DETAILED ACTION
This action is in response to the amendments filed on Mar. 16th, 2023. A summary of this action:
Claims 1-20 have been presented for examination.
Claims 2, 4-5, 10, 12-13, 15, 17-18 are objected to because of informalities
Claim 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process without significantly more.
Claim(s) 1, 6, 14, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Yu, et al. "Toward precise osteotomies: a coarse-to-fine 3D cut plane planning method for image-guided pelvis tumor resection surgery." IEEE Transactions on Medical Imaging 39.5 (2019): 1511-1523 taken in view of Thomas et al., US 2016/0070436
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Yu, et al. "Toward precise osteotomies: a coarse-to-fine 3D cut plane planning method for image-guided pelvis tumor resection surgery." IEEE Transactions on Medical Imaging 39.5 (2019): 1511-1523 taken in view of Thomas et al., US 2016/0070436 taken in further view of Bojarksi et al., US 2012/0209394
Claim(s) 7 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Yu, et al. "Toward precise osteotomies: a coarse-to-fine 3D cut plane planning method for image-guided pelvis tumor resection surgery." IEEE Transactions on Medical Imaging 39.5 (2019): 1511-1523 taken in view of Thomas et al., US 2016/0070436 taken in further view of Brett Hauber, A., Angelyn O. Fairchild, and F. Reed Johnson. "Quantifying benefit–risk preferences for medical interventions: an overview of a growing empirical literature." Applied health economics and health policy 11.4 (2013): 319-329.
Dependent claim 2, and its parallel claims 10 and 15, are not rejected under § 102/103. The closest combination of art of record is the below relied upon combination, taken in view of Bojarski et al., US 20120209394, see ¶¶ 952-964 and ¶ 453 – however, this does not fairly teach what is recited it dependent claim 2, in particular “each subsequent resection plan in the series has one more cut plane than the previous cut plan” – rather Bojarski simply teaches optimizing the number of cuts (and corresponding cut planes), wherein per ¶ 453: “Certain embodiments directed to implants or implant designs optimized to achieve minimal implant thickness can include a higher number of bone cuts, for example, six, seven, eight, nine or more bone cuts, on the inner, bone-facing surface of the implant” – but it does not teach such a particular claimed application of a “series” of plans wherein subsequent resection plans in the applied series of plans have one more cut plane than the prior/previous cut plan in the series as required by this claim’s express recitation.
To clarify on the plain meaning of “series”, as this term is not expressly defined in the instant disclosure, see Merriam Webster Dictionary, Definition of series, accessed electronically on July 15th, 2026, URL: merriam-webster(dot)com/dictionary/series: “a number of things or events of the same class coming one after another in spatial or temporal succession” – and note the context of the claim with its use of express terms such as “subsequent” and “previous” are consistent with this plain meaning of “series”.
Second closest reference is Tanji. US 2016/0125603. Abstract, cf. 1-2, 10A-10B, 18, 20, 25, and accompanying descriptions. See ¶¶ 83-88, 124-130, 160-166, 175
Also see Hill, US 2021/0282858. ¶¶ 67-73, 78-87, 95-96, 99-101, 106-130
Dependents of claim 2, and its parallels, are not rejected under § 102/103 solely for the recitation of the subject matter in claim 2, and its parallels.
This action is non-final
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 2, 4-5, 10, 12-13, 15, 17-18 are objected to because of the following informalities:
The claims have issues with antecedent basis. The Examiner suggests amending the claims such that the first recitation of each distinct element uses articles such as “a”/”an”, later recitations referring back to the same distinct element uses articles such as “the”/”said”, to use disambiguating modifiers (e.g., first, second, etc.) when there are multiple distinct elements with the same base term, and that the use of modifiers for each distinct element is kept consistent. Below is a non-exhaustive list of examples of these issues:
Representative Claim 2 and its parallels: “one or more resections” wherein claim 1 already recites “a resection” – Examiner notes given the context of claim 2, to amend claim 2 to use a disambiguating modifier, e.g. second resections, to ensure clarity
Representative Claim 4 and its parallels – multiple articles of “the” for elements (“the total number…the need…”) first recited in claim 4. Examiner suggests articles of a/an for first recitation of a new element
Representative Claim 4 and its parallels: “one of the resection” – there is a singular resection element in claim 1, but claim 2 has a distinct plural resections. Examiner suggest more expressly referring back to the element in claim 2
Representative Claim 5 and its parallels: “the presence of innate…”, but this is the first recitation – similar for other elements in this claim
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The dependent claims inherit the deficiencies of the claims they depend upon.
MPEP § 2173.05(b)(IV): “A claim term that requires the exercise of subjective judgment without restriction may render the claim indefinite. In re Musgrave, 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). Claim scope cannot depend solely on the unrestrained, subjective opinion of a particular individual purported to be practicing the invention. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350, 75 USPQ2d 1801, 1807 (Fed. Cir. 2005));”
Independent claims recite the term “optimal” for “selecting an optimal resection plan…” based on two scores. At issue is that this is a subjective term, and no objective standard is given ¶¶ 81-82) for POSITA to know what exactly is required to make this plan the “optimal” plan, e.g. is it the highest of the two scores, etc. See ¶ 81: “The line representing all of the planar optimized resection plans provides a baseline to help determine which resection plan should be selected.” And ¶ 82: “Indeed, system 100 may have a default setting in which it will automatically identify a preferred plan or couple of plans that are located at a transition in the line's slope from positive trending upward in benefit to zero trending toward more complex with no added benefit. For example, in case 13, the 3-CP planar resection plan and the 6-CP planar resection plan may be identified by system 100 as preferred plans as these plans are located at a slope transition to zero.” – this does not provide an objective standard to ascertain which plan is the optimal plan to select, as recited in the claim, but rather instead conveys a way for selecting “preferred” plans.
As such, it is left solely to the subjective opinion of POSITA what “optimal” requires for metes and bounds of the claimed invention, rendering the claim indefinite.
Examiner suggests deleting the term “optimal”.
