DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office Action is responsive to the amendment filed on 12 May 2026. As directed by the amendment: claims 1, 2, 7-10, 14, 18, and 19 have been amended and claims 13 and 17 are cancelled. Claims 1-12, 14-16, and 18-20 currently stand pending in the application.
The amendments to the claims are sufficient to overcome the claim objections listed in the previous action, which are correspondingly withdrawn. However, further claim objections as necessitated by the current amendments are presented below.
The amendments to the claims are sufficient to overcome the relevant rejections under 35 U.S.C. 112(a) and 35 U.S.C. 112(b) listed in the previous action, which are correspondingly withdrawn. The cancellation of claim 17 has rendered moot the relevant rejections under 35 U.S.C. 112(a) and 35 U.S.C. 112(b) listed in the previous action, which are correspondingly withdrawn. Further rejections under 35 U.S.C. 112(b) as necessitated by current amendments are presented below.
Response to Arguments
Applicant's arguments, filed 12 May 2026, as to the rejections under 35 USC § 101, have been fully considered but they are not persuasive. Applicant submits that claims 7 and 12 have been amended to closely reflect the language of claim 1, which was not rejected under 35 USC § 101. Examiner respectfully submits that claim 1 is a method claim that positively recites the step of performing the knee arthroplasty procedure. Claims 7 and 12, although amended to reflect the language of claim 1, are not method claims, and do not positively recite the step of performing the knee arthroplasty procedure. Claims 7 and 19 recite software instructions that instruct the processor(s) to perform the knee arthroplasty procedure; software instructions are an additional element which do not improve the functioning of a computer.
Applicant’s arguments with respect to the rejections under 35 USC § 103 have been considered but are moot because the new ground of rejection does not rely on any combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claim 1-12, 14-16, and 18-20 are objected to because of the following informalities: improper antecedence. Appropriate correction is required. The following amendments are suggested:
In claim 1 / line 3: “a plurality of hypothetical geometries,”
In claim 2 / line 3: “to reflect [[a]] the selection of the one or more”
In claim 7 / lines 12-13: “for the
In claim 19 / line 9: “joint performance”
In claim 19 / line 17: “a current patient”
In claim 19 / lines 23-24: “with [[a]] the current patient,”
In claim 19 / line 26: “from [[a]] the plurality of images”
In claim 19 / line 30: “[[ing]] a”
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 15 is rejected under 35 U.S.C. 112(b) 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.
As to claim 15, the limitation “the preoperative patient model” (line 3) renders the claim indefinite because it lacks proper antecedent basis in the claims. For examination purposes, the limitation will be interpreted as the surgical plan.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 7-12, 16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In accordance with MPEP 2106.04, each of Claims 7-12, 16, and 18-20 have been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1 per MPEP 2106.04(a)
Each of Claims 7-12, 16, and 18-20 recites at least one step or instruction for concepts performed in the human mind, which is grouped as a mental process in MPEP 2106.04(a)(2)(III) or a certain method of organizing human activity in MPEP 2106.04(a)(2)(II) or mathematical concept in MPEP 2106.04(a)(2)(I). The claimed limitations involve observation, evaluation, and/or judgment, which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Accordingly, each of Claims 7-12, 16, and 18-20 recites an abstract idea.
