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 .
Status of Claims
Claims 1-51 were previously pending and subject to a non-final Office Action having a notification date of July 11, 2025 (“non-final Office Action”), with claims 31-51 being withdrawn. Following the non-final Office Action, Applicant filed an amendment on September 25, 2025 (the “Amendment”), amending claims 1, 9-11, 13-21, 29, and 30; canceling claims 2, 12, 22, and 31-51; and adding new claims 52-54.
The present Final Office Action addresses pending claims 1, 3-11, 13-21, 23-30, and 52-54 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §112
These rejections are withdrawn in view of the Amendment.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
The rejection of claims 11 and 13-20 under 35 USC 101 as not being directed to one of the four statutory categories is withdrawn in view of the Amendment.
On page 11 of the Amendment, Applicant takes the position that the new limitations calling for pairing each health failure probability with a postoperative DAPT duration for the target patient and outputting a recommended DAPT duration to treat the target patient based on the paired set of health failure probabilities is not practically mentally performable. The Examiner disagrees.
As set forth in the rejection below, a person (e.g., data scientist) could practically in their mind with pen and paper analyze clinical data of a population of patients administered dual antiplatelet therapy (DAPT) medications for various durations (e.g., 1, month, 3 months, etc.) some of which resulted in health failures (e.g., ischemia, bleeding) to identify a set of preoperative baseline characteristics (e.g., "significant" contributors for future health failure risk prediction such as reference vessel diameter, DAPT duration, fasting BG, etc.), and determine a set of health failure probabilities for a target patient based on the set of preoperative baseline characteristics and a set of preoperative target characteristics for the target patient. For instance, in the case where the target characteristics are similar (e.g., within some threshold similarity level) to the baseline characteristics of a portion of the patient population that had bleeding events, the data scientist could estimate a high probability that the target patient will experience a bleeding event.
Furthermore, the data scientist could also practically in their mind with pen and paper pair each health failure probability with a different respective DAPT duration (thereby forming a paired set of health failure probabilities) to determine how the respective DAPT duration affects the health failure probability. For instance, in the case where a first sub-portion of the above portion of the patient population generally did not have a bleeding event after taking a particular DAPT for 28 days but did have a bleeding event after taking the particular DAPT for 90 days, then the data scientist could pair a 28 day DAPT with bleeding risk resulting in a relatively low probability of bleeding risk for the target patient and pair a 90 day DAPT with bleeding risk resulting in a relatively high probability of bleeding risk for the target patient. The data scientist could then review, select, and "output" a recommended DAPT duration to treat the target patient based on the paired set.
The 35 USC 101 rejection is maintained.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §103
Starting at page 12 of the Amendment, Applicant takes the position that Itu does not disclose "pair, by the machine learning model, each probability in the set of health failure probabilities with a postoperative … antiplatelet therapy … for the target patient" as recited in independent claim 1. Specifically, Applicant takes the position that [0060] of Itu merely discloses that antiplatelet agents may be recommended which may be just a standard dose/duration if the PMI rises to some unstated threshold level and that there is no discussion regarding pairing different levels of risk with various/differing aspects of antiplatelet therapy. The Examiner disagrees.
Paragraph [0060] of Itu specifically states "Antithrombotic and antiplatelet (e.g., clopidogrel, prasugrel) agents may be recommended based on the level of PMI risk" (Emphasis added). Furthermore, the end of [0025] and [0072] note how a plurality of risks can be determined in addition to PMI such as major bleeding, thrombosis, etc. Further, [0060], [0062], and [0069] discuss how the recommended medication (antiplatelet agents per [0060]) is to prevent/limit the risk of PMI (and/or other risks per [0072]) due to a PCI (procedure/operation) and how the recommendation can be generated before/during/after the PCI; accordingly, the recommended antiplatelet agents are "postoperative" antiplatelet agents because they are taken to limit/prevent the risk of PMI (and/or other risks per [0072]) after the PCI and/or because they are determined and taken after the PCI). Still further, [0061] discloses how PMI risk (which is part of a set of health failure risks/probabilities per [0072]) can be used to select courses of action from a look-up table (whereby each risk would be paired with a respective course of action (antiplatelet agents per [0060]) such that a paired set of health failure probabilities results as that is how look-up tables function). Therefore, the antiplatelet therapy is determined/paired for each probability in the "set of health failure probabilities."