Representative dependent claim 4 (also claims 12 and 17) recite the term “novel tooling” – no objective standard is provided (¶¶ 53, 55) for POSITA to ascertain what is “novel tooling” and what tooling is not “novel”, as such its left solely to the subjective opinion of POSITA what the requirements of this are, rendering the claim indefinite.
Examiner suggests deleting this factor, as ¶ 55 merely provides subjective scope for when such tooling is required, i.e. it is purely subjective whether a cut is “elaborate”, and the specification does not set forth any particular structure that would be actually required in such tooling.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process without significantly more.
Examiner suggests amending to incorporate the ¶ 88 to integrate the abstract idea into a practical application, e.g. “Where additive manufacturing is to be used, the implant model may be transferred to an additive manufacturing application which uses the implant model to print the physical implant. Thus, the void filling implant may be formed layer-by-layer using an additive layer manufacturing (ALM), i.e., 3D printing, process so no separate connection mechanism is necessary to bring together any of the features of such implant”.
Step 1
Claims 1 and 8 are directed towards the statutory category of a process.
Claim 14 is directed towards the statutory category of an article of manufacture.
Claims 8, 14, and the dependents thereof, are rejected under a similar rationale as representative claim 1, and the dependents thereof.
Step 2A – Prong 1
The claims recite an abstract idea of a mental process. See MPEP § 2106.04(a)(2).
The mental process recited in claim 1 is:
applying, by the one or more processors executing a resection planning application, a plurality of resection plans to the virtual bone model, each of the plurality of resection plans having at least one virtual control boundary representing a resection of specified geometry and defining a region of bone to be removed, each of the plurality of resection plans having at least one complexity factor and at least one benefit factor associated therewith, the at least one complexity factor and the at least one benefit factor being determined by the one or more processors in the background of the resection planning application upon application of each of the resection plans to the virtual bone model; - a mental process, but for the mere instructions to do it in a computer environment.
To clarify, a surgeon is readily able to mentally visualize in their own mind, or with the aid of pen and paper (e.g. by making sketches to aid the mental visualization) the application of bone resection (bone removal) plans to a mental model of a bone, e.g. mentally visualizing an area of a femur to be removed by a later surgery, and mentally judge the complexity and benefits of the resection plan, including of doing this for a plurality of plans. To clarify, ¶ 49: “A manual resection plan is a plan that is prepared by a surgeon based on experience, knowledge, and best practices and provides a baseline comparison value.” And ¶ 63: “It should be understood that the weightings assigned to the complexity and benefit factors described above are exemplary. A surgeon may override the default settings of system 10 and apply additional factors and/or assign different weights” and ¶ 82: “The surgeon may also override the default preferred plan selection criteria and instead apply their own criteria to system 100 for automated preferred plan identification” and ¶ 51: “In the example method described herein, these factors, which fit into the general categories mentioned above, include the number of cuts, the type of cut, the type of tooling that may be required to make the cut, the amount of bone removed by each cut, whether a cut preserves or sacrifices specified anatomy, and whether the cuts help constrain a void filling implant”
To clarify, a surgeon is readily able to mentally judge how many cuts will be required, and whether or not it is a complex surgery from the result, similar with the type of cut, and the type of tooling (e.g. mentally judged as part of instructing the nursing staff what tools should be on hand for the surgery), the amount of bone to be removed, etc.
As to the “virtual control boundary”, but for the recitation of “virtual” (mere instructions to do it on a computer/in a computer environment), this is a mental process – cf. 13-14. A surgeon is readily able to mentally visualize a bone of a patient, and mentally visualize a region of the bone to be removed, e.g. mentally visualizing that the top portion of the bone needs to be removed, so they visualize where a bone saw would have to go through the bone to remove the region desired for the surgery.
determining, by the one or more processors, a complexity score for each of the resection plans based on the at least one complexity factor, wherein the complexity score is determined in the background of the resection planning application; determining, by the one or more processors, a benefit score for each of the resection plans based on the at least one benefit factor, wherein the benefit score is determined in the background of the resection planning application; and selecting an optimal resection plan from the plurality of resection plans based on the complexity and benefit scores of the plurality of resection plans for execution in the surgical procedure.
A mental process of simply having judged/evaluated what factors are present and values for those factors, e.g. the “number of cuts” or the like (¶ 51), a surgeon is then readily able to tabulate these such as with pen and paper, and mentally evaluate a score for both complexity and benefit based on such tabular representations, and from that select the optimal plan (e.g. the one with the highest score).
Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2).
To clarify, see the USPTO 101 training examples, available at https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility. In particular, with respect to the physical aids, see example # 45, analysis of claim 1 under step 2A prong 1, including: “Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation.”; also see example # 49, analysis of claim 1, under step 2A prong 1: “Moreover, the recited mathematical calculation is simple enough that it can be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation.”.
As such, the claims recite a mental process.
Step 2A, prong 2
The claimed invention does not recite any additional elements that integrate the judicial exception into a practical application. Refer to MPEP §2106.04(d).
The following limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f), including the “Use of 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 integrate a judicial exception into a practical application or provide significantly more”:
The preambles of the claims
by the one or more processors executing a resection planning application… in the background of the resection planning application
… virtual…
Claim 8: executing the optimal resection plan on the mammalian subject to remove a portion of the bone and form a corresponding void; and implanting a void filling implant based on the virtual void filling implant model into the void. – see MPEP § 2106.04(d)(2): “The treatment or prophylaxis limitation must be "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). For example, consider a claim that recites mentally analyzing information to identify if a patient has a genotype associated with poor metabolism of beta blocker medications. This falls within the mental process grouping of abstract ideas enumerated in MPEP § 2106.04(a). The claim also recites "administering a lower than normal dosage of a beta blocker medication to a patient identified as having the poor metabolizer genotype." This administration step is particular, and it integrates the mental analysis step into a practical application. Conversely, consider a claim that recites the same abstract idea and "administering a suitable medication to a patient." This administration step is not particular, and is instead merely instructions to "apply" the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application” – “a void filling implant” is akin to “a suitable medication” as it encompasses all applications of the judicial exception without restriction on what this implant is. The “optimal resection plan” execution is in view of ¶ 89: “The plan can be executed entirely manually by the surgeon, robotically, or by the surgeon with a robotically assisted haptic feedback system.” – i.e. without restriction on how this is to be done. Thus, these steps are considered “apply it”
The following limitations are adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g):
generating, by one or more processors, a virtual bone model from a medical image taken of a bone of a mammalian subject; - mere data gathering
A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. See MPEP § 2106.04(d).