Specifically, Claims 7 and 19 recite (underlined = abstract idea; bolded = additional element; bolded and underlined = either):
7. A computer-assisted surgical system for performing a knee arthroplasty procedure, the computer-assisted surgical system comprising (additional elements):
a first set of software instructions that instructs one or more processors to perform, using a plurality of hypothetical geometries, a plurality of simulations of a joint performance for an anatomical model comprising a model of a knee based on one or more motion profiles, wherein a plurality of simulation results are stored in a simulation database (either observation which is a mental process or additional element that is insignificant extra-solution activity, e.g., data collection);
a second set of software instructions that instructs the one or more processors to generate, using the plurality of simulation results stored in the simulation database, a statistical model for the anatomical joint performance as a function of one or more geometries associated with the anatomical model and a set of prosthetic implant implantation parameters (either observation which is a mental process or additional element that is insignificant extra-solution activity, e.g., data collection);
a third set of software instructions that instructs the one or more processors to receive a plurality of patient images containing at least a preoperative image of one or more knee joint components of a patient in each of a plurality of flexion positions, and calculate a surgical plan by applying the statistical model to one or more preoperative knee geometries extracted from the plurality of patient images, wherein the surgical plan comprises a set of proposed knee implant implantation parameters for which a predicted prosthetic knee implant performance satisfies a set of predetermined performance criteria (either observation which is a mental process or additional element that is insignificant extra-solution activity, e.g., data collection); and
a fourth set of software instructions that instruct the one or more processors to perform the knee arthroplasty procedure in accordance with the surgical plan based on the set of proposed knee implant implantation parameters (either observation which is a mental process or additional element that is insignificant extra-solution activity, e.g., data collection).
19. A system for performing a knee arthroplasty procedure, the system comprising: (additional element)
one or more processors (additional element); and
a non-transitory, computer-readable medium storing instructions that, when executed, cause the one or more processors to (additional element):
generate a simulation database by:
performing, using a plurality of hypothetical geometries, a plurality of simulations of a joints performance for an anatomical model comprising a model of a knee undergoing one or more predetermined motion profiles, wherein a plurality of simulation results are stored in the simulation database;
generate, using the plurality of simulation results, a statistical model for the joint performance as a function of one or more geometries associated with the anatomical model and a set of prosthetic implant implantation parameters (either observation, evaluation, or judgement which is a mental process or additional element that is insignificant extra-solution activity, e.g., data collection);
receive a plurality of images of one or more knee joints of a patient, wherein the plurality of images depict the one or more knee joints in each of a plurality of flexion positions (either observation which is a mental process or additional element that is insignificant extra-solution activity, e.g., data collection);
calculate, by applying the statistical model to one or more preoperative patient-specific parameters associated with a current patient, a surgical plan, wherein the one or more preoperative patient-specific parameters correspond to one or more preoperative knee geometries identified from a plurality of images associated with the current patient, and wherein the surgical plan comprises a set of proposed implantation parameters for which a predicted prosthetic knee implant performance satisfies a set of predetermined performance criteria (either observation, evaluation, or judgement which is a mental process or additional element that is insignificant extra-solution activity, e.g., data collection); and
perform the knee arthroplasty procedure using a computer-assisted surgical system in accordance with the surgical plan comprising the set of proposed implantation parameters (either observation which is a mental process or additional element that is insignificant extra-solution activity, e.g., data collection).
Further, the dependent claims merely include limitations that either further define the abstract idea (and thus do not make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the claimed functions/steps are performed.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea as in MPEP 2106.04(a).
Step 2A, Prong 2 per MPEP 2106.04(d)
The above-identified abstract idea in independent Claims 7 and 19 (and dependent claims) is not integrated into a practical application under MPEP 2106.04(d) because the additional elements (identified above in the independent claims), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use according to MPEP 2106.05(h). More specifically, the additional elements of: computer-assisted surgical system, simulation database, software instructions, non-transitory computer-readable media, and processors are generically recited computer elements in the independent claims and their dependent claims which do not improve the functioning of a computer, or any other technology or technical field according to MPEP 2106.04(d)(1) and 2106.05(a). Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine according to MPEP 2106.05(b), effect a transformation according to MPEP 2106.05(c), provide a particular treatment or prophylaxis according to MPEP 2106.04(d)(2) or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception according to MPEP 2106.04(d)(2) and 2106.05(e). Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer in accordance with MPEP 2106.05(f). For at least these reasons, the abstract idea identified above in independent Claims 7 and 19 and their dependent claims is not integrated into a practical application in accordance with MPEP 2106.04(d).
Moreover, the above-identified abstract idea is not integrated into a practical application in accordance with MPEP 2106.04(d) because the claimed method and system merely implements the above-identified abstract idea (mental process) using rules (computer instructions) executed by a computer (non-transitory computer-readable media and processors as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer according to MPEP 2106.05(f). Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims according to MPEP 2106.05(a). That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 7 and 19 and their dependent claims is not integrated into a practical application under MPEP 2106.04(d)(I).
