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, 10, and 18 being independent.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 17/517,251 (“Prior Application”), fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of the present application.
Specifically, independent claims 1, 10, and 18 (and thus the dependent claims by virtue of their dependency) in the present application recite, inter alia, a combination of three different AI models, the second and third that respectively generate CTG and maternal health analysis data and the first that generates labor and delivery predictions applicable to one or more fetuses and a corresponding mother by analyzing the CTG and maternal health analysis data. However, the Prior Application does not disclose or support at least these limitations.
Accordingly, claims 1-20 of the present application are not entitled to the benefit of the Prior Application and thus the effective filing date of claims 1-20 of the present application is April 10, 2024, the actual filing date of the present application.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: "training component" in claim 6, "alert component" in claim 8, and "output component" in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-9 are directed to a system (i.e., a machine), claims 10-17 are directed to a method (i.e., a process), and claims 18-20 are directed to a computer program product including a non-transitory computer-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 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A system, comprising:
a processor that executes computer-executable components stored in memory, wherein the computer-executable components comprise:
a first artificial intelligence (AI) model that generates first data comprising one or more labor and delivery predictions applicable to one or more fetuses and a mother of the one or more fetuses, during labor, by analyzing second data comprising cardiotocography (CTG) analysis data of the one or more fetuses and the mother generated by a second AI model and third data comprising maternal health analysis data of the mother generated by a third AI model, wherein the first AI model is a multistage AI model comprising respective models directed to predicting respective ones of the one or more labor and delivery predictions.
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). For instance, a medical professional (e.g., OBGYN) could practically in their mind with pen and paper analyze "CTG analysis data" such as particular patterns/features in CTG traces and "maternal health analysis data" such as prior pregnancy complications, genetic disorders, etc. to generate labor and delivery predictions applicable to one or more fetuses and a mother of the one or more fetuses, during labor, such as predictions of fetal hypoxia, fetal acidemia, fetal acidosis, labor progression indicating a C-section, cervical dilation progression, a postpartum hemorrhage risk assessment, a preeclampsia prediction, a labor induction recommendation, a sepsis possibility, etc. 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).
The foregoing underlined limitations recite also recite "certain methods of organizing human activity" because they relate to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). These recitations, under their broadest reasonable interpretation, are similar to the concept of 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 2-9, 11-17, 19, 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, 11, and 19 recite how the first data includes one or more types of data selected from a group comprising fetal hypoxia, fetal acidemia, fetal acidosis, labor progression indicating a C-section, cervical dilation progression, a postpartum hemorrhage risk assessment, a preeclampsia prediction, a labor induction recommendation, a sepsis possibility, and one or more additional labor and delivery predictions which just further defines the abstract idea(s) discussed above.
-Claims 3, 12, and 20 recite how the second data is generated by processing fetal heart rate (FHR) data of the one or more fetuses and uterine activity (UA) data of the mother, where the second data comprises one or more types of data selected from a group consisting of an FHR baseline value calculation, an FHR acceleration, an FHR deceleration, a contraction, an FHR variability value calculation, fetal tracing classification, and one or more additional CTG analysis data types, all of which just further defines the abstract idea(s) discussed above.
-Claims 4, 13, and 20 recite how the third data is generated by employing rule-based algorithms to process electronic medical records (EMRs) and health parameters of the mother, where the third data comprises one or more types of data selected from a group consisting of maternal health related risk factors, pregnancy related complications, dystocia, genetic disorders and one or more additional maternal health analysis data types, all of which just further defines the abstract idea(s) discussed above.
-Claims 5 and 14 recites how the second data and the third data are generated during fetal monitoring of the one or more fetuses and maternal monitoring of the mother, where the second data and the third data are available for analysis during the fetal monitoring of the one or more fetuses and the maternal monitoring of the mother, all of which just further defines the abstract idea(s) discussed above.
-Claims 6 and 15 call for identifying patterns in training cardiotocograph data that correspond to defined physiological events associated with respective fetuses and mothers of the fetuses represented in the training cardiotocograph data which is practically performable in the human mind such as by a medical professional visually reviewing and thinking about CTG trace features and/or the like and thus just further defines the abstract idea(s) discussed above.