MPEP 2106.04(II)(A)(2) “…Instead, under Prong Two, a claim that recites a judicial exception is not directed to that judicial exception, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. Prong Two thus distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception…Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B” and MPEP § 2106(I): “Mayo, 566 U.S. at 80, 84, 101 USPQ2dat 1969, 1971 (noting that the Court in Diamond v. Diehr found “the overall process patent eligible because of the way the additional steps of the process integrated the equation into the process as a whole,”” – and see MPEP § 2106.05(e).
To further clarify, MPEP § 2106.04(II)(A)(1): “Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (citing Mayo, 566 US at 71, 101 USPQ2d at 1965). Yet, the Court has explained that ‘‘[a]t some level, all inventions embody, use, reflect, rest upon, or apply laws of nature, natural phenomena, or abstract ideas,’’ and has cautioned ‘‘to tread carefully in construing this exclusionary principle lest it swallow all of patent law” See also Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335, 118 USPQ2d 1684, 1688 (Fed. Cir. 2016) ("The ‘directed to’ inquiry, therefore, cannot simply ask whether the claims involve a patent-ineligible concept, because essentially every routinely patent-eligible claim involving physical products and actions involves a law of nature and/or natural phenomenon").”
As a point of clarity, RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." discussed in MPEP § 2106.04(II)(A)(2) as well as MPEP § 2106.04(I): “Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea") (emphasis in original).
The claimed invention does not recite any additional elements that integrate the judicial exception into a practical application. Refer to MPEP §2106.04(d).
Step 2B
The claimed invention does not recite any additional elements/limitations that amount to significantly more.
The following limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f), including the “Use of 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 integrate a judicial exception into a practical application or provide significantly more”:
The preambles of the claims
by the one or more processors executing a resection planning application… in the background of the resection planning application
… virtual…
Claim 8: executing the optimal resection plan on the mammalian subject to remove a portion of the bone and form a corresponding void; and implanting a void filling implant based on the virtual void filling implant model into the void. – see MPEP § 2106.04(d)(2): “The treatment or prophylaxis limitation must be "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). For example, consider a claim that recites mentally analyzing information to identify if a patient has a genotype associated with poor metabolism of beta blocker medications. This falls within the mental process grouping of abstract ideas enumerated in MPEP § 2106.04(a). The claim also recites "administering a lower than normal dosage of a beta blocker medication to a patient identified as having the poor metabolizer genotype." This administration step is particular, and it integrates the mental analysis step into a practical application. Conversely, consider a claim that recites the same abstract idea and "administering a suitable medication to a patient." This administration step is not particular, and is instead merely instructions to "apply" the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application” – “a void filling implant” is akin to “a suitable medication” as it encompasses all applications of the judicial exception without restriction on what this implant is. The “optimal resection plan” execution is in view of ¶ 89: “The plan can be executed entirely manually by the surgeon, robotically, or by the surgeon with a robotically assisted haptic feedback system.” – i.e. without restriction on how this is to be done. Thus, these steps are considered “apply it”
The following limitations are adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g):
generating, by one or more processors, a virtual bone model from a medical image taken of a bone of a mammalian subject; - mere data gathering
In addition, the above insignificant extra-solution activities are also considered as well-understood, routine, and conventional activities, as discussed in MPEP § 2106.05(d):
generating, by one or more processors, a virtual bone model from a medical image taken of a bone of a mammalian subject; - conventional data gathering in view of:
Carrillo, Fabio, et al. "An automatic genetic algorithm framework for the optimization of three-dimensional surgical plans of forearm corrective osteotomies." Medical image analysis 60 (2020): 101598. Page 2, paragraph split between the columns. Also see § 1.1 ¶¶ 1-2, and § 2.1.
Carrillo, Fabio, et al. "A time saver: Optimization approach for the fully automatic 3D planning of forearm osteotomies." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer International Publishing, 2017. § 2 ¶ 1
Zhang, Yu, et al. "Toward precise osteotomies: a coarse-to-fine 3D cut plane planning method for image-guided pelvis tumor resection surgery." IEEE Transactions on Medical Imaging 39.5 (2019): 1511-1523. § 1 ¶ 2
As such, the claims are directed towards a mental process without significantly more.
Regarding the dependent claims
Claim 2 is merely further limiting the abstract idea for a surgeon to consider a few, e.g. two, different plans in series
Claim 3 is further limiting the mental process as well by merely specifying what type of resection should be in the mental process of the surgeon, in a second series of plans, e.g. two more plans
Claim 4 – further limiting the mental process, e.g. a surgeon is readily able to mentally judge that a surgery is complex if it requires a high number of cuts, or special surgical cuts such as a blind cut or a fan cut
Claim 5 – further limiting the mental process to what desired results of the plan are, e.g. reducing the amount of bone waste.
Claim 6 – merely comparing a few numbers is a mental judgement
Claim 7 – such a plot is readily a mental process, such as on pen and paper. To clarify, it’s a 2D line chart – cf. 16-17. A person is readily able to draw such a chart on graph paper with physical aids, e.g. a pencil and a ruler, so as to plot a table of a few data points. Selecting at the “transition location” is merely the mental process of observing where the knee/elbow of the curve is (the region the curve flattens)
Claim 9 – adding another step to the mental process, given the generic nature of what is recited
Claims 10-13 and 15-20 are rejected under similar rationales as their representative claims above.
