DETAILED ACTION
This action is made in response to the amendments/remarks filed on January 20, 2026. This action is made final.
Claims 1-3, 5-7, 9, 11, 13, 17-18, 22, 24-27, and 29-32 are pending. Claims 4, 8, 10, 12, 14-16, 19-21, 23, and 28 have been cancelled. Claims 1, 9, 24, and 32 have been amended. Claims 1, 24, and 32 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/amendments filed January 20, 2026 with respect to the prior art rejection have been fully considered. The rejection of claims 1-3, 5-7, 9, 11, 13, 17-18, 22, 24-27, and 29-32 have been withdrawn in light of the new claim amendments.
Applicant's arguments filed January 20, 2026 with respect to the 101 eligibility rejection have been fully considered but they are not persuasive.
Applicant argues the amended claims integrate the alleged abstract idea are integrated into a practical application. Applicant states the present claims recite specific technical means for achieving improved machine learning systems akin to those of Ex parte Desjardins. However, the examiner respectfully disagrees.
Contrary to Applicant’s assertions, the present claims do not present an improvement to machine learning. Initially, the Examiner notes that Ex parte Desjardins does not represent a substantive change in subject matter eligibility analysis; there has been no indication by the Office that this decision impacts the how claims involving machine learning are analyzed at the Examiner level. The decision is specific to the facts before the Appeals Review Panel and follows the subject matter eligibility analysis set forth in MPEP 2106. As found by the Panel, the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" represents “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application-“improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel.
Applicant’s claims do not provide such an improvement. Initially, there is no training within the claims so this argument falls on its face from the outset. Even assuming there was, there is no indication in the cited portion of the Specification that the claimed invention provides an improvement as to how model is trained. Improving the accuracy of a machine learning model by supplying it with specific data is not an improvement to how the model is trained within the meaning of Desjardins. This is how all machine learning models are optimized (i.e., select training data, train the model, compare the output to validation data, receive feedback, adjust the parameters of the training data according to the comparison/feedback, and repeat until an accuracy threshold is met). Put another way, the particular way the machine learning model of applicant’s invention uses the data to train itself is not improved nor is there an improvement to computer component or system performance based upon adjustments to parameters of the machine learning model, which is the holding of Desjardins. Applicant is merely improving the accuracy of the model by optimizing the data selected/used by the model. Improving the accuracy/prediction of a model is not an improvement by any measure in MPEP 2106.
Examiner’s position is also supported by the decision in Recentive Analytics, Inc. v. Fox Corp. Recentive held that non-specifically claimed training of an AI/ML algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12.
Applicant further contends a technical problem exists in the conventional preprocessing techniques that undermine generalization of machine learning models (see Remarks page 8). However, the Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by the machine learning models. The problem of predicting injury risk was not a problem cause by the machine learning model, but is a problem that existed and/or exists regardless of whether a machine learning model is involved in the process. At best, Applicant’s identified problem is a health/injury management problem. Because no technological problem is present, the claims do not provide a practical application.
Furthermore, insomuch as Applicant contends the process of scaling the data is an non-conventional technical improvement. The examiner respectfully disagrees. As discussed below, the process of scaling data to predict an event recites the use of mathematical relationships, formulas, equations, and/or mathematical calculations for a specific purpose (i.e., predicting an injury event). That is, Applicant’s purported non-conventional technical improvement comprises a mathematical construct and does not amount to a practical application or significantly more.
Applicant’s argument that the claims do not preempt all approaches to predicting injury risk is not persuasive. MPEP 2106.04(I) states that “questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test referred to by the Office as Steps 2A and 2B).” Thus, pre-emption concerns are fully addressed and made moot upon application of the two-part Alice Corp. subject matter eligibility analysis, as provided in the basis of rejection. Further, by definition, the claimed invention would preempt the identified abstract idea.
Accordingly, for the aforementioned reasons, and those below, claims 1-3, 5-7, 9, 11, 13, 17-18, 22, 24-27, and 29-32 are rejected under 35 U.S.C. 101.
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 1-3, 5-7, 9, 11, 13, 17-18, 22, 24-27, and 29-32 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 1-3, 5-7, 9, 11, 13, 17-18, and 22 recite a method of predicting a musculoskeletal risk, which is within the statutory category of a process. Claim 24-27, and 29-31 recite a method of predicting a musculoskeletal risk, which is within the statutory category of a process. Claim 32 recites recite a system of predicting a musculoskeletal risk, which is within the statutory category of a machine.
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 1-3, 5-7, 9, 11, 13, 17-18, 22, 24-27, and 29-32, 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, 25, and 32 (claim 1 being representative)
retrieving a first dataset comprising running-related data for inference by a trained machine learning model; comparing a first distribution of the first dataset to a second distribution of a second dataset comprising running-related data used to train the machine learning model; determining, based on the comparison, whether a distribution mismatch between the first dataset and the second dataset exceeds a threshold indicative of non-representativeness of the second dataset relative to the first dataset; based on the determination, selecting one of the first dataset or the second dataset; scaling a runner profile from the first dataset based on the selected one of the first dataset or the second dataset to condition the runner profile for inference; inputting, into the trained machine learning model, the runner profile; and predicting, using the trained machine learning model, a risk of a musculoskeletal injury based on the runner profile.
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) and/or mental process 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, but for the noted computer elements, the claim encompasses a person following rules or instructions to process data to predict a risk of musculoskeletal injury or a clinician evaluating a person running and assessing whether they are at risk of injury. 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, scaling data upon itself recites a mathematical concept – the above limitations are steps in a mathematical concept such as mathematical relationships, mathematical formulas or equations, and mathematical calculations. If a claim, under its broadest reasonable interpretation, is directed towards a mathematical concept, then it falls within the Mathematical Concepts grouping of abstract ideas. See MPEP § 2106.04(a)(2).
The mathematical concept is claimed in such a generalized manner that the mathematical concept also encompasses a person mentally performing the math, see MPEP § 2106.04(a)(2) as well, including that for a mental process “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675.
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 system”, “at least one processor”, and “at least one memory”—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)(2) 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 trained machine learning model and (2) use of the trained machine learning model to make predictions. When given the broadest reasonable interpretation in light of the nonexistent description of machine learning model training in the disclosure, training of machine learning model with the noted data amounts to a mathematical concept that creates data associations. As such, this training of the machine learning model is interpreted to be subsumed within the identified abstract idea and the use of the 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 trained 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 into another form. 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 system”, “at least one processor”, and “at least one memory”—see Specification Fig. 5, [0094], [0095] 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).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements (1) the trained machine learning model and (2) use of the trained machine learning model to make predictions 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 training of the model is considered part of the abstract idea and thus cannot provide a practical application. Regarding (2), the use of the trained 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, 3, 5, and 6 merely recites comparing the data using statistical techniques or visualization techniques, claim 7 merely recites scaling of the data, claims 9, 11, 13, 17, 25-27, 29, 30 merely recites the type of data, claim 18 merely describes predicting the risk by classifying the data, claims 22 and 31 merely recite adjusting a plan based on the predicted risk, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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
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