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 (“RCE”), 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 April 20, 2026, has been entered.
Status of Claims
Claims 1-20 were previously pending and subject to a Final Office Action having a notification date of January 28, 2026 (“Final Office Action”). Following the Final Office Action, Applicant filed an amendment on February 24, 2026 (“Previous Response”), which resulted in an Advisory Action dated March 3, 2026 indicating that the Previous Response did not place the application in condition for allowance. Applicant then filed the RCE along with an amendment on April 20, 2026 (“Amendment”), amending claims 1, 8, and 15; canceling claims 16-20; and adding new claims 21-25.
The present non-final Office Action addresses pending claims 1-15 and 21-25 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
Applicant’s remarks regarding the rejection of claims 15-20 under 35 USC 101 as not being directed to one of the four statutory categories are convincing and therefore this rejection is withdrawn.
On page 14 of the Amendment, Applicant takes the position that "any such [abstract idea]" recited in the present claims is allegedly integrated into a particular, technical use that improves clinical pharmacokinetics/dosing while improving computer operation given the specific machine learning architecture, training regimen, and constrained objectives which is allegedly "what Desjardins requires be credited." The Examiner disagrees.
Initially, almost the entirety of each of the independent claims amounts to "mental processes" because it is practically performable in the human mind with pen and paper as set forth in the detailed example provided by the Examiner at pages 7-8 of the Final Office Action dated January 28, 2026 ("Final Office Action") and again hereinbelow. Furthermore, the present claims recite "certain methods of organizing human activities" as set forth at page 8 of the Final Office Action and again hereinbelow. Furthermore, the Examiner disagrees that an improvement to "clinical pharmacokinetics" is an improvement to technology because pharmacokinetics is the study of how the human body affects substances/drugs after administration rather than involving computers/technology. Still further, Applicant's position that computer operation is improved "given the specific machine learning architecture, training regimen, and constrained objectives" is inapposite as the present claims do not recite training in the first place, the Examiner is unaware what is encompassed by "constrained objectives," and the cr-GAN is a known type of ML model recited at a high level in the claims.
Applicant's position that "to access said optimal isobole...' is an alleged "constraint" on the cr-GAN is irrelevant because calculating dosage data of a pharmaceutical to access said optimal isobole is practically performable in the human mind with pen and paper. Essentially, the present independent claims are performable in the human mind with pen and paper and then Applicant has added performing such mentally-performable limitations "with a cr-GAN" which is not what Desjardins and other appropriate authorities have indicated renders claims patent-eligible.
On page 15 of the Amendment, Applicant again recites high level limitations of the present claims (i.e., rather than any specific limitations) and asserts that PK dosing and operation of the specific ML system are improved. The Examiner disagrees because improvements to PK dosing is not an improvement to computers/technology as noted above and there are absolutely no features/limitations of the cr-GAN recited in the claims that amount to an improvement of ML technology as alleged by Applicant. Again, Applicant has added performing mentally-performable limitations "with a cr-GAN" which is not what Desjardins and other appropriate authorities have indicated renders claims patent-eligible.
Regarding Applicant's position on page 15 that the present claims need not recite any details regarding how the cr-GAN is trained/used (e.g., including losses, weights, training, etc.) because Desjardins indicates that improvements described in the specification and apparent to a PHOSITA need not be explicitly recited in the claims, the Examiner respectfully asserts that such interpretation of Desjardins is incorrect. More specifically, while Desjardins indicated that "improvements" such as effectively learning new tasks in succession, reduced usage of system storage capacity, and reduced system complexity did not need to be actually recited in the claims, Desjardins reaffirmed that the claims must include the components/steps of the invention that provide the improvement recited in the specification (which the Examiner asserts would correspond in the present case to the details regarding how the cr-GAN is trained/used such as losses/weights/training/etc. that provide technological improvements described in the specification).
On page 16 of the Amendment, Applicant takes the position that the present claims are distinct from those in Recentive Analytics because the present claims allegedly "explain how the machine learning technology achieves a feasibility-respecting pharmacokinetic parameter sampling that drives a toxicity-constrained dosing solution. However, which steps/limitations, other than generically reciting "with a/said constrained optimization generative adversarial network," explain how the "machine learning technology" achieves a "feasibility-respecting pharmacokinetic parameter sampling that drives a toxicity-constrained dosing solution"? The mentally-performable steps of monitoring the patient measure, conditioning the patient parameters, parameterizing the PK model, sampling the conditioned model parameters, and calculating dosage data? These limitations are part of the abstract idea and thus cannot provide an improvement of itself. Applicant did not invent the cr-GAN. 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.