In relation to Applicant's suggestion that [0060] of Itu insinuates that merely a "standard" dose/duration of the antiplatelet agent is recommended, the Examiner notes that [0060] does not specifically disclose recommending a "standard" dose/duration of the antiplatelet agent based on PMI risk level. Even if [0060] of Itu did disclose or suggest that merely a "standard" dose/duration of the antiplatelet agent can be recommended based on PMI risk level, however, such disclosure would still read on "pair, by the machine learning model, each probability in the set of health failure probabilities with a postoperative … antiplatelet therapy … for the target patient" as recited in the present claims because the present claims do not require that each respective risk level is paired with an aspect of antiplatelet therapy different from those of other risk levels. For instance, the present claims encompass the antiplatelet therapy aspects being the same for a plurality of different risk levels.
Applicant next takes the position that Zhao does not teach a recommended duration of DAPT therapy based on probabilities but instead merely recognizes a "standard predetermined duration" of 12 months as a guideline per 1:53-57 of Zhao.
However, claim 1 of Zhao specifically recites, inter alia, "determining an anti-platelet medication regimen for the subject based on the probability score, wherein the anti-platelet medication regimen includes a dual anti-platelet therapy type and a duration after the stent implantation," where "[the] probability score [represents] a risk of stent thrombosis in the subject based on the input value of the baseline angiographic variable to allow a physician to assess the risk of stent," which directly reads on pairing health risk probabilities to a DAPT duration as recited in the present claims.
Applicant's suggestion that Zhao does not teach pairing health risk probabilities with a DAPT duration as recited in present claims because the DAPT duration in claim 1 of Zhao is limited to merely 12 months as disclosed in the background of Zhao (1:53-57) is flawed at least because i) the present claims do not even limit the recited DAPT duration from being a standard duration in the first place and/or ii) one of ordinary skill in the art would not understand a claim of a patent (i.e., Zhao) that recites determining a DAPT duration based on a health risk probability to be limited, defined, and/or tied to a specific duration disclosed in the background of the patent (notwithstanding that "at least 12 months" (1:56-57 of Zhao)(emphasis added) is not even a specific duration in the first place and instead encompasses a various durations). In contrast, the ordinary artisan would understand that the DAPT duration determined in claim 1 of Zhao is based on the determined health risk probability score as claimed rather than necessarily being a standard 12 month duration as already known in the background of Zhao.
On page 13 of the Amendment, Applicant then asserts that Sirota makes no mention of the AUC values being "time-dependent" AUC values as recited in the present claims. However, [0086], [0095], and [0096] of Sirota disclose how the ML model analyses biomarkers and generates predictions over time such as a patient's disease status over specific time periods such as future risks. Accordingly, the AUC value of greater than 0.8 of the ML model that generates such predictions per [0010]-[0012], [0151]-[0152], and [0282] is a "time-dependent" AUC value.
The 35 USC 103 rejections are maintained.
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, 3-11, 13-21, 23-30, and 52-54 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more:
Subject Matter Eligibility Criteria - Step 1:
Claims 1, 3-10, and 52 are directed to a system (i.e., a machine), claims 11, 13-20, and 53 are directed to a non-transitory computer readable storage medium (i.e., a manufacture), and claims 21, 23-30, and 54 are directed to a method (i.e., a process). Accordingly, claims 1, 3-11, 13-21, 23-30, and 52-54 are all within at least one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A computing system comprising:
a processor; and
a memory coupled to the processor, the memory including a set of instructions, which when executed by the processor, cause the computing system to:
identify a set of preoperative baseline characteristics associated with a procedure on a pooled patient population;
determine, by a machine learning model, a set of health failure probabilities for a target patient based on the set of preoperative baseline characteristics and a set of preoperative target characteristics, wherein the set of preoperative target characteristics correspond to the target patient, and wherein the set of preoperative baseline characteristics and a number of characteristics in the set of preoperative baseline characteristics yield time-dependent area under the curve (AUC) values of greater than 0.8 by decision tree procedure in the machine learning model;
pair, by the machine learning model, each probability in the set of health failure probabilities with a postoperative dual antiplatelet therapy (DAPT) duration for the target patient to form a paired set of health failure probabilities; and
output a recommended DAPT duration to treat the target patient based on the paired set of health failure probabilities.
