DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/19/2026 has been entered.
Response to Arguments
Claim Objection
Applicant's arguments filed 03/19/2026 have been fully considered. In light of the amendment and remarks, the objection is withdrawn.
Claim Interpretation and Rejection Under 112
Applicant's arguments filed 03/19/2026 have been fully considered. Applicant argues that the term “scoring unit” was removed from claim 1 entirely and amended in claim 17 to recite “scoring unit processor” and thus the interpretation and 112 rejections should be withdrawn. In response to the arguments, in light of the amendments, the claim interpretation and the 112 rejections have been withdrawn.
Rejection Under 101
Applicant's arguments filed 03/19/2026 have been fully considered.
Applicant argues that the claims are evaluated at too high a level of generality and that the Office Action strips away all machine learning specific steps and reduces the claims to following steps to predict a patient’s likelihood of rejecting their transplant. The amended claims recite specific technical steps including receiving parameter weights from the ML model that analyzes and selects parameters associated with the transplant rejection through iterative subset selection, generating a predictive score, and outputting a predictive probability user interface. According to Desjardins the examiners should not evaluate the claims at such a high level of generality.
In response to Applicant’s argument, the entire exemplary claim was reproduced in the analysis of the rejection, where the abstract idea is underlined. Only then is that underlined abstract idea summarized. Therefore, the abstract idea aligns with the exact claim limitations and cannot be an evaluation of high generality. Some of the limitations at issue in this argument fall under the abstract idea while others are additional elements. However, the machine learning is considered an additional element due to its recited high level of generality and thus amounts to applying the abstract idea in a computer environment. See the updated rejection for further clarification.
Applicant argues that the claims are not a method of organizing human activity. The Office Action conclusion strips away all computer implemented terms from the claims in contravention of the directives provided by Desjardins and its associated guidance. The amended claims recite technical methods steps where the model iteratively selecting the parameter subsets and outputs parameter weights.
In response to Applicant’s argument, the claims recite steps to determine a risk for a patients transplant rejection. Following rules or instructions is part of managing personal behavior or relationships and thus falls under organizing human activity. Additionally, selecting and inputting parameters into the model is not a technical improvement. The model itself it not being improved, data is merely being defined and input into the model. This is mere data processing. This is in contrast to Desjardins where they were solving a technical problem.
Applicant argues that the claims recite a practical application by reciting a technical workflow with a trained machine learning model that outputs parameter weights based on analyzing and selecting parameters associated with transplant rejection, generating a predictive score and outputting a predictive probability user interface. Desjardins warned that excluding AI from patent protection jeopardizes America’s leadership in critical emerging technology. The claims set forth in the Office Action treats a specific machine learning based transplant monitoring system as nothing more than an abstract idea on generic hardware which is precisely the type of overbroad reasoning that Desjardins cautioned against.
In response to Applicant’s argument, the claims do not recite a practical applicant since the additional elements do not amount to anything more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. The claims of Desjardins contrast Applicant’s claims since Desjardins was solving a technical problem. Here, Applicant claims that are directed at “predicting transplant rejection” (spec. [0020]) are merely recite a business problem, not a technical problem. The use of the machine learning helps to carry out the steps in order to determine that predicted risk of transplant rejection, thus amounting to invoking the use of computer/tool to carry out the abstract idea. See the updated rejection below for further clarification.