As such, the claims are directed towards a mental process without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6, 14, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Yu, et al. "Toward precise osteotomies: a coarse-to-fine 3D cut plane planning method for image-guided pelvis tumor resection surgery." IEEE Transactions on Medical Imaging 39.5 (2019): 1511-1523 taken in view of Thomas et al., US 2016/0070436
Regarding Claim 1
Zhang teaches:
A computer-implemented method of selecting a bone resection plan for implementation in a surgical procedure, the method comprising: (Zhang, abstract)
generating, by one or more processors, a virtual bone model from a medical image taken of a bone of a mammalian subject; (Zhang, abstract, then see § II.A, then see figure 1, in particular note # 1-4 in fig. 1 which visually depicts the generation of a virtual bone model from CT and MR images)
applying, by the one or more processors executing a resection planning application, a plurality of resection plans to the virtual bone model, each of the plurality of resection plans having at least one virtual control boundary representing a resection of specified geometry and defining a region of bone to be removed, each of the plurality of resection plans having at least one complexity factor and at least one benefit factor associated therewith, the at least one complexity factor and the at least one benefit factor being determined by the one or more processors in the background of the resection planning application upon application of each of the resection plans to the virtual bone model; …determining, by the one or more processors, a benefit score for each of the resection plans based on the at least one benefit factor, wherein the benefit score is determined in the background of the resection planning application; and selecting an optimal resection plan from the plurality of resection plans based on the … benefit scores of the plurality of resection plans for execution in the surgical procedure. (Zhang, as cited above, incl. the abstract and § II.A, and fig. 1, noting in fig. 1 in particular # 6-7, then see § II.B to clarify on the generation of the dangerous region
followed by § II.C which discusses the “2D Cut Plane Planning Method”, in particular: “…Then, intuitively, 3D cut plane planning can be transformed to 2D cut line planning, which aims to plan a set of 2D cut lines [examples of the virtual control boundary] that minimize the area of resected specimen in the 2D space. Finally, the 3D cut planes for bone tumor resection can be solved from the 2D cut lines according to the projection parameters… In the 2D projection space, our 2D cut plane planning method is proposed to plan a set of 2D cut lines that locate outside the dangerous region while minimizing the area of the virtually resected specimen by the planned cut lines. In practice, the cut lines should be strictly outside the outmost points of the dangerous region (i.e., convex hull points of the dangerous region) so that the cut lines can be constrained to locate outside the dangerous region. As the area of the region inside the convex hull of the dangerous region is constant, thus minimization of the area of the resected specimen is equivalent to minimization of the area of the region enclosed by the cut lines, convex hull of the dangerous region (red points in Fig. 4c) and two boundary lines of safe region (green points in Figs. 3b and 4b)… 2) 2D Cut Line Planning With Segmented Boundary-Constrained Linear Regression: 2D cut line planning mainly consists of two procedures: 1) partitioning the convex hull segment into an appropriate number of portions, and 2) fitting each portion with a cut line… Therefore, starting from the two main purposes of cut line planning, we propose a segmented boundary-constrained linear regression method to deal with the 2D cut line planning task… With a convex hull segment H = {p1, p2, · · · , pM} and two boundary lines L1 and L2, the proposed segmented boundary-constrained linear regression can be expressed to regress K cut lines Li (defined by the surgeons) outside H while minimizing the area of the region enclosed by H, L1, L2 and Li… Secondly, the proposed boundary-constrained linear regression method is separately applied on each short convex hull segment to regress a cut line, that minimizes the area of the region enclosed by the short convex hull segment, two boundary lines and the regressed cut line. Finally, the initially regressed cut lines are jointly optimized to minimize the area of the region enclosed by the long convex hull segment, two boundary lines and the regressed cut lines, which is equivalent to minimize the area of virtually resected specimen… In the last step, the initially regressed cut lines are jointly optimized to minimize the area of resected specimen. Note that, after initial regression, the two boundary lines of Si naturally become its two adjacent cut lines… Therefore, the boundary-constrained linear regression method is iteratively applied on the simultaneously updated Si , Lin i−1 and Lin i , to gradually minimize the area of resected specimen. After a few iterations, K optimal cut lines L ∗ = L1,∗, L2,∗, · · · , LK,∗ can be obtained” – to clarify, in each optimization iteration of Zhang, a plurality of “cut lines” are applied to the bone model, scored based on their ability to “minimize the area of resected specimen” [i.e. the amount of bone to be cut is scored], and the optimal plan (the optimal set of cut lines) is selected “After a few iterations”, e.g. last paragraph of § II.C.2.a (p. 1517, col. 1, ¶ 2) is “The proposed segmented boundary-constrained linear regression method is outlined in Algorithm 2, for better understanding of this method. Moreover, in Fig. 7, our segmented boundary-constrained linear regression method is demonstrated on a convex hull segment (extracted in Fig. 4c) to verify its efficacy. It can be seen from Fig. 7 that the cut lines are gradually optimized to minimize the area of the region enclosed by H, L1, L2 and L ∗ in only one iteration” – see the last subsection (p. 1517 # 3) for clarification on creating the 3D cut planes
Zhang then further continues the optimization in 3D with the 3D cut planes – see subsection (D) starting on p. 1517: “Even though cut planes can be more efficiently optimized in the projected 2D space than that in the original 3D space, minimization of the specimen’s area in the 2D space is not equivalent to minimization of the specimen’s volume in the 3D space. Therefore, we further proposed a coarse-to-fine 3D cut plane planning method by incorporating a 3D cut plane refinement scheme with our previously proposed 2D cut plane planning method. For each specific pelvis tumor resection surgery, we firstly adopt our 2D cut plane planning method to plan K initial 3D cut planes CK = {c1, c2, · · · , cK }. Then, for the purpose of minimizing the volume of the resected specimen, we leverage a newly proposed 3D cut plane refinement scheme to finely tune CK in the 3D space. So, in this part, we have mainly introduced the 3D cut plane refinement scheme… Firstly, before tuning the current cut plane c j , the other cut planes are kept fixed and used to virtually resect the 3D bone. Secondly, the triangle facets near c j are found out from TN , and taken as the candidate plane set Dj for updating c j . Specifically, if the distance d j i,k from any one vertex pt i,k of a triangle facet ti in TN to c j is less than the preset threshold dth (set as 10mm in this work), then ti is pushed to Dj . Thirdly, c j is iteratively fitted to each candidate plane ti in Dj , and