Applicant's continued insistence at the middle of page 16 of the Amendment that the present claims improve pharmaceutical dosing/pharmacokinetics is again respectfully irrelevant because pharmaceutical dosing/pharmacokinetics is part of the abstract idea rather than related to computers/technology. Furthermore, and as noted above, there are no details of the cr-GAN in the present claims such that the present claims amount to performing mentally-performable steps "with a cr-GAN" which is just reciting the idea of a solution and equivalent to the words "apply it" (see MPEP § 2106.05(f)).
At the bottom of page 16 of the Amendment, Applicant takes the position that the present claims "recite specific structure and training behavior of the cr-GAN." This is not true. Where do the claims recite any training behavior of the cr-GAN? They do not. Furthermore, there is no specific structure recited of the cr-GAN beyond performing the abstract idea "with the cr-GAN." Applicant also asserts that the present claims recite "explicit control constraints" of the cr-GAN such as "toxicity avoidance via accessing an optimal isobole for dosing and physiologically grounded pharmacokinetic parameter conditioning through the constrained optimization generative adversarial network" and thus "incorporate the architecture and specificity the Recentive Analytics decision explains is necessary for patent eligibility." The Examiner disagrees. Similar to the claims in Recentive, the present claims "do not delineate steps through which the machine learning technology achieves an improvement." See, e.g., IBM v. Zillow Grp., Inc., 50 F.4th 1371, 1381 (Fed. Cir. 2022) (holding abstract a claim that "d[id] not sufficiently describe how to achieve [its stated] results in a non-abstract way," because "[s]uch functional claim language, without more, is insufficient for patentability under our law." Recentive, p. 13.
"Instead of disclosing "a specific implementation of a solution to a problem in the software arts," Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or "a specific means or method that solves a problem in an existing technological process," Koninklijke, 942 F.3d at 1150, the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment. This new environment is event scheduling and the creation of network maps." Recentive, p. 13. Similarly, in the present case, a known ML algorithm (i.e., cr-GAN) is used in a new environment and the environment is pharmaceutical dosing to access an "optimal isobole" in a manner that avoids toxic effects while maintaining efficacy.
The Examiner notes how the Recentive decision saw "no merit to Recentive's argument that its patents are eligible because they apply machine learning to this new field of use. We have long recognized that "[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment." Intell. Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015); see also Alice, 573 U.S. at 222; Parker v. Flook, 437 U.S. 584, 593 (1978); Stanford, 989 F.3d at 1373 (rejecting argument that a claim was not abstract where patentee contended "the specific application of the steps [was] novel and enable[d] scientists to ascertain more haplotype information than was previously possible")." Recentive, p. 14.
Regarding Applicant's position at the top of page 17 of the Amendment that the present claims are not directed to an abstract idea at least at Step Prong Two because they include "a specific machine learning architecture to access optimal isoboles and physiologically grounded pharmacokinetic parameter conditioning through the constrained optimization generative adversarial network," the Examiner repeats the Examiner's position that the claims do not recite any technical details of the cr-GAN (e.g., in relation to training, weights, execution, etc.) and asserts "the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment." Recentive, p. 13.
At pages 17-18 of the Amendment, Applicant asserts the present claims are contribute to an "inventive concept" and thus include elements that amount to "significantly more" than the abstract idea (where such elements are presumably "additional limitations" because the limitations directed to the abstract idea cannot provide significantly more than itself) because the claims are inventive over the prior art. However, while any novelty in the implementation of an idea may be considered under step 2B of the Alice analysis, it does not necessarily turn an abstraction into something concrete. Ultramercial, Inc. V. Hulu, LLC, 772 F .3d 709, 715 (Fed. Cir. 2014). In the present case, the Examiner has considered the novelty of the claims (as evidenced by the prior art rejections being withdrawn) but ultimately determined that the majority of the claim limitations are practically mentally performable with pen and paper and that doing so "with a cr-GAN" just amounts to reciting the idea of a solution which is equivalent to the words "apply it" (see MPEP § 2106.05(f). The Examiner also disagrees with Applicant's position that the present claims are "necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computers" similar to DDR Holdings, LLC because calculating dosage data of a pharmaceutical to access an optimal isobole that avoids toxic effects while maintaining efficacy has nothing to do with computer-rooted technology to overcome a problem specifically arising in the realm of computers but instead relates to practically mentally performable and/or "certain methods of organizing human activities" abstract ideas.
The 35 USC 101 rejection is maintained.