Accordingly, independent Claims 7 and 19 and their dependent claims are each directed to an abstract idea according to MPEP 2106.04(d).
Step 2B per MPEP 2106.05
None of Claims 7-12, 16, and 18-20 include additional elements that are sufficient to amount to significantly more than the abstract idea in accordance with MPEP 2106.05 for at least the following reasons.
These claims require the additional elements of: computer-assisted surgical system, simulation database, software instructions, non-transitory computer-readable media, and processors.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, MPEP 2106.05(d)(II) along with Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, the computing system 150, database and server are shown as a schematic drawing (see e.g. FIGS. 1 and 4) and described as general purpose (par. [0077]).
Accordingly, in light of Applicant’s specification, the claimed terms computer-assisted surgical system, simulation database, software instructions, non-transitory computer-readable media, and processors are reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process. See MPEP 2106.05(f).
Furthermore, Applicant’s specification does not describe any special programming or algorithms required. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see MPEP 2106.05(d)(I)(2) and 2106.07(a)(III)). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications along with MPEP 2106.05(d)(I)).
The recitation of the above-identified additional limitations in Claims 7-12, 16, and 18-20 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See MPEP 2106.05(f) along with Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. See MPEP 2106.05(a) along with McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, per MPEP 2106.05(a), the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, Claims 7-12, 16, and 18-20 are directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself or providing a technical solution to a problem in a technical field according to MPEP 2106.05(a), or (ii) providing meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself according to MPEP 2106.04(d)(2) and 2106.05(e).
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in Claims 7-12, 16, and 18-20 do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment according to MPEP 2106.05(h). When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment according to MPEP 2106.05(h). When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself according to MPEP 2106.04(d)(2) and 2106.05(e). Moreover, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity according to MPEP 2106.05(g). As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application as required by MPEP 2106.05.
Therefore, for at least the above reasons, none of the Claims 7-12, 16, and 18-20 amounts to significantly more than the abstract idea itself. Accordingly, Claims 7-12, 16, and 18-20 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-12, 15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. US 2013/0211531 to Steines et al. (hereinafter, “Steines”), in view of U.S. Patent Application Publication No. US 2011/0054486 to Linder-Ganz et al. (hereinafter, “Linder-Ganz”) and U.S. Patent No. US 8,126,736 to Anderson et al. (hereinafter, “Anderson”).
As to claim 1, Steines discloses a method for performing a knee arthroplasty procedure (par. [0600]), the method comprising: calculating a surgical plan, wherein one or more preoperative patient-specific parameters correspond to one or more preoperative knee geometries identified from a plurality of images associated with the current patient (par. [0436]), and wherein the surgical plan comprises a set of proposed knee implant implantation parameters (e.g. proposed bone resections for maximum amount of bone and ligament preservation; implant selection or design) for which a predicted prosthetic knee implant performance satisfies a set of predetermined performance criteria (maximum amount of bone preservation; preserving, restoring, or enhancing the patient's joint kinematics) (par. [0705], [1126], [1240]); and performing the knee arthroplasty procedure using a computer-assisted surgical system in accordance with the set of proposed knee implant implantation parameters (par. [0548]-[0554]).
As to claim 2, Steines discloses the method of claim 1, further comprising selecting one or more postoperative patient activities from a plurality of model activities (par. [0439]-[0440]; all may be chosen), wherein the one or more motion profiles is chosen to reflect a selection of one or more postoperative patient activities (biomotion model simulates the activities; biomotion model is individualized by the imaging data).
As to claim 3, Steines discloses the method of claim 1, wherein the set of proposed knee implant implantation parameters include at least one of a set of proposed bone resections and tissue releases, a set of proposed implant positions, and an implant selection configured to achieve a desired post-operative patellofemoral joint and femoral-tibial joint geometry (implant selection that maximizes preservation of bone anatomy and/or restores joint-line location and/or joint gap width, par. [1126]).
