DETAILED ACTION
Applicant’s arguments, filed 02/19/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application. Applicant has amended their claims, filed 02/19/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Applicant canceled claims 6 and 18. Thus, claims 1-5, 7-17, and 19-20 are the current claims hereby under examination.
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 .
Claim Objections
Claims 1 and 15 are objected to because of the following informalities:
Claim 1, line 10 and claim 15, line 13 should read “for each of the factors …”.
Appropriate correction is required.
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, 7-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without
significantly more.
Analysis of independent claims 1 and 15:
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 1 is directed to a computer implemented method, which describes one of the four statutory categories of patentable subject matter, i.e., a method. Claim 15 is directed to a system, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Therefore, further consideration is necessary regarding claims.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 1 and 15 recite an abstract idea. In particular, the claims generally recite
the following:
Calculating one or more factors based on the current blood glucose level of the user indicative of a risk of a hypoglycemic or hyperglycemic condition, wherein each factor is a probability of the hypoglycemic condition or the hyperglycemic condition for the user; and
Determining, with the processor, for each of the factors, whether the factor exceeds a probability threshold for that factor and thus indicates that the hypoglycemic condition or the hyperglycemic condition is likely for the user;
These elements recited in claims 1 and 15 are drawn to an abstract idea since they are directed towards mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
“Calculating one or more factors based on the current blood glucose level of the user indicative of a risk of a hypoglycemic or hyperglycemic condition, wherein each factor is a probability of the hypoglycemic condition or the hyperglycemic condition for the user” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably view blood glucose levels and determine a probability of a patient’s risk of hypoglycemia or hyperglycemia therefrom. There is nothing to suggest an undue level of complexity in “Calculating one or more factors based on the current blood glucose level of the user indicative of a risk of a hypoglycemic or hyperglycemic condition, wherein each factor is a probability of the hypoglycemic condition or the hyperglycemic condition for the user”.
“Determining, with the processor, for each of the factors, whether the factor exceeds a probability threshold for that factor and thus indicates that the hypoglycemic condition or the hyperglycemic condition is likely for the user” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably set a threshold for a calculated probability to indicate a hypoglycemic or hyperglycemic condition. There is nothing to suggest an undue level of complexity in “Determining, with the processor, for each of the factors, whether the factor exceeds a probability threshold for that factor and thus indicates that the hypoglycemic condition or the hyperglycemic condition is likely for the user”.
Prong Two: Claims 1 and 15 do not recite additional elements that integrate the exception
into a practical application. Therefore, the claims are "directed to" the abstract idea. The additional
elements merely:
Recite the words "apply it" or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “software” (claim 15) and “a processor” (claims 1 and 15)) and
Add insignificant extra-solution activity (the pre-solution activity of: (e.g., "receiving at the processor an indication of a current blood glucose level of the user" (claims 1 and 15), "a blood glucose sensor" (claim 15)); the post-solution activity of: (e.g. “with the processor, outputting an alert to the user via a user interface of the automatic drug delivery system when more than a threshold number of the factors indicate that the hypoglycemic condition or the hyperglycemic condition for the user is likely and wherein the threshold number is more than two” (claims 1 and 15), “a drug delivery device” (claim 15), and “a user device” (claim 15))).
As a whole, the additional elements merely serve to gather information to be used by the
abstract idea, while generically implementing it on a computer. There is no practical application because
the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed
remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the
abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1 and 15 do not include additional elements, alone or in combination, that are
sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the
same reasons as described above. E.g., all elements are directed to implementing the abstract ideas on
generic processing components, the pre-solution activity of using generic data-gathering components,
and generic post-solution activities, which merely facilitate the abstract idea.
Per the Berkheimer requirement, the additional elements are well-understood, routine, and
conventional. For example, “a blood glucose sensor” as disclosed in the Applicant’s specification in paragraph 0042, “The analyte sensor 108 may be configured to detect one or multiple different analytes, such as glucose, lactate, ketones, uric acid, sodium, potassium, alcohol levels or the like, and output results of the detections, such as measurement values or the like. The analyte sensor 108 may, in an exemplary embodiment, be configured as a continuous glucose monitor (CGM) to measure a blood glucose values”. As another example, “a drug delivery device” as disclosed in the Applicant’s specification in paragraph 0002, “Many conventional automated drug delivery (ADD) systems are well known, including, for example, wearable drug delivery devices. The drug delivery device can be designed to deliver any type of liquid drug to a user. In specific embodiments, the drug delivery device can be, for example, an OmniPod® drug delivery device manufactured by Insulet Corporation of Acton, Massachusetts. The drug delivery device can be a drug delivery device such as those described in U.S. Pat. No. 7,303,549, U.S. Pat. No. 7,137,964, or U.S. Pat. No. 6,740,059, each of which is incorporated herein by reference in its entirety”.