updated as the candidate plane that yields the minimum volume of resected specimen. Geometrically, the volume of resected specimen can be expressed as the volume of the region composed of those bone points on tumor side of CK , which can be calculated by the alpha-shape method [15]. Here, whether a point pl lies on tumor side of c j can be judged by the signed distance from pl to c j , as calculated in Eq. (8). If d j l ≥ 0, then pl lies on tumor side of c j . Afterwards, the other cut planes are also tuned with the same refinement method… Repeating the above three steps for a few iterations, the cut planes will be gradually refined to minimize the volume of resected specimen, i.e., preserve maximum volume of healthy bone. Further, we have outlined the proposed cut plane refinement scheme in Algorithm 3, and illustrated its major procedures in Fig. 8, to help introduce this scheme… Through integrating both the effective 2D cut plane planning method and 3D cut plane refinement scheme, our coarse to fine 3D cut plane planning method will be capable of planing a set of precise cut planes for each pelvis tumor resection surgery. The cut planes planned by our coarse-to fine planning method can not only ensure the entire resection of the dangerous region, but also preserve larger amount of healthy bone than those planned by our 2D method” – see fig. 8 to further clarify
Then, see § III.A.2: “The quality of planned cut planes can be assessed by the following two criteria: (1) the cut planes should be strictly outside the tumor boundary with a margin o smaller than the safe margin, so that resection of the entire dangerous region (i.e., the tumor region and its surrounding tissue with the safe margin) can be ensured. As a result, the recurrence rate of bone tumor can be largely decreased [2], [8]–[10]; and (2) based on criterion (1), the planned cut planes should lead to resection of minimum amount of healthy bone, for the purpose of improving the functional outcomes of the patients. Moreover, time cost of the cut plane planning method is also an important criterion for surgeons. Therefore, we have compared our method with other different approaches to cut plane planning by assessing the margins of the planned cut planes, the volumes of the virtually resected specimens, and the time costs of the plannings. Criterion (1) can be achieved either qualitatively or quantitatively. The qualitative method is to observe whether the cut planes are strictly outside the dangerous region. The quantitative method is to evaluate the error in the safe margin (ESM) [19] of each cut plane, which is defined as the difference between the actual margin of this cut plane against tumor boundary and the desired safe margin. If the ESM value of a cut plane is no less than zero, then this cut plane obtains a margin no smaller than the safe margin and satisfies criterion (1). Moreover, a non-zero ESM value indicates the planning method did not place this cut plane along the dangerous region’s boundary: a cut plane with a positive ESM value leads to overresection of healthy bone, whereas a cut plane with a negative ESM value results in residuals of dangerous region. Therefore, a zero ESM value is desired for a cut plane according to criterion (1), which indicates the planning method placed the cut plane at the optimal position. Criterion (2) can be implemented through the volume of the virtually resected specimen, which can be computed by the alpha-shape method [15]. The optimal cut planes are desired to minimize the volume of resected specimen with criterion (1) satisfied. Execution time of different planning methods were assessed for the criterion of time costs in the plannings.”
to clarify on the BRI of the factors and scoring, instant ¶¶ 51 and 58 – Zhang’s criterion (1) and (2) are examples of the benefit factors and associated scoring, to further clarify see the “quantitative method” for critieria (1) of Zhang as cited above, clarified on in § III.B.1 ¶¶ 3-4 and table II with numeric scoring, i.e.: “MINIMUM ESM VALUES OF EACH SET OF CUT PLANES FOR EACH SURGERY RESPECTIVELY PLANNED BY THE MANUAL CUT PLANE PLANNING METHOD (minESMm), OUR 2D CUT PLANE PLANNING METHOD (minESM2), AND OUR COARSE-TO-FINE 3D CUT PLANE PLANNING METHOD (minESM3). (UNIT:mm)”, along with see the above discussed (in the optimization sections) scoring of the optimization goal (e.g. algorithm 3, the “Compute the volume” and corresponding description in the text cited above; e.g. in algorithm 2 the “Compute the area” and corresponding description in the text cited above, Examiner noting the goal is to “min”/minimize these scores for the optimal plan) and see further clarification in § III.B.2 including table III
as to the complexity factors, see instant disclosure ¶ 53 to clarify on the BRI, e.g. “the total number of CPs to be used” – e.g. ¶ 51 “the number of cuts” – see Zhang, p. 1511, last paragraph: “To be specific, the manual methods are usually performed by an experienced surgeon, and mainly include six procedures:…5) through observing the relative position of 3D bone and tumor, the surgeon initially places an appropriate number of cut planes around the 3D tumor with a margin equal to the desired safe margin on a single image slice;” – then see p. 1514, col. 2, ¶ 2: “2D cut line planning mainly consists of two procedures: 1) partitioning the convex hull segment into an appropriate number of portions, and 2) fitting each portion with a cut line.” To p. 1515 col. 1, ¶ 1: “regress K cut lines Li (defined by the surgeons)… Firstly, the convex hull segment is partitioned into K short segments with K −1 appropriate breakpoints” – i.e. the number of cuts is a factor associated with the resection plan, and wherein K is determined (the number of breakpoints to separate the cuts) is determined by the processor, which is an example of a complexity factor
as to the background of the planning application, Zhang as cited above teaches these are algorithms run on the computer, these would have been computed automatically in the background and the results shown to the user (see figures of Zhang to clarify, e.g. cf. 9)
Zhang does not explicitly teach the following, but Zhang in view of Thomas teaches:
determining, by the one or more processors, a complexity score for each of the resection plans based on the at least one complexity factor, wherein the complexity score is determined in the background of the resection planning application;…complexity score and… (Zhang, as was cited above for the optimization of the cut plane during the planning to minimize bone waste/the amount removed by optimizing the cutting paths