As to claim 5, Steines discloses the method of claim 1, wherein the plurality of images comprises a plurality of x-ray images (par. [0396]).
As to claim 6, Steines discloses the method of any of claim 1, wherein the performing of the knee arthroplasty procedure comprises: attaching fiducial markers to a femur and a tibia of the patient (par. [0437]; RF markers applied to the limb of concern, limbs of concern including the femur and tibia, par. [0390]), and performing one of: attaching fiducial markers to a patella of the patient (par. [0437]; RF markers applied to the limb of concern, limbs of concern including the patella, par. [0390]), and registering a feature of the patella with the computer-assisted surgical system using a probe.
As to claim 15, Steines discloses the method of claim 1, wherein the set of proposed knee implant implantation parameters (implant selection) is selected from a plurality of sets of proposed knee implant implantation parameters (library of implants) (par. [0327]).
Steines is silent as to performing, using a plurality of hypothetical geometries a plurality of simulations of a joint performance for an anatomical model comprising a model of a knee, based on one or more motion profiles, wherein a plurality of simulation results are stored in a simulation database; generating, using the plurality of simulation results, a statistical model for the joint performance as a function of one or more geometries associated with the anatomical model and a set of prosthetic implant implantation parameters; calculating, by applying the statistical model to one or more preoperative patient-specific parameters associated with a current patient, a surgical plan, wherein the one or more preoperative patient-specific parameters correspond to one or more preoperative knee geometries identified from a plurality of images associated with the current patient (claim 1); the set of proposed knee implant implantation parameters is selected based on the preoperative patient model (claim 15).
Linder-Ganz teaches performing, using a plurality of hypothetical geometries (geometry of each of a plurality of available prosthetic devices, par. [0101]) a plurality of simulations of a joint performance for an anatomical model comprising a model of a knee, based on one or more motion profiles (walking, running, riding a bicycle, standing up, sitting down, etc., par. [0101]), wherein a plurality of simulation results are stored in a simulation database (where the system memory is a database that stores the simulation results at least long enough to obtain scores for the prosthetic devices based on the simulation results, par. [0100]-[0101]); generating, using the plurality of simulation results, a score for the joint performance as a function of one or more geometries (of the relevant prosthetic device) associated with the anatomical model and a set of prosthetic implant implantation parameters (for implantation of the relevant prosthetic device); calculating, by applying the score to one or more preoperative patient-specific parameters (bone measurements of candidate knee) associated with a current patient, a surgical plan (the best-fitted prosthetic device(s), par. [0102]), wherein the one or more preoperative patient-specific parameters correspond to one or more preoperative knee geometries identified from a plurality of images associated with the current patient (bone measurements from CT/MRI scans of candidate knee), and wherein the surgical plan comprises: a set of proposed knee implant implantation parameters (for implanting the best-fitted prosthetic device) for which a predicted prosthetic knee implant performance satisfies a set of predetermined performance criteria (optimal load bearing or motion) (par. [0100]-[0102]).
Anderson teaches generating a statistical model for joint performance as a function of one or more geometries associated with the anatomical model and a set of prosthetic implant implantation parameters (preoperative statistical summary of patient condition that takes into account comparison to other patients in the simulation database and statistical success of treatment plans i.e. performance based on preoperative geometry and parameters, col. 13 / lines 13-50; col. 14 / line 64 – col. 15 / line 17; col. 15 / line 47 – col. 16 / line 12; col. 41 / lines 52-58). Anderson teaches the set of proposed knee implant implantation parameters is selected based on the preoperative patient model (based on statistical success of treatment plans).
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include in Steines’ method, the simulating and modeling steps as taught by Linder-Ganz, to obtain a level of confidence in a particular outcome resulting from a particular treatment, so that the surgical plan for the current patient has the best chance of success based on the patient’s preoperative geometry (as compared/similar to those of hypothetical patients) and parameters such as desired results. As applied to Steines, the joint performance will be as related to the knee, with the hypothetical patient information providing knee joint performance for calculation of a surgical plan.
