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 21, 22, 24-35, and 37-40 were previously pending and subject to a non-final Office Action having a notification date of November 26, 2025 (“non-final Office Action”). Following the non-final Office Action, Applicant filed an amendment on February 25, 2026 (the “Amendment”), amending claims 21 and 34.
The present Final Office Action addresses pending claims 21, 22, 24-35, and 37-40 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
Starting at page 9 of the Amendment, Applicant generally takes the position that existing systems that provide for pregnancy disorder risk detection are "technically" deficient because they allegedly cannot detect pregnancy disorders in the first trimester due to being based upon limited insights and overly simplifying risk in a binary manner through conventional rules-based algorithms while failing to consider it as a continuous measure with uncertainty.
At pages 11-13 of the Amendment, Applicant then goes through various "technological" solutions described in the present specification such as transforming EHR data into "outcome-relevant sub-pluralities of health parameters of prior patients" that are known to have a pregnancy disorder diagnosis based on satisfying predefined clinical criteria and a first trimester time interval, training two or more ML models to generate a numerical/continuous probability for the pregnancy disorder outcome, shrinking the values for the outcome-relevant sub-pluralities of parameters based on the pregnancy disorder outcome probability (e.g., based on their importance), updating a classification threshold based on a risk probability cutoff, and inputting first trimester data of a new patient into a best-performing one of the ML models to facilitate early detection of pregnancy disorders.
At pages 14-15 of the Amendment, Applicant then asserts that many of the claim limitations (e.g., the receiving, training, updating, and generating steps) reflect the above-noted "technical" solutions/improvements discussed in the specification to overcome the "technical" deficiencies of rules-based algorithms including the inability to detect pregnancy disorders in the first trimester of pregnancy and consider risk as a continuous value with uncertainty.
However, it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP §2106.05(a)(II). For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (“This invention makes the trader faster and more efficient, not the computer. This is not a technical solution to a technical problem”). In the present case, improving the manner in which the pregnancy disorder risk is determined (e.g., in the first trimester of pregnancy, as a continuous value, etc.) is an improvement to the "mental processes" and "certain methods of organizing human activity" discussed herein but does not improve computers or technology.
Almost all of above the "technical improvements" asserted by Applicant (e.g., the health parameter curation process, the health parameter shrinking/ML model reduction process, the best performing ML model selection process, the classification threshold updating process, etc.) are practically mentally performable with pen and paper and relate to certain methods of organizing human activity as noted herein.
Furthermore, the recitations of machine learning in the independent claims ("training two or more machine learning models using the training data," the (practically-mentally-determinable) steps of generating the parameter values and probability and shrinking the parameter values being "via the respective two or more machine learning models," and the (practically-mentally-determinable) step of generating the high risk prediction using the patient care information and updated classification threshold being via inputting the patient care information "to the best-performing reduced machine learning model") are at such a high level of generality as to 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)) as noted repeatedly herein.
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. 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. “[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., p. 12.
With specific respect to Applicant's contention on page 14 that the present claims consider interdependencies of parameters to generate continuous values, the Examiner notes that the present claims do not recite such features. Even if the present claims did recite considering such interdependencies of parameters to generate continuous values, the Examiner asserts that a medical professional familiar with patients that have and have not developed pregnancy disorders along with their first trimester health parameters (e.g., vitals, blood panels, weight, etc.) would be readily able in their mind with pen and paper to consider certain combinations and levels of first trimester health parameters that are typically associated with various different types of pregnancy disorder outcomes (e.g., preeclampsia, gestational diabetes, etc.). In the case where particular health parameters generally do not largely contribute to a probability of a particular pregnancy disorder outcome (e.g., their presence/degree/level do not significantly increase/decrease the probability), the medical professional could shrink/reduce their contributions to zero to simplify the analytical process.
Still further, even if the present claims recited that part of the training/execution of the ML models included considering such interdependencies of parameters to generate continuous values, Applicant specifically notes at the bottom of page 11 to the top of page 12 of the Amendment that the mere use of certain ML models enables consideration of interdependencies between each parameter/feature provided as input to the ML model. In other words, while mere recitation of certain types of ML models (e.g., MLP, LR, etc., not recited in present claims) might necessarily consider such interdependencies of parameters to generate continuous values, such mere recitation would amount to reciting an idea of a solution under MPEP 2106.05(f) rather than any details regarding how such ML models consider such interdependencies of parameters to generate continuous values.
