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, 7, 10, 18, 27 and 33 have been amended.
Claim 34 is newly presented.
Claims 1-5, 7-22, 25-30 and 32-34 as presented May 20, 2026 are currently pending and considered below.
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 1-5, 7-22, 25-30 and 32-34 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 amended claims recite “increasing a frequency, based on the transmitting of the signal to assign the workflow, of the glucose monitoring administered to the individual”. A review of the specification reveals a “preventative GDM treatment workflow may be assigned” or a “treatment plan” may be assigned to the “individual's electronic health record”, and this assigned plan may include “blood glucose tests at an increased frequency” ([0054], [0056]). However, the specification fails to provide written description support for the system, computer-readable medium or method executing the administration of the blood glucose test or increased monitoring. Describing a decision-support system that updates a database with a recommended care plan does not provide support for a system or processor that actively and physically administers increased medical monitoring or testing to an individual. Because the specification only supports assigning a plan or workflow that includes a recommendation for increased testing, while the amended claims recite actual administration of the increased monitoring as an operation based on the signal, new matter has been introduced. 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 “increasing a frequency, based on the transmitting of the signal to assign the workflow, of the glucose monitoring to be administered to the individual”.
As to claims 33 and 34, claim 33 recites “the oral glucose tolerance test is administered to the individual”, and claim 34 recites “administering, based on transmitting the signal to assign the workflow item, the oral glucose tolerance test to the individual”. A review of the specification reveals that as part of the individual’s workflow, if there is an “uncontrolled blood glucose level at 24-28 weeks”, then the “individual will undergo the oral glucose tolerance test” ([0055], [0053]). However, the specification fails to provide written description support for the system, computer-readable medium or method executing the administration of the test. Describing a decision-support system that updates a database with a recommended care plan, resulting in a patient undergoing a test at a later visit, does not provide support for the system, processor or claimed method that actively and physically administers the test to an individual. Because the specification only supports assigning a plan recommending for a test, while the claims recite actual administration of the test as an operation, new matter has been introduced. For the purposes of compact prosecution, the claim 33 will be interpreted in a manner as best understood by the Examiner and consistent with Applicant’s specification, wherein “transmitting a signal to assign a workflow item indicating the individual is to undergo an oral glucose tolerance test at a period of 24-28 weeks into the current pregnancy of the individual”. For the purposes of compact prosecution, the claim 34 will be interpreted in a manner as best understood by the Examiner and consistent with Applicant’s specification, wherein “transmitting a signal, in response to detecting that the blood glucose level is uncontrolled, to assign in the EHR a workflow item indicating the individual is to undergo an oral glucose tolerance test at a period of 24-28 weeks into the current pregnancy of the individual”.
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 1-5, 7-22, 25-30 and 32-34 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, 5, 7, 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.
Claim 33 recites “the oral glucose tolerance test”. There is insufficient antecedent basis for this limitation in either the preceding lines of claim 33 or in the claim upon which it depends, independent claim 10. As best understood by the Examiner, the recitation of “the oral glucose tolerance test” in claim 33 will be treated as “an oral glucose tolerance test” for the purposes of compact prosecution.
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 § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 16 and 17 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 16 fails to further limit the subject matter of the claim upon which it depends, claim 10. The subject matter of claim 16 was amended into independent claim 10 in the most recent response/amendment by the Applicant. Dependent claim 6 was similarly directed towards the same concept, but, was cancelled when incorporated into independent claim 1. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim 17 is rejected under 35 U.S.C. 112(d) due to its dependence on claim 16.