It further would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize a statistical model of the joint performance, since Linder-Ganz teaches that it is important to understand the probable effects or outcomes of performing a particular surgical procedure (the score), based on the data collected and the patient in question, and a statistical model would allow analysis of multiple potential distributions, as taught by Anderson, to determine the statistical success of treatment plans i.e. performance based on preoperative geometry and parameters.
As to claim 4, Steines discloses the method of claim 1, wherein the performing of the knee arthroplasty procedure using the computer-assisted surgical system comprises: tracking patient bone resections in real-time (par. [0522]); and updating the surgical plan based on the tracked patient bone resections (real-time intra-operative adjustments), but is silent as to modeling patellar tracking during the knee arthroplasty procedure and updating the surgical plan based on the modeled patellar tracking.
Steines does disclose modeling the trochlea and patella (par. [0390]), and does disclose a biomotion model that simulates how the knee and thus the patella move under various activities of daily life (par. [0439]) and that the implant should optimize tracking of the patella (par. [0651]).
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to perform the modeling of the patellar tracking during the knee arthroplasty procedure so that the surgical plan can be updated based on the modeled patellar tracking, as well as the tracked patient bone resections, since Steines discloses that optimizing tracking of the patella is important to optimize patient kinematics and thus to the implant selection and/or design, and modeling this in real time would ensure that the resections happening in real time are complementary to optimized patient kinematics.
As to claim 7, Steines discloses a computer-assisted surgical system for performing a knee arthroplasty procedure (par. [0519], [0600]), the computer-assisted surgical system comprising: software instructions that instructs one or more processors to perform the steps of a method (par. [0516]); a simulation database (library, par. [0327]) (par. [0329]); a third set of software instructions that instructs the one or more processors to receive a plurality of patient images (par. [0436]; each step is performed by a software-directed computer, par. [0495]) containing at least a preoperative image of one or more knee joint components of a patient in each of a plurality of flexion positions (par. [0490]; images of joint kinematics implicitly comprise a plurality of flexion positions as the knee passes through the flexion positions); and calculate a surgical plan comprising a set of proposed knee implant implantation parameters (e.g. proposed bone resections for maximum amount of bone preservation; implant selection or design; each step is performed by a software-directed computer) for which a predicted prosthetic knee implant performance satisfies a set of predetermined performance criteria (maximum amount of bone preservation; preserving, restoring, or enhancing the patient's joint kinematics) (par. [0705], [1126], [1240]), and a fourth set of software instructions that instruct the one or more processors to perform the knee arthroplasty procedure in accordance with the surgical plan based on the set of proposed knee implant implantation parameters (par. [0522], [0548]-[0554]).
As to claim 8, Steines discloses the computer-assisted surgical system of claim 7, further comprising a fifth set of software instructions that instructs the one or more processors to receive a selection of one or more postoperative patient activities from a plurality of model activities (par. [0439]-[0440]; all may be chosen; each step is performed by a software-directed computer).
As to claim 9, Steines discloses the computer-assisted surgical system of claim 7, wherein the set of proposed knee implant implantation parameters includes a set of proposed bone resections and tissue releases configured to achieve a desired post-operative patellofemoral joint and femoral-tibial joint geometry (maximum amount of bone and ligament preservation; preserving, restoring, or enhancing the patient's joint kinematics; par. [0705], [1126]).
As to claim 11, Steines discloses the computer-assisted surgical system of claim 7, wherein the plurality of patient images comprises a plurality of x-ray images (par. [0396]).
As to claim 12, Steines discloses the computer-assisted surgical system of claim 7, wherein the computer-assisted surgical system comprises fiducial markers configured to be affixed to a femur, a tibia, and a patella of the patient (par. [0437]; RF markers applied to the limb of concern, limbs of concern including the femur, tibia, and patella; par. [0390]).
