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 .
Response to Amendment
In response to amendment filed July 2, 2026, claims 1, 9, 11 and 19 are amended. Claims 10 and 20 are cancelled and new claim 24-25 are added. Claims 1-9, 11-19 and 21-25 are pending.
Response to Arguments
Applicant’s arguments, see Remarks, filed July 2, 2026, with respect to the specification objection has been fully considered and are persuasive in view of the amendment. The objection of the specification has been withdrawn.
Applicant's arguments filed July 2, 2026 have been fully considered but they are not persuasive with respect to the rejection of claims under 35 U.S.C 101.
In response to applicant’s arguments that the claims do not recite an abstract idea, Examiner respectfully disagrees. The abstract ideas are identified as the steps of “generating; receiving; and determining”. That is, as broadly and reasonably interpreted, the above recited steps of generating, receiving and determining are steps that can practically be performed in the mind or with the aid of pen/paper. a physician can perform the claimed step of generating by mentally noting information received from a sensor including glucose concentrations. The physician can then receive that information mentally and use that information to mentally determine a glucose feature combination and then output a gestational diabetes mellitus prediction mentally and/or with the aid of a pen/pencil and paper. Thus, the claims can be readily interpreted as being a mere application of a mental process on a computer. An improved abstract idea is still an abstract idea even if such an abstract idea results in more accurate results. Applicant refers to the tangible claim elements that are in addition to the abstract idea (i.e., the additional elements). Applicant argues that because the additional elements are tangible, the claims are not directed to an abstract idea. However, this is not the test for eligibility. “As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility.” MPEP 2106.04(d) I. citing Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USOQ2d 1976, 1983-84 (2014). Thus, applicant’s identification of the additional elements in the claims as not being abstract does not influence the eligibility analysis. Applicant should not conflate the abstract idea with the elements of the claim that are in addition to the abstract idea (i.e. additional elements).
In response to Applicant’s argument that the claims integrate the abstract idea into practical application, Examiner respectfully disagrees. An improvement to the technology cannot be in the abstract idea. MPEP 2106.05a. “[L]imitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.” MPEP 2106.05(h). “[T]he additional element in Flook ... was not sufficient to make the claim eligible, because it was merely an incidental or token addition to the claim that did not alter or affect how the process steps of calculating the alarm limit value were performed.” Further, the data-gathering step of “receiving” and the data-output steps of “causing and displaying” do not add a meaningful limitation to the claims as they are insignificant extra-solution activity. Furthermore, the abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computer components for generating; transmitting; receiving; determining; applying; calculating; executing; and displaying merely invoke a computer as a tool to perform an existing process. The additional elements are identified in the 101 section below as being the elements that comprise a continuous analyte monitoring system; a sensor electronics module; a computing device; a memory; a processor; a machine learning model; and a graphical user interface. When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Applicant’s arguments, see Remarks, filed July 2, 2026, with respect to the rejection(s) of claims under 35 U.S.C 102 and 35 U.S.C 103 have been fully considered and are persuasive in view of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Dugan (US 20150289822 A1).
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 1-9, 11-19 and 21-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-9, 11-19 and 21-25 are all within at least one of the four categories.
The independent claim 1 recites:
generate sensor data packages including measured glucose concentration;
receive sensor data packages;
determine a glucose feature combination from the measured glucose concentration levels
generate a quantitative risk value score for a patient on based on the glucose feature combination.
determine a GDM prediction for the patient by comparing the quantitative risk value score to one or more threshold values determined based on a gestational week of the patient
The independent claim 11 recites:
generate sensor data packages including measured glucose concentration;
receive sensor data packages;
determine a glucose feature combination from the measured glucose concentration levels
generate a quantitative risk value score for a patient on based on the glucose feature combination.
determine a GDM prediction for the patient by comparing the quantitative risk value score to one or more threshold values determined based on a gestational week of the patient
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019. See footnotes 14 and 15.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018) (performing a resampled statistical analysis to generate a resampled distribution).
