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 are pending in the present application with claims 1 and 11 being independent.
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 5 and 15 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.
Each of these claims calls for a "survey" user interface from which user configuration inputs are received by which the subset of health parameters are selected. While [0061] of the present application (and parent app. no. 18/389,192) disclose a user interface for stipulating such configuration inputs, neither this paragraph nor any other paragraph appears to disclose a "survey" user interface.
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), claims 11-20 are directed to a system (i.e., a machine). 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 system for predicting a pregnancy disorder outcome in a patient comprising:
a computer comprising at least one processor, configured to:
receive input data including a plurality of health parameters for a plurality of prior patients having experienced at least one pregnancy disorder outcome;
for a pregnancy disorder outcome of the at least one pregnancy disorder outcome, execute a machine-learning model on the input data to train the machine-learning model to generate an outcome risk for the pregnancy disorder outcome based upon a subset of one or more health parameters corresponding to the pregnancy disorder outcome, thereby resulting in a trained machine-learning model;
obtain a plurality of health data records for a patient containing the plurality of health parameters for the patient having timestamps within a first trimester of pregnancy and including the subset of one or more health parameters corresponding to the pregnancy disorder outcome; and
generate the outcome risk by executing the trained machine-learning model using the subset of one or more health parameters of the patient, the outcome risk indicating a probability for the patient developing the pregnancy disorder outcome.
The Examiner submits that the foregoing underlined limitations recite “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, other clinical experts, 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.) for prior patients having experienced a pregnancy disorder outcome (e.g., preeclampsia); determine (e.g., based on their experience, guidelines, etc.) a subset of the health parameters that "correspond" to (i.e., are highly correlated to) the pregnancy disorder outcome; obtain/review health data records for a current patient containing the plurality of health parameters having timestamps within a first trimester of pregnancy and including the subset of health parameters corresponding to the pregnancy disorder outcome; and generate an outcome risk indicating a probability for the patient developing the pregnancy disorder outcome using the subset of one or more health parameters of the patient. For instance, in the case where the subset of health parameters of the current patient are "close" to (e.g., using any appropriate distance measuring algorithm) those of the prior patients having experienced the pregnancy disorder outcome, the medical professional could generate a "high" risk of the current patient having the pregnancy disorder outcome now or in the future.
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).
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).
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 2, 3, 5-10, 12, 13, and 15-20 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 recite how receiving the input data includes determining one or more exclusion conditions for excluding at least one health parameter of the plurality of health parameters in response to determining that the at least one health parameter satisfies a corresponding exclusion condition which is practically performable in the human mind with pen and paper ("mental processes").
-Claims 3 and 13 call for selecting the subset of one or more health parameters corresponding to the pregnancy disorder outcome which is practically performable in the human mind with pen and paper ("mental processes").
-Claims 5 and 15 call for selecting the subset of one or more health parameters corresponding to the pregnancy disorder outcome according to one or more user configuration inputs received via a survey user interface from a client computing device which is practically performable in the human mind with pen and paper ("mental processes").
-Claims 6 and 16 call for iteratively updating the one or more heath parameters for the patient based upon the outcome risk as determined for the patient which is practically performable in the human mind with pen and paper ("mental processes").
-Claims 7 and 17 call for obtaining the health records for the patient from an EHR and recite how the timestamp of each health data record is within a time interval threshold relative to the first trimester of pregnancy, all of which just further defines the "mental processes" discussed above.
-Claims 8 and 18 call for generating the outcome risk as a numerical or semantic classification value which just further defines the "mental processes" discussed above.
-Claims 9 and 19 call for updating a classification threshold based upon the outcome risk generated for the patient which is practically performable in the human mind with pen and paper ("mental processes").
-Claims 10 and 20 call for identifying the pregnancy for the patient based upon the plurality of the health parameters for the patient which is practically performable in the human mind with pen and paper ("mental processes").
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 predicting a pregnancy disorder outcome in a patient comprising:
a computer comprising at least one processor, configured to:
receive input data including a plurality of health parameters for a plurality of prior patients having experienced at least one pregnancy disorder outcome;
for a pregnancy disorder outcome of the at least one pregnancy disorder outcome, execute a machine-learning model on the input data to train the machine-learning model to generate an outcome risk for the pregnancy disorder outcome based upon a subset of one or more health parameters corresponding to the pregnancy disorder outcome, thereby resulting in a trained machine-learning model;
obtain a plurality of health data records for a patient containing the plurality of health parameters for the patient having timestamps within a first trimester of pregnancy and including the subset of one or more health parameters corresponding to the pregnancy disorder outcome; and
generate the outcome risk by executing the trained machine-learning model using the subset of one or more health parameters of the patient, the outcome risk indicating a probability for the patient developing the pregnancy disorder outcome.