Claim Objections
Claim 22 is objected to because of the following informalities:
In claim 22, line 3, it appears that “satisfying” should be changed to --satisfies--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21, 22, and 25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding new claim 21, Applicant refers to [0028], [0036], and [0043] of the present specification. However, neither these portions nor any other portions of the specification appear to disclose executing the parameterized PK model with the sampled mechanistic model parameters as required in claim 21.
Regarding new claim 22, Applicant refers to [0024]-[0033] and [0047] of the present specification. However, neither these portions nor any other portions of the specification appear to disclose that the iterative sampling occurs until convergence of a distribution of PK parameters [satisfy] patient specific conditional variables as required in claim 22.
Regarding new claim 25, Applicant refers to [0029] and [0047] of the present specification. While [0029] discloses minimizing divergence between a given prior and generated model parameters, neither these portions nor any portions of the specification appear to disclose that the sampling minimizes a divergence between a generated parameter distribution and a target distribution conditioned on the patient data as required in claim 25.
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 1-15 and 21-25 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 1-7 and 21-25 are directed to a system (i.e., a machine), claims 8-14 are directed to a method (i.e., a process), and claim 15 is directed to a product including a non-transitory computer readable storage medium (i.e., a manufacture). Accordingly, claims 1-15 and 21-25 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 system to access an optimal isobole for a therapeutic target for a patient, said system comprising:
a memory; and
a processor in communication with said memory, said processor being configured to perform operations, said operations comprising:
monitoring said patient for a patient measure, wherein said patient measure is selected from the group consisting of a baseline patient state, an observed change from said baseline patient state, and a therapeutic target;
conditioning patient parameters with said patient measure;
parameterizing a pharmacokinetic model with said patient parameters to obtain a parameterized pharmacokinetic model;
iteratively sampling, with a constrained optimization generative adversarial network, mechanistic model parameters of said parameterized pharmacokinetic model;
calculating dosage data of a pharmaceutical to access said optimal isobole for said therapeutic target for said patient, wherein said dosage data is calculated with said sampled patient parameters derived from said sampled mechanistic model parameters sampled by said constrained optimization generative adversarial network, and wherein said optimal isobole avoids toxic effects while maintaining efficacy; and
communicating said dosage data to a user.
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 medical professional could readily in their mind with pen and paper monitor (e.g., watch, perceive) a patient for a patient measure (e.g., baseline state such as oxygen levels, etc.), use the patient measure (the baseline oxygen levels) to "condition" (e.g., adjust, update) patient parameters (e.g., surgical status, etc.), parameterize/develop a PK model with the patient parameters (e.g., indicative of how a pharmaceutical is absorbed/distributed through a patient's body) to obtain a parameterized PK model, iteratively (e.g., repeatedly, at least a few times) sample mechanistic model patient parameters of the PK model (e.g., obtaining a portion of the parameters, such as in relation to clearance, absorption, etc.), and calculate a dosage data of a pharmaceutical with the sampled mechanistic parameters.
For instance, based on a particular patient urination/surgical status as well as absorption/distribution of a particular pharmaceutical within a patient’s body, the medical professional could calculate/determine (e.g., based on their experience/clinical guidelines/etc.) a particular effective dosage of a combination of drugs that achieves a particular inhibition or cure rate a defined confidence level (e.g., access optimal isobole for a therapeutic target for the patient) in a manner that avoids toxic effects while maintaining efficacy.
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 that were equivalent to "mental processes" in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, the 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). For instance, 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). MPEP 2106.04(a)(2)(II)(C).
Furthermore, dependent claims 3-6, 10-13, 22, and 25 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, 4, 10, and 11 call for selecting the patient measure from a monitoring stream (e.g., a neurocritical monitoring stream) which just further defines the abstract idea(s) discussed above.
-Claims 5 and 12 call for modeling a neurocritical care measure (e.g., brain swelling, etc.) with an associated pharmacokinetic model (e.g., how a particular pharmaceutical is absorbed, metabolized, etc. in the body) and assessing an effect of the neurocritical care measure on the pharmacokinetic model (e.g., assessing how such brain swelling affects the absorption, metabolism, etc. of the pharmaceutical) which just further defines the abstract idea(s) discussed above.
Claims 6 and 13 recite how the patient is a critical care unit patient which just further defines the abstract idea(s) discussed above.
Claim 22 recites how the iterative sampling occurs until convergence of a distribution of PK parameters [satisfy] patient specific conditional variables which just further defines the abstract idea(s) discussed above.
Claim 25 recites how the sampling minimizes a divergence between a generated parameter distribution and a target distribution conditioned on the patient data which just further defines the abstract idea(s) discussed above.