The 35 USC 101 rejection is 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 21, 22, 24-35, and 37-40 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 21, 22, and 24-33 are directed to a method (i.e., a process) and claims 34, 35, and 37-40 are directed to a system (i.e., a machine). Accordingly, claims 21, 22, 24-35, and 37-40 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 34 includes limitations that recite at least one abstract idea. Specifically, independent claim 34 recites:
A system for detecting pregnancy disorders via machine learning models for first trimesters of pregnancies using continuous variable classification threshold satisfaction, comprising:
a computing device comprising at least one processor, configured to:
receive, from one or more databases, input data for training data associated with a pregnancy disorder outcome, wherein the input data includes respective pluralities of health parameters associated with a time interval that satisfy a first trimester of pregnancy time interval, for respective prior patients having (i) experienced the pregnancy disorder outcome and (ii) satisfy a defined clinical criteria for the pregnancy disorder outcome;
select respective outcome-relevant sub-pluralities of the respective pluralities of health parameters, wherein selecting is based upon the respective outcome-relevant sub-pluralities of parameters for the respective prior patients respectively satisfying a threshold availability criterion among the respective pluralities of health parameters received from the one or more database;
allocate the respective outcome-relevant sub-pluralities of parameters for respective prior patients into the training data and testing data;
training two or more machine learning models using the training data, wherein the training comprises:
generating, via the respective two or more machine learning models, (i) parameter values for the outcome-relevant sub-pluralities of parameters and (ii) a probability for the pregnancy disorder outcome based on the parameter values for the outcome-relevant sub-pluralities of parameters;
reducing the respective two or more machine learning models, to have a reduced set of outcome-relevant sub-pluralities of parameters and corresponding parameter values, based on shrinking the parameter values for the outcome-relevant sub-pluralities of parameters respective to the probability for the pregnancy disorder outcome;
evaluate performance of the two or more reduced machine learning models using the testing data to select a best-performing reduced machine learning model based upon statistical comparisons of the performance among the two or more reduced machine learning models for the pregnancy disorder outcome;
update a classification threshold for the selected best-performing machine learning model as a risk probability cutoff for a given patient being high risk for developing the pregnancy disorder outcome based on a sensitivity or specificity threshold associated with the defined clinical criteria; and
subsequent to updating the classification threshold for the best-performing reduced machine learning model, generating, based on providing patient care information of a patient that satisfies the first trimester of pregnancy time interval as input to the best-performing reduced machine learning model, a prediction indicating that the patient is high risk for developing the pregnancy disorder outcome based on the updated classification threshold.
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., OBGYN, clinical expert, etc.) could practically review health parameters (e.g., clinical data such as current/past medications, vitals, pre-existing conditions; lab/screening test results such as blood counts, urinalysis results, ultrasound readings, etc.) from the first trimester for prior patients having experienced a pregnancy disorder outcome (e.g., preeclampsia); select (e.g., based on their experience) a sub-plurality (subset) of the health parameters meeting some threshold level of availability/presence in the reviewed health parameters (e.g., particular number of times mentioned, etc.); allocate (e.g., divide/separate) the respective sub-pluralities into training data and testing data; generate parameter values for the outcome-relevant sub-pluralities of parameters (e.g., determining a likelihood that each of the sub-pluralities of parameters indicates or does not indicate the pregnancy disorder outcome); generate a probability for the pregnancy disorder outcome based on the parameter values for the outcome-relevant sub-pluralities of parameters (e.g., via aggregating/combining the above parameters values to arrive at an "overall" probability/likelihood of the pregnancy disorder outcome occurring); "shrink" the parameter values based on the overall probability for the pregnancy disorder outcome so that the ML models have a reduced set of outcome-relevant sub-pluralities of parameters and corresponding parameter values; evaluate performance of the reduced ML models using the testing data to select a best-performing reduced ML model based upon statistical comparisons of the performance among the two or more reduced ML models (e.g., via comparing outputs of the reduced ML models to respective ground truth and determining which of the reduced ML models has the best performance); update (e.g., increase, decrease) a classification threshold (e.g., risk probability cutoff for a given patient being high risk for developing the pregnancy disorder outcome) using a sensitivity or specificity threshold; and generate a prediction that a patient is high risk for developing the pregnancy disorder outcome based on patient care information collected during the first trimester and the updated classification threshold (e.g., comparing the patient care information to the reduced set of outcome-relevant parameter sub-plurality, determining a probability of the pregnancy disorder outcome based on the comparison, and comparing the probability to the updated threshold).