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 computer 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 executing an ML model to train the model to generate an outcome risk for the pregnancy disorder outcome based upon a subset of one or more health parameters corresponding to one of the pregnancy disorder outcomes and then executing the trained ML model to generate the (mentally-determinable) outcome risk, 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 actually occurs. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. 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 ML model is trained to perform the (mentally-determinable) selection of the health parameter subset 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)). These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs in a manner that provides a technological improvement to the ML process itself. 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 8 and 18 recite how the numerical/semantic classification value is "for a classification layer of the machine-learning 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)). These additional limitations provide only a result-oriented solution and lack details as to how the classification layer is actually implemented in a manner that provides a technological improvement to the ML process itself.
-Claims 9 and 19 recite how the classification threshold is "for a classification layer of the machine-learning 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)). These additional limitations provide only a result-oriented solution and lack details as to how the classification layer is actually implemented in a manner that provides a technological improvement to the ML process itself.
-Claims 10 and 20 recite how the identifying of the pregnancy is performed by "executing a pregnancy detection machine-learning model trained to detect the pregnancy" 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)). These additional limitations provide only a result-oriented solution and lack details as to how the pregnancy ML model is actually trained/executed in a manner that provides a technological improvement to the ML process itself.
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 system including a computer 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 executing an ML model to train the model to generate an outcome risk for the pregnancy disorder outcome based upon a subset of one or more health parameters corresponding to one of the pregnancy disorder outcomes and then executing the trained ML model to generate the (mentally-determinable) outcome risk, 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 actually occurs. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. 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.
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 reasons the same as 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 ML model is trained to perform the (mentally-determinable) selection of the health parameter subset 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)). These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs in a manner that provides a technological improvement to the ML process itself. 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 8 and 18 recite how the numerical/semantic classification value is "for a classification layer of the machine-learning 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)). These additional limitations provide only a result-oriented solution and lack details as to how the classification layer is actually implemented in a manner that provides a technological improvement to the ML process itself.
-Claims 9 and 19 recite how the classification threshold is "for a classification layer of the machine-learning 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)). These additional limitations provide only a result-oriented solution and lack details as to how the classification layer is actually implemented in a manner that provides a technological improvement to the ML process itself.
-Claims 10 and 20 recite how the identifying of the pregnancy is performed by "executing a pregnancy detection machine-learning model trained to detect the pregnancy" 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)). These additional limitations provide only a result-oriented solution and lack details as to how the pregnancy ML model is actually trained/executed in a manner that provides a technological improvement to the ML process itself.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-5, 7, 8, 11, 13-15, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2022/0175324 to Holder et al. ("Holder") in view of U.S. Patent App. Pub. No. 2024/0339220 to McElrath et al. ("McElrath"):
Regarding claim 1, Holder discloses a computer-implemented method for predicting a pregnancy disorder outcome in a patient ([0028], [0084], [0094], [0095] disclose predicting various types of pregnancy disorders), comprising:
receiving, by a computer, input data including a plurality of health parameters for a plurality of prior patients having experienced at least one pregnancy disorder outcome ([0037], [0065] and Figures 3 and 7 disclose/illustrate how fetal monitoring system 300 (computer) receives training data from historical data database 316 that includes various health parameters (input data) along with matched fetal/maternal outcomes (which can be pregnancy disorders per [0028], [0084], [0094], [0095]) for historical plurality of patients (health parameters for respective prior patients having experienced the pregnancy disorder outcome); also see [0072]);
for a pregnancy disorder outcome of the at least one pregnancy disorder outcome, executing, by the computer, a machine-learning model on the input data to train the machine-learning model to generate an outcome risk for the pregnancy disorder outcome based upon a subset of one or more health parameters corresponding to the pregnancy disorder outcome, thereby resulting in a trained machine-learning model ([0065]-[0069], [0072], [0101] disclose training an AI engine (one or more ML models) using the training data to predict the one or more pregnancy disorder outcomes such as preterm labor risk, preeclampsia risk, etc., while [0066]-[0068] disclose how the training utilizes a set of features (subset of health parameters) extracted from the historical patient data that correspond to one or more maternal/fetal outcomes);
obtaining, by a computer, a plurality of health data records for a patient containing the plurality of health parameters for the patient ([0070] discloses acquiring patient data for a particular patient which includes the same health parameters as the training data per [0099]) … and including the subset of one or more health parameters corresponding to the pregnancy disorder outcome ([0070] discloses extracting the features (i.e., the "subset" of the health parameters) used to train the model); and
generating, by the computer, the outcome risk by executing the trained machine-learning model using the subset of one or more health parameters of the patient ([0070] discloses processing the features/subset with the ML model to generate the one or more predicted outcomes), the outcome risk indicating a probability for the patient developing the pregnancy disorder outcome (the risk can be a probability of the adverse predicted outcomes occurring per [0071]).