-Claim 7 recites how at least some of the training cardiotocograph data comprises annotated cardiotocograph data annotated with information identifying the patterns and the defined physiological events that respectively correspond to the patterns which is mentally reviewable by a medical professional and thus just further defines the abstract idea(s) discussed above.
-Claims 8 and 16 call for generating an alert component that generates an alert in response to the first data being indicative of an emergency situation which is practically performable in the human mind with pen and paper and relates to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions) and therefore just further defines the abstract idea(s) discussed above.
-Claims 9 and 17 call for displaying the first data for further analysis of the first data to identify actions to be executed by the entity for safe delivery of the one or more fetuses which again just further defines the abstract idea(s) discussed above.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system, comprising:
a processor that executes computer-executable components stored in memory, wherein the computer-executable components comprise:
a first artificial intelligence (AI) model that generates first data comprising one or more labor and delivery predictions applicable to one or more fetuses and a mother of the one or more fetuses, during labor, by analyzing second data comprising cardiotocography (CTG) analysis data of the one or more fetuses and the mother generated by a second AI model and third data comprising maternal health analysis data of the mother generated by a third AI model, wherein the first AI model is a multistage AI model comprising respective models directed to predicting respective ones of the one or more labor and delivery predictions.
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 processor that executes computer-executable components stored in memory, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of the first, second, and third AI models for respectively generating the first, second, and third data, where the first AI model is a "multistage" AI model having respective models for predicting respective ones of the one or more labor and delivery predictions, 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. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 13. Using existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved does not confer patent-eligibility. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 15.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 1 and analogous independent claims 10 and 18 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 1 and analogous independent claims 10 and 18 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
-Claims 6 and 15 call for training the first AI model to generate the first data by employing the second data and the third data as input data to the first AI model and employing known labor and delivery predictions corresponding to the input data as output data for the first AI model which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). 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. For instance, as the first AI model is already configured to receive the second and third data and output labor and delivery predictions, then specifying that the model is trained based on the input second and third data and known labor and delivery predictions corresponding to the input data (the same type of data configured to be input into and output from the model) does not recite any specific details regarding how the training is accomplished. Claims 6 and 15 also generically recite how the second AI model is trained with a supervised ML process which again does 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.
-Claim 7 recites how the supervised machine learning process comprises employing the annotated cardiotocograph data as ground truth which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[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 16 recite how the alert is generated by an "alert component" or the "device" which amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
-Claims 9 and 17 recite how the first data is displayed at a "device" by an "output component" or the "device" which amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
components stored in memory, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of the first, second, and third AI models for respectively generating the first, second, and third data, where the first AI model is a "multistage" AI model having respective models for predicting respective ones of the one or more labor and delivery predictions, 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. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 13. Using existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved does not confer patent-eligibility. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 15.
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
-Claims 6 and 15 call for training the first AI model to generate the first data by employing the second data and the third data as input data to the first AI model and employing known labor and delivery predictions corresponding to the input data as output data for the first AI model which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). 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. For instance, as the first AI model is already configured to receive the second and third data and output labor and delivery predictions, then specifying that the model is trained based on the input second and third data and known labor and delivery predictions corresponding to the input data (the same type of data configured to be input into and output from the model) does not recite any specific details regarding how the training is accomplished. Claims 6 and 15 also generically recite how the second AI model is trained with a supervised ML process which again does 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.
