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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 15 September 2025 has been entered.
Response to Amendment
This Office Action is responsive to the amendment filed on 15 September 2025. As directed by the amendment: claims 1, 2, 7, 8, 11, and 14-19 have been amended and claim 13 is cancelled. Claims 1-12 and 14-20 currently stand pending in the application.
The amendments to the claims are sufficient to overcome the claim objection listed in the previous action, which is correspondingly withdrawn. However, further claim objections in view of the current amendments are presented below.
The cancellation of claim 13 has rendered moot the relevant rejections under 35 U.S.C. 112(b) and 35 U.S.C. 112(d) listed in the previous action, which are correspondingly withdrawn.
Response to Arguments
Applicant's arguments, filed 15 September 2025, as to the rejections under 35 USC § 101, have been fully considered but they are not persuasive. Applicant first refers to the claimed subject matter of independent claim 1, which is not rejected under 35 USC § 101. If all of the arguments are directed to claim 1 (which may be concluded as there is no specific reference to any claim rejected under 35 USC § 101), the arguments are moot. In the interest of compact prosecution, the arguments will be addressed as best understood in light of the rejected claims. However, in general, without specifically pointing out the claim language of the rejected claims, the arguments amount to a general allegation that the claims define a patentable invention.
In response to applicant's arguments regarding synthetically generated parametric simulation results based on hypothetical input parameters, and a generalized virtual model with parametrized geometries (see Arguments, pages 10-11); unconventional arrangement of otherwise generic computing features, and on-demand/on-the-fly computation (page 13), it is noted that the features upon which applicant relies are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant contends that if the simulation results can be performed mentally upon evaluating a patient’s condition there would be no need for further mental computation of a statistical model and patient customization of the same. Examiner respectfully maintains that generation of a simulation database comprising simulation results of hypothetical geometries as claimed in claims 7 and 19 can be performed as a mental process; for example, the practitioner can observe or visualize or imagine, based on prior experience and imagination, how a hypothetical knee geometry, e.g. one with particular disease or for a particular gender patient, would move during flexion. Even if the feature is not an abstract idea, it is insignificant extra-solution activity since as claimed it comprises data collection which falls under extra-solution activity. The generation of a preoperative statistical patient model is also a mental process of evaluation or judgement, e.g. the practitioner can imagine how a knee implant would perform based on prior experience and imagination. A statistical model would be made patient-specific by evaluation or judgement of the patient’s knee. Simulation results that are observed or visualized or imagined would be precomputed by the brain and stored in the practitioner’s memory.
Applicant contends that there is an improved technological implementation of the allegedly computerized abstract idea, having various advantageous features with regards to complexity of implementation and performance outcome trade-offs which is still associated with a technological solution corresponding to an unconventional combination of technical features. Examiner respectfully maintains that the claimed invention is not a technical improvement that improves the performance of the computer itself or provides a technical solution (via specialized programming or algorithms), but rather is the conducting of the abstract ideas by automating mental tasks, since the computing system, database and server are shown only as schematics and described as general purpose (par. [0077]) with no specialized programming or algorithms. Simply using a computer or other machinery in its ordinary capacity to receive, store, or transmit data does not provide significantly more.
Applicant contends that the claimed subject matter recites an alternative implementation whereby generalized simulations of stock anatomical/implant virtual model is executed using a range of hypothetical parameter values. Because they are pre-computed and not patient-specific, they can be conducted at any time and stored in a database and then used to generate a model which can then be personalized to the specific patient. Applicant appears to be distinguishing the streamlined system and process over the prior art. Much of this language is not claimed in rejected claims 7 and 19, much less in a manner which overcomes a rejection under 35 USC 101. Computationally speeding up a process would not amount to significantly more, if the claims do not reflect how the computer is used beyond its ordinary capacity. The alternative implementation appears to be a reordering of computational steps. Using a computer efficiently is still using the same computer.