These recitations, under their broadest reasonable interpretation, are similar to how the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis were 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). 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).
Furthermore, 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). The claim 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).
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 22, 24-30, 32, 35, 37, and 39 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claims 22 and 35 call for determining exclusion conditions for excluding any of the respective pluralities of health parameters based upon a determination of whether any respective prior patients meet the exclusion conditions (e.g., over a certain age, have certain preexisting conditions, etc.) which can be practically performed in the human mind with pen and paper ("mental processes") and relates to managing relations/following rules ("certain methods of organizing human activity").
-Claims 24 and 37 call for transforming respective associated sub-pluralities having a number of parameters from the respective pluralities of health parameters into a respective transformed parameter having a fewer number of parameters and replacing the respective sub-pluralities with the respective transformed parameters which again can be practically performed in the human mind with pen and paper ("mental processes") and relates to managing relations/following rules ("certain methods of organizing human activity"). For instance, a person could easily manipulate the parameters in any appropriate manner to result in a fewer number of parameters and then replace the original number with the fewer number of parameters.
-Claim 25 calls for identifying one or more missing parameters in one or more of the respective pluralities of health parameters which can be practically performed in the human mind with pen and paper ("mental processes") and relates to managing relations/following rules ("certain methods of organizing human activity").
-Claim 26 calls for identifying at least one missing parameter as informative toward the pregnancy disorder outcome which can be practically performed in the human mind with pen and paper ("mental processes") and relates to managing relations/following rules ("certain methods of organizing human activity").
-Claim 27 calls for identifying at least one missing parameter as non-informative and imputing a representative value to replace the missing parameter which again can be practically performed in the human mind with pen and paper ("mental processes") and relates to managing relations/following rules ("certain methods of organizing human activity").
-Claim 28 recites how the imputation is based upon K-nearest neighbors which just further defines the "mental processes" and "certain methods of organizing human activity" of claim 27.
-Claim 29 calls for transforming "complex input data" having multiple values of respective dependent parameters for respective independent parameters into a number of finite parameters which again can be practically performed in the human mind with pen and paper ("mental processes") and relates to managing relations/following rules ("certain methods of organizing human activity").
-Claim 30 calls for balancing the training data by undersampling a majority input parameter or oversampling a minority input parameter in response to determining that the testing data produces an imbalanced distribution of an outcome variable which again can be practically performed in the human mind with pen and paper ("mental processes") and relates to managing relations/following rules ("certain methods of organizing human activity") and also amounts to mathematical calculations ("mathematical concepts") such as a resampled statistical analysis to generate a resampled distribution (SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016 (Fed. Cir. 2018)).
-Claims 32 and 39 recite identifying one or more pregnancy indicators for the patient based upon one or more types of data stored in one or more data records associated with the patient and detecting an instance of a pregnancy in response to determining that the one or more pregnancy indicators satisfy a pregnancy detection threshold which again can be practically performed in the human mind with pen and paper ("mental processes") and relates to managing relations/following rules ("certain methods of organizing human activity").
