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 1-20 were previously pending and subject to a non-final Office Action having a notification date of December 5, 2025 (“non-final Office Action”). Following the non-final Office Action, Applicant filed an amendment on March 5, 2026 (the “Amendment”), amending claims 1-20.
The present Final Office Action addresses pending claims 1-20 in the Amendment.
Terminal Disclaimer
The terminal disclaimer filed on March 5, 2026 ("Terminal Disclaimer"), disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent No. 12,051,487 has been reviewed and is accepted. The Terminal Disclaimer has been recorded.
Response to Arguments
Response to Applicant’s Arguments Regarding Double Patenting Claim Rejections
These rejections are withdrawn in view of the Terminal Disclaimer.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
On page 3 of the Amendment, Applicant takes the position that "the current process is not claiming the allegedly abstract idea of diagnostics itself, but rather the process of training and refining generative models tailored to "determining a clinical trial configuration" that could (potentially) be used in performing such diagnostics." The Examiner disagrees.
The Examiner initially notes that the present claims do recite numerous practically performable mental processes and/or mathematical concepts such as, inter alia, generating result data comprising predicted panel data for a set of one or more digital subjects in a target RCT; deriving in part using a Bayesian analysis of one or more of the plurality of predicted outcomes: a point estimate and an uncertainty estimate for outcomes of the target RCT; determining one or more decision rules for the target RCT based, at least in part, on the point estimate and the uncertainty estimate; using the one or more decision rules to generate a set of one or more trial characteristics, to be used, for implementing the target RCT; etc. as discussed in more detail in the below rejection.
Furthermore, and notwithstanding that the claims never recite "refining" the generative model(s) as alleged by Applicant, but the independent claims never even recite training the generative model(s) in the first place. Instead, they superficially recite that "the set of one or more generative models comprises a neural network trained, at least in part, based on the RCT data" 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” at the high level of generality as claimed (see MPEP § 2106.05(f)). Claims drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results, are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024). 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. 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. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
While dependent claims 4 and 14 recite how the one or more generative models are "pre-trained" on certain historical data and dependent claims 5 and 15 call for actually pre-training the generative model(s) on the certain historical data, these claims still do not place any limits on how such pre-training proceeds such that these limitations also just 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” at the high level of generality as claimed (see MPEP § 2106.05(f)).
At the bottom of page 3, Applicant then asserts that "this process" (presumably, the aforementioned "training and refining generative models" which is not even recited in the first place as noted above) is implemented to determine a specific breakdown of actual trial participants and generative model output participants (with the generation of the latter being the cumulative step) which allegedly avoids wasteful processing while maintaining the same capacity to detect an observable effect relative to standards established by decision rules derived by any previously digital twin outputs. However, all of the recited limitations directed to generating result data comprising predicted panel data for a set of one or more digital subjects in a target RCT; deriving in part using a Bayesian analysis of one or more of the plurality of predicted outcomes: a point estimate and an uncertainty estimate for outcomes of the target RCT; determining one or more decision rules for the target RCT based, at least in part, on the point estimate and the uncertainty estimate; using the one or more decision rules to generate a set of one or more trial characteristics, to be used, for implementing the target RCT; etc. which result in generation of an "optimal" number of digital subjects are part of the abstract idea(s) and thus cannot provide a "practical application" of itself.
On pages 3-4 of the remarks of the Amendment, Applicant asserts that the amendments to claim 1 allegedly align with certain legal principles in several key respects. First, Applicant takes the position that using decision rules to derive an "optimal" number of digital subjects while reaching an ideal ("sufficient" as claimed) threshold of power for the RCT and then generating such optimal number with the generative model(s) allegedly automates the task of "weighing the computational/statistical efficiency associated with generating a certain [number] of artificial subjects for a clinical trial" which is allegedly "consistent with McRO's holding that processes of automating tasks humans are capable of performing are patent eligible if properly claimed." The Examiner disagrees that the present claims are consistent with the holdings in McRO.
