DETAILED ACTION
This action is made in response to the amendments/remarks filed on April 30, 2026. This action is made final.
Claims 1, 2, 5-7, 10-12, 15-17, 20, 53, and 54 are pending. Claims 1, 2, and 5-7, 10-12, 15-17, 20, 53 and 54 have been amended. Claims 3-4, 13-14, and 21-52 have been previously cancelled. Claims 8, 9, 18, 19, 55, and 56 are presently cancelled. Claims 1, 11, and 53 are independent claims.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed April 30, 2026 have been fully considered but they are not persuasive.
As to the 101 rejection, Applicant argues the claims are not directed to certain methods of organizing human activity. However, the examiner respectfully disagrees.
MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to decide on a particular prosthetic based on patient and implant characteristics. The Examiner notes that Applicant’s Background describes pre-operative planning for joint repair and placement is a human task that may be assisted with various tools (e.g., see [0002]). Furthermore, the Examiner submits that healthcare itself is inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to decide on a particular prosthetic based on patient and implant characteristics, the claimed invention is directed to an abstract idea.
Applicant further argues the claims are not directed to a mental process, however, the examiner respectfully disagrees.
Per MPEP 2106.04(a)(2)(III) a claimed invention may encompass an abstract idea if it represents concepts that can be practically performed in the human mind (with or without the aid of pencil and paper or a computer) such as observations, evaluations, judgments, and opinions. Under the broadest reasonable interpretation, the identified features of the claim encompass mental process because it represents decion on a particular prosthetic based on patient and implant characteristics (see Spec. [0002]). This is one of more of observations, evaluations, judgments, and opinions that a surgeon can make in determining a particular prosthetic for a particular patient. Because the identified features of the claim can be performed in the human mind, the claims are directed to an abstract idea. The recitation of a computing system and machine-learned models are recited at a high level of generality and represent no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). Furthermore, these limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers and machine learning models (see MPEP 2106.05(h)).
Applicant further argues the claims are integrated into a practical application in that they improve “techniques related to implant selection”. However, the examiner respectfully disagrees.
MPEP 2106.04(d)(1) states “the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” Here, there is no improvement to a computer nor is there an improvement to another technology. Because neither type of improvement is present in the claims, an improvement to technology is not present and there is no practical application.
Insomuch as applicant asserts the claims are directed to an “improvement to techniques related to implant selection”, such an improvement is not an improvement to a computer, technology, or other technical field. Rather, the problem of implant selection is not a problem caused by the computer, machine learning model, or other technology, but exists and/or exists regardless of whether a computer, machine learning model, or other technology is involved in the process. At best, Applicant’s identified problem is a healthcare management problem. Because no technological problem is present, the claims do not provide a practical application.
As to the previous 102 rejection, Applicant asserts Khan teaches comparison of tissue, but fails to teach comparison of the prosthetic implants. However, the examiner respectfully disagrees. As illustrated in at least Figs. 5-7, [0339], [0349], Khan teaches determining compatibility information based on both patient characteristics and implant characteristics, wherein a comparison is performed based on those characteristics to determine an optimal fit. As such, Khan teaches the claimed limitation. Nonetheless, for the purposes of compact prosecution, newly cited MacDonald is further relied upon as teaching the claimed limitation.
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, 2, and 5-12, 15-20, and 53-56 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.
Claims 1, 2, and 5-10 recite a method of predicting operational duration of an implant, which is within the statutory category of a process. Claims 10, 11 and 15-20 recite a system for predicting operational duration of an implant, which is within the statutory class of a machine. Claims 53-56 recites a non-transitory computer readable memory performing instructions for predicting operational duration of an implant, which is within the statutory class of a manufacture.
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. ___ (2014). Claims 1, 2, and 5-12, 15-20, and 53-56, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
MPEP 2106 Step 2A – Prong 1:
The bolded limitations of:
Claims 1, 11, and 53 (claim 1 being representative)
obtaining, by a computing system, patient characteristics of a patient; obtaining, by the computing system, prosthetic implant characteristics of a prosthetic implant; determining, by the computing system, information indicative of an operational duration of the prosthetic implant based on the patient characteristics and the prosthetic implant characteristics, wherein determining the information indicative of the operational duration of the prosthetic implant comprises: receiving, with a machine-learned model of the computing system, the patient characteristics and the prosthetic implant characteristics; applying, with the computing system, model parameters of the machine-learned model to the patient characteristics and the prosthetic implant characteristics, wherein the model parameters of the machine-learned model are generated based on a machine learning dataset, wherein the machine learning dataset includes one or more of pre- operative scans of a set of patients, patient characteristics of the set of patients, information indicative of previous surgical plans using different prosthetic implants, and information indicative of surgical results; and determining the information indicative of the operational duration of the prosthetic implant from the application of the model parameters of the machine-learned model; and outputting, by the computing system, the information indicative of the operational duration of the prosthetic implant.
as presently drafted, under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions). For example, but for the noted computer elements, the claim encompasses a person following rules or instructions to collect and process data in the manner described in the abstract idea. For example, a surgeon can, based on patient data and implant data, predict the longevity of the implant. The examiner further notes that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). If the claim limitation, under its broadest reasonable interpretation, covers managing persona behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally, 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. For example, a surgeon can, based on patient data and implant data, predict the longevity of the implant. 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).