The Examiner submits that the foregoing underlined limitations constitute “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, a person (e.g., data scientist) could practically in their mind with pen and paper analyze clinical data of a population of patients administered dual antiplatelet therapy (DAPT) medications for various durations (e.g., 1, month, 3 months, etc.) some of which resulted in health failures (e.g., ischemia, bleeding) to identify a set of preoperative baseline characteristics (e.g., "significant" contributors for future health failure risk prediction such as reference vessel diameter, DAPT duration, fasting BG, etc.), and determine a set of health failure probabilities for a target patient based on the set of preoperative baseline characteristics and a set of preoperative target characteristics for the target patient. For instance, in the case where the target characteristics are similar (e.g., within some threshold similarity level) to the baseline characteristics of a portion of the patient population that had bleeding events, the data scientist could estimate a high probability that the target patient will experience a bleeding event.
Furthermore, the data scientist could also practically in their mind with pen and paper pair each health failure probability with a different respective DAPT duration (thereby forming a paired set of health failure probabilities) to determine how the respective DAPT duration affects the health failure probability. For instance, in the case where a first sub-portion of the above portion of the patient population generally did not have a bleeding event after taking a particular DAPT for 28 days but did have a bleeding event after taking the particular DAPT for 90 days, then the data scientist could pair a 28 day DAPT with bleeding risk resulting in a relatively low probability of bleeding risk for the target patient and pair a 90 day DAPT with bleeding risk resulting in a relatively high probability of bleeding risk for the target patient. The data scientist could then review, select, and "output" a recommended DAPT duration to treat the target patient based on the paired set.
These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III). Claims “directed to collection of information, comprehending the meaning of that collected information, and indication of the results, all on a generic computer network operating in its normal, expected manner,” fail step one of the Alice framework. In re Killian, 45 F.4th 1373, 1380 (Fed. Cir. 2022). Claims directed to “collecting, analyzing, manipulating, and displaying data’’ are abstract. Univ. of Fla. Research Found., Inc. v. General Elec. Co., 916 F.3d 1363, 1368 (Fed. Cir. 2019). Claims directed to organizing, storing, and transmitting information determined to be directed to an abstract idea. Cyberfone Sys., L.L.C. v. CNN Interactive Grp., Inc., 558 F. App’x 988, 992 (Fed. Cir. 2014).
The Examiner also submits that the foregoing underlined limitations constitute “certain methods of organizing human activity” because they relate to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). These limitations are similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982).
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 3-5, 8, 13-15, 18, 23-25, 28 and 52-54 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claims 3, 13, and 23 call for outputting a recommended DAPT duration for the target patient corresponding to the lowest probability in the set of health failure probabilities) which just further defines the abstract idea(s) discussed above.
-Claims 4, 5, 14, 15, 24, and 25 recite how the health failure probabilities are to be associated with a time to first ischemic event or first bleeding event which just further defines the abstract idea(s) discussed above.
-Claims 8, 18, and 28 recite how the procedure on the pooled patient population is a stent procedure which just further defines the abstract idea(s) discussed above.
-Claims 52-54 call for applying a "variable importance measure" to evaluate an importance measure of each real variable of a plurality of real variables in the machine learning model as initially trained and removing each real variable when its importance measure is less than a maximum importance measure for a plurality of shadow variables including randomized versions of the real variable which is practically performable in the human mind with pen and paper (e.g., processing the variables through any appropriate variable importance algorithm or through process to determine an "importance measure" and then comparing each importance measure to those of corresponding "shadow variables").
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A computing system comprising:
a processor (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)); and
a memory coupled to the processor, the memory including a set of instructions, which when executed by the processor, cause the computing system to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
identify a set of preoperative baseline characteristics associated with a procedure on a pooled patient population;
determine, by a machine learning model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), a set of health failure probabilities for a target patient based on the set of preoperative baseline characteristics and a set of preoperative target characteristics, wherein the set of preoperative target characteristics correspond to the target patient, and wherein the set of preoperative baseline characteristics and a number of characteristics in the set of preoperative baseline characteristics yield time-dependent area under the curve (AUC) values of greater than 0.8 by decision tree procedure in the machine learning model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f));
pair, by the machine learning model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), each probability in the set of health failure probabilities with a postoperative dual antiplatelet therapy (DAPT) duration for the target patient to form a paired set of health failure probabilities; and
output a recommended DAPT duration to treat the target patient based on the paired set of health failure probabilities.