-Claim 7 recites how the supervised machine learning process comprises employing the annotated cardiotocograph data as ground truth which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[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 16 recite how the alert is generated by an "alert component" or the "device" which amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
-Claims 9 and 17 recite how the first data is displayed at a "device" by an "output component" or the "device" which amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
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, 8-12, 14, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0200746 to Holder et al. ("Holder") in view of U.S. Patent App. Pub. No. 2021/0056413 to Cheung ("Cheung"):
Regarding claim 1, Holder discloses a system (Figure 1), comprising:
a processor (processor 322 in Figure 3) that executes computer-executable components stored in memory ([0145]), wherein the computer-executable components comprise:
a first artificial intelligence (AI) model (AI engine 302 of Figure 1) that generates first data comprising one or more labor and delivery predictions applicable to one or more fetuses and a mother of the one or more fetuses ([0081]-[0082] discloses how AI engine 302 generates predicted outcomes 380 regarding fetal/maternal biometric/physiological parameters such as C-section delivery, preeclampsia, neonatal destination after birth, other neonatal complications, etc. per [0086]-[0108]), during labor (the end of [0035]), by analyzing second data comprising cardiotocography (CTG) analysis data of the one or more fetuses and the mother ([0081] discloses how the AI engine 302 analyzes first features 350 extracted from current first patient data 346 where the current first patient data 346 can include fetal heart rate, uterine contractions, etc. ("CTG analysis data") per [0036], [0077], [0085]; therefore, the features of the first patient data 346 is "second data comprising CTG analysis data") generated by a second … model ([0056] discloses how a signal analysis module/model can extract the first features (the above "CTG analysis data") from the first patient data 346) and third data comprising maternal health analysis data of the mother ([0081] discloses how the AI engine 302 analyzes second features 351 extracted from current second patient data 347, where the second patient data can include e.g., biometric data, blood oxygen level, mental health assessment features, SDoH assessments, etc. per [0004], [0077]; therefore, the features of the second patient data is "third data comprising maternal health analysis data")) generated by a third … model ([0056] discloses how signal analysis modules/models can extract the first and second features (the above "CTG analysis data" and the above "maternal health analysis data") from the first and second patient data; therefore, the signal analysis module that extracts the first features is a "second" model and the signal analysis module that extracts the second features is a "third" model), wherein the first AI model is a multistage AI model comprising respective models directed to predicting respective ones of the one or more labor and delivery predictions (Figure 1, [0086]-[0108], [0111] illustrate/disclose how the AI model makes use of a plurality of different ML/AI models that respectively predict respective ones of the one or more labor/delivery predictions).
However, Holder appears to be silent regarding the signal analysis modules (the "second" and "third" models) that extract the above-noted first and second features specifically being AI models.
Nevertheless, Cheung teaches ([0047]-[0048] and Figure 1) that it was known in the healthcare informatics and ML art to utilize different NNs (different AI models) to extract relevant features from different types of input data for combined input into another NN (another AI model) for generating a prediction of a patient based on the extracted features. This arrangement advantageously makes use of different NN architectures that are specifically tuned to extract relevant features of different data types for generating optimal analyses ([0003]).
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 second and third models of Holder to be second and third AI models configured/tuned to extract different data types (the CTG and maternal health data of Holder) to advantageously generate optimal analyses/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.
Regarding claim 2, the Holder/Cheung combination discloses system of claim 1, further including wherein the first data comprises one or more types of data selected from a group comprising fetal hypoxia, fetal acidemia, fetal acidosis, labor progression indicating a C-section, cervical dilation progression, a postpartum hemorrhage risk assessment, a preeclampsia prediction, a labor induction recommendation, a sepsis possibility, and one or more additional labor and delivery predictions ([0086]-[0108] of Holder discloses C-section delivery, preeclampsia, neonatal destination after birth, other neonatal complications, etc.; also, [0084] discloses fetal acidemia/hypoxia).
Regarding claim 3, the Holder/Cheung combination discloses system of claim 1, further including wherein the second AI model generates the second data by processing fetal heart rate (FHR) data of the one or more fetuses and uterine activity (UA) data of the mother ([0081] of Holder discloses how the first features 350 (the recited "second data") are extracted from first patient data 346 acquired by wearable device 150 while [0036] discloses how the data from the wearable device 150 can include fetal cardiac activity (FHR) and uterine activity (UA)), and wherein the second data comprises one or more types of data selected from a group consisting of an FHR baseline value calculation, an FHR acceleration, an FHR deceleration, a contraction, an FHR variability value calculation, fetal tracing classification, and one or more additional CTG analysis data types ([0056] of Holder discloses how the signal analysis module (the "second model") can extract baseline of the first patient data (which would thus include baseline FHR, a baseline variability, number of accelerations/decelerations, etc.; furthermore, the second model is a second AI model per the above-discussed combination with Cheung).