Applicant’s arguments with respect to the rejections under 35 U.S.C. 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 and 14-18 are objected to because of the following informalities: improper antecedence. Appropriate correction is required. The following amendments are suggested:
Claim 1 / line 13: “the
Claim 7 / line 8: “[[, t]]
Claim 7 / line 16: “a second set of software instructions that instructs the one or more processors”
Claim 10 / line 2: “a fourth set”
Claim 14 / line 3: “the
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-12 and 14-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As to claim 1, the limitations of generating a statistical model and generating a preoperative patient model, where the preoperative patient model is generated by applying the statistical model to one or more preoperative patient-specific parameters associated with a current patient profile and one or more implant implantation parameters, 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 calculating the surgical plan from the preoperative patient model, are not supported by the specification as originally filed. The specification refers to the patient model as the statistical patient model, and not as two models generated separately. The specification does not support each of the two models being a function of the particular claimed limitations, or the patient model (and not the statistical model) being used to calculate the surgical plan.
As to claim 7, the limitations of instructions to generate a statistical model and instructions to generate a preoperative patient model, where the preoperative patient model is based on the statistical model and preoperative knee geometries extracted from the patient images; where the preoperative patient model satisfies a set of performance criteria, are not supported by the specification as originally filed. The specification refers to the patient model as the statistical patient model, and not as two models generated separately. The specification does not support each of the two models being a function of the particular claimed limitations, or the patient model (and not the statistical model) being used to satisfy performance criteria.
As to claim 17, the limitation that the set of recommended knee implant implantation parameters is selected based on the preoperative patient model and the plurality of patient images is not supported by the specification as originally filed. The specification does not recite the set of implantation parameters being based on a preoperative patient model, which itself uses the patient images, as well as the patient images.
As to claim 19, the limitations of a statistical model and a preoperative patient model; and calculating the surgical plan from the preoperative patient model, are not supported by the specification as originally filed. The specification refers to the patient model as the statistical patient model, and not as two models generated separately. The specification does not recite the preoperative patient model is generated using the second set of results and the preoperative knee geometry.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 and 14-20 are 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.
In claim 1 / lines 8-9, the limitation “simulating a plurality of hypothetical geometries in a plurality of simulations” renders the claims indefinite, because it is unclear if and how to simulate a simulation. For examination purposes, the limitation will be interpreted as meaning that a plurality of simulations are performed.
Further as to claim 1, the limitation “one or more implant implantation parameters” (lines 18-19) renders the claims indefinite, because it is unclear if this refers to the previously recited set of prosthetic implant implantation parameters (claim 1 / lines 15-16), or to different implant implantation parameters. For examination purposes, the limitation will be interpreted in the former instance.
It is further unclear how the statistical model, which is a function of a set of prosthetic implant implantation parameters, is also applied to one or more implant implantation parameters to generate the preoperative patient model (the implant implantation parameters both are used to generate the statistical model and are used by the statistical model). For examination purposes, the later limitation will be interpreted as deleted.
Further as to claim 1, the limitation “the predicted prosthetic knee implant performance” (lines 25-26) lacks proper antecedent basis in the claim. For examination purposes, the limitation will be interpreted as a predicted prosthetic knee implant performance.
As to claim 7, the limitation “the one or more knee joint components of a patient” (lines 17-18) renders the claims indefinite because it is unclear if this refers back to the previously recited one or more knee joint components (claim 7 / line 4), or to different knee joint components. For examination purposes, the limitation will be interpreted in the latter instance.
As to claim 17, it is unclear how the set of implantation parameters is based on a preoperative patient model, which itself uses the patient images, as well as the patient images. For examination purposes, the later limitation will be interpreted as deleted.
In claim 19 / lines 12-13, the limitation “simulating a first set of results of a plurality of hypothetical geometries in a plurality of simulations” renders the claims indefinite, because it is unclear if and how to simulate a simulation. For examination purposes, the limitation will be interpreted as meaning that a plurality of simulations are performed.
Further as to claim 19, the limitation “the one or more knee joints performance” (line 13) lacks proper antecedent basis in the claim. For examination purposes, the limitation will be interpreted as one or more knee joints performance.
