DETAILED ACTION
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 1-12, 14-22 and 25-33 have been amended.
Claims 1-22 and 25-33 as presented September 30, 2025 are currently pending and considered below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 30, 2025 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-22 and 25-33 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 claims contain 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. This is a new matter rejection.
As to claim 1, 10 and 18, the claims recite “the individual is 24 or fewer weeks into a current pregnancy”. This claim language defines a range that explicitly includes week 24 of pregnancy. A review of the specification reveals “conventional screening for GDM occurs at about 24-28 weeks of pregnancy”, and that “it is desirable” to identify the risk of GDM “early on in pregnancy, e.g., earlier than 24 weeks.” ([0012]). The system can “determine or assess if an individual is at risk of developing GDM earlier than is performed in conventional systems and processes, e.g., before 24 weeks of pregnancy” ([0013]). Furthermore, paras. [0015], [0017], [0058] and [0061] consistently describe the invention as receiving medical information where the individual is “less than 24 weeks” into a pregnancy. By reciting “24 or fewer weeks” and including week 24, the claims broaden the scope of the invention to overlap into the “conventional screening” window, which is not supported by the specification. Therefore, the claimed range of “24 or fewer weeks” constitutes new matter unsupported by the original disclosure. For the purposes of compact prosecution, the claim will be interpreted in a manner as best understood by the Examiner and consistent with Applicant’s specification, wherein the individual is less than 24 weeks into pregnancy, which is consistent with at least [0012], [0013], [0015], [0017], [0058] and [0061] of Applicant’s originally filed specification.
As to claim 33, the claim recites the step of determining that the individual requires preventative treatment for GDM and based on this determination, performing a response action comprising “initiating administration of an oral glucose tolerance test”. A review of the specification reveals “preventative GDM treatment workflow can include…blood glucose tests at an increased frequency” [0054]. The specification further indicates that while on the “preventative GDM treatment workflow”, or even if on the “non-GDM risk workflow”, the individual may undergo a “blood glucose test at 24-28 weeks” ([0055], [0052], Fig. 3). If the blood glucose level at 24-28 weeks is “uncontrolled”, then an “oral glucose tolerance test” may be performed ([0055], [0053], Fig. 3). Thus, the specification describes the administering of blood glucose tests while on preventative treatment for GDM, and the oral glucose tolerance test is strictly conditioned upon the finding of an uncontrolled blood glucose level at 24-28 weeks. The specification fails to describe a response action of “initiating administration of an oral glucose tolerance test” for an individual requiring preventative treatment for GDM. For the purposes of compact prosecution, claim 33 will be interpreted in a manner as best understood by the Examiner and consistent with Applicant’s specification, wherein initiating administration of an oral glucose tolerance test to the individual that has an uncontrolled blood glucose level at 24-28 weeks.
Claims 2-9, 21, 22 and 25-30 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), second paragraph due to their dependence on claim 1.
Claims 11-17, 32 and 33 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), second paragraph due to their dependence on claim 10.
Claims 19, 20 and 31 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), second paragraph due to their dependence on claim 18.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention
Claims 1-22 and 25-33 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 10 and 18 recite “determining…that the individual requires treatment for GDM”. A review of the specification reveals that an individual may be assessed before 24 weeks of pregnancy on the risk of developing GDM using machine learning models in response to receiving medical information associated with the individual. If it is determined at less than 24 weeks of pregnancy that the individual is at risk for developing GDM, a “preventative treatment plan” may pursued (e.g., see [0051], [0017], [0054] and Fig. 3). In addition, at 24-28 weeks, the individual who is on a preventative treatment workflow may undergo a glucose test. If this test reflects an uncontrolled blood glucose level at 24-28 weeks, the individual may undergo a conventional oral glucose tolerance test (OGTT). An individual with a normal OGTT may be assigned a continued preventative GDM workflow. However, a “treatment plan” may be assigned to an individual that has an abnormal OGTT result at 24-28 weeks of pregnancy (e.g., see [0054]-[0056] and Fig. 3).
Claims 1, 10 and 18 require the particular instance of medical information associated with the individual that is applied to the machine learning model to determine the need for treatment be received when the individual is 24 weeks or fewer into a pregnancy. This is contrary to the disclosure that explicitly describes the medical information received before 24 weeks being used to determine the need for preventative treatment. Therefore, it is unclear from the claims and the specification, if the individual is 24 weeks or fewer into pregnancy and requires preventative treatment for GDM, or if the individual is 24-28 weeks into pregnancy and requires treatment for GDM. For the purposes of compact prosecution, the claim will be interpreted in a manner as best understood by the Examiner and consistent with Applicant’s specification, wherein the individual is less than 24 weeks into pregnancy and requires preventative treatment for GDM, which is consistent with at least [0012], [0017], [0051], [0054]-[0056] and Fig. 3 of Applicant’s originally filed specification.
Claims 4-8, 16, 17, 20 and 33 recite “preventative treatment” for GDM. However, “preventative treatment” in these claims are inconsistent with claims 1, 10 and 18, which recite “that the individual requires treatment for GDM”. The specification explicitly distinguishes “treatment” from “preventative treatment” as two separate clinical concepts/workflows (“it is desirable to begin treatment for GDM, or preventative treatment for a GDM risk” [0012]). The “preventative treatment” plan or workflow includes measures such as “diet counseling” and “exercise” for those at risk for GDM ([0054]). In contrast, the “treatment plan” is assigned to the individual only after a diagnosis of GDM is confirmed from an abnormal oral glucose tolerance test and includes “counseling on various medications including insulin” ([0056]). Because the specification defines “preventative treatment” as a distinct clinical path for preventing disease in high-risk patients ([0054]) and “treatment” as a distinct clinical path for managing active disease ([0056]), it is unclear if “treatment” in the independent claims encompass “preventative treatment”. A patient cannot simultaneously require treatment for an active disease and a preventative treatment to stop that same disease from occurring. Thus, the dependent claims 4-8, 16, 17, 20 and 33 are rendered indefinite due to inconsistent terminology regarding the scope of “treatment” when compared to the independent claims.
Claim 26 recites the “one or more elements correspond to items selected from a group comprising a Random Forest model, a logistic regression machine learning method, and a neural network”. However, this description of a “elements” in claim 26 is inconsistent with claims 1, 10 and 18, which recite the machine learning model is trained based on “one or more elements indicating whether gestational diabetes mellitus (GDM) treatment is needed based on the instances”. It is unclear how “elements” can simultaneously be a decision outcome indicating whether GDM treatment is needed and also correspond to a type of a machine learning model (e.g. a Random Forest Model). For the purposes of compact prosecution, claim 26 will be interpreted in a manner as best understood by the Examiner, wherein the machine learning electronic model corresponds to items selected from a group comprising a Random Forest model, a logistic regression machine learning method, and a neural network, which is consistent with at least [0037] of Applicant’s originally filed specification.