Regarding claim 5, the Holder/Cheung combination discloses system of claim 1, further including wherein the second data and the third data are generated during fetal monitoring of the one or more fetuses and maternal monitoring of the mother, and wherein the second data and the third data are available for analysis by the first AI model during the fetal monitoring of the one or more fetuses and the maternal monitoring of the mother ([0035], [0042], [0053], [0073]-[0074], [0081], [0111] of Holder disclose obtaining the first and second patient data, extracting the first and second features (the second and third data) and using the AI model (the first AI model) to generate the predicted outcomes based on the first and second features (the second and third data) during a current monitoring session of the fetus and mother).
Regarding claim 8, the Holder/Cheung combination discloses system of claim 1, further including an alert component that generates an alert in response to the first data being indicative of an emergency situation ([0028] of Holder discloses alert triggering and [0116] discloses how a provider module (alert component) generates reports (alert) when a predicted outcome is determined to be an adverse outcome (an emergency situation).
Regarding claim 9, the Holder/Cheung combination discloses system of claim 1, further including an output component that displays the first data at a device accessible to an entity for further analysis of the first data to identify actions to be executed by the entity for safe delivery of the one or more fetuses ([0115]-[0116] of Holder discloses how a provider or patient module (output component) generates and displays reports with the predicted (adverse) outcomes (first data) and suggested actions for the provider or patient to address the predicted adverse outcomes such as intervention suggestion, care plan suggestion, etc.; as the purpose of the disclosure of Holder is to provide a system to support real-time decision-making by a clinical team to decrease overall costs associated with adverse maternal and fetal outcomes per [0028], [0084], then the identified suggested intervention would necessarily be for "safe delivery" of the one or more fetuses).
Claims 10-12, 14, 16, and 17 are rejected in view of the Holder/Cheung combination as respectively discussed above in relation to claims 1-3, 5, 8, and 9.
Claims 18 and 19 are rejected in view of the Holder/Cheung combination as respectively discussed above in relation to claims 1 and 2.
Claims 4, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0200746 to Holder et al. ("Holder") in view of U.S. Patent App. Pub. No. 2021/0056413 to Cheung ("Cheung"), and further in view of U.S. Patent App. Pub. No. 2022/0101987 to Tuysuzoglu et al. ("Tuysuzoglu"):
Regarding claim 4, the Holder/Cheung combination discloses system of claim 1, further including wherein the third AI model generates the third data by employing … algorithms to process electronic medical records (EMRs) and health parameters of the mother (the end of [0004] of Holder discloses how the second patient data can include medical history and/or profile of the patient including current conditions such as gestational diabetes (collectively, EHRs and health parameters of the mother); accordingly, when the "third" model/signal analysis module extracts the "third" data from the second patient data per [0056] of Holder, it extracts/generates such third data from the "EHRs and health parameters of the mother"; furthermore, the third model is a third "AI" model per the combination with Cheung as discussed previously (where AI models necessarily employ algorithms)), and wherein the third data comprises one or more types of data selected from a group consisting of maternal health related risk factors, pregnancy related complications, dystocia, genetic disorders and one or more additional maternal health analysis data types (as the second patient data from which the third data/features are extracted includes e.g., mental health assessment features, SDoH assessments, current conditions such as gestational diabetes, etc. (which amount to of maternal health related risk factors, pregnancy related complications, additional maternal health analysis data types, etc. per [0004], [0077], then the features (third data) of such second patient data necessarily also includes one or more data types selected from a group consisting of maternal health related risk factors, pregnancy related complications, one or more additional maternal health analysis data types, etc.).
However, the Holder/Cheung combination might not specifically discloses the algorithms of the third AI model to be rule-based algorithms.
Nevertheless, Tuysuzoglu teaches ([0069]) that it was known in the healthcare informatics and machine learning art for a rule-based NN to associate a level of risk to medical data (which can include recorded patient data such as temperature, weight, medical history, etc. including textual data per [0043]-[0044]) based on a set of predetermined rules so that a trained NN can associate higher risk to more urgent conditions to ensure that more urgent pieces of medical data are analyzed in a prioritized manner ([0071]).