The claimed steps of generating; receiving; and determining can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
Examples of ineligible claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.;
claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.
a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
With respect to the pending claims, for example, a physician can perform the claimed step of generating by mentally noting information received from a sensor including glucose concentrations. The physician can then receive that information mentally and use that information to mentally determine a glucose feature combination and then output a gestational diabetes mellitus prediction mentally and/or with the aid of a pen/pencil and paper. Thus, the claims can be readily interpreted as being a mere application of a mental process on a computer.
Regarding the dependent claims 2-8 and 12-23, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. For example, dependent claims recite steps (e.g. generating; determining; applying; calculating; executing; and displaying) that can be performed in the mind. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
This judicial exception (abstract idea) in claims 1-9, 11-19 and 21-25 is not integrated into a practical application because:
The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for generating; transmitting; receiving; determining; applying; calculating; executing; and displaying merely invoke a computer as a tool.
The data-gathering step (measuring; and receiving) and the data-output step (transmitting; displaying) do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computer that is used as a tool for generating; transmitting; receiving; determining; applying; calculating; executing; and displaying.
The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to provide information about a gestational diabetes mellitus prediction.
The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer for generating; transmitting; receiving; determining; applying; calculating; executing; and displaying. The claims do not apply the obtained data to a particular machine. Rather, the data is merely output in an post-solution step.
The additional elements are identified as follows: a continuous analyte monitoring system; a sensor electronics module; a computing device; a memory; a processor; a machine learning model; and a graphical user interface
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
the patent publication cited herewith:
Frank (US 20210401330 A1).
see [0034]-[0035]; [0039]; [0055]; and [0154]-[0157]
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional elements as those claimed. See option III. A. 2. in the Berkheimer memorandum.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 9, 11-13, 19 and 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Frank (US 20210401330 A1; previously cited) in view of Dugan (US 20150289822 A1).
With respect to claim 1, Frank discloses a system for predicting gestational diabetes mellitus (GDM) (see paragraph 0034-0035: environment #100 to predict diabetes where the type of diabetes includes gestational diabetes mellitus (GDM)), the system comprising:
a continuous analyte monitoring (CAM) system (see paragraph 0039: wearable glucose monitoring device #104 is a continuous glucose monitoring system), including:
an analyte sensor configured to measure at least glucose concentration levels (see paragraph 0055: wearable glucose monitoring device #104 has a sensor #202; and see paragraph 0059: sensor #202 can be glucose sensor that detects analytes in blood indicative of glucose levels), and
a sensor electronics module (SEM) (see paragraph 0055: wearable glucose monitoring device #104 has a sensor module #204) configured to:
generate sensor data packages including measured glucose concentration levels (see paragraph 0061: sensor module #204 generates glucose measurements #110 and further generates observational device data #214 which is a communicable package of data that includes at least one glucose measurement #110, sensor identification #216, sensor status #218 and so forth), and
transmit the sensor data packages (see paragraph 0055: sensor module #104 may be housed or otherwise implanted within housing of transmitter #2087; and see paragraph 0062: transmitter #208 may transmit the observational data #214 wirelessly as a stream of data to a computing device); and
a computing device (see paragraph 0154: computing device #1102 that is representative of computing systems and/or devices that may implement the various techniques disclosed herein) comprising:
a memory storing executable instructions (see paragraph 0155-0157: 1102 includes one or more computer readable media #1106), and
a processor, in data communication with the memory, (see paragraph 0155-0157: 1102 includes processing system #1104): the processor configured to execute the instructions to cause the computing device to:
receive the sensor data packages (see paragraph 0154: computing device #1102 has prediction system #114; see paragraph 0091-0096: prediction system #114 may have preprocessing manager #402 and machine learning model #404 which receives glucose measurements),
determine a glucose feature combination from the measured glucose concentration levels (see paragraph 0091-0096: prediction system #114 may have preprocessing manager #402 and machine learning model #404 which receives glucose measurements to generate data such as one or more feature vectors to be provided as input to machine learning model #404 that data can be reported in connection with diabetes classification #116), and
generate a quantitative risk value score for a patient based on the glucose feature combination (see paragraph 0053: prediction system outputs a diabetes classification which indicates whether it is predicted a person has diabetes or is predicted to experience adverse effects associated with diabetes; and see paragraph 0027: diabetes classification indicates whether is user is at risk for developing diabetes and the type of diabetes can include gestational diabetes mellitus (GDM)).