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The additional elements merely amount to instructions to apply the exception using generic computer components (“a computing system”, “a memory”, “one or more processors”, "a non-transitory computer readable storage medium”—all recited at a high level of generality). Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(I) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claim further recites the additional elements of (1) a machine-learned model generated based on a machine learning dataset and (2) use machine-learned model to make a determination/prediction. When given the broadest reasonable interpretation of generating a machine-learned model (e.g., training), generating/training of a machine-learned model with the noted data amounts to a mathematical concept that creates data associations. As such, this generating/training of the model is interpreted to be subsumed within the identified abstract idea and the use of the generated/trained model provides nothing more than mere instructions to implement the abstract idea, supra. July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of item (c) at Pgs. 7-9. Regarding (2), the use of the machine-learned model provides nothing more than mere instructions to implement an abstract idea on a generic computer (“apply it”). See MPEP 2106.05(f). MPEP 2106.05(f); July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of items (d) and (e) at Pgs. 8-9.
The claims only manipulate abstract data elements as part of performing the abstract idea. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2. Moreover, the additional elements recited are known and conventional generic computing elements (“a computing system”, “a memory”, “one or more processors”, "a non-transitory computer readable storage medium”-- see Specification Fig. 9, [0078], [0098], [0170], [0171] describing the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and 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 the statutory disclosure requirements). Therefore, these additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept that amounts to significantly more. See MPEP 2106.05(f).
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, translating, and displaying data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions).
Furthermore, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements (1) a machine-learned model generated based on a machine learning dataset and (2) use machine-learned model to make a determination/prediction were considered to be part of the abstract idea and “apply it,” respectively. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. Regarding (1), the generating (e.g., training) of the machine-learned model is considered part of the abstract idea and thus cannot provide a practical application. Regarding (2), the use of the machine-learned model represented saying “apply it.” Item (2) has been revaluated under the “significantly more” analysis and does not provide “significantly more” to the abstract idea. MPEP 2106.05(A) indicates also indicates that merely adding the words “apply it” or equivalent use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims
The limitations of dependent but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Claims 2, 5-10 (12, 15-20, 53-56) merely determining the operational duration of the implant using specific patient or implant characteristics, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, and 5-12, 15-20, and 53-56 is/are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Khan et al. (USPPN: 2021/0128247; hereinafter Khan)
As to Claim 1, Khan teaches A computer-implemented method (e.g., see Abstract, Figs. 1, 14) comprising:
obtaining, by a computing system, patient characteristics of a patient (e.g., see Fig. 5, [0034], [0177], [0278] wherein the system collects various characteristics of a patient);
obtaining, by the computing system, respective prosthetic implant characteristics of a prosthetic implant of each of a plurality of prosthetic implants (e.g., see Figs. 5, 7, [0115], [0322], [0344] wherein various information relating to the physical implant is collected, wherein multiple changes to the implant characteristics and corresponding calculations can be performed, wherein the multiple variations reads upon the “plurality”);
determining, by the computing system, information indicative of a respective operational duration of each of the plurality of prosthetic implants based on the patient characteristics and the respective prosthetic implant characteristics of the plurality of prosthetic implants (e.g., see Figs. 1, 4-7, 11-13, [0316], [0324], [0370] teaching a system for predicting implantation performance and longevity using patient and implant characteristics), wherein determining the information indicative of the respective operational duration of each of the plurality of prosthetic implant comprises:
receiving, with a machine-learned model of the computing system, the patient characteristics and the respective prosthetic implant characteristics (e.g., see [0353], [0371]-[0373], [0377] wherein machine learning models are used to predict how long an implantation will last using patient data and implant data);
applying, with the computing system, model parameters of the machine-learned model to the patient characteristics and the respective prosthetic implant characteristics, wherein the model parameters of the machine-learned model are generated based on a machine learning dataset, wherein the machine learning dataset includes one or more of pre-operative scans of a set of patients, patient characteristics of the set of patients, information indicative of previous surgical plans using different prosthetic implants, and information indicative of surgical results (e.g., see [0274], [0370]-[0377], [0382], [0383], [0389] wherein the machine learning models are trained using datasets including medical records and other patient data, including pre-operative scans and historical data of actual implantation performance on similar patients); and
determining the information indicative of the respective operational duration of each of the plurality of prosthetic implants from the application of the model parameters of the machine-learned model to the patient characteristics and the respective prosthetic implant characteristics (e.g., see [0386]-[0391] wherein the predictions generated using the machine learning models provide insight into impact of the implantation, including its longevity and factors in patient characteristics);
comparing the information indicative of the respective operational duration of each of the plurality of prosthetic implants with each other; selecting one of the plurality of prosthetic implants based on the comparison of the information indicative of the respective operational duration of each of the plurality of prosthetic implants; and outputting information indicating that the selected prosthetic implant is a recommended prosthetic implant (e.g., see Fig. 13, [0344]-[0348], [0398],[0399], [0403] wherein changes can be made to the implant/tissue to generate an ideal fit and calculations for predicting implantation performance and longevity can be performed for each change such that an implant and actions are selected that are determined to be optimal).