Claims 2-9, 21, 22 and 25-30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph due to their dependence on claim 1.
Claims 11-17, 32 and 33 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph due to their dependence on claim 10.
Claims 19, 20 and 31 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph due to their dependence on claim 18.
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 1-22 and 25-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-9, 21, 22 and 25-30 recite a system for determining treatment for an individual with GDM, which is within the statutory category of a machine. Claims 10-17, 32 and 33 recite non-transitory media having instructions that when executed by the one or more processors, cause the one or more processors to determining treatment for an individual with GDM, which is within the statutory category of an article of manufacture. Claims 18-20 and 31 recite a method for initiating preventative treatment for an individual with GDM, which is within the statutory category of a process.
Step 2A - Prong One:
Regarding Prong One of Step 2A, 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. An "abstract idea" judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Representative independent claim 1 includes limitations that recite at least one abstract idea.
Specifically, independent claim 1 recites: A system having one or more processors configured to facilitate a plurality of operations, the operations comprising:
receiving via the one or more processors a particular instance of medical information associated with glucose monitoring for an individual, wherein the individual is 24 or fewer weeks into a current pregnancy;
in response to receiving the particular instance of medical information, applying a machine learning electronic model to data associated with the particular instance of medical information, wherein:
(a) the machine learning electronic model is trained based on (a) data associated with instances of the medical information and (b) one or more elements indicating whether gestational diabetes mellitus (GDM) treatment is needed based on the instances, and
(b) the instances correspond to medical information for prior pregnancies;
after the applying of the machine learning electronic model to the data associated with the particular instance of medical information, determining via the one or more processors and based on the applying of the machine learning electronic model to the data associated with the particular instance of medical information that the individual requires treatment for GDM; and
initiating one or more electronic data transmissions, based on the applying of the machine learning electronic model to the data associated with the particular instance of medical information, wherein the one or more electronic data transmissions are sent to a destination selected from a group comprising a medical-information electronic health record system and an electronic memory associated with a distributed microprocessor-based computing network, one or more response actions are performed based at least on the determining and in response to the initiating, and the one or more response actions comprise increasing a frequency of the glucose monitoring administered to the individual.
The underlined limitations constitute concepts performed in the human mind and mathematical concepts. That is, other than reciting steps as performed by the generic computer components, nothing in the claim elements precludes the steps from practically being performed in the mind. The claim encompasses a mental process of receiving a particular instance of medical information and performing one or more response actions comprising increasing the frequency of glucose monitoring. The identified abstract idea, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind except for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind except for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Additionally, the claim encompasses an abstract idea that falls under the mathematical concepts grouping because applying a model to the data, training the model and determining based on applying the model that the individual requires treatment for GDM under its broadest reasonable interpretation, represents mathematical calculations (see MPEP 2106.04(a)(2)). The abstract idea for Claims 10 and 18 are identical as the abstract idea for Claim 1, because the only difference between Claim 1 and 10 is that Claim 1 recites a system, whereas Claim 10 recites one or more non-transitory media, and because the only difference between Claims 1 and 18 is that Claim 1 recites a system, whereas Claim 18 recites a method. Any limitations not identified above as part of the limitation in the mind or mathematical concepts, are deemed “additional elements” and will be discussed further in detail below. Accordingly, claims 1, 10 and 18 recite at least one abstract idea.
Similarly, dependent claims 2-3, 6-8, 13-17, 19, 22, 32 and 33 further narrow the abstract idea described in the independent claims. Claims 2, 3 and 19 describe the medical information. Claims 5 and 10 further describe the individual. Claims 6-8, 16 and 17 further describe the workflow. Claims 11-15 describe the medical encounter. Claim 21 describes updating the model. Claims 22 further describes the training. Claim 32 describe assessing the individual for GMD at a time of the medical encounter. Claim 33 describes the medical information and response actions. Claims 6, 11, 12, 16 and 33 partially narrow the abstract idea as described above, and also introduce additional element(s) which will be discussed in Step 2A Prong 2 and Step 2B. These limitations only serve to further limit the abstract idea and hence, are directed toward fundamentally the same abstract ideas as independent claims 1, 10 and 18, even when considered individually and as an ordered combination.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. 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 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."
In the present case, claims 1-22 and 25-33 as a whole do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. The additional elements or combination of additional elements, beyond the above-noted at least one abstract idea will be described as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the “abstract idea(s)”).
Specifically, independent claim 1 recites: A system having one or more processors configured to facilitate a plurality of operations, the operations comprising:
receiving via the one or more processors a particular instance of medical information associated with glucose monitoring for an individual, wherein the individual is 24 or fewer weeks into a current pregnancy;
in response to receiving the particular instance of medical information, applying a machine learning electronic model to data associated with the particular instance of medical information, wherein:
(a) the machine learning electronic model is trained based on (a) data associated with instances of the medical information and (b) one or more elements indicating whether gestational diabetes mellitus (GDM) treatment is needed based on the instances, and
(b) the instances correspond to medical information for prior pregnancies;
after the applying of the machine learning electronic model to the data associated with the particular instance of medical information, determining via the one or more processors and based on the applying of the machine learning electronic model to the data associated with the particular instance of medical information that the individual requires treatment for GDM; and
initiating one or more electronic data transmissions, based on the applying of the machine learning electronic model to the data associated with the particular instance of medical information, wherein the one or more electronic data transmissions are sent to a destination selected from a group comprising a medical-information electronic health record system and an electronic memory associated with a distributed microprocessor-based computing network, one or more response actions are performed based at least on the determining and in response to the initiating, and the one or more response actions comprise increasing a frequency of the glucose monitoring administered to the individual.
The independent claims recite the additional elements of a system, processors, non-transitory media, machine learning, electronic data, electronic health record system, and an electronic memory associated with a distributed microprocessor-based computing network that implement the identified abstract idea. The system, processors, non-transitory media, machine learning and an electronic memory associated with a distributed microprocessor-based computing network are not described by the applicant and are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. See paras. [0014]-[0015] and [0038] of the specification. The electronic data and electronic health record system are recited at a high-level of generality such that they are generally linking the use of a judicial exception to a particular technological environment or field of use, and thus, do not integrate a judicial exception into a practical application.