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, 4-7, 9-18, 20-23, 25-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 1-2, 4-7, 9-16 are drawn to a method, which is within the four statutory categories (i.e. process). Claims 17-18, 20-23, 25-32 are drawn to a system, which is within the four statutory categories (i.e. apparatus). Claim 33 is drawn to a non-transitory computer-readable storage medium, which is within the four statutory categories (i.e. manufacture).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, claim 1 (and substantially similar with independent claim 17 and 33) recites:
A computer-implemented method for outputting a predictive probability user interface in a transplant monitoring system, the computer- implemented method comprising:
receiving, via a computer or an input function, transplant recipient data of an individual transplant recipient comprising a set of transplant recipient parameters, wherein the set of transplant recipient parameters comprises donor-derived cell-free DNA (dd-cfDNA) and does not comprise a histological parameter;
receiving, from a trained machine learning model, one or more parameter weights, wherein the trained machine learning model is trained to output the one or more parameter weights based on analyzing and selecting parameters from a training cohort dataset that are associated with transplant rejection, and wherein training the machine learning model comprises:
analyzing a first set of model parameters for associations between a training cohort dataset and a corresponding training cohort transplant rejection information;
iteratively selecting one or more subsequent subsets of model parameters from the first set of model parameters or a preceding subset of model parameters;
selecting a last subset of model parameters from the one or more subsequent subsets of model parameters, wherein the last subset of model parameters comprises independent variables associated with transplant rejection and meeting one or more second criteria; and
outputting the one or more parameter weights corresponding to the last subset of model parameters;
generating a predictive score based on the set of transplant recipient parameters of the received transplant recipient data and the one or more parameter weights output from the trained machine learning model; and
outputting a predictive probability user interface for display to a graphical user interface, the predictive probability user interface depicting a predicted probability of whether or an extent to which transplant rejection in the individual transplant recipient will occur in the future based on the predictive score.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behavior or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the computer, processors, display, storage medium, trained machine learning model, predictive probability user interface, the limitations in the context of this claim encompass following steps to predict a patient’s likelihood of rejecting their transplant. By following these steps recited, we can evaluate the risk and therefore generate a score related to this risk for the patient’s transplant rejection. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behavior or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2, 4-7, 9-16, 18, 20-23, 25-32 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, claim 1 (and substantially similar with independent claim 17 and 33) recites:
A computer-implemented method for outputting a predictive probability user interface in a transplant monitoring system, the computer- implemented method comprising: (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
receiving, via a computer or an input function (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), transplant recipient data of an individual transplant recipient comprising a set of transplant recipient parameters, wherein the set of transplant recipient parameters comprises donor-derived cell-free DNA (dd-cfDNA) and does not comprise a histological parameter;
receiving, from a trained machine learning model, one or more parameter weights, wherein the trained machine learning model is trained to output the one or more parameter weights (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
based on analyzing and selecting parameters from a training cohort dataset that are associated with transplant rejection, and wherein training the machine learning model comprises: (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
analyzing a first set of model parameters for associations between a training cohort dataset and a corresponding training cohort transplant rejection information;
iteratively selecting one or more subsequent subsets of model parameters from the first set of model parameters or a preceding subset of model parameters;
selecting a last subset of model parameters from the one or more subsequent subsets of model parameters, wherein the last subset of model parameters comprises independent variables associated with transplant rejection and meeting one or more second criteria; and
outputting the one or more parameter weights corresponding to the last subset of model parameters; (merely insignificant extrasolution activity steps as noted below, see MPEP 2106.05(g))
generating a predictive score based on the set of transplant recipient parameters of the received transplant recipient data and the one or more parameter weights output from the trained machine learning model; and (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
outputting a predictive probability user interface for display to a graphical user interface, the predictive probability user interface (merely insignificant extrasolution activity steps as noted below, see MPEP 2106.05(g)) depicting a predicted probability of whether or an extent to which transplant rejection in the individual transplant recipient will occur in the future based on the predictive score.
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of the computer, processors, display, storage medium, trained machine learning model, predictive probability user interface, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0023], [0037], [0042], [0096]-[0098], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of outputting parameter weights, outputting a predicted probability, amounts to insignificant extrasolution activity, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2, 4-7, 9-16, 18, 20-23, 25-32 recite additional limitations that further narrow or define the abstract idea, and claims 2, 4-7, 9-16, 18, 20-23, 25-32 additional limitations which generally link the abstract idea to a particular technological environment or field of use). 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 and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
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 discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the computer, processors, display, storage medium, trained machine learning model, predictive probability user interface, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. [0023], [0037], [0042], [0096]-[0098]); using the computer, processors, display, storage medium, trained machine learning model, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application. The following represent examples that courts have identified as insignificant extrasolution activities (e.g. see MPEP 2106.05(g)): outputting parameter weights, outputting a predicted probability, e.g., outputting or providing access to the information, Symantec, 838 F.3d at 1321 and MPEP 2106.05(g)(3)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, further define the abstract idea or are generally linking the abstract idea to a particular field of environment. 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. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
Subject Matter Free of Prior Art
Claims 1-2, 4-7, 9-18, 20-23, 25-33 are free of prior art over Moshkevich et al. (US 2021/0198733) in view of University (WO 2021/021657). The prior art references, or reasonable combination thereof, could not be found to disclose, or suggest all of the limitations found in the independent claims. The closest prior art is Moshkevich et al. (US 2021/0198733), which teaches methods for determining the status of an allograft within a transplant recipient from genotypic data measured from a sample of DNA. University (WO 2021/021657) teaches using prediction models to generate predictions based on adjustments of the first predictions. The references taken solely, or in combination, fail to provide the required limitations, and modification of any complementary combination of the references of record would be impermissible hindsight and not provide any advantages over their present application. The dependent claims are also free of prior art due to their corresponding dependency of the independent claims.
Conclusion
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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