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 algorithms of the third AI model of the Holder/Cheung combination to be rule-based algorithms similar to as taught by Tuysuzoglu so that a trained ML model (e.g., NN, etc.) can associate higher risk to more urgent conditions to ensure that more urgent pieces of medical data are analyzed in a prioritized manner. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claims 13 and 20 are rejected in view of the Holder/Cheung/Tuysuzoglu combination similar to the rejection of claim 4 above.
Claims 6, 7, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0200746 to Holder et al. ("Holder") in view of U.S. Patent App. Pub. No. 2021/0056413 to Cheung ("Cheung"), and further in view of NPL "Detection of preventable fetal distress during labor from scanned cardiotocogram tracings using deep learning" to Frasch et al. ("Frasch"):
Regarding claim 6, the Holder/Cheung combination discloses system of claim 1, further including a training component that trains the first AI model to generate the first data by employing the second data and the third data as input data to the first AI model and employing known labor and delivery predictions corresponding to the input data as output data for the first AI model ([0080] of Holder discloses how the AI engine applies a training set of the first and second features (the recited second and third data) and associated outcomes obtained for each of a plurality of patients (known labor and delivery predictions corresponding to the input data as output data) to train the one or more ML models to predict maternal/fetal outcomes (a training components that trains the first AI model to generate the first data), …
However, the Holder/Cheung combination appears to be silent regarding the training component training the second AI model by employing a supervised machine learning process to identify patterns in training cardiotocograph data that correspond to defined physiological events associated with respective fetuses and mothers of the fetuses represented in the training cardiotocograph data.
Nevertheless, Frasch teaches (page 10, third paragraph) that it was known in the healthcare informatics and machine learning art to perform supervised training of an AI/ML model using FHR and UC data (training cardiotocograph data associated with fetuses and mothers of the fetuses)) and classifying (page 4 and page 12, second paragraph) patterns in EFM/cardiotocography data as signatures/features/events (e.g., Point A, Point B, etc.) associated with respective fetuses and mothers (top of page 3). Accordingly, as part of using the training data to train the AI/ML model per the third paragraph of page 10, such graphical patterns in cardiotocograph graphical tracings (e.g., Figure 1 on page 3) of the training data corresponding to physiological events associated with hearts of the fetuses and uteruses of the mothers of the fetuses during labor are identified in the training data. This arrangement advantageously identifies critical features in the EFM/CTG data that indicate critical and timely points of either conservative or operative intervention to thereby tailor treatment approaches to specific patient parameters resulting in improved outcomes (page 12).
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 trained the second AI model of the Holder/Cheung combination by employing a supervised machine learning process to identify patterns in training cardiotocograph data that correspond to defined physiological events associated with respective fetuses and mothers of the fetuses represented in the training cardiotocograph data similar to as taught by Frasch to advantageously identify critical features in the EFM/CTG data that indicate critical and timely points of either conservative or operative intervention to thereby tailor treatment approaches to specific patient parameters resulting in improved outcomes. 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 15 is rejected in view of the Holder/Cheung/Frasch combination similar to as discussed in relation to claim 6.
Regarding claim 7, the Holder/Cheung/Frasch combination discloses system of claim 6, further including wherein at least some of the training cardiotocograph data comprises annotated cardiotocograph data annotated with information identifying the patterns and the defined physiological events that respectively correspond to the patterns (page 10, second, third, and fifth paragraphs of Frasch discuss how the FHR/UC data (the training cardiotocograph data) can have markers/labels/annotations assigned to features (events) for use in supervised training of the ML model; the particular cardiotocograph data making up each particular event are graphical patterns), and wherein the supervised machine learning process comprises employing the annotated cardiotocograph data as ground truth (as the above-noted markers/labels of the patterns are used in supervised training of the ML model, it is employed as "ground truth" because that is how supervised training performs; similar to as discussed above, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have trained the second AI model of the Holder/Cheung combination by employing a supervised machine learning process to identify patterns in annotated training cardiotocograph data that correspond to defined physiological events associated with respective fetuses and mothers of the fetuses represented in the training cardiotocograph data and using the annotated data as ground truth similar to as taught by Frasch to advantageously identify critical features in the EFM/CTG data that indicate critical and timely points of either conservative or operative intervention to thereby tailor treatment approaches to specific patient parameters resulting in improved outcomes. 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.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686