The independent claims further recite the additional element of initiating data transmissions. The dependent claims 6, 9, 11, 12, 20 and 33 recite initiating a notification, transmitting a notification, transmitting a signal and entering medical information. Under practical application, initiating a notification, transmitting a notification, transmitting a signal and entering medical information are forms of extra-solution activity. MPEP 2106.5(g) indicates the term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Therefore, even in combination, these additional elements do not integrate the abstract idea into a practical application.
The dependent claims 4, 6, 9, 16, 20 and 25-31 recite additional element(s) beyond those already recited in the independent claims that implement the identified abstract idea. Claims 4 and 26-31 further describe the machine learning. Claims 6, 9, 16, 20 and 25 describe the electronic health record. Claim 11 describes an electronic medical device. However, these functions do not integrate a practical application more than the abstract idea because:
the machine learning represents mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations); and,
the electronic health record and electronic medical device generally link the use of a judicial exception to a particular technological environment or field of use.
Accordingly, the claims as a whole do not integrate the abstract idea into a practical application as they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
Regarding Step 2B, 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 the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
When viewed as a whole, claims 1-22 and 25-33 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite processes that are routine and well-known in the art and simply implements the process on a computer(s) is not enough to qualify as "significantly more."
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a system, processors, non-transitory media, machine learning and an electronic memory associated with a distributed microprocessor-based computing network to perform the noted steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). In addition, the additional elements of a electronic data and electronic health record system generally link the use of a judicial exception to a particular technological environment or field of use, and thus, do not amount to significantly more than the judicial exception.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of initiating a notification, transmitting a notification, transmitting a signal and entering medical information were considered extra-solution activity. Regarding initiating a notification, transmitting a notification, transmitting a signal and entering medical information this has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2106.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such, the claims also do not recite significantly more than the abstract idea and are not patent eligible.
The dependent claims 4, 6, 9, 16, 20 and 25-31 recite additional element(s) beyond those already recited in the independent claims that implement the identified abstract idea. Claims 4 and 26-31 further describe the machine learning. Claims 6, 9, 16, 20 and 25 describe the electronic health record. Claim 11 describes an electronic medical device. However, these functions are not deemed significantly more than the abstract idea because:
the machine learning represents mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations); and,
the electronic health record and electronic medical device generally link the use of a judicial exception to a particular technological environment or field of use.
Therefore, claims 1-22 and 25-33 are rejected under 35 USC §101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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.
Claims 1, 4, 6, 7, 9, 10, 16-18, 21, 22 and 25-31 are rejected under 35 U.S.C. 103 as being unpatentable over Peri (US 2021/0118574 A1) in further view of Davis (US 2021/0345925 A1).
Regarding claim 1, Peri teaches: A system having one or more processors configured to facilitate a plurality of operations, the operations comprising:
receiving via the one or more processors a particular instance of medical information associated with glucose monitoring for an individual, wherein the individual is the individual is 24 or fewer weeks into a current pregnancy; (the computational system for predicting risks utilizes software with robust computational infrastructure for using diverse technologies for processing of data, such as smartphones, iPads, computers (i.e. the processor), e.g. see [0041-[0443]; acquiring the pregnant women’s characteristics including “Glucose Plasma (mg/dL)” and “Fasting Blood Sugar”; the medical information includes the number of gestational weeks of the patient, e.g. see [0049], Table 1; “system predicts the risks to mother…early enough during the pregnancy, before the risks actually manifest” [0028]; interventions starting “before 16 weeks of pregnancy” [0170] and at the “16th to 24th week of pregnancy” [0179] (In order to intervene at or before 16 weeks, the system must have received and processed the data at or before 16 weeks. At or before 16 weeks is “24 or fewer weeks”.))
the system predicts the risks to the mother and fetus, including gestational diabetes, early enough during pregnancy, before the risks actually manifest, so as to drive interventions in the patients identified to have a high risk probability, e.g. see [0028], [0033], [0153])
in response to receiving the particular instance of medical information, applying a machine learning electronic model to data associated with the particular instance of medical information, wherein: (“The suite of AI algorithms comprise a set of machine learning models/techniques trained to learn” [0036]; “forwarding the preprocessed/cleaned data to the AI suite for exploration of factors associated with risks” [0039]; the “MIHIC System consumes the input data and utilizes advanced machine learning…to output a MIHIC score” [0153])
(a) the machine learning electronic model is trained based on (a) data associated with instances of the medical information and (b) one or more elements indicating whether gestational diabetes mellitus (GDM) treatment is needed based on the instances, and (b) the instances correspond to medical information for prior pregnancies; (the machine learning models are trained on millions of medical records having medical, clinical and biological characteristics of the mothers to attain the ability to generalize maternal and infant risks; the models are trained to extract information/data, assemble knowledge from the extracted data and map the assembled data to the characteristics of associated maternal risks to perform a risk prediction, e.g. see [0074], [0036]; “the algorithm learns from the training data” [0090]; the probability of a particular risk (e.g. gestational diabetes) is converted to a MIHIC score based on the highest and least probability from the training data set, e.g. see [0083]-[0090]; the MIHIC score represents the risk of gestational diabetes during pregnancy; for high risk patients, interventions (i.e. treatment) may be implemented to correct the preventable conditions that lead to gestational diabetes, e.g. see [0153]-[0154])
after the applying of the machine learning electronic model to the data associated with the particular instance of medical information, determining via the one or more processors and based on the applying of the machine learning electronic model to the data associated with the particular instance of medical information […]; and (the system consumes the input data and utilizes machine learning models to output the MIHIC score representing the risk of gestational diabetes during pregnancy; for high risk patients, interventions (i.e. treatment) may be implemented to correct the preventable conditions that lead to gestational diabetes, e.g. see [0153]-[0154], [0074]; “predicting the risks…so as to drive interventions in the patients identified that have a high risk probability”, e.g. see [0028])
initiating one or more electronic data transmissions, based on the applying of the machine learning electronic model to the data associated with the particular instance of medical information, wherein the one or more electronic data transmissions are sent to a destination selected from a group comprising a medical-information electronic health record system and an electronic memory associated with a distributed microprocessor-based computing network, (“The data acquisition modules collect data…from information systems of clinics [EHRs]…and transform them into structured data format”, e.g. see [0034]; “distribution of data” using “Cloud Servers”, e.g. see [0043]; the individual MIHIC score risk score for each pregnant mother for maternal risk can aid clinicians and caregivers in clinical decision making, by displaying the maternal health as risk scores in an interactive web interface, e.g. see [0041], [0048], [0038]; Fig. 1 illustrates the output of the risk score from the AI/ML models onto the MIHIC System interface of the clinician device, also see [0011])
one or more response actions are performed based at least on the determining and in response to the initiating, and the one or more response actions comprise increasing a frequency of the glucose monitoring administered to the individual. (interventions including “more frequent monitoring of bio-markers etc.” such as glucose [0002], Table 1; “monitor blood sugar” as an intervention for GDM, e.g. see [0156], [0154])
Peri does not teach:
determining via the one or more processors that the individual requires treatment for GDM
However, Davis in the analogous art teaches:
determining via the one or more processors that the individual requires treatment for GDM (“A data processing system is configured to identify treatment responsive to a health risk determined from feature data”, e.g. see abstract; “The data processing system is configured to determine that the health risk factors are present in the patient and subsequently determine what treatment can be applied to avoid adverse health outcomes…to treat disease, such as gestational diabetes” [0008]; “The data processing system described in this document is configured to detect health risks in patients and cause treatment responsive to the detection.” [0006])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri to include determining via the one or more processors that the individual requires treatment for GDM as taught by Davis, for the purposes of “Quick, accurate, and indirect detection of health risks” that accelerate the “discovery and treatment of medical issues” (Davis [0004]).