As to claim 18, Steines discloses the computer-assisted surgical system of claim 7, further comprising a seventh set of software instructions that instructs the one or more processors to: receive a location of a probe with respect to a patella of the patient; and register at least one feature of the patella based on the received location (par. [0437]; RF markers, i.e. probes, applied to the limb of concern and registered to measure movement, limbs of concern including the patella, par. [0390]; each step is performed by a software-directed computer).
Steines is silent as to a first set of software instructions that instructs one or more processors to perform, using a plurality of hypothetical geometries, a plurality of simulations of a joint performance for an anatomical model comprising a model of a knee based on one or more motion profiles, wherein a plurality of simulation results are stored in a simulation database; a second set of software instructions that instructs the one or more processors to generate, using the plurality of simulation results stored in the simulation database, a statistical model for the anatomical joint performance as a function of one or more geometries associated with the anatomical model and a set of prosthetic implant implantation parameters; and calculate a surgical plan by applying the statistical model to one or more preoperative knee geometries extracted from the plurality of patient images (claim 7).
Linder-Ganz teaches performing, using a plurality of hypothetical geometries (geometry of each of a plurality of available prosthetic devices, par. [0101]), a plurality of simulations of a joint performance for an anatomical model comprising a model of a knee based on one or more motion profiles (walking, running, riding a bicycle, standing up, sitting down, etc., par. [0101]), wherein a plurality of simulation results are stored in a simulation database (where the system memory is a database that stores the simulation results at least long enough to obtain scores for the prosthetic devices based on the simulation results, par. [0100]-[0101]); generating, using the plurality of simulation results stored in the simulation database, a score for the anatomical joint performance as a function of one or more geometries (of the relevant prosthetic device) associated with the anatomical model and a set of prosthetic implant implantation parameters (for implantation of the relevant prosthetic device); and calculating a surgical plan (the best-fitted prosthetic device(s), par. [0102]) by applying the score to one or more preoperative knee geometries extracted from the plurality of patient images (bone measurements of candidate knee), wherein the surgical plan comprises a set of proposed knee implant implantation parameters (for implanting the best-fitted prosthetic device) for which a predicted prosthetic knee implant performance satisfies a set of predetermined performance criteria (optimal load bearing or motion) (par. [0100]-[0102]).
Anderson teaches generating a statistical model of joint performance as a function of one or more geometries for the anatomical model and a set of prosthetic implant implantation parameters (preoperative statistical summary of patient condition that takes into account comparison to other patients in the simulation database and statistical success of treatment plans i.e. performance based on preoperative geometry and parameters, col. 13 / lines 13-50; col. 14 / line 64 – col. 15 / line 17; col. 15 / line 47 – col. 16 / line 12; col. 41 / lines 52-58). Anderson teaches the set of proposed knee implant implantation parameters is selected based on the preoperative patient model (based on statistical success of treatment plans) and the patient images (since the model is based on the images).
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include in Steines’ method, the simulating and modeling steps as taught by Linder-Ganz, to obtain a level of confidence in a particular outcome resulting from a particular treatment, so that the surgical plan for the current patient has the best chance of success based on the patient’s preoperative geometry (as compared/similar to those of hypothetical patients) and parameters such as desired results. As applied to Steines, the joint performance will be as related to the knee, with the hypothetical patient information providing knee joint performance for calculation of a surgical plan. Since Steines discloses the system has software instructions stored thereon that instructs one or more processors to perform each of the steps of a method (par. [0516]), it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include in the system sets of software instructions that instructs one or more processors to perform each of the steps taught by Linder-Ganz.
It further would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize a statistical model of the anatomical joint performance, since Linder-Ganz teaches that it is important to understand the probable effects or outcomes of performing a particular surgical procedure (the score), based on the data collected and the patient in question, and a statistical model would allow analysis of multiple potential distributions, as taught by Anderson, to determine the statistical success of treatment plans i.e. performance based on preoperative geometry and parameters.
As to claim 10, Steines discloses the computer-assisted surgical system of claim 7, further comprising a sixth set of software instructions that instructs the one or more processors to: track patient bone resections in real-time (par. [0522]); and update the surgical plan based on the tracked patient bone resections (real-time intra-operative adjustments), but is silent as to modeling patellar tracking during the knee arthroplasty procedure and updating the surgical plan based on the modeled patellar tracking.