As to claim 11, the claim is directed to the system implementing the method of claim 1 and further recites a memory and one or more processors (e.g., see Fig. 14 of Khan) and are similarly rejected.
As to claim 53, the claim is directed to the non-transitory computer-readable medium implementing the method of claims 1 and is similarly rejected.
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.
Claim(s) 1, 2, 5-7, 10-12, 15-17, 20, 53, and 54 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. (USPPN: 2021/0128247; hereinafter Khan) in further view of MacDonald et al. (USPPN: 2014/0013565; hereinafter MacDonald).
As to Claim 1, Khan teaches A computer-implemented method (e.g., see Abstract, Figs. 1, 14) comprising:
obtaining, by a computing system, patient characteristics of a patient (e.g., see Fig. 5, [0034], [0177], [0278] wherein the system collects various characteristics of a patient);
obtaining, by the computing system, respective prosthetic implant characteristics of a prosthetic implant of each of a plurality of prosthetic implants (e.g., see Figs. 5, 7, [0115], [0322], [0344] wherein various information relating to the physical implant is collected, wherein multiple changes to the implant characteristics and corresponding calculations can be performed, wherein the multiple variations reads upon the “plurality”);
determining, by the computing system, information indicative of a respective operational duration of each of the plurality of prosthetic implants based on the patient characteristics and the respective prosthetic implant characteristics of the plurality of prosthetic implants (e.g., see Figs. 1, 4-7, 11-13, [0316], [0324], [0370] teaching a system for predicting implantation performance and longevity using patient and implant characteristics), wherein determining the information indicative of the respective operational duration of each of the plurality of prosthetic implant comprises:
receiving, with a machine-learned model of the computing system, the patient characteristics and the respective prosthetic implant characteristics (e.g., see [0353], [0371]-[0373], [0377] wherein machine learning models are used to predict how long an implantation will last using patient data and implant data);
applying, with the computing system, model parameters of the machine-learned model to the patient characteristics and the respective prosthetic implant characteristics, wherein the model parameters of the machine-learned model are generated based on a machine learning dataset, wherein the machine learning dataset includes one or more of pre-operative scans of a set of patients, patient characteristics of the set of patients, information indicative of previous surgical plans using different prosthetic implants, and information indicative of surgical results (e.g., see [0274], [0370]-[0377], [0382], [0383], [0389] wherein the machine learning models are trained using datasets including medical records and other patient data, including pre-operative scans and historical data of actual implantation performance on similar patients); and
determining the information indicative of the respective operational duration of each of the plurality of prosthetic implants from the application of the model parameters of the machine-learned model to the patient characteristics and the respective prosthetic implant characteristics (e.g., see [0386]-[0391] wherein the predictions generated using the machine learning models provide insight into impact of the implantation, including its longevity and factors in patient characteristics);
comparing the information indicative of the respective operational duration of each of the plurality of prosthetic implants with each other; selecting one of the plurality of prosthetic implants based on the comparison of the information indicative of the respective operational duration of each of the plurality of prosthetic implants; and outputting information indicating that the selected prosthetic implant is a recommended prosthetic implant (e.g., see Fig. 13, [0344]-[0348], [0398],[0399], [0403] wherein changes can be made to the implant/tissue to generate an ideal fit and calculations for predicting implantation performance and longevity can be performed for each change such that an implant and actions are selected that are determined to be optimal).