as taken in view of Thomas, abstract, see ¶ 90, see ¶¶ 93-102, in particular ¶ 93: “The method also includes assigning a score to the one or more trajectory paths [see ¶¶ 58-59 to clarify] to quantify how well the one or more trajectory paths satisfy the surgical intent, and based on a comparison of these scores, the best surgical path is calculated.” – then, see ¶¶ 94-101 which give examples of complexity factors to be scored, then see ¶ 102: “These metrics will change depending on the surgical tool being inserted and the surgery being performed. Hence, the score presented to alternative trajectories will incorporate both the type of surgery and specific tools that are planned to be used in the procedure. This also provides the surgeon an opportunity to evaluate the pros and cons of using different surgical techniques and tools for a certain procedure, then see ¶ 103: “The method may also include comparing the scores of the one or more point-wise surgical trajectory paths to a surgical intent score of a path of the shortest distance between the one or more target locations and the closest entry point( s ). It is noted that most surgeries currently performed presently use a straight linear path from the surface to the target which corresponds to the shortest distance. Therefore this method performs a score comparison between the more prominently used shortest distance surgical trajectory path and the alternate path being proposed, allowing the difference to be noted by the user for future consideration. In some cases the straight path approach may give the best score in which case, which may also be considered by the user.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Zhang on a system which optimized the resection plan to optimize the cutting planes in the plan with the teachings from Thomas on scoring surgical trajectory “to quantify how well the one or more trajectory paths satisfy the surgical intent” The motivation to combine would have been that such a scoring which ensure a more optimal surgical plan, e.g. Thomas, ¶ 94: “For surgery involving structures such as, but not limited to, nerves, blood vessels, ligaments, tendons, organs etc, the surgical path's incident angle relative to an individual structures may be used to determine the average amount of damage expected to be sustained by the structure, where a steeper incident angle ( closer to orthogonal with the structure would cause more damage and therefore correspond to a worse score than a more parallel incident angle which ( closer to parallel with the structure) would cause less damage and therefore correspond to a better score”, e.g. ¶ 95: “For example depending on the type of surgical device, its shape and size, longer trajectories may cause the device to apply force over a larger area which may result in greater trauma overall than if the path was shorter. Therefore in this case a shorter path would correspond to a better score whereas a longer path would correspond to a worse score”, e.g. ¶ 96: “The number of waypoints used for specifically changing directions could also be used to score. For example if the surgical device is rigid, the higher the number of directional changes that occur the, and the greater the directional change angle(s), the more the tissue is forced to deform. This deformation of the tissue in various orientations with respect to the surgical device may cause additional internal strain and wear on surrounding tissue causing damage thereto. In this manner, a higher number of directional changes, and higher angle (s) of directional change, would correspond to a lower surgical path score.” – see ¶¶ 97-101 for more clarification.
Regarding Claim 6
Zhang in view of Thomas teaches:
The method of claim 1, wherein the selecting step includes comparing the complexity and benefit scores of the plurality of resection plans. (Zhang as was taken in view of Thomas as cited above for claim 1, incl. Thomas ¶¶ 93 ad 103 as discussed above)
Regarding Claim 14.
Rejected under a similar rationale as claim 1 above.
Regarding Claim 19.
Rejected under a similar rationale as claim 6 above.
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Yu, et al. "Toward precise osteotomies: a coarse-to-fine 3D cut plane planning method for image-guided pelvis tumor resection surgery." IEEE Transactions on Medical Imaging 39.5 (2019): 1511-1523 taken in view of Thomas et al., US 2016/0070436 taken in further view of Bojarksi et al., US 2012/0209394
Regarding Claim 8
Rejected under a similar rationale as claim 1 above, wherein Zhang in view of Thomas does not explicitly teach the following, however Zhang taken in view of Thomas and Carrillo teaches:
generating, by the one or more processors executing a CAD application, a virtual void filling implant model based on the optimal resection plan; and executing the optimal resection plan on the mammalian subject to remove a portion of the bone and form a corresponding void; and implanting a void filling implant based on the virtual void filling implant model into the void.
See Zhang, as was taken in view of Thomas above, on “A Coarse-to-Fine 3D Cut Plane Planning Method for Image-Guided Pelvis Tumor Resection Surgery” (Zhang, title, and abstract)
As taken in further view of Bojarski, abstract, then cf. 26, then see ¶¶ 466-479 which discusses the “assessment process” of “…selecting and/ or designing one or more features and/ or feature measurements of an implant component and, optionally, of a corresponding resection cut strategy and/or guide tool that is adapted… Correcting a joint deformity and/or a limb alignment deformity can include, for example, generating a virtual model of the patient's joint, limb, and/or other relevant biological structure(s ); virtually correcting the deformity and/or aligning the limb; and selecting and/or designing one or more surgical steps ( e.g., one or more resection cuts), one or more guide tools, and/or one or more implant components to physically perform and/or accommodate the correction…” – incl. seeing ¶ 470 in particular, and ¶ 479: “For example, a feedback mechanism can be used to determine the effect that changes in features intended to maximize bone preservation (e.g., implant component thickness( es), bone cut number, cut angles, cut orientations, and related resection cut number, angles, and orientations) have on other parameters such as limb alignment, deformity correction, and/or joint kinematic parameters, for example, relative to selected parameter thresholds. Accordingly, implant component features and/or feature measurements ( and, optionally, resection cut and guide tool features and/or feature measurements) can be modeled virtually and modified reiteratively to achieve an optimum solution for a particular patient” – then, cf. 26 and accompanying description, in particular noting ¶ 481 incl.: “The obtained patient's biological features and feature measurements, implant component features and feature measurements, and, optionally, resection cut and/or guide tool features and/or feature measurements then can be assessed to determine the optimum implant component features and/or feature measurements, and optionally, resection cut and/or guide tool features and/or feature measurements, that achieve one or more target or threshold values for parameters of interest 2630 ( e.g., by maintaining or restoring a patient's healthy joint feature). As noted, parameters of interest can include, for example, one or more of (1) joint deformity correction; (2) limb alignment correction; (3) bone, cartilage, and/or ligaments preservation at the joint; (4) preservation, restoration, or enhancement of one or more features of the patient's biology, for example, trochlea and trochlear shape; ( 5) preservation, restoration, or enhancement of joint kinematics, including, for example, ligament function and implant impingement; (6) preservation, restoration, or enhancement of the patient's joint-line location and/or joint gap width; and (7) preservation, restoration, or enhancement of other target features”
Then, see ¶¶ 482-483: “Once the one or more optimum implant component features and/or feature measurements are determined, the implant component(s) can be selected 2640, designed 2650, or selected and designed 2640, 2650…. Optionally, one or more resection cut features and/ or feature measurements can be selected 2660, designed 2670, or selected and further designed 2660, 2670. For example, a resection cut strategy selected to have some optimum features and/or feature measurements can be designed further using one or more CAD software programs or other specialized software to optimize additional features or measurements of the resection cuts, for example, so that the resected surfaces substantially match optimized bone-facing surfaces of the selected and designed implant component. This process can be repeated as desired”