However, Holder appears to be silent regarding the health data records containing the health parameters for the patient having timestamps within a first trimester of pregnancy.
Nevertheless, McElrath teaches ([0022]-[0024], [0100]-[0101]) that it was known in the healthcare informatics art to execute a model (ML model per [0081]) on first trimester diagnostic features of a patient (where such first trimester diagnostic features would necessarily be associated with timestamps indicating their collection during the first trimester) to infer an adverse gestational outcome which advantageously allows customized treatment tracks to be implemented for individual patients and predicts risk levels with improved accuracy over existing technology ([0033]-[0034]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the health data records containing the health parameters for the patient to have timestamps within a first trimester of pregnancy in the system of Holder as taught by McElrath to advantageously allows customized treatment tracks to be implemented for individual patients and predicts risk levels with improved accuracy over existing technology. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 3, the Holder/McElrath combination discloses the method according to claim 1, further including selecting, by the computer, the subset of one or more health parameters corresponding to the pregnancy disorder outcome ([0068] and [0108] of Holder disclose selecting features of the historical patient data (the subset of health parameters) that correspond to one or more pregnancy disorder outcomes)).
Regarding claim 4, the Holder/McElrath combination discloses the method according to claim 3, further including wherein the machine-learning model is trained to select the subset of one or more health parameters for the pregnancy disorder outcome ([0068], [0070], [0102] of Holder disclose how a feature extractor of the AI engine can extract the features, where the feature extractor would necessarily need to be trained in order to extract the appropriate features for prediction of the particular outcome(s)).
Regarding claim 5, the Holder/McElrath combination discloses the method according to claim 3, further including wherein the computer selects the subset of one or more health parameters corresponding to the pregnancy disorder outcome according to one or more user configuration inputs received via a survey user interface from a client computing device ([0068] of Holder discloses manually selecting the features (i.e., the subset of health parameters) by a subject matter expert which would necessarily be via a UI, such as the UI of [0040] and/or [0047], whereby the entered information amounts to "user configuration inputs" corresponding to the subset of health parameters to be selected for the ML model; furthermore, the UI is a "survey" UI because it is soliciting information from the expert).
Regarding claim 7, the Holder/McElrath combination discloses the method according to claim 1, further including wherein the computer obtains the plurality of health records for the patient from one or more databases, including an electronic health record (EHR) stored in an EHR database ([0038] of Holder discloses how the system can obtain patient data from an EHR for use in predicting maternal/fetal outcomes), and wherein the timestamp of each health data record of the plurality of health data records of the patient is within a time interval threshold relative to the first trimester of pregnancy (McElrath teaches ([0022]-[0024], [0100]-[0101]) that it was known in the healthcare informatics art to execute a model (ML model per [0081]) on first trimester diagnostic features of a patient (where such first trimester diagnostic features would necessarily be associated with timestamps indicating their collection within a time interval threshold relative to the first trimester of pregnancy) to infer an adverse gestational outcome which advantageously allows customized treatment tracks to be implemented for individual patients and predicts risk levels with improved accuracy over existing technology ([0033]-[0034]); accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the timestamp of each health data record of the plurality of health data records of the patient to be within a time interval threshold relative to the first trimester of pregnancy in the system of Holder as taught by McElrath to advantageously allows customized treatment tracks to be implemented for individual patients and predicts risk levels with improved accuracy over existing technology. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Regarding claim 8, the Holder/McElrath combination discloses the method according to claim 1, further including wherein the computer generates the outcome risk as a numerical or semantic classification value ([0071] of Holder discloses how the predicted outcome can be a predicted value/probability which would necessarily be a numerical and/or semantic classification value) for a classifier layer of the machine-learning model ([0102] of Holder discloses how the ML model can be an NN which necessarily has a "classifier" layer that outputs the prediction).
Claims 11, 13-15, 17, and 18 are rejected in view of the Holder/McElrath combination as respectively discussed above in relation to claims 1, 3-5, 7, and 8.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2022/0175324 to Holder et al. ("Holder") in view of U.S. Patent App. Pub. No. 2024/0339220 to McElrath et al. ("McElrath"), and further in view of Int'l Pub. No. WO 2023/192224 to Bellesia et al. ("Bellesia"):
Regarding claim 2, the Holder/McElrath combination discloses the method according to claim 1, but appears to be silent regarding wherein receiving the input data includes determining, by the computer, one or more exclusion conditions for excluding at least one health parameter of the plurality of health parameters in response to determining that the at least one health parameter satisfies a corresponding exclusion condition.