Regarding claim 4, Peri and Davis teach the system of claim 1 as described above.
Davis teaches determining that the individual requires preventative treatment for gestational diabetes mellitus as described above.
Peri further teaches:
wherein a classification model is utilized by the one or more processors (the machine learning models are selected from but not limited to logistic regression, Support Vector Machine regression and neural networks (i.e. a classification model), e.g. see [0036])
Regarding claim 6, Peri and Davis teach the system of claim 1 as described above.
Peri does not teach:
wherein the operations further comprise transmitting by the one or more processors a signal to assign a workflow for preventative treatment of GDM in an electronic health record associated with the individual
However, Davis in the analogous art teaches:
wherein the operations further comprise transmitting by the one or more processors a signal to assign a workflow for preventative treatment of GDM in an electronic health record associated with the individual (the system “determine[s] what treatment can be applied” and can be “integrated into an electronic medical record (EMR)”; presenting “interactive controls that facilitate treatment”, e.g. see [0008], [0031])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri to include transmitting by the one or more processors a signal to assign a workflow for preventative treatment of GDM in an electronic health record associated with the individual as taught by Davis, for the purpose of assisting the provider (Davis [0042]).
Regarding claim 7, Peri and Davis teach the teach the system of claim as described above.
Davis teaches the workflow for preventative treatment of GDM as described above.
Peri further teaches:
administering blood glucose monitoring tests to the individual, counseling for dietary modifications, and counseling for lifestyle modifications (interventions including “more frequent monitoring of bio-markers etc.” such as glucose [0002], Table 1; lifestyle and dietary interventions including “monitor blood sugar” for patients at risk for GDM, e.g. see [0154]-[0161])
Regarding claim 9, Peri and Davis teach the system of claim 1 as described above.
Peri further teaches:
wherein the operations further comprise transmitting by the one or more processors an electronic notification […] associated with the individual that the individual is at risk of GDM (displaying on a webpage the insights regarding the maternal health condition (e.g. GDM) as a risk score stratified into low, medium and high risk and as graphical charts on the interactive dashboard using various risk indicators of pregnant women (i.e. an electronic notification), e.g. see [0038], [0081])
Peri does not teach:
transmitting an electronic notification in an electronic health record
However, Davis in the analogous art teaches:
transmitting an electronic notification in an electronic health record (“alerts…can be presented below the patient status” in the “client device…integrated into an electronic medical record (EMR)”, e.g. see [0040], [0031])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri to include transmitting an electronic notification in an electronic health record as taught by Davis, for the purpose of assisting the provider (Davis [0042]).
Claims 10 and 18 recite substantially similar limitations as those already addressed in claim 1, and, as such are rejected for similar reasons as given above.
Claim 16 recites substantially similar limitations as those already addressed in claim 6, and, as such is rejected for similar reasons as given above.
Regarding claim 17, Peri and Davis teach the one or more non-transitory media of claim 16 as described above.
Davis teaches the workflow for preventative treatment of GDM as described above.
Peri further teaches:
administering a set of blood glucose monitoring tests to the individual, counseling for dietary modifications, and counseling for lifestyle modifications (interventions including “more frequent monitoring of bio-markers etc.” such as glucose [0002], Table 1; lifestyle and dietary interventions including “monitor blood sugar” for patients at risk for GDM, e.g. see [0154]-[0161])
Regarding claim 21, Peri and Davis teach the system of claim 1 as described above.
Peri further teaches:
wherein the operations further comprise updating the machine learning electronic model by the one or more processors based on information associated with additional instances of the medical information (the system utilizes machine learning models with self-leaning capabilities to learn continuously from the data provided; the system continuously receives real-time feed-back from caregivers and improvises the risk scores on a perpetual basis, e.g. see [0050], [0081], [0126])
Regarding claim 22, Peri and Davis teach the system of claim 1 as described above.
Peri further teaches:
wherein the training is based at least in part on historical information from individuals having completed pregnancies and results of blood glucose testing administered by a medical professional to each individual during each completed pregnancy (the system is trained on historical medical records of pregnant mothers to attain the ability to generalize maternal and infant risks, e.g. see [0074], [0024]; input data includes fasting blood sugar, post prandial blood sugar, plasma glucose (lab test performed by a medical professional), e.g. see Table 1, [0099], [0049])
Regarding claim 25, Peri and Davis teach the system of claim 1 as described above.
Peri further teaches:
wherein the machine learning electronic model is trained by the one or more processors based on instances of medical data corresponding to electronic health records from one or more medical facilities and corresponding to medical information for prior completed pregnancies (the machine learning models are trained on millions of medical records having medical, clinical and biological characteristics of the mothers to attain the ability to generalize maternal and infant risks, e.g. see [0074], [0036]; “data from information systems of clinics”, e.g. see [0034]; “Gathering data from multiple sources-Nurses, Doctors, Clinicians, Labs, Hospitals etc.;”, e.g. see [0042])
Regarding claim 26, Peri and Davis teach the system of claim 1 as described above.