To clarify that this includes a void filling implant, cf. 27 as discussed starting in ¶ 520: “FIG. 27 is an illustrative flow chart showing exemplary steps taken by a practitioner in assessing a joint and selecting and/or designing a suitable replacement implant component”, e.g. ¶ 527: “For example, in designing an implant for a total knee replacement comprising a femoral component and a tibial component, one component can include one or more patient-specific features and the other component can be selected from a library”, e.g. ¶ 528: “Then, the images and data in the patient-specific database can be accessed and a patient-specific and/or patient-engineered partial or total joint replacement implant using the patient's original anatomy, not affected by arthritic deformity yet, can be generated” – i.e. its replacing the portion of the bone that was removed
As to performing the implantation, see ¶¶ 950-951: “For surgeons and medical professionals, this process also provides a simplified surgical technique. The selected and/or designed bone cuts and, optionally, other features that provide a patient-adapted fit for the implant components eliminates the complications that arise in the surgical setting with traditional, misfitting implants. Moreover, since the process and implant component features are predetermined prior to surgery, model images of the surgical steps can be provided to the surgeon as a guide….As noted above, the design of an implant component can include manufacturing or machining the component in accordance with the implant design specifications. Manufacturing can include, for example, using a designed mold to form the implant component.” – then, see ¶ 967: “Then, surgery was performed on the cadaveric patient to perform the predetermined ( e.g., designed) resection cuts and to implant the components designed for the particular patient”, e.g. ¶ 968: “Three surgical procedures were performed to implant femoral and tibial implant components for three different cadaveric patients. Prior to each surgery, patient adapted implant components (femoral and tibial implant components) and guide tools were designed (including manufacturing) in conjunction with a resection cut design. Then, the guide tools were used during surgery to prepare the predetermined resection cuts and place the implant components”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Zhang on “Toward Precise Osteotomies: A Coarse-to-Fine 3D Cut Plane Planning Method for Image-Guided Pelvis Tumor Resection Surgery” (Zhang, title), see abstract and § I to clarify, as Zhang’s system is focused on the resection of the bone itself, with the teachings from Bojarski on “The exemplary process described above yields both a predetermined surgical resection design for altering articular surfaces of a patient's bones during surgery and a design for an implant that specifically fits the patient, for example, following the surgical bone resectioning” (Bojarski, ¶ 946)
The motivation to combine would have been that “The exemplary process described above, including the resulting patient-adapted implants and predetermined bone resectioning design, offers several advantages over traditional primary and revision implants and related processes. For example, it allows for one or more pre-primary implants such that a subsequent replacement or improvement can take the form of a primary implant. Specifically, because the process described herein can minimize the amount of bone that is resected, enough bone stock may remain such that a subsequent procedure may be performed with a traditional, primary, off-the-shelf implant. This offers a significant advantage for younger patients who may require in their lifetime more than a single revision for an implant. In fact, the exemplary process described above may allow for two or more pre-primary implants or procedures before so much bone stock is sacrificed that a traditional, primary implant is required. The advantageous minimal bone resectioning and therefore minimal bone loss that is achieved with this process arises from the fact that the bone-facing surfaces of the implants are derived for each patient based on patient-specific data, such as, for example, data derived from images of the patient's joint, size or weight of the patient, size of the joint, and size, shape and/or severity of defects and/or disease in the joint. This patient-adapted approach allows for the bonefacing surface of the implant components to be optimized with respect to any number of parameters, including minimizing bone loss, using any number of resection cuts and corresponding implant component bone cuts and bone cut facets to conserve bone for the patient. With traditional implants, the implant's bone-facing surface includes standard bone cuts and the resection cuts to the patient's bone are made to fit those standard bone cuts…For surgeons and medical professionals, this process also provides a simplified surgical technique. The selected and/or designed bone cuts and, optionally, other features that provide a patient-adapted fit for the implant components eliminates the complications that arise in the surgical setting with traditional, misfitting implants” (Bojarski, ¶¶ 947-950
Regarding Claim 9
Zhang, in view of Thomas and Bojarski above teaches:
The method of claim 8, further comprising assessing, by the one or more processors, assessing the virtual void filling implant model, and revising the complexity and benefit score on the based on the assessment of the virtual void filling implant model. (Zhang, as was taken in view of Thomas and Bojarski above teaches this – in particular note the scoring of Thomas and Bojarski as discussed above, see a similar such “assessment” in Bojarski as was cited above (¶¶ 466-479 and cf. 26 along with accompanying description), in particular noting ¶¶ 482-483 as cited above, incl.: “Once the one or more optimum implant component features and/or feature measurements are determined, the implant component(s) can be selected 2640, designed 2650, or selected and designed 2640, 2650. For example, an implant component having some optimum features and/or feature measurements can be designed using one or more CAD software programs or other specialized software to optimize additional features or feature measurements of the implant component….For example, a resection cut strategy selected to have some optimum features and/or feature measurements can be designed further using one or more CAD software programs or other specialized software to optimize additional features or measurements of the resection cuts, for example, so that the resected surfaces substantially match optimized bone-facing surfaces of the selected and designed implant component. This process can be repeated as desired””
Claim(s) 7 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Yu, et al. "Toward precise osteotomies: a coarse-to-fine 3D cut plane planning method for image-guided pelvis tumor resection surgery." IEEE Transactions on Medical Imaging 39.5 (2019): 1511-1523 taken in view of Thomas et al., US 2016/0070436 taken in further view of Brett Hauber, A., Angelyn O. Fairchild, and F. Reed Johnson. "Quantifying benefit–risk preferences for medical interventions: an overview of a growing empirical literature." Applied health economics and health policy 11.4 (2013): 319-329.