The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process. As part of its analysis, the McRO court examined the specification, which described the claimed invention as improving computer animation through the use of specific rules, rather than human artists, to set morph weights (relating to facial expressions as an animated character speaks) and transition parameters between phonemes (relating to sounds made when speaking). In this regard, implementation of the particular claimed rules in McRO improved the existing computer animation (i.e., technical) process. In contrast, the "decision rules" recited in the present claims just improve a mental process (not a technological process) of deriving/determining some "optimal" number of digital and actual subjects while reaching a "sufficient" power threshold for a target RCT.
Second, Applicant takes the position that the present claims parallel the Vanda framework because the decision rules, point estimate, and uncertainty estimate are applied in a "concrete manner" by determining the number of digital subjects to be generated, thereby integrating the mathematical and analytical steps into the "practical application" of configuring neural network outputs that will subsequently implemented in a clinical trial. The Examiner respectfully asserts that the Vanda framework is inapplicable because the present claims do not administer any treatment to treat a particular disease or disorder.
Third, Applicant asserts "[M]uch like the claims found eligible in Ex Parte Kannan [Appeal No. 2018-004925 (PTAB Decision on Appeal, June 11, 2019)]- where a machine learning model's numerical assessment was used to tailor a survey - the amended claim recites that a statistical/machine learning analysis (the Bayesian-derived decision rules) produces a concrete output (the optimal number of digital subjects) that is then used to tailor a later evaluation by generating digital subjects to supplement actual subjects while satisfying a power threshold, thus ensuring that the judicial exception is applied in a manner that meaningfully limits and directs the claim toward a practical result." (Annotation added). Notwithstanding that Kannan is not a precedential opinion in the first place, the abstract idea steps of analyzing commercial interactions/transactions between a customer and a merchant to derive a numerical assessment correlated with the customer's predicted intent when compared to a threshold are integrated into a practical application via the steps calling for tailoring/generating a survey including a question, options to answer the question, and a specific appearance selected from a design library corresponding to the predicted customer intent based on a specific mapping and weighting function. In contrast, the abstract idea steps in the present claims are merely used to determine some "optimal" number of digital subjects and then generate such optimal number of digital subjects as opposed to generating and delivering the customized survey design (or something equivalent) in the manner recited in Kannan.
On page 5 of the remarks of the Amendment, Applicant takes the position that deriving, from the one or more decision rules, an optimal number of digital subjects to supplement actual subjects of the RCT while reaching an ideal/sufficient threshold of power for the RCT and generating, using the set of one or more generative models, the optimal number of digital subjects" provides a technical improvement to the field of RCT design by reducing the number of subjects required for different arms of the RCT, increasing the statistical power of the trial, and/or minimizing the number of actual subjects required for the control group. However, deriving an "optimal" number of digital subjects based on decision rules derived based on point/uncertainty estimates and generating the optimal number of digital subjects for use in configuring an RCT design is all part of the abstract idea and thus cannot provide a technical improvement of itself.
Applicant next asserts that claim 1, like USPTO Example 47's claim 3, does not merely report analytical results to a practitioner but instead uses such results to take concrete, automated actions: deriving, from the one or more decision rules, an optimal number of both new digital subjects and actual subjects to include in the target RCT, while reaching a sufficient power threshold for the target RCT and then generating, using the set of one or more generative models, the optimal number of digital subjects. "Just as Example 47's steps (e)-(f) used the ANN's detection (via a network analysis subprocess ostensibly performable using a basic computer) to automatically perform network security responses (i.e., drop malicious packets and block future traffic), amended claim 1 uses the decision rules to automatedly derive specific trial enrollment parameters and then generate digital subjects that make up the clinical trial." The Examiner disagrees that the present claims are similar to claim 3 of Example 47.
Example 47 explains that claim 3 was patent eligible because it included the "additional limitations" of dropping malicious packets in real-time and blocking future traffic from the source address which provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets as discussed in the background. The Examiner additionally notes that dropping malicious packets in real-time and blocking future traffic from the source address are not practically performable in the human mind and thus are "additional limitations." In contrast, deriving, from the one or more decision rules, an optimal number of both new digital subjects and actual subjects to include in the target RCT, while reaching a sufficient power threshold for the target RCT and then generating the optimal number of digital subjects as asserted by Applicant are practically performable in the human mind with pen and paper as discussed herein.