Peri further teaches:
wherein the one or more elements correspond to items selected from a group comprising of a Random Forest model, a logistic regression machine learning method, and a neural network (the system applies advanced Artificial intelligence and Deep learning methods, including but not limited to Neural Networks, Bayesian Networks, Decision Trees, Random Forests, etc. to generate risk scores, e.g. see [0031]; machine learning models are selected from logistic regression, Support Vector Machine and neural networks, e.g. see [0036])
Regarding claim 27, Peri and Davis teach the system of claim 1 as described above.
Peri further teaches:
wherein the machine learning electronic model is trained based on (i) an algorithm corresponding to at least one item selected from a group comprising a Random Forest model, a logistic regression machine learning method, and a neural network and (ii) and the data associated with the instances (the system applies advanced Artificial intelligence and Deep learning methods, including but not limited to Neural Networks, Bayesian Networks, Decision Trees, Random Forests, etc. to generate risk scores, e.g. see [0031]; machine learning models are selected from logistic regression, Support Vector Machine and neural networks, e.g. see [0036]; the machine learning models are trained on millions of medical records having medical, clinical and biological characteristics of the mothers to attain the ability to generalize maternal and infant risks, e.g. see [0074], [0036])
Regarding claim 28, Peri and Davis teach the system of claim 27 as described above.
Peri further teaches:
wherein the machine learning electronic model corresponds to a Random Forest algorithm (the system applies methods including Random forests, e.g. see [0031])
Regarding claim 29, Peri and Davis teach the system of claim 27 as described above.
Peri further teaches:
wherein the machine learning electronic model is trained based on one or both of a logistic regression algorithm and a neural network algorithm (the machine learning models are trained to learn to extract information/data from the structured and unstructured data, assemble knowledge and map the assembled data to characteristics of associated maternal, fetal and infant risks; the machine learning models are selected from but not limited to logistic regression, SVM regression and neural networks, e.g. see [0036])
Regarding claim 30, Peri and Davis teach the system of claim 27 as described above.
Peri further teaches:
wherein the machine learning electronic model is automatically applied to the data associated with the particular instance of medical information in response to receiving via the one or more processors the particular instance of medical information (the system continuously receives real-time feed-back from caregivers and improvises the scores on a perpetual basis (construed as automatically applying the machine learning model in response to receiving the particular instance of medical information), e.g. see [0126])
Regarding claim 31, Peri and Davis teach the one or more non-transitory media of claim 10 as described above.
Peri further teaches:
wherein training the machine learning electronic model comprises configuring a machine learning neural network algorithm based on the data associated with instances (the trained machine learning models include but not limited to convolutional neural network (CNN), recurrent neural network (RNN) and long short-term memory model (LSTM)., e.g. see [0036]; [0105] provides an explanation of the deep Neural Network architecture for predicting risk)
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Peri and Davis in further view of Smith (US 2010/0137263 A1).
Regarding claim 2, Peri and Davis teach the system of claim 1 as described above.
Peri further teaches:
wherein the medical information comprises: an indication of whether the individual has undergone a prior GDM diagnosis, an indication of whether the individual has undergone a birth of a prior child having macrosomia […] (family medical history, medical history of e.g. gestational diabetes, “Weight of Last child born” (construed to include child having macrosomia), i.e. see [0049], Table 1)
Peri and Davis do not teach:
a blood cortisol level measured for the individual
However, Smith in the analogous art of determining the risks of pregnancy associated conditions (e.g. see [0001]) teaches:
a blood cortisol level measured for the individual (“Cortisol levels” proved useful for predicting pregnancy outcomes [0234])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri and Davis to include a blood cortisol level measured for the individual as taught by Smith, for the purposes of being useful for predicting adverse pregnancy outcomes (Smith [0234]).
Claims 3, 14, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Peri and Davis in further view of Roberts (US 2018/0114600 A1) and Smith.
Regarding claim 3, Peri and Davis teach the system of claim 1 as described above.
Peri further teaches:
wherein the medical information is collected from the individual during the current pregnancy and includes: age, body mass index, […], non-invasive systolic blood pressure, non-invasive diastolic blood pressure, estimated creatinine clearance, ethnicity, family medical history, medical history of the individual indicating whether the individual has undergone (i) a prior pregnancy, ii) a prior GDM diagnosis, iii) or birth of any prior child having macrosomia, […] (the pregnant woman’s characteristics include age, BMI, systolic blood pressure, diastolic blood pressure (construed to be non-invasive), serum creatinine (used to calculate creatinine clearance), race (i.e. ethnicity), family medical history, medical history, gestational diabetes, “Weight of Last child born” (construed to include child having macrosomia), e.g. see [0049], Table 1)
Peri and Davis do not teach:
wherein the medical information includes: heart or pulse rate
However, Roberts in the analogous art of determining the risk of a complication of pregnancy (e.g. see [0012]) teaches:
wherein the medical information includes: heart or pulse rate (the clinical information includes pulse rate, e.g. see [0161])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri and Davis to include medical information of heart rate as taught by Roberts, for the purposes of predicting early in pregnancy the risks of the main pregnancy complications and employing interventions and management strategies for those at risk (Roberts [0069], [0220]).
Peri, Davis and Roberts do not teach:
a blood cortisol level measured for the individual
However, Smith in the analogous art teaches:
a blood cortisol level measured for the individual (“Cortisol levels” proved useful for predicting pregnancy outcomes [0234])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri, Davis and Roberts to include a blood cortisol level measured for the individual as taught by Smith, for the purposes of being useful for predicting adverse pregnancy outcomes (Smith [0234]).
Regarding claim 14, Peri and Davis teach the one or more non-transitory media of claim 12 as described above.
Peri further teaches:
wherein the medical information is collected for the individual, […], and comprises: age, body mass index, […], non-invasive systolic blood pressure, non-invasive diastolic blood pressure, estimated creatinine clearance, ethnicity, family medical history, medical history associated with any prior pregnancy, any prior GDM diagnosis, birth of any prior child having macrosomia, […] (the pregnant woman’s characteristics include age, BMI, systolic blood pressure, diastolic blood pressure (construed to be non-invasive), serum creatinine (used to calculate creatinine clearance), race (i.e. ethnicity), family medical history, medical history, gestational diabetes, “Weight of Last child born” (construed to include child having macrosomia), e.g. see [0049], Table 1)
Peri and Davis do not teach:
the medical information is collected during the medical encounter, and comprises: heart or pulse rate
However, Roberts in the analogous art teaches:
the medical information is collected during the medical encounter, and comprises: heart or pulse rate (“Women…prior to 15 weeks' gestation...were interviewed and examined by a research midwife at 15±1…weeks”; this was the “first antenatal visit” where data and samples were taken, e.g. see [0266], [0311]; data was “entered into an internet-accessed…centralised database” [0268]; the clinical information includes pulse rate, e.g. see [0161])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri and Davis to include medical information of heart rate as taught by Roberts, for the purposes of predicting early in pregnancy the risks of the main pregnancy complications and employing interventions and management strategies for those at risk (Roberts [0069], [0220]).