Regarding Claim 7
While Zhang in view of Thomas does not explicitly teach the following, Zhang in view of Thomas and Hauber teaches:
The method of claim 6, wherein comparing the complexity and benefit scores includes plotting, by the one more more processors, the complexity and benefit scores on a line in a two-dimensional Cartesian coordinate system and selecting, by the one or more processors, a resection plan at a transition location between a positive slope and a zero slope of the line. (Zhang, as was taken in view of Thomas above,
Taken in further view of Hauber, cf. 1 which shows a transition region between a positive slope and a zero slope of the line (Examiner notes that the end of the line shown is at, or nearly at zero; see Titanium Metals Corp. of America v. Banner, 778 F.2d 775, 783, 227 USPQ 773, 779 (Fed. Cir. 1985) as briefly discussed in MPEP § 2144.05(I)) wherein in Hauber (pp. 320-321, paragraph between the pages): “In this graph, benefits are plotted on the horizontal axis, risks are plotted on the vertical axis, and a threshold is plotted above which risks outweigh benefits and below which benefits outweigh risks.” – to clarify, p. 321, col. 2, ¶ 2: “For
the purpose of describing this framework, we define the benefit–risk threshold as the maximum risk of harm decision makers would accept for a realized improvement in a beneficial outcome. Thus, the horizontal axis is an index of that improvement and the vertical axis is the associated risk or probability of harm” – i.e. one selects treatments in the region of the “Net Effectiveness Benefit” and “Net Safety Margin” – see p. 322 col. 1, ¶¶ 2-3 to clarify)
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Zhang, as was modified by Thomas on a system scoring resection plans with multiple scores (Thomas, ¶¶ 93 and 103 in particular as cited above) with the teachings from Hauber on “a welfare-theoretic conceptual framework underlying the measurement of benefit–risk preferences in pharmaceutical and medical treatment decisions” (Hauber, abstract). The motivation to combine would have been that “The simplest method of combining benefit–risk preference information and clinical results in benefit–risk decisions is to plot the clinical data corresponding to point A in Fig. 1 relative to the benefit–risk threshold.” (Hauber, § 4 ¶ 4)
Regarding Claim 20.
Rejected under a similar rationale as claim 7 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DiGioia, III et al., US 6,205,411. Abstract, cf. 12(a) and accompanying description, also see col. 2-3, paragraph split between the columns.
Gueziec et al., US 6,301,495. Abstract, cf. 3 and accompanying description, also see col. 5 description of # 1060
Schoenefeld et al., US 9,345,548. Abstract, cf. 1 and 1A # 130 to 140 and accompanying description. Cf. 13A, in particular # 130 to 140 and accompanying description. Cf. 21, # 1178 and accompanying description. Cf. 6 and accompanying description. See col. 5-7 in particular.
Aram et al., US 10,149,722. Abstract, cf. 1 # 24-26, and accompanying description, incl. in col. 6-8.
Park, US 11,439,467. Abstract, col. 7-8.
McKinnon et al., US 2014/0244220. Abstract, see figures for part # 104 and accompanying description, and ¶¶ 56-60.
Otto et al., US 2022/0104881. Abstract, cf. 26c-h and accompanying description
Bell et al., US 2022/0338935. ¶¶ 189, 202-207
Metcalfe et al., US 2023/0087313. Abstract, cf. 20-21, ¶¶ 52, 143, 146, 150-151, 253-255.
Belei, P., et al. "Computer-assisted single-or double-cut oblique osteotomies for the correction of lower limb deformities." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 221.7 (2007): 787-800. Abstract, § 4 and its subsections.
Caprara, Sebastiano, et al. "Bone density optimized pedicle screw instrumentation improves screw pull-out force in lumbar vertebrae." Computer Methods in Biomechanics and Biomedical Engineering 25.4 (2022): 464-474. Abstract, cf. 1, pp. 466-469, cf. 6.
Carrillo, Fabio, et al. "An automatic genetic algorithm framework for the optimization of three-dimensional surgical plans of forearm corrective osteotomies." Medical image analysis 60 (2020): 101598. Abstract, pp. 3-11
Carrillo, Fabio, et al. "A time saver: Optimization approach for the fully automatic 3D planning of forearm osteotomies." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer International Publishing, 2017. Abstract, table 1 and accompanying description, § 2 and subsections.
Hill, Dave, et al. "Automated resection planning for bone tumor surgery." Computers in Biology and Medicine 137 (2021): 104777. Publication by inventive entity, along with other authors, related to instant application. See §§ 2.1-3. § 4.3, including second to last paragraph.
Ren, Hongliang, et al. "Coverage planning in computer-assisted ablation based on genetic algorithm." Computers in biology and medicine 49 (2014): 36-45. Abstract, §§ 3-5
Schkommodau, Erik, et al. "Computer-assisted optimization of correction osteotomies on lower extremities." Computer Aided Surgery 10.5-6 (2005): 345-350. Abstract and pp. 346-349.
Lynd, Larry D., and Bernie J. O'brien. "Advances in risk-benefit evaluation using probabilistic simulation methods: an application to the prophylaxis of deep vein thrombosis." Journal of clinical epidemiology 57.8 (2004): 795-803. See figure 5 and accompanying description,)
Aghdasi, Nava. Computer-Aided Pre-operative Planning System for Skull Base Surgery. Diss. 2017. See pp. 42, 45-55
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/David A Hopkins/Primary Examiner, Art Unit 2188
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