Frank does not disclose determining a GDM prediction for the patient by comparing the quantitative risk value score to one or more threshold values based on a gestational week of the patient.
Dugan teaches determining a GDM prediction for a patient by comparing a quantitative risk value score to one or more thresholds based on a gestational week of the patient (see paragraph 0186-0188: risk value or risk thresholds are adjusted based on a gestational age of user; and see paragraph 0204: gestational age modified instant activity risk score).
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 Frank with the teachings of Dugan because it would have resulted in the predictable result of creating a customized risk threshold to compare whether a patient has diabetes (Dugan: [01896]-[0188]).
With respect to claim 2, all limitations of claim 1 apply in which Frank further discloses wherein:
the computing device is further configured to generate a quantitative GDM risk value based on the glucose feature combination (see paragraph 0027 and 0150-0151: diabetes classification is a classification of a user in a diabetes class, a prediabetes class or a no diabetes class or a value indicative of one of those classes); and
the quantitative GDM risk value has a range from a minimum GDM risk value to a maximum GDM risk value (see paragraph 0027 and 0150-0151: diabetes classification corresponds to a risk level with a high risk, low risk or no risk where high risk a maximum and low risk is a minimum).
With respect to claim 3, all limitations of claim 1 apply in which Frank further discloses wherein the glucose feature combination includes at least four glucose features (see paragraph 0095-0096: extracted glucose features may include one or more of mean glucose, median glucose, inter quartile range of the glucose measurements 110, variance of the glucose measurements 110, nocturnal hyperglycemia, differences between average glucose during waking hours and average glucose during sleeping hours, day-to-day variability of glucose, day-to-night variability of glucose, statistical distributions of glucose, threshold percentile of glucose (e.g., statistically significant threshold percentile such as 94.sup.th percentile or greater), 10 to 90 percentile range of glucose, standard deviation of glucose, mean of daily difference (MODD), and mean amplitude of glycemia excursions (MAGE), to name just a few).
With respect to claim 9, all limitations of claim 2 apply in which Frank further discloses wherein:
the processor is configured to execute a machine learning (ML) model to generate the quantitative GDM risk value (see paragraph 0091-0096: prediction system #114 may have preprocessing manager #402 and machine learning model #404 which receives glucose measurements to generate data such as one or more feature vectors to be input to machine learning model #404); and
the ML model is trained based on a combination of glucose features extracted from historical glucose data, and clinical GDM diagnoses associated with the historical glucose data (see paragraph 0104-0110: machine learning model #404 is trained to predict diabetes classification #116 using historical data, historical glucose measurements and outcome data from which diabetes classification can be derived);
the sensor data packages are received by the computing device over a wireless connection (see paragraph 0055: sensor module #204 may be housed or otherwise implemented within a hosing of transmitter #208; and see paragraph 0062: transmitter #208 may transmit the observation device data #214 wirelessly as a stream of data to a computing device); and
the processor is configured to execute further instructions to cause the computing device to display the quantitative GDM risk value in a graphical user interface (GUI) (see paragraph 0154: computing device #1102 that is representative of computing systems and/or devices that may implement the various techniques disclosed herein; and see paragraph 0127-0129: user interface displays a diabetes classification #116 to a user).