Peri, Davis and Roberts do not teach:
a blood cortisol level
However, Smith in the analogous art teaches:
a blood cortisol level (“Cortisol levels” proved useful for predicting pregnancy outcomes [0234])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri, Davis and Roberts to include a blood cortisol level as taught by Smith, for the purposes of being useful for predicting adverse pregnancy outcomes (Smith [0234]).
Regarding claim 15, Peri, Davis, Roberts and Smith teach the one or more non-transitory media of claim 14 as described above.
Peri does not teach:
wherein at least a portion of the medical information is obtained from the individual at an initial medical encounter associated with the current pregnancy of the individual
However, Roberts in the analogous art teaches:
wherein at least a portion of the medical information is obtained from the individual at an initial medical encounter associated with the current pregnancy of the individual (“Women…prior to 15 weeks' gestation...were interviewed and examined by a research midwife at 15±1…weeks”; this was the “first antenatal visit” where data and samples were taken, e.g. see [0266], [0311]; data was “entered into an internet-accessed…centralised database” [0268])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri to include at least a portion of medical information obtained from the individual at the initial medical encounter as taught by Roberts, for the purposes of predicting early in pregnancy the risks of the main pregnancy complications and employing interventions and management strategies for those at risk (Roberts [0069], [0220]).
Regarding claim 19, Peri and Davis teach the method of claim 18 as described above.
Peri further teaches:
wherein the medical information is obtained for the individual and comprises age, body mass index, […], systolic blood pressure, diastolic blood pressure, estimated creatinine clearance, ethnicity, family medical history, any medical history of the individual associated with a prior pregnancy, any prior gestational diabetes mellitus (GDM) diagnosis, any birth of a prior child having macrosomia, […] (the pregnant woman’s characteristics include age, BMI, systolic blood pressure, diastolic blood pressure (construed to be non-invasive), serum creatinine (used to calculate creatinine clearance), race (i.e. ethnicity), family medical history, medical history, gestational diabetes, “Weight of Last child born” (construed to include child having macrosomia), e.g. see [0049], Table 1)
Peri and Davis do not teach:
the medical information comprises heart rate and wherein at least a portion of the medical information is obtained from the individual at an initial pregnancy medical appointment
However, Roberts in the analogous art teaches:
the medical information comprises heart rate and wherein at least a portion of the medical information is obtained from the individual at an initial pregnancy medical appointment (“Women…prior to 15 weeks' gestation...were interviewed and examined by a research midwife at 15±1…weeks”; this was the “first antenatal visit” where data and samples were taken, e.g. see [0266], [0311]; data was “entered into an internet-accessed…centralised database” [0268]; the clinical information includes pulse rate, e.g. see [0161])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri and Davis to the medical information comprises heart rate and wherein at least a portion of the medical information is obtained from the individual at an initial pregnancy medical appointment as taught by Roberts, for the purposes of predicting early in pregnancy the risks of the main pregnancy complications and employing interventions and management strategies for those at risk (Roberts [0069], [0220]).
Peri, Davis and Roberts do not teach:
a blood cortisol level
However, Smith in the analogous art teaches:
a blood cortisol level (“Cortisol levels” proved useful for predicting pregnancy outcomes [0234])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri, Davis and Roberts to include a blood cortisol level as taught by Smith, for the purposes of being useful for predicting adverse pregnancy outcomes (Smith [0234]).
Claims 5, 8, 11-13, 20, 32 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Peri and Davis in further view of Roberts.
Regarding claim 5, Peri and Davis teach the system of claim 1 as described above.
Davis teaches determining that the individual requires preventative treatment for gestational diabetes mellitus as described above.
Peri further teaches:
wherein the individual is in a first trimester of pregnancy when the individual is determined via the one or more processors to require preventative treatment for GDM
wherein the individual is […] early in pregnancy when it is determined that the individual requires preventative treatment for GDM (the system predicts the risks to the mother and fetus, including gestational diabetes, early enough during pregnancy, before the risks actually manifest, so as to drive interventions in the patients identified to have a high risk probability, e.g. see [0028], [0033], Table 1)
Peri and Davis do not teach:
wherein the individual is in a first trimester of pregnancy
However, Roberts in the analogous art teaches:
wherein the individual is in a first trimester of pregnancy (the lifestyle and/or clinical information may comprise information at 13 weeks (of pregnancy) or less, e.g. see [0099]; Table 21 shows a “High Risk” GDM protocol starting in the “first trimester”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri and Davis to include the individual is in a first trimester of pregnancy as taught by Roberts, for the purposes of predicting early in pregnancy the risks of the main pregnancy complications and employing interventions and management strategies for those at risk (Roberts [0069], [0220]).
Regarding claim 8, Peri and Davis teach the system of claim 7 as described above.
Davis teaches the workflow for preventative treatment of GDM as described above.
Peri and Davis do not teach:
administering multiple blood glucose monitoring tests to the individual during the current pregnancy […] (interventions including “more frequent monitoring of bio-markers etc.” such as glucose [0002], Table 1; interventions including “monitor blood sugar” for patients at risk for GDM, e.g. see [0154], [0156])
However, Roberts in the analogous art teaches:
administering blood glucose monitoring test to the individual beginning in a first trimester of current pregnancy (an antenatal intervention and/or management strategy for a subject considered to be at risk for a complication of pregnancy such as gestational diabetes; management strategy for a subject at moderate or high risk for gestational diabetes of increased monitoring, lifestyle changes and/or treatment, e.g. see [0220], [0222]; Table 21 shows a “High Risk” GDM protocol of administering an Oral Glucose Tolerance Test (OGTT) in the “first trimester”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri and Davis to include administering blood glucose monitoring test to the individual beginning in a first trimester as taught by Roberts, for the purposes of preventing complications associated with GDM (Roberts [0009]-[0010]).
Regarding claim 11, Peri and Davis teach the one or more non-transitory media of claim 10 as described above.