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the computing system including processor and memory with instructions, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of how the ML model somehow performs the (mentally performable) determination of the health failure probabilities and (mentally performable) pairing of the probabilities with DAPT durations such that the preoperative baseline characteristics and number thereof somehow yield time-dependent AUC values of greater than 0.8 by a decision tree procedure in the ML model, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). For instance, how is the ML model trained and executed to determine the health failure probabilities pair the probabilities with DAPT durations in a manner resulting in time-dependent AUC values of greater than 0.8 by a decision tree procedure? These additional limitations provide only a result-oriented solution and lack details as to how training and execution of the ML model actually occurs.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. Id. Using existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved does not confer patent-eligibility. Id., p. 15.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 1 and analogous independent claims 11 and 21 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 1 and analogous independent claims 11 and 21 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 6, 7, 16, 17, 26, and 27 recite different types of ML models (e.g., Random Survival Forests model, Gradient Boosting model) and thus do no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)) and amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)).
Claims 9, 10, 19, 20, 29, and 30 recite how the preoperative baseline characteristics and number thereof yield time-dependent AUC values of greater than 0.85 or 0.9 by decision tree procedure in the machine learning model which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). For instance, how is the ML model trained and executed to determine the health failure probabilities pair the probabilities with DAPT durations in a manner resulting in time-dependent AUC values of greater than 0.85 or 0.9 by a decision tree procedure? These additional limitations provide only a result-oriented solution and lack details as to how training and execution of the ML model actually occurs.
Claims 52-54 recite how the ML model is trained via "Boruta automation" including performing an initial training the ML model based on a training data set which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12.
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations of the computing system including processor and memory with instructions, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of how the ML model somehow performs the (mentally performable) determination of the health failure probabilities and (mentally performable) pairing of the probabilities with time-dependent DAPT durations such that the preoperative baseline characteristics and number thereof somehow yield time-dependent AUC values of greater than 0.8 by a decision tree procedure in the ML model, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). For instance, how is the ML model trained and executed to determine the health failure probabilities pair the probabilities with DAPT durations in a manner resulting in time-dependent AUC values of greater than 0.8 by a decision tree procedure? These additional limitations provide only a result-oriented solution and lack details as to how training and execution of the ML model actually occurs.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. Id. Using existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved does not confer patent-eligibility. Id., p. 15.
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Claims 6, 7, 16, 17, 26, and 27 recite different types of ML models (e.g., Random Survival Forests model, Gradient Boosting model) and thus do no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)) and amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)).
Claims 9, 10, 19, 20, 29, and 30 recite how the preoperative baseline characteristics and number thereof yield time-dependent AUC values of greater than 0.85 or 0.9 by decision tree procedure in the machine learning model which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). For instance, how is the ML model trained and executed to determine the health failure probabilities pair the probabilities with DAPT durations in a manner resulting in time-dependent AUC values of greater than 0.85 or 0.9 by a decision tree procedure? These additional limitations provide only a result-oriented solution and lack details as to how training and execution of the ML model actually occurs.
Claims 52-54 recite how the ML model is trained via "Boruta automation" including performing an initial training the ML model based on a training data set which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12.
Therefore, claims 1, 3-11, 13-21, 23-30, and 52-54 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 5, 8-11, 14, 15, 18-21, 24, 25, and 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2021/0251577 to Itu et al ("Itu") in view of U.S. Patent No. 10,748,659 to Zhao et al. ("Zhao") and U.S. Patent App. Pub. No. 2023/0298696 to Sirota et al. ("Sirota"):
Regarding claim 1, Itu discloses a computing system (Figure 4) comprising:
a processor (processor 42 in Figure 4); and
a memory coupled to the processor, the memory including a set of instructions, which when executed by the processor, cause the computing system to (memory 44 in Figure 4 which includes instructions per [0079] and [0085]-[0087]):
identify a set of preoperative baseline characteristics associated with a procedure on a pooled patient population ([0030], [0038] discloses various training data (baseline characteristics) data of historical patients (pooled patient population) associated with PCI (a procedure) per [0025], [0038], where at least some of the training data/baseline characteristics (e.g., demographic, genetic, etc.) are "preoperative" baseline characteristics);