With respect to claim 11, Frank discloses a method for predicting gestational diabetes mellitus (GDM) (see paragraph 0034-0035: environment #100 to predict diabetes where the type of diabetes includes gestational diabetes mellitus (GDM)), the method comprising:
at a continuous analyte monitoring (CAM) system (see paragraph 0039: wearable glucose monitoring device #104 is a continuous glucose monitoring system):
measuring at least glucose concentration levels (see paragraph 0055: wearable glucose monitoring device#104 has a sensor #202; and see paragraph 0059: sensor #202 can be glucose sensor that detects analytes in blood indicative of glucose levels),
generating sensor data packages including measured glucose concentration levels (see paragraph 0061: sensor module #204 generates glucose measurements #110 and further generates observational device data #214 which is a communicable package of data that includes at least one glucose measurement #110, sensor identification #216, sensor status #218 and so forth), and
transmitting the sensor data packages (see paragraph 0055: sensor module #104 may be housed or otherwise implanted within housing of transmitter #2087; and see paragraph 0062: transmitter #208 may transmit the observational data #214 wirelessly as a stream of data to a computing device); and
at a computing device (see paragraph 0154: computing device #1102 that is representative of computing systems and/or devices that may implement the various techniques disclosed herein):
receiving the sensor data packages (see paragraph 0154: computing device #1102 has prediction system #114; see paragraph 0091-0096: prediction system #114 may have preprocessing manager #402 and machine learning model #404 which receives glucose measurements),
determining a glucose feature combination from the measured glucose concentration levels (see paragraph 0091-0096: prediction system #114 may have preprocessing manager #402 and machine learning model #404 which receives glucose measurements to generate data such as one or more feature vectors to be provided as input to machine learning model #404 that data can be reported in connection with diabetes classification #116), and
generating a quantitative risk value score for a patient based on the glucose feature combination (see paragraph 0053: prediction system outputs a diabetes classification which indicates whether it is predicted a person has diabetes or is predicted to experience adverse effects associated with diabetes; and see paragraph 0027: diabetes classification indicates whether is user is at risk for developing diabetes and the type of diabetes can include gestational diabetes mellitus (GDM)).
Frank does not disclose determining a GDM prediction for the patient by comparing the quantitative risk value score to one or more threshold values based on a gestational week of the patient.
Dugan teaches determining a GDM prediction for a patient by comparing a quantitative risk value score to one or more thresholds based on a gestational week of the patient (see paragraph 0186-0188: risk value or risk thresholds are adjusted based on a gestational age of user; and see paragraph 0204: gestational age modified instant activity risk score).
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 Frank with the teachings of Dugan because it would have resulted in the predictable result of creating a customized risk threshold to compare whether a patient has diabetes (Dugan: [01896]-[0188]).
With respect to claim 12, all limitations of claim 11 apply in which Frank further discloses wherein:
generating a quantitative GDM risk value based on the glucose feature combination (see paragraph 0027 and 0150-0151: diabetes classification is a classification of a user in a diabetes class, a prediabetes class or a no diabetes class or a value indicative of one of those classes); and
wherein the quantitative GDM risk value has a range from a minimum GDM risk value to a maximum GDM risk value (see paragraph 0027 and 0150-0151: diabetes classification corresponds to a risk level with a high risk, low risk or no risk where high risk a maximum and low risk is a minimum).
With respect to claim 13, all limitations of claim 11 apply in which Frank further discloses wherein the glucose feature combination includes at least four glucose features (see paragraph 0095-0096: extracted glucose features may include one or more of mean glucose, median glucose, inter quartile range of the glucose measurements 110, variance of the glucose measurements 110, nocturnal hyperglycemia, differences between average glucose during waking hours and average glucose during sleeping hours, day-to-day variability of glucose, day-to-night variability of glucose, statistical distributions of glucose, threshold percentile of glucose (e.g., statistically significant threshold percentile such as 94th percentile or greater), 10 to 90 percentile range of glucose, standard deviation of glucose, mean of daily difference (MODD), and mean amplitude of glycemia excursions (MAGE), to name just a few).