Further as to claim 19, the limitation “a second set of results of a statistical model” (lines 15-16) renders the claims indefinite because it is unclear if the first set of results is also of the statistical model, or if there is another first set of results of the statistical model on which the second set is based. For examination purposes, the limitation will be interpreted as “of a statistical model” deleted.
Further as to claim 19, the limitation “the predictive performance assessment for the proposed implantation parameters” (lines 30-31) renders the claims indefinite because it is unclear if this refers to the previously recited predictive performance assessment for an implant component, or to a different predictive performance assessment for the proposed implantation parameters. For examination purposes, the limitation will be interpreted in the latter instance, as a predictive performance assessment for the proposed implantation parameters.
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 and 16-20 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 and 16-20 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 and 16-20 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 and 16-20 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 simulation database comprising a plurality of previously performed anatomical simulation results of one or more knee joint components, associated with a plurality of hypothetical geometries, for a model of a knee, undergoing a predetermined motion profile; (either observation which is a mental process, or additional element that is insignificant extra-solution activity, e.g., data collection)
a first set of software instructions that instructs one or more processors to generate, using the simulation database, a statistical model of a prosthetic knee implant performance as a function of one or more knee geometries and a set of prosthetic knee implant implantation parameters (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 one or more processors to receive a plurality of patient images containing at least an image of the one or more knee joint components of a patient in each of a plurality of flexion positions, and generate a preoperative patient model, based on the statistical model of the prosthetic knee implant performance and one or more preoperative knee geometries extracted from the plurality of patient images (either observation which is a mental process or additional element that is insignificant extra-solution activity, e.g., data collection); and
a third set of software instructions that instructs the one or more processors to determine a set of recommended knee implant implantation parameters for which the preoperative patient model satisfies a set of predetermined post-operative performance criteria, and to update a surgical plan of the computer-assisted surgical system to reflect the set of recommended 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:
simulating a first set of results of a plurality of hypothetical geometries in a plurality of simulations of the one or more knee joints performance for a model of a knee undergoing one or more predetermined motion profiles; and
generating, using machine learning, a second set of results of a statistical model of the one or more knee joints performance for the model of the knee, based on the first set of results (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);
define a preoperative knee geometry for the patient based on the plurality of images (either observation, evaluation, or judgement which is a mental process);
generate, using the second set of results and the preoperative knee geometry, a preoperative patient model configured to provide a predictive performance assessment for an implant component based on the preoperative knee geometry and a set of implantation parameters (evaluation or judgement which is a mental process); and
calculate, based on the preoperative patient model, a surgical plan comprising a set of proposed implantation parameters, wherein the predictive performance assessment for the proposed implantation parameters 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).
Further, dependent Claims 8-12, 16-18, and 20 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 8-12, 16-18, and 20) 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 and 16-20 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 and 16-20 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 and 16-20 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 and 16-20 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 and 16-20 amounts to significantly more than the abstract idea itself. Accordingly, Claims 7-12 and 16-20 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, 17, and 18 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 No. US 9,601,030 to Ratcliffe et al. (hereinafter, “Ratcliffe”) 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: 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]), the plurality of images comprising an image of a knee joint of the current patient in 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); calculating a surgical plan including 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 the 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 the selected 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 simulating a plurality of hypothetical geometries in 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 the plurality of simulations are stored in a simulation database; generating, using the simulation database, a statistical model of the anatomical joint performance as a function of one or more geometries for the anatomical model and a set of prosthetic implant implantation parameters; generating, by applying the statistical model to one or more preoperative patient-specific parameters associated with a current patient profile and one or more implant implantation parameters, a preoperative patient model, 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; calculating, from the preoperative patient model, the surgical plan (claim 1); the set of proposed knee implant implantation parameters is selected based on the preoperative patient model (claim 15).