While generic use of "one or more generative models" to generate the already (mentally) determined "optimal" number of digital subjects is not something performable in the human mind, it just amounts to 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 drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results, are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024). 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. Therefore, claim 3 of Example 47 is not similar to the present claims.
The 35 USC 101 rejection is maintained.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
-In claim 1, line 13, "in is" should be changed to --is in--.
-In claim 11, line 14, "in is" should be changed to --is in--.
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 1-20 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.
As set forth in MPEP 2163(1), an applicant shows possession of the claimed invention by describing the claimed invention with all of its limitations using such descriptive means as words, structures, figures, diagrams, and formulas that fully set forth the claimed invention. Lockwood v. Am. Airlines, Inc., 107 F.3d 1565, 1572, 41 USPQ2d 1961, 1966 (Fed. Cir. 1997).
However, the appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement.” Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002). MPEP 2163.02(V). That is, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. Id.
In the present case, each of independent claims 1 and 11 now how generating the set of one or more trial characteristics comprises:
deriving, from the one or more decision rules, an optimal number of both new digital subjects and actual subjects to include in the target RCT, while reaching a sufficient power threshold for the target RCT; and
generating, using the set of one or more generative models, an additional set of digital subjects with a quantity of the optimal number of digital subjects, where each of the additional set of digital subjects is generated in a specialized format configured to be integrated with the actual subjects.
However, the Examiner cannot identify any portion of the original specification (including [0044], [0055], [0062], and [0081] as mentioned by Applicant on page 1 of the Remarks in the Amendment) supporting the above limitations (e.g., using such descriptive means as words, structures, figures, diagrams, and formulas that fully set forth the claimed invention as set forth in MPEP 2163(1)), such that a person skilled in the art at the time the application was filed would not have recognized that the inventors were in possession of the invention as claimed in view of the disclosure of the application as filed.
Claims 2-10 and 12-20 are rejected based on their dependency from claims 1 or 11.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more:
Subject Matter Eligibility Criteria - Step 1:
Claims 1-10 are directed to a method (i.e., a process) and claims 11-20 are directed to a non-transitory machine-readable medium (i.e., a manufacture). Accordingly, claims 1-20 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 11 includes limitations that recite at least one abstract idea. Specifically, independent claim 11 recites:
A non-transitory machine-readable medium containing instructions for determining a clinical trial configuration, where execution of the instructions by a processor causes the processor to perform a process that comprises:
receiving randomized control trial (RCT) data, wherein the RCT data comprises panel data from subjects of at least one previous RCT;
generating, using a set of one or more generative models, result data comprising predicted panel data for a set of one or more digital subjects in a target RCT,
wherein the set of one or more generative models comprises a neural network trained, at least in part, based on the RCT data;
wherein a digital subject of the set of one or more digital subjects corresponds to a particular subject included in the RCT data; and
wherein the predicted panel data for the digital subject comprises a plurality of predicted outcomes on characteristics of the digital subject in a case where the digital subject is in a control arm of the target RCT;
deriving, in part using a Bayesian analysis of one or more of the plurality of predicted outcomes: a point estimate and an uncertainty estimate for outcomes of the target RCT;
determining one or more decision rules for the target RCT based, at least in part, on the point estimate and the uncertainty estimate; and
using the one or more decision rules to generate a set of one or more trial characteristics, to be used, for implementing the target RCT, wherein generating the set of one or more trial characteristics comprises:
deriving, from the one or more decision rules, an optimal number of both new digital subjects and actual subjects to include in the target RCT, while reaching a sufficient power threshold for the target RCT; and
generating, using the set of one or more generative models, an additional set of digital subjects with a quantity of the optimal number of digital subjects, where each of the additional set of digital subjects is generated in a specialized format configured to be integrated with the actual subjects.
