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 .
DETAILED ACTION
Response to Amendment
In the Amendment dated 18 December 2025, the following occurred:
Replacement drawings were provided.
Claims 1-20 are pending.
Information Disclosure Statement
The Information Disclosure Statements (IDS) submitted on 20 November 2025 is in compliance with the provisions of 37 CFR 1.97 and has been fully considered by the Examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(I). The following figures are unsatisfactory for reproduction because they are blurry:
Fig. 1B, 1C, 2A, 2B, 3-5, 6A, 6B
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The Specification is objected to because the paragraphs are incorrectly numbered. Para. 0006 follows Para. 0046. Appropriate correction is required.
Subject Matter Free of Art
Claims 1-20 include subject matter that is free of prior art. The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within independent claim 1. In particular, the cited prior art fails to expressly teach or suggest the combination of:
applying a first machine learning model to determine, based at least on a clinical data of a patient, a risk score for the patient;
in response to the risk score for the patient exceeding a first threshold, applying a second machine learning model to determine a first probability of the risk score being a false positive;
in response to the risk score for the patient failing to exceed the first threshold, applying a third machine learning model to determine a second probability of the risk score being a false negative; and
determining, based at least on the risk score, the first probability of the risk score being the false positive, and the second probability of the risk score being the false negative, one or more clinical recommendations for the patient.
The closest prior art Vladimirova et al. (U.S. 2020/0105413) teaches applying a first machine learning model to clinical data; applying a second machine learning model to the output of the first machine learning model; and determining a recommendation. However, Vladimirova fails to teach that the first machine learning model determines a risk score.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite a system for patient stratification, and therefore meet step 1.
Step 2A1
The limitations of (Claim 1) …determin[ing], based at least on a clinical data of a patient, a risk score for the patient; in response to the risk score for the patient exceeding a first threshold, …determin[ing] a first probability of the risk score being a false positive; in response to the risk score for the patient failing to exceed the first threshold, …determin[ing] a second probability of the risk score being a false negative; and determining, based at least on the risk score, the first probability of the risk score being the false positive, and the second probability of the risk score being the false negative, one or more clinical recommendations for the patient, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
That is, other than reciting a system implemented by at least one data processor and at least one memory, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the data processor/memory, this claim encompasses a person analyzing risk score data to determine probabilities of false positives and false negatives in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of at least one data processor and at least one memory that implement the identified abstract idea. The processor/memory is not exclusively described by the applicant and is recited at a high-level of generality (i.e., generic computer components) such that it amounts no more than mere instructions to apply the exception using a generic computer or components thereof. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim further recites the additional elements of using first, second, and third machine learning models to determine and analyze the risk scores. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). The Examiner notes that the machine learning models are described in the Specification at Para. 0014, 0017 as encompassing a convolutional neural network, a recurrent neural network, a regression model, an instance-based model, a regularization model, a decision tree, a random forest, a Bayesian model, a clustering model, an associative model, a deep learning model, a dimensionality reduction model, an ensemble model, and/or feed forward neural networks. Alternatively, or in addition, the implementation of the machine learning models to the clinical data merely confines the use of the abstract idea (i.e., the trained models) to a particular technological environment or field of use (the noted types of ML) and thus fails to add an inventive concept to the claims. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of first, second, and third machine learning models were determined to represent “apply it” on a generic computer. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(I)(A) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such the claim is not patent eligible.
Claims 2-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim 2 merely describes determining a conformity metric, determining the risk score, and rejecting the clinical data, which further defines the abstract idea.
Claim 3 merely describes encoding the clinical data and determining the conformity metric, which further defines the abstract idea.
Claim 4 merely describes the conformity metric, which further defines the abstract idea.
Claims 5 and 6 merely describe the one or more conformal sets, which further defines the abstract idea.
Claim 7 merely describes the first probability of the risk score being the false positive and the second probability of the risk score being the false negative, which further defines the abstract idea.
Claim 8 merely describes determining an uncertainty and determining the one or more clinical recommendations, which further defines the abstract idea.
Claims 9-11 merely describe the uncertainty, which further defines the abstract idea.
Claim 12 further describes the machine learning models as a feed forward neural network. The prior art of record indicates that using a feed forward neural network is well-understood, routine, conventional activity in the field (see Brewer et al., US 7020521 at Col. 19, Line 29-36; Baker, US 2021/0342683 at Para. 0027; Malkosh et al., US 2022/0138383).
Claims 13-16 merely describe the one or more clinical recommendations, which further defines the abstract idea.
Claim 17 merely describes the one or more additional labs, which further defines the abstract idea.
Claim 18 merely describes the set of most important features, which further defines the abstract idea.
Claim 19 merely describes determining a measured clinical outcome, determining an expected clinical outcome, and determining an adjustment, which further defines the abstract idea.
Claim 20 merely describes decomposing the difference and determining the adjustment, which further defines the abstract idea.
Response to Arguments
Drawings
Regarding the drawing objection(s), the Applicant has submitted replacement drawings which have alleviated several drawing issues; however, additional issues remain. As such, the objection is maintained.
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
…the high false alarm rate may be due to for example, uncertainty in the coefficients of the prediction model, data quality and characteristics, healthcare-specific variations in data generating process, etc.
Regarding (a), the Examiner respectfully disagrees. The listed problems are problems related to the data or how the data is analyzed (the abstraction). They are not problems caused by the computer. The Examiner prospectively notes that there is also no improvement to the machine learning model(s) as in Desjardins; there is no indication that the invention reduces storage and/or system complexity. See Decision on Request for Rehearing, Ex parte Desjardins at Pg. 7.
…the lack of privacy for the client is also an issue… Moreover, computation demands may be reduced and provide a practical application.
Regarding (b), the Examiner respectfully disagrees. The listed problem is not caused by the computer. It is a problem that exists independent of the computer and is thus not a technical problem. Further, and more importantly, this purported problem is not even solved by Applicant’s claims.
Moreover, computation[al] demands may be reduced and provide a practical application.
Regarding (c), the Examiner respectfully disagrees. The Examiner initially notes that “computational demands” does not appear at all in the as-filed disclosure. Thus this represents unsupported supposition on the part of the Applicant. Even assuming this is true, there is no indications in the claim that computational demands are reduced. Finally, Applicant’s statement (“may be reduced”) indicates that computational demands may also not be reduced. We do not know, because there is no discussion of computational demands in the disclosure.
Claim 1 clearly provides a specific neural network architecture…
Regarding (d), the Examiner respectfully disagrees. There is no particular architecture claimed; using multiple machine learning models does not describe the “architecture” of a model, merely the number of models. Further, the neural networks used by the claim do not appear until claim 12, and there they are just described as feed forward neural networks, which are well-understood, routine, and conventional in the art as evidenced by the prior art of record.
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Antoniades et al. (U.S. 2023/0113005) which discloses a method for stratifying patients according to their risk.
Haber et al. (U.S. 2021/0142915) which discloses a system for clinical predictive analysis.
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 CAMRYN B LEWIS whose telephone number is (703)756-1807. The examiner can normally be reached Monday - Friday, 11:00 am - 8:00 pm EST.
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/CAMRYN B LEWIS/
Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683