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 .
Response to Arguments
Rejection Under 101
Applicant's arguments filed 08/04/2026 have been fully considered.
Applicant argues that the amended claims recite a practical application. The claims recite an interactive user interface workflow where a score is generated in response to the actuation event. The amended claims defines a specific manner of receiving input through a structured interface display with specific output elements, which is more than mere outputting a result. It is a defined state change of a specific graphical user interface triggered by actuation of a submission button.
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. While the amended claims recite receiving data in response to an actuation event, the event itself is not positively recited. The additional elements do not amount to a practical application. See the updated rejection below for further clarification.
Applicant argues that the amended claims are analogous to Example 37 where the claims were found to be integrated into a practical application. The amended claims recite a specific manner of receiving input via a defined interactive graphical user interface and in response to a specific actuation event, automatically updating the same interface with output elements to depict the projected transplant dataset. Just as in Example 37, the combination of receiving input via a GUI and automatically displaying specific GUI elements in a defined manner (e.g., based on individual transplant recipient relative to a reference set) constitutes a specific improvement to the user interface.
In response to Applicant’s argument, as discussed above the actuation event is not positively recited as applicant appears to argue. Additionally, the amended claims are not like Example 37 because in that example, the icons were moved closer to start menu but were organized by considering the usage of each icon. In contrast, Applicant’s output to a GUI is to cause the display of a text box that depicts a predicted probability of rejection. This is merely outputting the determined rejection risk. The courts have routinely held that outputting a result does not amount to practical application. See the updated rejection 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, 25-39 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, 34-39 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 comprising:
receiving, from a user device displaying an interactive medical analysis tool graphical user interface and in response to an actuation event corresponding to actuation of a submission button of the interactive medical analysis tool graphical user interface, transplant recipient data of an individual transplant recipient comprising a set of transplant recipient parameters, wherein the interactive medical analysis tool graphical user interface comprises one or more text boxes identifying respective transplant recipient parameters and one or more corresponding input boxes configured to receive user input values for the identified respective transplant recipient parameters via user interaction with the corresponding input boxes, wherein the transplant recipient data comprises the user input values entered into the one or more input boxes corresponding to the set of transplant recipient parameters, including a user input value for donor-derived cell-free DNA (dd-cfDNA), and wherein the set of transplant recipient parameters 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 informative of transplant rejection 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:
receiving the training cohort dataset comprising at least a first set of candidate parameters and corresponding training cohort transplant rejection information of a plurality of transplant recipients;
analyzing the first set of candidate parameters of the training cohort dataset for associations between the first set of candidate parameters and the corresponding training cohort transplant rejection information;
reducing a dimensionality of the training cohort dataset by iteratively selecting one or more subsequent subsets of candidate parameters, wherein each subsequent subset is selected from the first set of candidate parameters or an immediately preceding subset of the one or more subsequent subsets of candidate parameters;
selecting a final subset of parameters from the immediately preceding subset of one or more subsequent subsets of candidate parameters, wherein the final subset of parameters comprises independent variables associated with transplant rejection and satisfying one or more final selection criteria; and
outputting the one or more parameter weights corresponding to the final subset of parameters, wherein each of the one or more parameter weights corresponds to a degree of association of a respective parameter of the final subset of parameters to transplant rejection;
in response to the actuation event, 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 informative of transplant rejection output from the trained machine learning model; and
outputting a predictive probability user interface component for updating the interactive medical analysis tool graphical user interface to cause display of a text box 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 and a projection of the transplant recipient data in a reference set.
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, user device, interactive medical analysis tool graphical user interface, trained machine learning model, predictive probability user interface component, 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, 34-39 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 comprising: (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
receiving, from a user device displaying an interactive medical analysis tool graphical user interface (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), and in response to an actuation event corresponding to actuation of a submission button of the interactive medical analysis tool graphical user interface, transplant recipient data of an individual transplant recipient comprising a set of transplant recipient parameters, wherein the interactive medical analysis tool graphical user interface comprises one or more text boxes identifying respective transplant recipient parameters and one or more corresponding input boxes configured to receive user input values for the identified respective transplant recipient parameters via user interaction with the corresponding input boxes, (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) wherein the transplant recipient data comprises the user input values entered into the one or more input boxes corresponding to the set of transplant recipient parameters, including a user input value for donor-derived cell-free DNA (dd-cfDNA), and wherein the set of transplant recipient parameters does not comprise a histological parameter;
receiving, from a trained machine learning model (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), one or more parameter weights, wherein the trained machine learning model is trained (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) to output the one or more parameter weights informative of transplant rejection 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))
receiving the training cohort dataset comprising at least a first set of candidate parameters and corresponding training cohort transplant rejection information of a plurality of transplant recipients;
analyzing the first set of candidate parameters of the training cohort dataset for associations between the first set of candidate parameters and the corresponding training cohort transplant rejection information;
reducing a dimensionality of the training cohort dataset by iteratively selecting one or more subsequent subsets of candidate parameters, wherein each subsequent subset is selected from the first set of candidate parameters or an immediately preceding subset of the one or more subsequent subsets of candidate parameters;
selecting a final subset of parameters from the immediately preceding subset of one or more subsequent subsets of candidate parameters, wherein the final subset of parameters comprises independent variables associated with transplant rejection and satisfying one or more final selection criteria; and
outputting the one or more parameter weights corresponding to the final subset of parameters, wherein each of the one or more parameter weights corresponds to a degree of association of a respective parameter of the final subset of parameters to transplant rejection;
in response to the actuation event, 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 informative of transplant rejection 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 component for updating the interactive medical analysis tool graphical user interface to cause display of a text box (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) and (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 and a projection of the transplant recipient data in a reference set.
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, user device, interactive medical analysis tool graphical user interface, trained machine learning model, predictive probability user interface component, 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 displaying a text box for the output, 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, 34-39 recite additional limitations that further narrow or define the abstract idea, and claims 2, 4-7, 9-16, 18, 20-23, 25-32, 34-39 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, user device, interactive medical analysis tool graphical user interface, trained machine learning model, predictive probability user interface component, 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, user device, interactive medical analysis tool graphical user interface, trained machine learning model, predictive probability user interface component, 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)): displaying a text box for the output, e.g., outputting or providing access to the information, 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, 25-39 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
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 AMANDA R COVINGTON whose telephone number is (303)297-4604. The examiner can normally be reached Monday - Friday, 10 - 5 MT.
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