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 (e.g., clinical trial sponsor) could practically in their mind with pen and paper generate predicted outcomes from characteristics of a digital subject (e.g., represented by particular demographic/clinical data, etc.) in a target RCT corresponding to an actual subject from a previous RCT (e.g., where a predicted outcome could be that a particular digital subject's weight and liver function will decrease after being administered various particular dosages of a weight loss pharmaceutical for various time periods based on the digital subject's demographic/clinical characteristics), derive a "point estimate" (e.g., 10lb weight reduction) and an uncertainty estimate (e.g., +/- 2lbs) for outcomes of the target RCT (e.g., particular change in pain scores, certain side effects, certain biomarker levels, etc.) based on an analysis of the predicted outcomes (e.g., decrease in weight and liver function), determine decision rules for the target RCT based on the point and uncertainty estimate (e.g., implement administration of the pharmaceutical in the target RCT similar to that administered to the digital subject when the uncertainty is less than 30% different than the point estimate), and use the decision rules to generate a set of one or more trial characteristics to be used for implementing the target RCT (e.g., lower type-1 and type-II error rates).
Furthermore, a clinical trial sponsor or the like could determine some "optimal" number of new digital subjects and actual subjects to include while reaching a "sufficient" power threshold for the target RCT based on the decision rules. The Examiner notes that there is no limit/details/etc. regarding what constitutes an "optimal" number or a "sufficient" power threshold. In this regard, the clinical trial sponsor could determine/derive/predict based on their experience/the decision rules/etc. that 40 new digital and actual subjects (an "optimal" number) for Phase I of the target RCT would reach a power threshold of 80% (a "sufficient" power threshold). Thereafter, the sponsor could generate an additional set of digital subjects (corresponding to the "optimal" number) in some "specialized" format (e.g., SDTM or the like).
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 found to be "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, using a Bayesian analysis to derive the point and uncertainty estimates amounts to "mathematical concepts" because it includes mathematical calculations and is similar to performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. Investpic, LLC (898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018)). MPEP 2106.04(a)(2)(I)(C).
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 2, 3, 5-8, 10, 12, 13, 15-18, and 20 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claims 2 and 12 call for determining treatment effects for a particular subject of the target RCT including evaluating an individualized response of the particular subject to the treatment which is practically performable in the human mind with pen and paper (e.g., via observations, analyses, etc.).
-Claims 3 and 13 recite how determining the treatment effects for the particular subject of the target RCT includes at least one of: comparing the panel data for the particular subject from the target RCT data with the predicted panel data for the corresponding digital subject; determining responses for the particular subject based on derived probabilities, wherein the derived probabilities are based on the plurality of predicted outcomes; or correcting treatment effects for the particular subject based on a determined bias for a generative model, of the set of one or more generative models, that generated the predicted panel data for the corresponding digital subject. All of these steps are practically performable in the human mind with pen and paper ("mental processes").
-Claims 5 and 15 recite how the historical data is used for determining a prior distribution used in deriving the point estimate and the uncertainty estimate including historical borrowing which is practically performable in the human mind with pen and paper ("mental processes") at such high level of generality.
-Claims 6 and 16 recite how the prior distribution is further applied to computing an expected sample size of the target RCT which just further defines the above abstract idea.
-Claims 7 and 17 recite how each of the optimal number of digital subjects includes trial data that describes observed values of multiple characteristics, for the subjects from the target RCT, at multiple discrete timepoints, which just further defines the above abstract idea.
-Claims 8 and 18 recite how at least one of the set of one or more digital subjects is generated in a form of a digital twin; and generating the result data includes generating a particular digital subject for each actual subject of the target RCT. These limitations just further define the above abstract idea.
-Claims 10 and 20 recite how the Bayesian analysis includes fitting a generalized linear model to at least one of the target RCT data and the plurality of predicted outcomes which just further defines the "mathematical concepts" discussed previously.