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 for detecting pregnancy disorders via machine learning 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)) for first trimesters of pregnancies using continuous variable classification threshold satisfaction, comprising:
a computing device comprising at least one processor, configured to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
receive, from one or more databases, input data for training data associated with a pregnancy disorder outcome, wherein the input data includes respective pluralities of health parameters associated with a time interval that satisfy a first trimester of pregnancy time interval, for respective prior patients having (i) experienced the pregnancy disorder outcome and (ii) satisfy a defined clinical criteria for the pregnancy disorder outcome (extra-solution activity (data gathering) as noted below, see MPEP § 2106.05(g));
select respective outcome-relevant sub-pluralities of the respective pluralities of health parameters, wherein selecting is based upon the respective outcome-relevant sub-pluralities of parameters for the respective prior patients respectively satisfying a threshold availability criterion among the respective pluralities of health parameters received from the one or more database;
allocate the respective outcome-relevant sub-pluralities of parameters for respective prior patients into the training data and testing data;
training two or more machine learning models using the training 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 the training comprises:
generating, via the respective two or more machine learning 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)), (i) parameter values for the outcome-relevant sub-pluralities of parameters and (ii) a probability for the pregnancy disorder outcome based on the parameter values for the outcome-relevant sub-pluralities of parameters;
reducing the respective two or more machine learning models, to have a reduced set of outcome-relevant sub-pluralities of parameters and corresponding parameter values, based on shrinking the parameter values for the outcome-relevant sub-pluralities of parameters respective to the probability for the pregnancy disorder outcome;
evaluate performance of the two or more reduced machine learning models using the testing data to select a best-performing reduced machine learning model based upon statistical comparisons of the performance among the two or more reduced machine learning models for the pregnancy disorder outcome;
update a classification threshold for the selected best-performing machine learning model as a risk probability cutoff for a given patient being high risk for developing the pregnancy disorder outcome based on a sensitivity or specificity threshold associated with the defined clinical criteria; and
subsequent to updating the classification threshold for the best-performing reduced machine learning model, generating, based on providing patient care information of a patient that satisfies the first trimester of pregnancy time interval as input to the best-performing reduced 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 prediction indicating that the patient is high risk for developing the pregnancy disorder outcome based on the updated classification threshold.
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 system including a computing device with 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, from one or more databases, input data for training data associated with a pregnancy disorder outcome, wherein the input data includes respective pluralities of health parameters, collected during the first trimester of pregnancy, for respective prior patients having experienced the pregnancy disorder outcome and satisfying a defined clinical criteria for the pregnancy disorder outcome, 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 training two or more ML models using the training data including performing the (practically-mentally-determinable) steps of generating the parameter values and probability and shrinking the parameter values to reduce the ML models as well as performing the (practically-mentally-determinable) step of generating the high risk prediction using the patient care information and updated classification threshold with 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)). These additional limitations provide only a result-oriented solution and lack details as to how the training and execution of the one or more ML models actually occurs.
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. 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. “[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., p. 12.
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 34 and analogous independent claim 21 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 34 and analogous independent claim 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 31 and 38 recite how the best-performing reduced ML model produces a reduced number of output variables associated with statistically related input 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 (see MPEP § 2106.05(f)). This additional limitation provides only a result-oriented solution and lacks details as to how the production of the reduced number of output variables associated with statistically related input parameters actually occurs.
-Claims 33 and 40 call for receiving a selection input indicating a known outcome relevance for an outcome-relevant sub-plurality of a plurality of health parameters from a computing device associated with a clinical expert which 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)).
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 34 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 system including a computing device with 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 training two or more ML models using the training data including performing the (practically-mentally-determinable) steps of generating the parameter values and probability and shrinking the parameter values to reduce the ML models as well as performing the (practically-mentally-determinable) step of generating the high risk prediction using the patient care information and updated classification threshold, 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 the training and execution of the one or more ML models actually occurs.
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. 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. “[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., p. 12.
Regarding the additional limitations directed to receiving, from one or more databases, input data for training data associated with a pregnancy disorder outcome, wherein the input data includes respective pluralities of health parameters, collected during the first trimester of pregnancy, for respective prior patients having experienced the pregnancy disorder outcome and satisfying a defined clinical criteria for the pregnancy disorder outcome 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 31 and 38 recite how the best-performing reduced ML model produces a reduced number of output variables associated with statistically related input 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 (see MPEP § 2106.05(f)). This additional limitation provides only a result-oriented solution and lacks details as to how the production of the reduced number of output variables associated with statistically related input parameters actually occurs.
-Claims 33 and 40 call for receiving a selection input indicating a known outcome relevance for an outcome-relevant sub-plurality of a plurality of health parameters from a computing device associated with a clinical expert which 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)).
Therefore, claims 21, 22, 24-35, and 37-40 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