outcomes, because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention, and because there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
Claim 104 is rejected in view of the Aubin/Besier/Yoshinaka/Roh/Anderson combination as discussed above in relation to claim 87.
Claims 88 and 105 are rejected under 35 U.S.C. 103 as being unpatentable over NPL "Preoperative Planning Simulator for Spinal Deformity Surgeries" to Aubin et al. ("Aubin") in view of U.S. Patent App. Pub. No. 2022/0249168 to Besier et al. ("Besier"), U.S. Patent App. Pub. No. 2021/0169576 to Yoshinaka et al. ("Yoshinaka"), and U.S. Patent App. Pub. No. 2019/0146458 to Roh et al. ("Roh"), and further in view of U.S. Patent App. Pub. No. 2015/0157416 to Andersson ("Andersson"):
Regarding claim 88, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 75, further including wherein the imaging data comprise at least one of three-dimensional CT, three-dimensional MRI images, or X-ray imaging … or any combination thereof (the top of the right column on page 2145 of Aubin discloses radiographs (X-ray imaging)).
However, the Aubin/Besier/Yoshinaka/Roh combination might be silent regarding the X-ray imaging specifically being in digital imaging and communications in medicine (DICOM) format.
Nevertheless, Andersson teaches ([0073]-[0075] and [0079]) that it was known in the healthcare informatics art to generate a 3D model of a patient's anatomy from X-ray imaging in DICOM format to facilitate simulation of a surgical approach as use of such imaging in a DICOM format is a known efficient manner of obtaining scan data of a patient.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the X-ray imaging of the Aubin/Besier/Yoshinaka/Roh combination to specifically being in DICOM format as taught by Andersson because DICOM format is a known efficient manner of obtaining scan data of a patient and because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
Claim 105 is rejected in view of the Aubin/Besier/Yoshinaka/Roh/Andersson combination as discussed above in relation to claim 88.
Claim 117 is rejected under 35 U.S.C. 103 as being unpatentable over NPL "Preoperative Planning Simulator for Spinal Deformity Surgeries" to Aubin et al. ("Aubin") in view of U.S. Patent App. Pub. No. 2022/0249168 to Besier et al. ("Besier"), U.S. Patent App. Pub. No. 2021/0169576 to Yoshinaka et al. ("Yoshinaka"), and U.S. Patent App. Pub. No. 2019/0146458 to Roh et al. ("Roh"), and further in view of U.S. Patent App. Pub. No. 2021/0322100 to Roche et al. ("Roche"):
Regarding claim 117, the Aubin/Besier/Yoshinaka/Roh combination discloses the method of claim 116, but appears to be silent regarding validating the machine learning algorithm based on a testing set comprising a testing portion of the historical data, the testing portion being distinct from the training portion.
Nevertheless, Roche teaches ([0090]) that it was known in the healthcare informatics and machine learning art to train a predictive algorithm (ML model per [0091]) to generate predictive surgical outcomes ([0002]) using a training set of previous patient data and test the algorithm using a distinct testing set of the previous patient data to advantageously evaluate a prediction error of the algorithm to allow for improvements to be made.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have validated the machine learning algorithm of the Aubin/Besier/Yoshinaka/Roh combination based on a testing set comprising a testing portion of the historical data, the testing portion being distinct from the training portion, similar to as taught by Roche to advantageously evaluate a prediction error of the algorithm to allow for improvements to be made. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Conclusion
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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