Ratcliffe teaches simulating a plurality of hypothetical geometries (a hypothetical patient made from averages, i.e. made from an averaged plurality; col. 7 / lines 1-28) in a plurality of simulations (changes in the mesh geometry by altering the shape of the hypothetical patient would result in a plurality; col. 7 / lines 1-28) of a joint performance (of the knee) for an anatomical model comprising a model of a knee based on one or more motion profiles (knee mechanics is based on a motion profile; col. 12 / line 20 – col. 13 / line 31), wherein the plurality of simulations are stored in a simulation database (col. 16 / lines 16-19); generating, using the simulation database, a probability model of the anatomical joint performance as a function of one or more geometries for the anatomical model (one or more of the mesh geometries) and a set of prosthetic implant implantation parameters (col. 1 / line 63 – col. 2 / line 11; col. 4 / lines 47-58; col. 13 / lines 32-61); generating, by applying the probability model to one or more preoperative patient-specific parameters associated with a current patient profile and one or more implant implantation parameters, a preoperative patient model (col. 13 / lines 48-61), 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 (col. 8 / lines 16-20; col. 17 / lines 43-60); calculating, from the preoperative patient model, the surgical plan (col. 14 / lines 50-60).
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).
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 Ratcliffe, 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 anatomical joint performance, since Ratcliffe teaches that it is important to understand the probable effects or outcomes of performing a particular surgical procedure, 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. Ratcliffe also contemplates the use of statistics (col. 4 / lines 19-22).
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: a simulation database (library, par. [0327]) comprising a plurality of previously performed anatomical simulation results of one or more knee joint components, associated with a plurality of hypothetical geometries, for a model of a knee, undergoing a predetermined motion profile (par. [0329]) (the plurality of simulations, previously performed so they can be included in the library, including the virtual modeling of standing and moving, i.e. walking, running, kneeling, etc., where each movement is a different simulation, wherein each of the simulations models one knee joint component of the patient, undergoing the motion profile that is predetermined, i.e. the standing or moving); a second set of software instructions that instructs 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 an image of the 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 a third set of software instructions that instructs the one or more processors to determine a set of recommended 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 the preoperative patient model satisfies a set of predetermined post-operative performance criteria (maximum amount of bone preservation; preserving, restoring, or enhancing the patient's joint kinematics) (par. [0705], [1126], [1240]), and to update a surgical plan of the computer-assisted surgical system to reflect the set of recommended knee implant implantation parameters (par. [0522]).
As to claim 8, Steines discloses the computer-assisted surgical system of claim 7, further comprising a fourth 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 recommended 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 17, Steines discloses the computer-assisted surgical system of claim 7, wherein the set of recommended knee implant implantation parameters (implant selection) is selected from a plurality of sets of knee implant implantation parameters (library of implants) (par. [0327]).
As to claim 18, 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 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 generate, using the simulation database, a statistical model of a prosthetic knee implant performance as a function of one or more knee geometries and a set of prosthetic knee implant implantation parameters; generate a preoperative patient model, based on the statistical model of the prosthetic knee implant performance and one or more preoperative knee geometries extracted from the plurality of patient images (claim 7); the set of recommended knee implant implantation parameters is selected based on the preoperative patient model and the plurality of patient images containing at least the image of the one or more knee joint components of the patient in each of a plurality of flexion positions (claim 17).
Ratcliffe teaches a simulation database (col. 16 / lines 16-19) comprising a plurality of previously performed anatomical simulation results of one or more knee joint components (changes in the mesh geometry by altering the shape of the hypothetical patient would result in a plurality; col. 7 / lines 1-28; previously performed in order for results to be stored in the database), associated with a plurality of hypothetical geometries (a hypothetical patient made from averages, i.e. made from an averaged plurality; col. 7 / lines 1-28), for a model of a knee, undergoing a predetermined motion profile (knee mechanics is based on a motion profile; col. 12 / line 20 – col. 13 / line 31); a first set of software instructions (col. 18 / lines 44-55) that instructs one or more processors to generate, using the simulation database, a probability model of an implant performance as a function of one or more knee geometries (one or more of the mesh geometries) and a set of prosthetic knee implant implantation parameters (col. 1 / line 63 – col. 2 / line 11; col. 4 / lines 47-58; col. 13 / lines 32-61); generate a preoperative patient model (col. 13 / lines 48-61), based on the probability model of the prosthetic knee implant performance and one or more preoperative knee geometries extracted from the plurality of patient images (col. 8 / lines 16-20; col. 17 / lines 43-60; col. 14 / lines 50-60).
