DETAILED ACTION
This action is made in response to the communication filed on May 26, 2022. This action is made non-final.
Claims 1, 2, and 5-12, 15-20, and 53-56 are pending. Claims 1, 2, and 5-12, 15-20 have been amended, claims 3-4, 13-14, and 21-52 have been cancelled, and claims 53-56 have been newly added by preliminary amendment filed on May 26, 2022.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed 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)(2) as being anticipated by 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, prosthetic implant characteristics of a prosthetic implant (e.g., see Fig. 5, [0115], [0322] wherein various information relating to the physical implant is collected);
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 (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 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 (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 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 operational duration of the prosthetic implant from the application of the model parameters of the machine-learned model (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
outputting, by the computing system, the information indicative of the operational duration of the prosthetic implant (e.g., see [0316], [0386]-[0391] wherein the predictions are provided, the prediction including longevity of the implantation).
As to claim 2, the rejection of claim 1 is incorporated. Khan further teaches wherein determining information indicative of the operational duration of the prosthetic implant comprises determining information indicative of a likelihood that the prosthetic implant will serve a function of the prosthetic implant 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).
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 target bone where the prosthetic implant is to be implanted (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 further teaches wherein the prosthetic implant characteristics of the prosthetic implant comprise one or more of a type of prosthetic implant or dimensions of the prosthetic implant (e.g., see [0322], [0325] wherein the implant characteristics can include specific materials (i.e., type) or the form/shape/structure (i.e., dimensions)).
As to claim 7, the rejection of claim 1 is incorporated. Khan further teaches wherein determining the information indicative of the operational duration of the prosthetic implant comprises determining information indicative of the operational duration of the prosthetic implant for a first surgical procedure, the method further comprising: determining information indicative of a plurality of operational durations for the prosthetic implant 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)).
As to claim 8, the rejection of claim 1 is incorporated. Khan further teaches wherein the prosthetic implant comprises a first prosthetic implant, the method further comprising: obtaining prosthetic implant characteristics of a plurality of prosthetic implants, wherein the plurality of prosthetic implants includes the first prosthetic implant; determining information indicative of the operational duration of each of the plurality of prosthetic implants based on the patient characteristics and respective prosthetic implant characteristics of the plurality of prosthetic implants; and outputting the information indicative of the respective operational duration of each of the plurality of prosthetic implants (e.g., see Figs. 1, 4-7, 11-13, [0316], [0324], [0344]-[0348], [0370] teaching a system for predicting implantation performance and longevity using patient and implant characteristics, wherein multiple changes to the implant characteristics and corresponding calculations can be performed).
As to claim 9, the rejection of claim 1 is incorporated. Khan further teaches wherein the prosthetic implant comprises a first prosthetic implant, the method further comprising: obtaining prosthetic implant characteristics of a plurality of prosthetic implants, wherein the plurality of prosthetic implants includes the first prosthetic implant; determining information indicative of the operational duration of each of the plurality of prosthetic implants based on the patient characteristics and respective prosthetic implant characteristics of the plurality of prosthetic implants (e.g., see Figs. 1, 4-7, 11-13, [0316], [0324], [0344]-[0348], [0370] teaching a system for predicting implantation performance and longevity using patient and implant characteristics, wherein multiple changes to the implant characteristics and corresponding calculations can be performed);
comparing the information indicative of the operational duration of each of the plurality of prosthetic implants, including the information indicative of the operational duration of the first prosthetic implant, with each other; selecting one of the plurality of prosthetic implants based on the comparison of the information indicative of the operational duration of each of the plurality of prosthetic implants; and outputting information indicating that the selected prosthetic implant is a recommended prosthetic implant, wherein outputting the information indicative of the operational duration of the prosthetic implant comprises outputting information indicative of the operational duration of the first prosthetic implant responsive to the first prosthetic implant being the selected one of the plurality of prosthetic implants (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 10, the rejection of claim 9 is incorporated. Khan further teaches further comprising: determining one or more feasibility scores for one or more of the plurality of prosthetic implants; comparing the one or more feasibility scores, 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 operational duration of each of the plurality of prosthetic implants and the comparison of the one or more feasibility scores (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).
As to claims 11, 12, and 15-20, the claims are directed to the system implementing the method of claims 1, 2, and 5-10 and further recites a memory and one or more processors (e.g., see Fig. 14 of Khan) and are similarly rejected.
As to claims 53-56, the claims are directed to the non-transitory computer-readable medium implementing the method of claims 1, 2, and 8-9 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STELLA HIGGS whose telephone number is (571)270-5891. The examiner can normally be reached Monday-Friday: 9-5PM.
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/STELLA HIGGS/Primary Examiner, Art Unit 3681