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 non-transitory machine-readable medium containing instructions for determining a clinical trial configuration, where execution of the instructions by a processor causes the processor to perform a process that comprises (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
receiving randomized control trial (RCT) data, wherein the RCT data comprises panel data from subjects of at least one previous RCT (extra-solution activity (data gathering) as noted below, see MPEP § 2106.05(g));
generating, using a set of one or more generative models (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)), result data comprising predicted panel data for a set of one or more digital subjects in a target RCT,
wherein the set of one or more generative models comprises a neural network trained, at least in part, based on the RCT data (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));
wherein a digital subject of the set of one or more digital subjects corresponds to a particular subject included in the RCT data; and
wherein the predicted panel data for the digital subject comprises a plurality of predicted outcomes on characteristics of the digital subject in a case where the digital subject is in a control arm of the target RCT;
deriving, in part using a Bayesian analysis of one or more of the plurality of predicted outcomes: a point estimate and an uncertainty estimate for outcomes of the target RCT;
determining one or more decision rules for the target RCT based, at least in part, on the point estimate and the uncertainty estimate; and
using the one or more decision rules to generate a set of one or more trial characteristics, to be used, for implementing the target RCT, wherein generating the set of one or more trial characteristics comprises:
deriving, from the one or more decision rules, an optimal number of both new digital subjects and actual subjects to include in the target RCT, while reaching a sufficient power threshold for the target RCT; and
generating, using the set of one or more generative models (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)), an additional set of digital subjects with a quantity of the optimal number of digital subjects, where each of the additional set of digital subjects is generated in a specialized format configured to be integrated with the actual subjects.
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 non-transitory machine-readable medium containing instructions executable by a 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 limitation of receiving RCT data including panel data from subjects of the RCT, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) 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)).
Regarding the additional limitations of how generation of the result data and additional set of digital subjects generically uses "one or more generative models" that includes a NN (generically) "trained based on the RCT data," 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)). Claims drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results, are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024). 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.
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. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
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 11 and analogous independent claim 1 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 11 and analogous independent claim 1 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 4 and 14 recite how the set of one or more generative models is pre-trained on historical data including at least one of: control arm data from historical control arms, patient registries, electronic health records, or real-world data 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)). Claims drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results, are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024). 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. Again, 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. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
-Claims 5 and 15 call for generically "pre-training" the one or more generative models (one of which is a neural network) which merely recites 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 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. 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. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
-Claims 9 and 19 recite how the set of one or more generative models includes at least one of a Conditional Restricted Boltzmann Machine, a statistical model, a generative adversarial network, a recurrent neural network, a Gaussian process, an autoencoder, an autoregressive model, or a variational autoencoder which does 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)).
-Claims 10 and 20 recite how the Bayesian analysis includes fitting a generalized linear model to at least one of the RCT data or the plurality of predicted outcomes which merely recites 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 11 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 non-transitory machine-readable medium containing instructions executable by a 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 how generation of the result data and additional set of digital subjects generically uses "one or more generative models" that includes a NN (generically) "trained based on the RCT data," 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)). Claims drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results, are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024). 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.
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. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
Regarding the additional limitations directed to receiving RCT data including panel data from subjects of the RCT which the Examiner submits merely adds insignificant extra-solution activity to the abstract idea (see MPEP § 2106.05(g)) as discussed above, the Examiner has reevaluated such limitations and determined such limitations to not be unconventional as they merely consist 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 4 and 14 recite how the set of one or more generative models is pre-trained on historical data including at least one of: control arm data from historical control arms, patient registries, electronic health records, or real-world data 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)). Claims drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results, are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024). 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. Again, 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. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
-Claims 5 and 15 call for generically "pre-training" the one or more generative models (one of which is a neural network) which merely recites 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 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. 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. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
-Claims 9 and 19 recite how the set of one or more generative models includes at least one of a Conditional Restricted Boltzmann Machine, a statistical model, a generative adversarial network, a recurrent neural network, a Gaussian process, an autoencoder, an autoregressive model, or a variational autoencoder which does 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)).
-Claims 10 and 20 recite how the Bayesian analysis includes fitting a generalized linear model to at least one of the RCT data or the plurality of predicted outcomes which merely recites 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 1-20 are ineligible under 35 USC §101.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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