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 system to access an optimal isobole for a therapeutic target for a patient, said system comprising:
a memory (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)); and
a processor in communication with said memory, said processor being configured to perform operations, said operations comprising (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
monitoring said patient for a patient measure, wherein said patient measure is selected from the group consisting of a baseline patient state, an observed change from said baseline patient state, and a therapeutic target;
conditioning patient parameters with said patient measure;
parameterizing a pharmacokinetic model with said patient parameters to obtain a parameterized pharmacokinetic model;
iteratively sampling, with a constrained optimization generative adversarial network (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)), mechanistic model parameters of said parameterized pharmacokinetic model;
calculating dosage data of a pharmaceutical to access said optimal isobole for said therapeutic target for said patient, wherein said dosage data is calculated with said sampled patient parameters derived from said sampled mechanistic model parameters sampled by said constrained optimization generative adversarial network (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)), and wherein said optimal isobole avoids toxic effects while maintaining efficacy; and
communicating said dosage data to a user (extra-solution activity (transmitting data) as noted below, see MPEP § 2106.05(g)).
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 memory and processor, 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 the sampling and calculating steps being performed with a constrained optimization GAN, 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)). These additional limitations provide only a result-oriented solution and lack details as to how sampling and calculating steps actually occur.
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.
Regarding the additional limitation of communicating the dosage data to the user, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (transmitting data) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
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 8 and 15 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 1 and analogous independent claims 8 and 15 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 2 and 9 call for streaming the patient measure to the constrained optimization generative adversarial network which amounts 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)).
-Claims 7 and 14 recite how the dosage data is calculated in real time which amounts 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)).
-Claim 21 calls for executing said parameterized pharmacokinetic model with the sampled mechanistic model parameters 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)). These additional limitations provide only a result-oriented solution and lack details as to how the execution of the PK model actually occurs.
-Claim 23 recites how the constrained optimization GAN includes a generator and discriminator 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)).
-Claim 24 recites how the constrained optimization GAN operates on outputs of the pharmacokinetic model which again 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)).
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 memory and processor, 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 the sampling and calculating steps being performed with a constrained optimization GAN, 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)). These additional limitations provide only a result-oriented solution and lack details as to how sampling and calculating steps actually occur.
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.
Regarding the additional limitations directed to communicating the dosage data to the user which the Examiner submits merely adds insignificant extra-solution activity to the abstract idea (see MPEP § 2106.05(g)), the Examiner has reevaluated such limitation and determined it to not be unconventional as it merely consists of receiving/transmitting data over a network. See Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); See MPEP 2106.05(d)(II).
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 2 and 9 call for streaming the patient measure to the constrained optimization generative adversarial network which amounts 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)).
-Claims 7 and 14 recite how the dosage data is calculated in real time which amounts 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)).
-Claim 21 calls for executing said parameterized pharmacokinetic model with the sampled mechanistic model parameters 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)). These additional limitations provide only a result-oriented solution and lack details as to how the execution of the PK model actually occurs.
-Claim 23 recites how the constrained optimization GAN includes a generator and discriminator 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)).
-Claim 24 recites how the constrained optimization GAN operates on outputs of the pharmacokinetic model which again 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)).
Therefore, claims 1-15 and 21-25 are ineligible under 35 USC §101 as being directed to an abstract idea without significantly more.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent App. Pub. No. 2023/0377747 to Molero Leon et al. discloses ([0216]) a modified Generative Adversarial Network (GAN) that can be used to predict pertinent subject data. Specifically, a population distribution across one or more variables (e.g., rate constants, dynamics, etc.) can be identified. A Generator network can identify a transformation of the distribution along one or more dimensions. The transformation can be defined at least in part based on subject-specific characteristics (e.g., weight, age) and/or data (e.g., one or more subject-associated rate constants, dynamic variables, etc.). A sampling technique (e.g., Monte Carlo technique) can sample from the transformed distribution, and a Discriminator network can predict whether the sample(s) correspond to the population or the subject. Accuracy of the predictions can be fed back to the Generator network until a threshold accuracy is obtained or a threshold number of iterations have occurred. The transformation can then be used to estimate subject-specific metrics (and/or uni- or multi-dimensional distributions thereof) that represent pharmacokinetics corresponding to an individual subject. This approach can facilitate using a limited and/or small number of subject-specific variable values to generate a subject-specific distribution that may more fully represent biological activity. A sampling technique (e.g., Monte-Carlo technique) may sample from the distribution to generate data to use to train another model (e.g., a pharmacokinetic model or neural network).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHON A. SZUMNY whose telephone number is (303) 297-4376. The examiner can normally be reached Monday-Friday 7-5.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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