Steines does disclose modeling the trochlea and patella (par. [0390]), and does disclose a biomotion model that simulates how the knee and thus the patella move under various activities of daily life (par. [0439]) and that the implant should optimize tracking of the patella (par. [0651]).
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to perform the modeling of the patellar tracking during the knee arthroplasty procedure so that the surgical plan can be updated based on the modeled patellar tracking, as well as the tracked patient bone resections, since Steines discloses that optimizing tracking of the patella is important to optimize patient kinematics and thus to the implant selection and/or design, and modeling this in real time would ensure that the resections happening in real time are complementary to optimized patient kinematics.
As to claim 19, Steines discloses a system for performing a knee arthroplasty procedure (par. [0600]), the system comprising: one or more processors (par. [0516]); and a non-transitory, computer-readable medium storing instructions that, when executed, cause the one or more processors to: receive a plurality of images (par. [0436]) of one or more knee joints of a patient, wherein the plurality of images depict the one or more knee joints in each of a plurality of flexion positions (par. [0490]; images of joint kinematics implicitly comprise a plurality of flexion positions as the knee passes through the flexion positions); and calculate a surgical plan comprising a set of proposed implantation parameters (e.g. proposed bone resections for maximum amount of bone and ligament preservation; implant selection or design) for which a predicted prosthetic knee implant performance satisfies a set of predetermined performance criteria (maximum amount of bone preservation; preserving, restoring, or enhancing the patient's joint kinematics) (par. [0705], [1126], [1240]); and perform the knee arthroplasty procedure using a computer-assisted surgical system in accordance with the surgical plan comprising the set of proposed implantation parameters (par. [0548]-[0554]).
Steines is silent as to generate a simulation database by: performing, using a plurality of hypothetical geometries, a plurality of simulations of a joints performance for an anatomical model comprising a model of a knee undergoing one or more predetermined motion profiles, wherein a plurality of simulation results are stored in the simulation database; generate, using the plurality of simulation results, a statistical model for the joint performance as a function of one or more geometries associated with the anatomical model and a set of prosthetic implant implantation parameters; calculate, by applying the statistical model to one or more preoperative patient-specific parameters associated with a current patient, a surgical plan, wherein the one or more preoperative patient-specific parameters correspond to one or more preoperative knee geometries identified from a plurality of images associated with the current patient.
Linder-Ganz teaches to generate a simulation database (where the system memory is a database that stores the simulation results at least long enough to obtain scores for the prosthetic devices based on the simulation results, par. [0100]-[0101]) by: performing, using a plurality of hypothetical geometries (geometry of each of a plurality of available prosthetic devices, par. [0101]), a plurality of simulations of a joints performance for an anatomical model comprising a model of a knee undergoing one or more predetermined motion profiles (walking, running, riding a bicycle, standing up, sitting down, etc., par. [0101]), wherein a plurality of simulation results are stored in the simulation database; generate, using the plurality of simulation results, a score for the joint performance as a function of one or more geometries (of the relevant prosthetic device) associated with the anatomical model and a set of prosthetic implant implantation parameters (for implantation of the relevant prosthetic device); calculate, by applying the score to one or more preoperative patient-specific parameters (bone measurements of candidate knee) associated with a current patient, a surgical plan (the best-fitted prosthetic device(s), par. [0102]), wherein the one or more preoperative patient-specific parameters correspond to one or more preoperative knee geometries identified from a plurality of images associated with the current patient (bone measurements from CT/MRI scans of candidate knee), and wherein the surgical plan comprises: a set of proposed knee implant implantation parameters (for implanting the best-fitted prosthetic device) for which a predicted prosthetic knee implant performance satisfies a set of predetermined performance criteria (optimal load bearing or motion) (par. [0100]-[0102]).