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 1-5, 7-22, 25-30 and 32-34 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 1-5, 7-9, 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 34 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 less than 24 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:
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 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;
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;
sending the one or more electronic data transmissions based on the initiating to a destination selected from a group comprising a medical-information electronic health record system (EHR) and an electronic memory associated with a distributed microprocessor-based computing network,
performing one or more response actions based at least on the determining and in response to the initiating, wherein performing the one or more response actions comprises: transmitting a signal to assign in an EHR, associated with the individual, a workflow comprising blood glucose monitoring at an increased frequency; and
increasing a frequency, based on the transmitting of the signal to assign the workflow, 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 determining that the individual requires treatment for GDM. 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 associated with the particular instance of medical information 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, 5, 7, 8, 10-17, 19, 21, 32, 33 and 34 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 7, 8, 16 and 17 further describe the workflow. Claims 11-15 describe the medical encounter. Claim 21 describes updating the model. Claim 32 describe assessing the individual for GMD at a time of the medical encounter. Claim 33 describes the medical information and response actions. Claim 34 describes detecting uncontrolled blood glucose administering further testing. Claims 7, 11, 12 and 34 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 1-5, 7-22, 25-30 and 32-34 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 less than 24 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:
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 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;
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;
sending the one or more electronic data transmissions based on the initiating to a destination selected from a group comprising a medical-information electronic health record system (EHR) and an electronic memory associated with a distributed microprocessor-based computing network,
performing one or more response actions based at least on the determining and in response to the initiating, wherein performing the one or more response actions comprises: transmitting a signal to assign in an EHR, associated with the individual, a workflow comprising blood glucose monitoring at an increased frequency; and
increasing a frequency, based on the transmitting of the signal to assign the workflow, of the glucose monitoring administered to the individual.
The independent claims recite the additional elements of a system, processors, non-transitory media, machine learning, training the machine learning model, EHR system, EHR 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. The machine learning and training of the machine learning generally apply the abstract idea without placing any limits on how the machine learning and its training function. Rather, these limitations only recite the outcome and do not include any details on how the outcome is accomplished (see MPEP 2106.05(f)). See paras. [0014]-[0015] and [0038] of the specification. The EHR system and EHR 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 receiving a particular instance of medical information, initiating data transmissions, sending data transmissions, transmitting a signal and performing one or more response actions comprising increasing the frequency of glucose monitoring. The dependent claims 7-9, 11, 12, 17, 20, 33 and 34 recite initiating a notification, transmitting a notification, transmitting a signal and administering glucose testing. Under practical application, receiving a particular instance of medical information, initiating data transmissions, sending data transmissions, performing one or more response actions comprising increasing the frequency of glucose monitoring, initiating a notification, transmitting a notification, transmitting a signal and administering glucose testing 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, 11, 22 and 25-30 recite additional element(s) beyond those already recited in the independent claims that implement the identified abstract idea. Claims 4, 22 and 25-30 further describe the machine learning and/or machine learning training. 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 and/or machine learning training represent mere instructions to apply the abstract idea; and,
the electronic medical device generally links 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 1-5, 7-22, 25-30 and 32-34 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. The machine learning and training of the machine learning generally apply the abstract idea without placing any limits on how the machine learning and its training function. Mere instructions to apply an exception cannot provide an inventive concept (“significantly more”). In addition, the additional elements of an EHR system and EHR 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 receiving a particular instance of medical information, initiating data transmissions, sending data transmissions, performing one or more response actions comprising increasing the frequency of glucose monitoring, initiating a notification, transmitting a notification, transmitting a signal and administering glucose testing were considered extra-solution activity. Regarding receiving a particular instance of medical information, initiating data transmissions, sending data transmissions, performing one or more response actions comprising increasing the frequency of glucose monitoring, initiating a notification, transmitting a notification, transmitting a signal and administering glucose testing 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, 11, 22 and 25-30 recite additional element(s) beyond those already recited in the independent claims that implement the identified abstract idea. Claims 4, 22 and 25-30 further describe the machine learning and/or machine learning training. Claim 11 describes an electronic medical device. However, these functions are not deemed significantly more than the abstract idea because:
the machine learning and/or machine learning training represent mere instructions to apply the abstract idea; and,
the electronic medical device generally links the use of a judicial exception to a particular technological environment or field of use.
Therefore, claims 1-5, 7-22, 25-30 and 32-34 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, 7, 9, 10, 16-18, 21, 22 and 25-30 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 less than 24 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”.))