Peri does not teach:
wherein the operations further comprise initiating by the one or more processors an electronic notification to an electronic medical device associated with a medical professional […]
However, Davis in the analogous art teaches:
wherein the operations further comprise initiating by the one or more processors an electronic notification to an electronic medical device associated with a medical professional […] (“delivering actionable information as part of routine prenatal care”, e.g. see [0037]; “The communication of risk to physicians and other providers through an interface…( e.g., received the same day as the risk is experienced by the patient).”, e.g. see [0030]; “displaying…on a client device”, e.g. see [0010])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri to include initiating by the one or more processors an electronic notification to an electronic medical device associated with a medical professional on a same day as a medical encounter of the medical professional with the individual as taught by Davis, for the purpose of enabling “treatment-seeking action in the moment” (Davis [0038]).
Peri and Davis do not teach:
on a same day as a medical encounter of the medical professional with the individual
However, Roberts in the analogous art teaches:
on a same day as a medical encounter of the medical professional with the individual (“Women…prior to 15 weeks' gestation...were interviewed and examined by a research midwife at 15±1…weeks”, e.g. see [0266]; data was “entered into an internet-accessed…centralised database” [0268])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri and Davis to include on a same day as a medical encounter of the medical professional with the individual as taught by Roberts, for the purposes of providing “risk estimates or prediction” throughout pregnancy (Roberts [0315]).
Regarding claim 12, Peri, Davis and Roberts teach the one or more non-transitory media of claim 11 as described above.
Peri does not teach:
wherein the electronic notification is transmitted by the one or more processors […]
However, Davis in the analogous art teaches:
wherein the electronic notification is transmitted by the one or more processors […] (“alerts…can be presented below the patient status” in the “client device…integrated into an electronic medical record (EMR)”, e.g. see [0040], [0031]; “delivering actionable information as part of routine prenatal care” and “in the moment”, e.g. see [0037])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri to include the electronic notification is transmitted by the one or more processors during the medical encounter as taught by Davis, for the purpose of enabling “treatment-seeking action in the moment” (Davis [0038]).
Peri and Davis do not teach:
during the medical encounter
However, Roberts in the analogous art teaches:
during the medical encounter (“Women…prior to 15 weeks' gestation...were interviewed and examined by a research midwife at 15±1…weeks”, e.g. see [0266]; data was “entered into an internet-accessed…centralised database” [0268])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri and Davis to include during the medical encounter as taught by Roberts, for the purposes of providing “risk estimates or prediction” throughout pregnancy” (Roberts [0315]).
Regarding claim 13, Peri, Davis and Roberts teach the one or more non-transitory media of claim 12 as described above.
Peri and Davis do not teach:
wherein the medical encounter is an initial medical encounter associated with the current pregnancy of the individual
However, Roberts in the analogous art teaches:
wherein the medical encounter is an initial medical encounter associated with the current pregnancy of the individual (“Women…prior to 15 weeks' gestation...were interviewed and examined by a research midwife at 15±1…weeks”; this was the “first antenatal visit” where data and samples were taken, e.g. see [0266], [0311])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri and Davis to include the medical encounter is an initial medical encounter associated with the current pregnancy of the individual as taught by Roberts, for the purposes of predicting early in pregnancy the risks of the main pregnancy complications and employing interventions and management strategies for those at risk (Roberts [0069], [0220]).
Regarding claim 20, Peri and Davis teach the method of claim 18 as described above.
Peri further teaches:
transmitting […] an electronic notification […] associated with the individual that the individual is at risk of GDM; and (displaying on a webpage the insights regarding the maternal health condition (e.g. GDM) as a risk score stratified into low, medium and high risk and as graphical charts on the interactive dashboard using various risk indicators of pregnant women (i.e. an electronic notification), e.g. see [0038], [0081])
[…] preventative treatment of GDM comprising a sequence of blood glucose monitoring tests, counseling for dietary modifications, and counseling for lifestyle modifications (interventions including “more frequent monitoring of bio-markers etc.” such as glucose [0002], Table 1; lifestyle and dietary interventions including “monitor blood sugar” for patients at risk for GDM, e.g. see [0154]-[0161])
Peri does not teach:
transmitting an electronic notification in an electronic health record
However, Davis in the analogous art teaches:
transmitting an electronic notification in an electronic health record (“alerts…can be presented below the patient status” in the “client device…integrated into an electronic medical record (EMR)”, e.g. see [0040], [0031])
transmitting a signal to assign a workflow for preventative treatment of GDM in the electronic health record (the system “determine[s] what treatment can be applied” and can be “integrated into an electronic medical record (EMR)”; presenting “interactive controls that facilitate treatment”, e.g. see [0008], [0031])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri to include transmitting an electronic notification in an electronic health record and transmitting a signal to assign a workflow for preventative treatment of GDM in the electronic health record as taught by Davis, for the purpose of assisting the provider (Davis [0042]).
Peri and Davis do not teach:
during an initial pregnancy medical appointment
However, Roberts in the analogous art teaches:
during an initial pregnancy medical appointment (“Women…prior to 15 weeks' gestation...were interviewed and examined by a research midwife at 15±1…weeks”; this was the “first antenatal visit” where data and samples were taken, e.g. see [0266], [0311]; data was “entered into an internet-accessed…centralised database” [0268])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri and Davis to include during an initial pregnancy medical appointment as taught by Roberts, for the purposes of predicting early in pregnancy the risks of the main pregnancy complications and employing interventions and management strategies for those at risk (Roberts [0069], [0220]).
Regarding claim 32, Peri and Davis teach the system of claim 1 as described above.