While Khan teaches determining information indicative of an operation duration of a prosthetic implant based on the patient characteristics and prosthetic implant characteristics, for the purposes of compact prosecution and in the same field of endeavor assessing and improving prosthetics/implant fit in a patient, MacDonald teaches respective prosthetic implant of each of a plurality of prosthetic implants (e.g., see Figs. 17, 23, [0010], [0011], [0072] wherein a plurality of potential prosthetics are identified); comparing the information indicative of the respective operational duration of each of the plurality of prosthetic implants with each other (e.g., see Figs. 17, 23, [0067], [0072] wherein the plurality of potential prosthetics are compared and assessed to determine a recommendation); selecting one of the plurality of prosthetic implants based on the comparison of the information indicative of the respective operational duration of each of the plurality of prosthetic implants (e.g., see Fig. 19, [0072] wherein a recommendation of the prosthetic with the best patient outcome is recommended); and outputting information indicating that the selected prosthetic implant is a recommended prosthetic implant (e.g., see Fig. 23, [0076] wherein the recommendation for a particular prosthetic is output to the user). MacDonald additionally teaches determining the information indicative of the respective operational duration of each of the plurality of prosthetic implants to the patient characteristics and the respective prosthetic implant characteristics (e.g., see Figs. 17-19, [0077]-[0079] wherein the potential prosthetics and customizations are compared to similarly situated patients to determine parameters that correlate to success in the operation).
Accordingly, it would have been obvious to modify Khan in view of MacDonald before the effective filing date of the application with a reasonable expectation of success. One would have been motivated to make the modification to perform comparative analysis of prosthetic implants among similar patients in pre-operative planning, thereby improving the likelihood of success in prosthetic implantations (e.g., see [0001]-[0009] of MacDonald).
As to claim 2, the rejection of claim 1 is incorporated. Khan-MacDonald further teaches wherein determining information indicative of the respective operational duration of each of the plurality of prosthetic implants comprises determining information indicative of a respective likelihood that each of the plurality of prosthetic implants will serve a function for a certain amount of time (e.g., see [0383]-[0390] wherein the system can be used to predict the amount of time the implant will degrade and require revision surgery. See rejection above wherein MacDonald teaches predicting best patient outcomes from a plurality of prosthetic implants in pre-operative planning).
As to claim 5, the rejection of claim 1 is incorporated. Khan further teaches wherein the patient characteristics include one or more age of the patient, gender of the patient, diseases of the patient, whether the patient is a smoker, or fatty infiltration at a target bone (e.g., see [0016], [0046], [0323] wherein patient characteristics can include age, habits, pre-existing infections/diseases, tissue characteristics/health including cartilage, fat, density, necrosis, etc.).
As to claim 6, the rejection of claim 1 is incorporated. Khan-MacDonald further teaches wherein the respective prosthetic implant characteristics of each of the plurality of prosthetic implants comprise one or more of a type of prosthetic implant or dimensions of each of the plurality of prosthetic implants (e.g., see [0322], [0325] wherein the implant characteristics can include specific materials (i.e., type) or the form/shape/structure (i.e., dimensions). See rejection above wherein MacDonald teaches predicting best patient outcomes from a plurality of prosthetic implants in pre-operative planning. See also [0003], [0010], [0050]-[0052] of MacDonald wherein each prosthetic implant can have different customizations/jigs (i.e., type) and size).
As to claim 7, the rejection of claim 1 is incorporated. Khan-MacDonald further teaches wherein determining the information indicative of the respective operational duration of each of the plurality of prosthetic implants comprises determining information indicative of the respective operational duration of each of the plurality of prosthetic implants for a first surgical procedure, the method further comprising: determining, by the computer system, information indicative of respective operational duration for each of the plurality of prosthetic implants for a plurality of surgical procedures (e.g., see [0259], [0265]-[0267], [0269], [0277], [0278] wherein the prediction about potential issues and longevity of the implants are performed in connection with a surgical procedure wherein predictions of implantation performance and durability are further calculated in real time for possible corrective actions (i.e. plurality of surgical procedures). See rejection above wherein MacDonald teaches predicting best patient outcomes from a plurality of prosthetic implants in pre-operative planning).
As to claim 10, the rejection of claim 9 is incorporated. Khan-MacDonald further teaches further comprising: determining, by the computing system, a respective feasibility score for each of the plurality of prosthetic implants; comparing, by the computing system, each respective feasibility score with one another, wherein selecting one of the plurality of prosthetic implants comprises selecting one of the plurality of prosthetic implants based on the comparison of the information indicative of the respective operational duration of each of the plurality of prosthetic implants and the comparison of each respective feasibility scores with one another (e.g., see Fig. 12, [0396]-[0403] wherein a numerical value can be used as a comparator value to determine optimal course of action for the implantation including implantation performance and longevity prediction. See Figs. 23-24 of MacDonald wherein a score is provided for each implant and compared to one another).
As to claims 11, 12, 15-17 and 20, the claims are directed to the system implementing the method of claims 1, 2, 5-7 and 10 and further recites a memory and one or more processors (e.g., see Fig. 14 of Khan) and are similarly rejected.
As to claims 53 and 54, the claims are directed to the non-transitory computer-readable medium implementing the method of claims 1, 2 and are similarly rejected.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/STELLA HIGGS/Primary Examiner, Art Unit 3681
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