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])
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 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 […]; (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; sending the one or more electronic data transmissions based on the initiating to a destination selected from a group comprising a medical-information electronic health record (EHR) system and an electronic memory associated with a distributed microprocessor-based computing network; (computational infrastructure using “Standalone/On-Premise/Cloud Servers” and “Organizing data in a main storage and in auxiliary storage devices”, e.g. see [0043]-[0044]; the individual MIHIC score risk score for each pregnant mother is transmitted to a Cloud Server for storage and displayed on an “interactive web interface”, e.g. see [0038], [0048], [0043]; 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])
performing one or more response actions based at least on the determining and in response to the initiating, wherein performing the one or more response actions comprises: […] a workflow comprising blood glucose monitoring at an increased frequency; and increasing a frequency, […], of the glucose monitoring administered to the individual. (the response to a high-risk prediction is to define a “pro-active approach comprising of clinical…interventions” (i.e. workflow) such as “more frequent monitoring of bio-markers” including glucose [0002], Table 1; the intervention workflow includes “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
transmitting a signal to assign in an EHR, associated with the individual, a workflow
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”, e.g. see [0008]; “The data processing system described in this document is configured to detect health risks in patients and cause treatment responsive to the detection.”, e.g. see [0006])
transmitting a signal to assign in an EHR, associated with the individual, a workflow (the machine learning “data processing system…is configured to detect health risks in patients and cause treatment responsive to the detection”, e.g. see [0006], [0002]; 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 determining via the one or more processors that the individual requires treatment for GDM and transmitting a signal to assign in an EHR, associated with the individual, a workflow 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 7, Peri and Davis teach the teach the system of claim as described above.
Peri teaches the workflow for preventative treatment of GDM as described above. Davis teaches transmitting signals to assign workflows in the EHR 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 […] 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 the EHR
However, Davis in the analogous art teaches:
transmitting an electronic notification in the EHR (“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.
Regarding claim 16, Peri and Davis teach the one or more non-transitory media of claim 10 as described above.
Peri teaches the workflow for preventative treatment of GDM as described above.
Peri does not teach:
wherein execution of the instructions further causes the one or more processors to: assign a workflow in the EHR
However, Davis in the analogous art teaches:
wherein execution of the instructions further causes the one or more processors to: assign a workflow in the EHR (the machine learning “data processing system…is configured to detect health risks in patients and cause treatment responsive to the detection”, e.g. see [0006], [0002]; 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 cause the one or more processors 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 17, Peri and Davis teach the one or more non-transitory media of claim 16 as described above.
Peri 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 EHRs 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) 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]).
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 and 32-34 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 […] 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 the EHR
transmitting a signal to assign a workflow in the EHR
However, Davis in the analogous art teaches:
transmitting an electronic notification in the EHR (“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 in the EHR (the machine learning “data processing system…is configured to detect health risks in patients and cause treatment responsive to the detection”, e.g. see [0006], [0002]; 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 the EHR and transmitting a signal to assign a workflow in the EHR 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 and Davis do 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 and Davis 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:
[…] the one or more processors are caused to determine that the individual requires preventative treatment for GDM and to 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 and Davis do not teach:
(a) 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, (b) based on the entered portion of the medical information
(c) the oral glucose tolerance test is administered to the individual at a period of 24-28 weeks into the current pregnancy of the individual
However, Roberts in the analogous art teaches:
(a) 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, (b) 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])
(c) the oral glucose tolerance test is administered to the individual at a period of 24-28 weeks into the current pregnancy of the individual (a workflow for assessing GDM includes an “Oral Glucose tolerance test (OGTT) at 28 weeks”, 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 and Davis 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 oral glucose tolerance test is administered to the individual at a period of 24-28 weeks into the current pregnancy of 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]).
Regarding claim 34, Peri and Davis teach the method of claim 18 as described above.
Davis teaches transmitting signals to assign workflows in the EHR as described above.