Nevertheless, Bellesia teaches ([0005]-[0007], [0058]) that it was known in the healthcare informatics and machine learning art to develop an ML model for predicting preeclampsia using a training cohort of patient data whereby health parameters of the cohort are excluded if the data indicates a fetal chromosomal or major structural abnormality, termination of pregnancy, missing data on pregnancy outcome, etc. (determining that at least one health parameter satisfies an exclusion condition) which would advantageously avoid unnecessary skewing of predictions from the ML model.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the receiving the input data of the Holder/McElrath combination to include determining, by the computer, one or more exclusion conditions for excluding at least one health parameter of the plurality of health parameters in response to determining that the at least one health parameter satisfies a corresponding exclusion condition as taught by Bellesia to advantageously avoid unnecessary skewing of predictions from the ML model. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim 12 is rejected in view of the Holder/McElrath/Bellesia combination as discussed above in relation to claim 2.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2022/0175324 to Holder et al. ("Holder") in view of U.S. Patent App. Pub. No. 2024/0339220 to McElrath et al. ("McElrath"), and further in view of U.S. Patent App. Pub. No. 2024/0090845 to Ohayon ("Ohayon"):
Regarding claim 6, the Holder/McElrath combination discloses the method according to claim 1, but appears to be silent regarding iteratively updating, by the computer, the one or more heath parameters for the patient based upon the outcome risk as determined for the patient.
Nevertheless, Ohayon ([0036]) teaches that it was known in the healthcare informatics and machine learning art to feed output results of an ML model as input back into the ML model until successive results are within a particular tolerance (which results in iteratively updating health parameters for a patient based upon a prediction from the ML model). This arrangement advantageously results in more accurate results by taking into account previous predictions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have iteratively updated, by the computer, the one or more heath parameters for the patient based upon the outcome risk as determined for the patient in the system of the Holder/McElrath combination as taught by Ohayon to advantageously result in more accurate results by taking into account previous predictions. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim 16 is rejected in view of the Holder/McElrath/Ohayon combination as discussed above in relation to claim 6.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2022/0175324 to Holder et al. ("Holder") in view of U.S. Patent App. Pub. No. 2024/0339220 to McElrath et al. ("McElrath"), and further in view of U.S. Patent No. 11,373,640 to Chen et al. ("Chen"):
Regarding claim 9, the Holder/McElrath combination discloses the method according to claim 1, further including (per [0108] of Holder) certain features of the fetal heart rate satisfying respective threshold(s) may be correlated to an increase in fetal pH blood levels and thus processed by the associated ML model to predict an increase in fetal pH blood levels (classification threshold for a given patient developing the pregnancy; e.g., if the fetal heart rate satisfies the threshold/cutoff, then the patient is likely to develop the increased fetal pH blood levels).
However, Holder might be silent regarding specifically regarding wherein the computer updates a classification threshold for a classifier layer of the machine-learning model based upon the outcome risk generated for the patient.
Nevertheless, Chen teaches (7:62-63 and 8:22-55) that it was known in the machine
learning art to adjust/update threshold probability scores of ML models based on feedback received on generated outputs from the model so that subsequent outputs from the model are more correlated with desired outputs.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the computer of the Holder/McElrath combination to update a classification threshold for a classifier layer of the machine-learning model ([0102] of Holder discloses how the ML model can be an NN which necessarily has a "classifier" layer that outputs the prediction) based on outputs generated by the model (which is the "outcome risk" in the case of Holder) as taught by Chen so that subsequent outputs from the model are more correlated with desired outputs thereby resulting in more accurate predictions. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim 19 is rejected in view of the Holder/McElrath/Chen combination as discussed above in relation to claim 9.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2022/0175324 to Holder et al. ("Holder") in view of U.S. Patent App. Pub. No. 2024/0339220 to McElrath et al. ("McElrath"), and further in view of U.S. Patent App. Pub. No. 2022/0142477 to Liang et al. ("Liang"):
Regarding claim 10, the Holder/McElrath combination discloses the method according to claim 1, but appears to be silent regarding identifying, by the computer, the pregnancy for the patient by executing a pregnancy detection machine-learning model trained to detect the pregnancy based upon the plurality of the health parameters for the patient.
Nevertheless, Liang teaches ([0005]-[0006]) that it was known in the healthcare informatics and machine learning art to utilize a trained computational/ML model to determine a time to delivery/gestational age (detect the pregnancy) based on measurements of metabolites of a pregnant individual (health parameters of a patient) to advantageously provide an improved method of estimating gestational age thereby allowing for improved obstetric care ([0003]-[0004]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have identified, by the computer, the pregnancy for the patient by executing a pregnancy detection machine-learning model trained to detect the pregnancy based upon the plurality of the health parameters for the patient in the system of the Holder/McElrath combination as taught by Liang to advantageously provide an improved method of estimating gestational age thereby allowing for improved obstetric care. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim 20 is rejected in view of the Holder/McElrath/Liang combination as discussed above in relation to claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The references cited on the attached PTO-892 disclose ML systems for analyzing various types of pregnant patient data and predicting various types of adverse pregnancy outcomes, gestational ages, and the like.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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