With respect to claim 19, all limitations of claim 12 apply in which Frank further discloses wherein:
generating the quantitative GDM risk value includes executing a machine learning (ML) model (see paragraph 0091-0096: prediction system #114 may have preprocessing manager #402 and machine learning model #404 which receives glucose measurements to generate data such as one or more feature vectors to be input to machine learning model #404); and
the ML model is trained based on a combination of glucose features extracted from historical glucose data, and clinical GDM diagnoses associated with the historical glucose data (see paragraph 0104-0110: machine learning model #404 is trained to predict diabetes classification #116 using historical data, historical glucose measurements and outcome data from which diabetes classification can be derived);
displaying the quantitative GDM risk value in a graphical user interface (GUI) (see paragraph 0154: computing device #1102 that is representative of computing systems and/or devices that may implement the various techniques disclosed herein; and see paragraph 0127-0129: user interface displays a diabetes classification #116 to a user),
wherein the sensor data packages are received over a wireless connection (see paragraph 0055: sensor module #204 may be housed or otherwise implemented within a hosing of transmitter #208; and see paragraph 0062: transmitter #208 may transmit the observation device data #214 wirelessly as a stream of data to a computing device).
With respect to claim 24, all limitations of claim 11 apply in which Dugan further teaches wherein: the GDM prediction includes a prediction that the patient has GDM or does not have GDM (see paragraph 0186-0188, 0204-0205: whether patient has diabetes); and the one or more threshold values include a diagnostic threshold that increases as the gestational week increases (see paragraph 0186-0188, 0204-0205: risk threshold value changes based on gestational age and is modified based on the gestational age).
With respect to claim 25, all limitations of claim 11 Dugan further teaches wherein: the GDM prediction includes a prediction that: the patient does not have GDM,the patient has a predisposition for GDM, or the patient has GDM (see paragraph 0186-0188, 0204-0205: whether patient has diabetes, inclined to get diabetes or does not have); the one or more threshold values include a screening threshold for each prediction; and each screening threshold depends on the gestational week of the patient (see paragraph 0186-0188, 0204-0205: risk threshold value changes based on gestational age and is modified based on the gestational age).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Frank in view of Dugan as applied to claims 3 and 13 above respectively, and further in view of Koike Akino (US 20230162023 A1; previously cited).
With respect to claim 4, all limitations of claim 3 apply in which Frank does not specifically disclose wherein: at least one glucose feature has a high performance metric; and at least one glucose feature has a high robustness metric that is relatively insensitive to analyte sensor bias.
Koike Akino teaches features that have high performance metric (see paragraph 0103: accuracy score) and features that have high robustness metric that is relatively insensitive to analyte sensor bias (see paragraph 0040: a neural network can have features that account for biases; and see paragraph 0008: preprocessing mechanism that enhance robust inference across data formats such as time-series signals).
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 Frank with the teachings of Koike Akino to have features with high performance metric and high robustness metric because it would have resulted in the predictable result of finding features that are insensitive to them as bio-signals are highly subject to variation depending on biological states of a subject and sensor’s imperfections (Koike Akino: see [0002]).
With respect to claim 14, all limitations of claim 13 apply in which Frank does not specifically disclose wherein: at least one glucose feature has a high performance metric; and at least one glucose feature has a high robustness metric that is relatively insensitive to analyte sensor bias.
Koike Akino teaches features that have high performance metric (see paragraph 0103: accuracy score) and features that have high robustness metric that is relatively insensitive to analyte sensor bias (see paragraph 0040: a neural network can have features that account for biases; and see paragraph 0008: preprocessing mechanism that enhance robust inference across data formats such as time-series signals).