determine, by a machine learning model, a set of health failure probabilities for a target patient based on the set of preoperative baseline characteristics and a set of preoperative target characteristics, wherein the set of preoperative target characteristics correspond to the target patient ([0031], [0037], [0038] discuss training an ML model based on the training data (the "set of preoperative baseline characteristics"), [0043]-[0053] discloses obtaining data for a current patient before a PCI procedure ("set of preoperative target characteristics"), and [0054]-[0058] and [0072] discloses inputting the obtained data for the current patient into the trained ML model to predict a risk of PMI, thrombosis, major bleeding, etc. ("set of health failure probabilities" for the current/target patient), …;
pair, by the machine learning model, each probability in the set of health failure probabilities with a postoperative … antiplatelet therapy … for the target patient ([0060] discloses how the AI-based system (which includes the above-noted ML model per [0027]) recommends antiplatelet agents (therapy) based on the level of PMI risk; furthermore, the end of [0025] and [0072] note how a plurality of risks can be determined in addition to PMI such as major bleeding, thrombosis, etc.; accordingly, the antiplatelet therapy would be determined/paired for each probability in the "set of health failure probabilities"; still further, [0060], [0062], and [0069] discuss how the recommended medication (antiplatelet agents per [0060]) is to prevent/limit the risk of PMI (and/or other risks per [0072]) due to a PCI (procedure/operation) and how the recommendation can be generated before/during/after the PCI; accordingly, the recommended antiplatelet agents are "postoperative" antiplatelet agents because they are taken to limit/prevent the risk of PMI (and/or other risks per [0072]) after the PCI and/or because they are determined and taken after the PCI) to form a paired set of health failure probabilities ([0060]-[0061] discloses how PMI risk (which is part of a set of health failure risks/probabilities per [0072]) can be used to select courses of action from a look-up table (whereby each risk would be paired with a respective course of action (antiplatelet agents per [0060]) such that a paired set of health failure probabilities results as that is how look-up tables function)); and
output a recommended [antiplatelet therapy] for the target patient based on the set of health failure probabilities ([0060]-[0061] discloses outputting/recommending one or more courses of action including recommended antiplatelet agents based on the PMI risk (which is a set of health failure risks/probabilities per [0072]).
However, Itu appears to be silent regarding the recommended postoperative antiplatelet therapy specifically being a dual antiplatelet therapy duration.
Nevertheless, Zhao teaches (claim 1) that it was known in the healthcare informatics art to determine a particular DAPT type and duration for a subject based on a probability score representing a risk of stent thrombosis based on baseline variables of the subject because DAPT is an accepted strategy for minimizing the risk of stent thrombosis (1:47-52) and because doing so would advantageously improve the efficacy and safety of the therapy (Abstract and 1:14-16).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the antiplatelet therapy of Itu to be a dual antiplatelet therapy as taught by Zhao because DAPT is an accepted strategy for minimizing the risk of stent thrombosis and for the recommended antiplatelet therapy of Itu to be a recommended DAPT duration as taught by Zhao to advantageously improve the efficacy and safety of the therapy. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Furthermore, while Itu discloses use of the "set of preoperative baseline characteristics" to develop the ML model as discussed above ([0031], [0037], [0038]), Itu might be silent regarding wherein the set of preoperative baseline characteristics and a number of characteristics in the set of preoperative baseline characteristics yield time-dependent area under the curve (AUC) values of greater than 0.8 by decision tree procedure in the machine learning model.
Nevertheless, Sirota teaches ([0010]-[0012], [0151]-[0152], and [0282]) that it was known in the machine learning and healthcare informatics art to select a number of biomarkers resulting in AUC values of greater than 0.8 by a decision tree/random forest procedure in an ML model used to predict/monitor a patient's disease status over specific time periods such as future risks ([0086], [0095], [0096]) as an ML model having an AUC (which is a "time-dependent" AUC as the ML model analyses biomarkers and generates predictions over time per ([0086], [0095], [0096])) of greater than 0.8 is known to correspond to good model performance and because decision trees/random forest procedures are known effective ML approaches for determining relationships between biomarker levels and disease risk ([0068]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the set of preoperative baseline characteristics and a number of characteristics in the set of preoperative baseline characteristics in the system of Itu/Zhao combination to yield time-dependent area under the curve (AUC) values of greater than 0.8 by decision tree procedure in the machine learning model as taught by Sirota because an ML model having a time-dependent AUC of greater than 0.8 is known to correspond to good model performance and because decision trees/random forest procedures are known effective ML approaches for determining relationships between biomarker levels and disease risk. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 4, the Itu/Zhao/Sirota combination discloses the computing system of claim 1, further including wherein the set of health failure probabilities are to be associated with a time to first ischemic event ([0007] and [0057] of Itu discloses predicting a risk of PMI (where an infarction (blockage) is a ischemic event because blood flow would be slowed due to the blockage) a number of days (e.g., 30, etc.) after PCI).