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 Ratcliffe, 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 anatomical joint performance, since Ratcliffe teaches that it is important to understand the probable effects or outcomes of performing a particular surgical procedure, 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. Ratcliffe also contemplates the use of statistics (col. 4 / lines 19-22).
As to claim 10, Steines discloses the computer-assisted surgical system of claim 7, further comprising a third 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.
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Steines in view of Ratcliffe, and U.S. Patent Application Publication No. US 2007/0233267 to Amirouche et al. (hereinafter, “Amirouche”), and Anderson.
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); define a preoperative knee geometry for the patient based on the plurality of images (as identified from previous images of the patient’s joint); 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), wherein the predictive performance assessment for the proposed implantation parameters 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]).
Steines is silent as to generate a simulation database by: simulating a first set of results of a plurality of hypothetical geometries in a plurality of simulations of the one or more knee joints performance for a model of a knee undergoing one or more predetermined motion profiles; generate a preoperative patient model configured to provide a predictive performance assessment for an implant component based on the preoperative knee geometry and a set of implantation parameters; and calculate, based on the preoperative patient model, the surgical plan.
Ratcliffe teaches simulating a plurality of hypothetical geometries (a hypothetical patient made from averages, i.e. made from an averaged plurality; col. 7 / lines 1-28) in a plurality of simulations (changes in the mesh geometry by altering the shape of the hypothetical patient would result in a plurality; col. 7 / lines 1-28) of a joint performance (of the knee) for an anatomical model comprising a model of a knee undergoing one or more motion profiles (knee mechanics is based on a motion profile; col. 12 / line 20 – col. 13 / line 31); generating, using the simulation database, a model configured to provide a predictive performance assessment for an implant component based on the preoperative knee geometry and a set of implantation parameters (col. 1 / line 63 – col. 2 / line 11; col. 4 / lines 47-58; col. 13 / lines 32-61); calculating, from the preoperative patient model, the surgical plan (col. 14 / lines 50-60).
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 Ratcliffe, 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.
Steines is silent as to generating, using machine learning, a second set of results of a statistical model of the one or more knee joints performance for the model of the knee, based on the first set of results; generating the preoperative patient model using the second set of results and the preoperative knee geometry.
Amirouche teaches using neural networks having a learning algorithm to predict new variables based on known variables to provide increased data for surgical planning without having to acquire more samples from a patient (par. [0013], [0042], [0048]).
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).
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, simulating a first set of results and generating, using machine learning, a second set of results based on the first set of results, since as taught by Amirouche, machine learning or neural networks allow prediction of new variables based on known variables to provide increased data for surgical planning without having to acquire more samples from a patient. As applied to Steines, the joint performance will be as related to the knee, with the results including patellofemoral joint and femoral-tibial joint performance.
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 Ratcliffe teaches that it is important to understand the probable effects or outcomes of performing a particular surgical procedure, 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. Ratcliffe also contemplates the use of statistics (col. 4 / lines 19-22).
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 Anderson (hereinafter, “Steines/Ratcliffe/Anderson”), as applied to claims 1-12, 15, 17, and 18 above, and further in view of U.S. Patent Application Publication No. US 2007/0233267 to Amirouche et al. (hereinafter, “Amirouche”).
Steines/Anderson are silent as to wherein the statistical model comprises a transfer function computed based on the plurality of the simulations of the knee 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/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/Anderson would be effectively trained and highly accurate.
Conclusion
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/TRACY L KAMIKAWA/Examiner, Art Unit 3775