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 Frank with the teachings of Koike Akino to have features with high performance metric and high robustness metric because it would have resulted in the predictable result of finding features that are insensitive to them as bio-signals are highly subject to variation depending on biological states of a subject and sensor’s imperfections (Koike Akino: see [0002]).
Claims 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Frank and Dugan as applied to claim 11 above, and further in view of Chorev (US 20210284751 A1; previously cited).
With respect to claim 21, all limitations of claim 11 apply in which Frank does not specifically disclose wherein the glucose concentration levels are measured prior to gestational week 24, and the GDM prediction is associated with the first trimester or the second trimester.
Chorev teaches measuring glucose levels prior to gestational week 24 (see paragraph 0171: obtaining baseline values where baseline values may, for example, be obtained from about 12 weeks to about 36 weeks of pregnancy, from about 20 weeks to about 36 weeks of pregnancy and/or from about 24 to about 28 weeks of pregnancy (including during week 24 of pregnancy, during week 25 of pregnancy, during week 26 of pregnancy, during week 27 of pregnancy and/or during week 28 of pregnancy wherein some baseline values may be concentration values of glucose), and the GDM prediction is associated with the first trimester or the second trimester (see paragraph 0166: first trimester is typically from about week 1 to week 12 and the second trimester is typically from 13 weeks to 28 weeks).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to have combined Frank with the teachings of Chorev to have measured glucose levels prior to gestational week 24 because it would have resulted in the predictable result of monitoring gestational diabetes mellitus for women in their first or second trimester (Chorev: see [0166]) to monitor women who are at risk of developing gestational diabetes (Chorev: see [0133]-[0134]).
With respect to claim 22, all limitations of claim 21 apply in which Chorev further teaches wherein the glucose concentration levels are measured during the first trimester (see paragraph 0171: obtaining baseline values where baseline values may, for example, be obtained from about 12 weeks to about 36 weeks of pregnancy, from about 20 weeks to about 36 weeks of pregnancy and/or from about 24 to about 28 weeks of pregnancy (including during week 24 of pregnancy, during week 25 of pregnancy, during week 26 of pregnancy, during week 27 of pregnancy and/or during week 28 of pregnancy wherein some baseline values may be concentration values of glucose), and the GDM prediction is associated with the first trimester (see paragraph 0166: first trimester is typically from about week 1 to week 12 and the second trimester is typically from 13 weeks to 28 weeks).
With respect to claim 23, all limitations of claim 21 apply in which Chorev further teaches wherein the glucose concentration levels are measured during the second trimester prior to gestational week 24 (see paragraph 0171: obtaining baseline values where baseline values may, for example, be obtained from about 12 weeks to about 36 weeks of pregnancy, from about 20 weeks to about 36 weeks of pregnancy and/or from about 24 to about 28 weeks of pregnancy (including during week 24 of pregnancy, during week 25 of pregnancy, during week 26 of pregnancy, during week 27 of pregnancy and/or during week 28 of pregnancy wherein some baseline values may be concentration values of glucose), and the GDM prediction is associated with the second trimester prior to gestational week 24 (see paragraph 0166: first trimester is typically from about week 1 to week 12 and the second trimester is typically from 13 weeks to 28 weeks).
Examiner Comment on Prior Art
The closest prior art of record Frank (US 20210401330 A1); Koike Akino (US 20230162023 A1); and Chorev (US 20210284751 A1), do not teach or suggest claims 5-8 and 15-18 more specifically the limitations of reciting the “the glucose feature combination includes at least an autocorrelation skew feature and at least a mean peak width feature” in combination with the other elements of the claim. Zalevsky (US 9668672 B2) teaches measuring peak amplitudes, mean temporal distances between temporal peaks and temporal pulse widths (see Col 14 lines 7-20) however does not disclose or suggest “a mean peak width feature”. Gunn (US 20200305741 A1) teaches known signal processing techniques include autocorrelation (see paragraph 0207) however does not disclose or suggest “an autocorrelation skew feature”.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.N.P./Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791