Anderson teaches generating a statistical model for joint performance as a function of one or more geometries associated with the anatomical model and a set of prosthetic implant implantation parameters (preoperative statistical summary of patient condition that takes into account comparison to other patients in the simulation database and statistical success of treatment plans i.e. performance based on preoperative geometry and parameters, col. 13 / lines 13-50; col. 14 / line 64 – col. 15 / line 17; col. 15 / line 47 – col. 16 / line 12; col. 41 / lines 52-58). Anderson teaches the set of proposed knee implant implantation parameters is selected based on the preoperative patient model (based on statistical success of treatment plans).
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include in Steines’ method, the simulating and modeling steps as taught by Linder-Ganz, to obtain a level of confidence in a particular outcome resulting from a particular treatment, so that the surgical plan for the current patient has the best chance of success based on the patient’s preoperative geometry (as compared/similar to those of hypothetical patients) and parameters such as desired results. As applied to Steines, the joint performance will be as related to the knee, with the hypothetical patient information providing knee joint performance for calculation of a surgical plan.
It further would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize a statistical model of the joint performance, since Linder-Ganz teaches that it is important to understand the probable effects or outcomes of performing a particular surgical procedure (the score), based on the data collected and the patient in question, and a statistical model would allow analysis of multiple potential distributions, as taught by Anderson, to determine the statistical success of treatment plans i.e. performance based on preoperative geometry and parameters.
As to claim 20, Steines discloses the system of claim 19, wherein the instructions, when executed, further cause the one or more processors to: track one or more patient bone resections in real-time (par. [0522]); and update the surgical plan based on the tracked patient bone resections (real-time intra-operative adjustments), but is silent as to modeling patellar tracking based on at least the one or more patient bone resections and updating the surgical plan based on the modeled patellar tracking.
Steines does disclose modeling the trochlea and patella (par. [0390]), and does disclose a biomotion model that simulates how the knee and thus the patella move under various activities of daily life (par. [0439]) and that the implant should optimize tracking of the patella (par. [0651]).
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to perform the modeling of the patellar tracking during the knee arthroplasty procedure so that the surgical plan can be updated based on the modeled patellar tracking, as well as the tracked patient bone resections, since Steines discloses that optimizing tracking of the patella is important to optimize patient kinematics and thus to the implant selection and/or design, and modeling this in real time would ensure that the resections happening in real time are complementary to optimized patient kinematics.
Claims 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Steines in view of Linder-Ganz and Anderson (hereinafter, “Steines/Linder-Ganz/Anderson”), as applied to claims 1-12, 15, and 18-20 above, and further in view of U.S. Patent Application Publication No. US 2007/0233267 to Amirouche et al. (hereinafter, “Amirouche”).
Steines/Linder-Ganz/Anderson are silent as to wherein the statistical model comprises a transfer function computed based on the plurality of the simulations of the joint performance associated with the plurality of hypothetical geometries (claim 14); wherein the statistical model comprises a transfer function based on the plurality of simulations (claim 16).
Amirouche teaches that neural networking principles may be applied to joint replacement procedures such as total knee arthroplasty (par. [0031]) to provide improved data acquisition ability and simplify the procedure (par. [0041]-[0047]). Known data can be passed through a trained neural network, which can predict and output at least one previously unknown data point. The outputted, predicted data values can assist the surgeon in determining whether to resect additional bone, release soft tissues, and/or select implant sizes. A transfer function is applied to input values to obtain output values in such a neural network.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention for the statistical model to comprise a transfer function computed based on the simulation data, since as taught by Amirouche, a transfer function as part of a neural network can predict and output data values that can assist the surgeon in determining whether to resect additional bone, release soft tissues, and/or select implant sizes, thus improving and simplifying the procedure. The statistical model in Steines/Linder-Ganz/Anderson is based on biomechanical simulation data from a plurality of biomechanical simulations, which would provide a substantial amount of known data to effectively train the neural network since neural networks learn by example based on input patterns (Amirouche, par. [0048]-[0049]), and therefore a neural network and transfer function applied to Steines/Linder-Ganz/Anderson would be effectively trained and highly accurate.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TRACY L KAMIKAWA/Examiner, Art Unit 3775
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