Peri further teaches:
detecting, during the blood glucose monitoring at the increased frequency […] (the response to a high-risk prediction is to define a “pro-active approach comprising of clinical…interventions” such as “more frequent monitoring of bio-markers” including glucose [0002], Table 1; the intervention workflow includes “monitor blood sugar” as an intervention for GDM, e.g. see [0156], [0154])
Peri and Davis do not teach:
detecting that a blood glucose level of the individual is uncontrolled;
in response to detecting that the blood glucose level is uncontrolled, to assign a workflow item comprising an oral glucose tolerance test; and
administering, based on assign the workflow item, the oral glucose tolerance test to the individual
However, Roberts in the analogous art teaches:
detecting that a blood glucose level of the individual is uncontrolled; (monitoring glucose in pregnant individuals, “If uncontrolled, GDM in the mother results in high glucose transport…elevating fetal insulin”, e.g. see [0009])
in response to detecting that the blood glucose level is uncontrolled, to assign a workflow item comprising an oral glucose tolerance test; and (when suspected of having GDM, the standard workflow involves assigning an OGTT, “Its presence is usually tested for at about 28 weeks gestation…and if positive it is definitively diagnosed by an oral glucose tolerance test.”, e.g. see [0009]; clinical workflows requiring an “OGTT” for varying risk levels, e.g. see Table 21, [0625])
administering, based on assign the workflow item, the oral glucose tolerance test to the individual (an intervention of an OGTT to the individual to verify GDM, e.g. see Table 21, [0625], [0009])
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 detecting that a blood glucose level of the individual is uncontrolled, in response to detecting that the blood glucose level is uncontrolled, to assign a workflow item comprising an oral glucose tolerance test and administering, the 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 rejection under 35 U.S.C. § 112(a) of Claims 1, 10, 18 and 33, the Applicant has amended the claims to overcome the bases of rejection. However, new grounds for rejection are made in view of the amendments.
Regarding the rejection under 35 U.S.C. § 112(b) of Claims 1, 4, 5, 7, 8, 10, 16-18, 20, 26 and 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 1-5, 7-22, 25-30 and 32-34, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive.
Applicant argues claim 1 cannot be directed to the abstract idea grouping of Certain Methods of Organizing Human Activity since no human is recited.
The Examiner respectfully disagrees that no human is recited. However, the claims have not been rejected under Certain Methods of Organizing Human Activity. Rather, as set forth in the prior Office Action, the claims are rejected as being directed to the abstract idea groupings of “Mental Processes” and “Mathematical Concepts”. See details above. The machine learning and training of the machine learning generally apply the abstract idea without placing any limits on how the machine learning and its training function. These limitations only recite the outcome and do not include any details on how the outcome is accomplished (see MPEP 2106.05(f)).
Applicant argues the claims integrate the judicial exception into a practical application.
The Examiner respectfully disagrees. The claim recites updating an EHR and increasing the frequency of glucose monitoring, which under MPEP 2106.05(g) is considered to be mere data gathering or insignificant extra-solution activity. Gathering additional medical data based on a mathematical determination does not amount to a practical application or provide significantly more.
Regarding the rejection under 35 U.S.C. § 103 of Claims 1-5, 7-22, 25-30 and 32-34, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive.
Applicant argues the cited portions of Peri, Davis, Smith, and Roberts, whether considered individually or in combination, fail to disclose “determining based on applying machine learning that an individual requires treatment for GDM, initiating data transmissions based on the applying and sending data based on the initiating, transmitting a signal to assign in an EHR a workflow comprising blood glucose monitoring at an increased frequency, and increasing a frequency (based on the transmitting of the signal to assign the workflow) of the glucose monitoring administered to the individual, as recited in claim 1”.
The Examiner respectfully disagrees. Applicant’s reliance on In re Robertson and the standard for inherency is misplaced. The rejection to claim 1 is maintained under 103 for Obviousness, not under 102 for Anticipation. The Examiner does not allege that the claimed combination is inherently present in a single reference, rather, the Examiner asserts the combination of the explicit teachings of the references would have made the claimed invention obvious to a person of ordinary school in the art. Under KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007), a claim is obvious if it is a predictable use of prior art elements according to their established functions.
Furthermore, as noted in the 112(a) rejection, the specification does not provide written description support for the limitation “increasing a frequency, based on the transmitting of the signal to assign the workflow, of the glucose monitoring administered to the individual”. The specification only supports updating a database with a care plan (see specification [0054] and [0056]). Therefore, “increasing a frequency, based on the transmitting of the signal to assign the workflow, of the glucose monitoring administered to the individual” is understood to be the system executing data transmissions to assign the workflow in the EHR, resulting in a care plan that dictates testing. Under this proper interpretation, the combination of Peri and Davis explicitly teach the claimed limitations as described above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.A./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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