Peri teaches a GDM prediction system using machine learning that relies on data input from Nurses, Doctors and Clinicians and processes unstructured data like Clinician notes/images/video (e.g. see [0041], [0031]). Peri further teaches the system continuously receives real-time feed-back from caregivers and improvises the scores on a perpetual basis (e.g. see [0126]) but does not explicitly teach:
Peri further teaches:
[…] wherein determining that the individual requires the treatment for GDM comprises (a) applying the machine learning electronic model to a set of data associated with at least a portion of the medical information collected […] and (b) assessing the individual for GDM […] based on the applying of the machine learning electronic model to the set of data (“The suite of AI algorithms comprise a set of machine learning models/techniques trained to learn” [0036]; “forwarding the preprocessed/cleaned data to the AI suite for exploration of factors associated with risks” [0039]; the “MIHIC System consumes the input data and utilizes advanced machine learning…to output a MIHIC score” [0153]; the system continuously receives real-time feed-back from caregivers and improvises the scores on a perpetual basis, e.g. see [0126])
Peri does not teach:
wherein the medical information is created during a medical encounter of the individual with a clinician
assessing the individual for GDM at a time of the medical encounter
However, Roberts in the analogous art teaches:
wherein the medical information is created during a medical encounter of the individual with a clinician (“Women…prior to 15 weeks' gestation...were interviewed and examined by a research midwife at 15±1…weeks”; this was the “first antenatal visit” where data and samples were taken, e.g. see [0266], [0311]; data was “entered into an internet-accessed…centralised database” [0268])
assessing the individual for GDM at a time of the medical encounter (“risk estimates or prediction can be obtained throughout pregnancy, which allows constant monitoring and update of predicted risk for individuals”; at the first visit, “by 15 weeks of gestation, the first group of low-risk women can be identified”, e.g. see [0315]; recommendations for interventions for “High Risk” for GDM of an OGTT in the first trimester for a patient with a “Specialist” care provider (The determination of “High Risk” must have occurred during the medical encounter.), e.g. see Table 21, [0625])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri to include the medical information is created during a medical encounter of the individual with a clinician and assessing the individual for GDM at a time of the medical encounter as taught by Roberts, for the purposes of updating a predicted risk when new predictors are available or when conditions change (Roberts [0315]).
Regarding claim 33, Peri and Davis teach the one or more non-transitory media of claim 10, as described above.
Peri further teaches:
[…] (a) determine that the individual requires preventative treatment for GDM and (b) perform the one or more response actions […] (the system consumes the input data and utilizes machine learning models to output the MIHIC score representing the risk of gestational diabetes during pregnancy; for high risk patients, interventions may be implemented to correct the preventable conditions that lead to gestational diabetes, e.g. see [0153]-[0154], [0074]; interventions including “more frequent monitoring of bio-markers etc.” such as glucose [0002], Table 1; “monitor blood sugar” as an intervention for GDM, e.g. see [0156], [0154])
Peri does not teach:
wherein at least a portion of the medical information is entered by the one or more processors into the medical-information electronic health record system at an initial pregnancy medical appointment of the individual, wherein entering the portion of the medical information by the one or more processors into the medical-information electronic health record system causes the one or more processors to, based on the entered portion of the medical information
wherein the one or more response actions further comprise initiating administration of an oral glucose tolerance test to the individual
However, Roberts in the analogous art teaches:
wherein at least a portion of the medical information is entered by the one or more processors into the medical-information electronic health record system at an initial pregnancy medical appointment of the individual, wherein entering the portion of the medical information by the one or more processors into the medical-information electronic health record system causes the one or more processors to, based on the entered portion of the medical information (“Women…prior to 15 weeks' gestation...were interviewed and examined by a research midwife at 15±1…weeks”; this was the “first antenatal visit” where data and samples were taken, e.g. see [0266], [0311]; data was “entered into an internet-accessed…centralised database” [0268]; “risk estimates or prediction can be obtained throughout pregnancy, which allows constant monitoring and update of predicted risk for individuals”; at the first visit, “by 15 weeks of gestation, the first group of low-risk women can be identified”, e.g. see [0315]; recommendations for interventions for “High Risk” for GDM of an OGTT in the first trimester for a patient with a “Specialist” care provider (The determination of “High Risk” must have occurred during the medical encounter.), e.g. see Table 21, [0625])
wherein the one or more response actions further comprise initiating administration of an oral glucose tolerance test to the individual (recommendations for interventions for “High Risk” for GDM of an OGTT in the first trimester, e.g. see Table 21, [0625])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Peri to include at least a portion of the medical information is entered by the one or more processors into the medical-information electronic health record system at an initial pregnancy medical appointment of the individual and the one or more response actions further comprise initiating administration of an oral glucose tolerance test to the individual as taught by Roberts, for the purposes of allowing for the “constant monitoring and update of [the] predicted risk for individuals” and employing “management strategies” to mitigate morbidity and mortality risks (Roberts [0315], [0010]-[0011]).
Response to Arguments
Regarding the objection to Claim 32, the Applicant has amended the claim to overcome the bases of objection.
Regarding the rejection under 35 U.S.C. § 112(a) of Claims 26 and 27, the Applicant has amended the claims to overcome the bases of rejection.
Regarding the rejection under 35 U.S.C. § 112(b) of Claims 1-22 and 25-33, Applicant’s amendments to the claims have not overcome the bases of rejection. See details above.
Regarding the rejection under 35 U.S.C. § 101 of Claims 1-22 and 25-33, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment and/or afforded by the present RCE.
Notably, the Applicant argues claim 1 cannot be directed to the abstract idea grouping of Certain Methods of Organizing Human Activity since no human is recited. However, the rejection has been updated in light of the new amendments, and the claims no longer recite an abstract idea under the grouping of Certain Methods of Organizing Human Activity. See details above.
Regarding the rejection under 35 U.S.C. § 103 of Claims 1-22 and 25-33, the Examiner has considered the Applicant’s arguments.
Applicant argues Peri fails to teach or suggest inputting data, associated with instances of the medical information, to a machine learning electronic model in response to receiving of the medical information, and determining based on the inputting that the individual requires treatment, as recited in claim 1.
The rejection has been updated in light of the new amendments. Applicant’s arguments directed to determining based on the inputting that the individual requires treatment are moot because the new grounds of rejection does not rely on Peri. See details above.
The arguments are not persuasive. Applicant’s assertion that Peri does not describe inputting data to the model is false. Peri provides explicit support for inputting or forwarding patient data to the machine learning model for processing. Peri teaches “forwarding the preprocessed/cleaned data to the AI suite for exploration of factors associated with risks” ([0039]) and for “learning how to generalize and predict” ([0074]). Peri further discusses, specifically for Gestational Diabetes, “MIHIC System consumes the input data and utilizes advanced machine learning…to output a MIHIC score” ([0153]).
In addition, Peri teaches applying the model in response to receiving information. Peri describes the system for “early detection” ([0028]) and “real-time feed-back” ([0126]), where the application of the model is a direct consequence of receiving new patient data. This is not a case of “possibilities or probabilities”. Peri discusses the sequential process of the acquiring patient data, pre-processing the data and forwarding the data to machine learning models ([0039], [0098], [0153]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference Pengetnze (US 2019/0122770 A1) discloses a clinical pregnancy preterm birth predictive system. Reference Thadhani (US 2008/0213794 A1) discloses a system screening for gestational diabetes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Aaisha Abdullah whose telephone number is (571)272-5668. The examiner can normally be reached Monday through Friday 8:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Choi can be reached on (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.A./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681