Regarding claim 5, the Itu/Zhao/Sirota combination discloses the computing system of claim 1, further including wherein the set of health failure probabilities are to be associated with a time to first bleeding event ([0072] of Itu discloses how the event can be major bleeding while [0057] discloses how the risk prediction can be a number of days (e.g., 1, 3, 30, etc.) in the future; furthermore, the end of [0025] of Itu notes how PMI is only used as an example such that the future risk prediction in [0057] applies to the major bleeding disclosed in [0072]).
Regarding claim 8, the Itu/Zhao/Sirota combination discloses the computing system of claim 1, further including wherein the procedure is to be a stent procedure ([0014], [0053] of Itu disclose how the PCI procedure includes use of stents).
Regarding claim 9, the Itu/Zhao/Sirota combination discloses the computing system of claim 1, further including wherein the set of preoperative baseline characteristics and the number of characteristics in the set of preoperative baseline characteristics yield time-dependent AUC values of greater than 0.85 by decision tree procedure in the machine learning model (Sirota teaches ([0010]-[0012], [0151]-[0152], and [0282]) that it was known in the machine learning and healthcare informatics art to select a number of biomarkers resulting in time-dependent AUC values of greater than 0.85 by a decision tree/random forest procedure in an ML model as an ML model having a time-dependent AUC of greater than 0.85 is known to correspond to good model performance and because decision trees/random forest procedures are known effective ML approaches for determining relationships between biomarker levels and disease risk ([0068]); therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the set of preoperative baseline characteristics and a number of characteristics in the set of preoperative baseline characteristics in the system of Itu/Zhao combination to yield time-dependent area under the curve (AUC) values of greater than 0.85 by decision tree procedure in the machine learning model as taught by Sirota because an ML model having a time-dependent AUC of greater than 0.85 is known to correspond to good model performance and because decision trees/random forest procedures are known effective ML approaches for determining relationships between biomarker levels and disease risk. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Regarding claim 10, the Itu/Zhao/Sirota combination discloses the computing system of claim 1, further including wherein the set of preoperative baseline characteristics and the number of characteristics in the set of preoperative baseline characteristics yield time-dependent AUC values of greater than 0.9 by decision tree procedure in the machine learning model (Sirota teaches ([0010]-[0012], [0151]-[0152], and [0282]) that it was known in the machine learning and healthcare informatics art to select a number of biomarkers resulting in time-dependent AUC values of greater than 0.9 by a decision tree/random forest procedure in an ML model as an ML model having a time-dependent AUC of greater than 0.9 is known to correspond to good model performance and because decision trees/random forest procedures are known effective ML approaches for determining relationships between biomarker levels and disease risk ([0068]); therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the set of preoperative baseline characteristics and a number of characteristics in the set of preoperative baseline characteristics in the system of Itu/Zhao combination to yield time-dependent area under the curve (AUC) values of greater than 0.9 by decision tree procedure in the machine learning model as taught by Sirota because an ML model having a time-dependent AUC of greater than 0.9 is known to correspond to good model performance and because decision trees/random forest procedures are known effective ML approaches for determining relationships between biomarker levels and disease risk. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Claims 11, 14, 15, and 18-20 are rejected in view of the Itu/Zhao/Sirota combination as respectively discussed above in relation to claims 1, 4, 5, and 8-10.
Claims 21, 24, 25, and 28-30 are rejected in view of the Itu/Zhao/Sirota combination as respectively discussed above in relation to claims 1, 4, 5, and 8-10.
Claims 3, 13, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2021/0251577 to Itu et al ("Itu") in view of U.S. Patent No. 10,748,659 to Zhao et al. ("Zhao") and U.S. Patent App. Pub. No. 2023/0298696 to Sirota et al. ("Sirota"), and further in view of U.S. Patent App. Pub. No. 2014/0314292 to Kamen et al. ("Kamen"):
Regarding claim 3, the Itu/Zhao/Sirota combination discloses the computing system of claim 2, but appears to be silent regarding wherein the recommended DAPT duration is to correspond to a lowest probability in the set of health failure probabilities.
Nevertheless, Kamen teaches ([0021], [0028]-[0033]) that it was known in the machine learning and healthcare informatics art to utilize machine learning techniques to analyze patient features to generate a cancer risk score/probability and then to recommend a treatment option corresponding to a lowest risk factor score which would advantageously recommend treatment options that limit negative health effects on a patient thereby improving patient outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the recommended DAPT duration to correspond to a lowest probability in the set of health failure probabilities in t