Further, "a user device", “a processor”, and “software” do not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric PowerGroup, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014); SAP Am. v. lnvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a
practical application and do not amount to significantly more than the above-judicial exception (the
abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing
that is not already present when looking at the elements taking individually. There is no indication that
the combination of elements improves the functioning of a computer, for example, or improves any
other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of
elements include a particular solution to a computer-based problem or a particular way to achieve a
desired computer-based outcome. Rather, the collective functions of the claimed invention merely
provide conventional computer implementation, i.e., the computer is simply a tool to perform the
process.
Analysis of the dependent claims:
Claims 2-5, 7-14, 16-17, and 19-20 depend from the independent claims. Dependent claims 2-5, 7-14, 16-17, and 19-20 merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
Further describe the abstract idea (“receiving glucose levels of the user over time and wherein the one or more risk factors include a factor indicating a linear relationship between a probability of hypoglycemia and a drop in the blood glucose level of the user or the probability of hyperglycemia and a rise in the blood glucose level of the user” (claims 2 and 16), “wherein the one or more risk factors include a factor indicating a linear relationship between a probability of hypoglycemia and a rate of change of the blood glucose level of the user below a predetermined threshold per unit time or a probability of hyperglycemia and a rate of change of the blood glucose level of the user above a predetermined threshold per unit time” (claims 3 and 16), “wherein the one or more risk factors include a factor indicating a linear relationship between a probability of hypoglycemia and an amount of insulin-on-board the user necessary to bring the blood glucose level of the user to zero or a probability of hyperglycemia and an amount of insulin-on-board the user necessary to bring the blood glucose level of the user to a predetermined threshold” (claims 4 and 16), “calculate an additional risk factor based on accuracy of previous alerts issued to the user” (claims 5 and 17), “a machine learning model providing weighting factors applicable to the risk factors, the weighting factors being dependent upon the accuracy of previous alerts” (claim 10), “wherein the method is executed periodically or each time a new blood glucose reading for the user is received” (claim 12), “wherein the risk of hypoglycemia is determined based on the assumption that the user intends on engaging in a specific activity” (claim 13), and “wherein an assessment of the risk of hypoglycemia in presence of the specific activity is calculated at a request of the user” (claim 14));
Further describe the pre-solution activity (“wherein the machine learning model is aggregated over a large population of users to provide weighting factors for new users of the automatic drug delivery system” (claim 11)); and
Further describe the post-solution activity (“wherein the alert comprises a text flag displayed on a screen of a user application running on a user device portion of the automatic drug delivery system” (claims 7 and 19), “wherein the alert comprises a modal window displayed on a screen of a user application running on a user device portion of the automatic drug delivery system” (claims 8 and 19), and “wherein the alert comprises a visual, audible or haptic feedback provided via a user interface of a wearable drug delivery device portion of the automatic drug delivery system” (claims 9 and 20)).
Taken alone or in combination, the additional elements do not integrate the judicial exception
into a practical application at least because the abstract idea is not applied, relied on, or used in a
meaningful way. The additional elements do not add anything significantly more than the abstract idea.
The collective functions of the additional elements merely provide computer/electronic implementation
and processing, and no additional elements beyond those of the abstract idea. There is no indication
that the combination of elements permits automation of specific tasks that previously could not be
automated. There is no indication that the combination of elements improves the functioning of a
computer, output device, improves technology other than the technical field of the claimed invention, etc. The result of the abstract idea does not cause the computing device and/or application to perform
different. The result of the abstract idea does not cause output of the user-accessible output. The user-
accessible output does not cause or confirm that the user adjusts their posture. Therefore, the claims
are rejected as being directed to non-statutory subjection matter.
Examiner notes that claims 10 and 11, while directed towards a machine learning model, broadly claims the use of a machine learning model to weight risk factors trained over a large population. Without the specific recitation of the type of machine learning model or algorithms used to perform this calculation and training, claims 10 and 11 do not integrate the judicial exception into a practical application. Therefore, claims 1-5, 7-17, and 19-20 are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-10, 12-13, 15-17, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rack-Gomer (US 20150289821 – cited by Applicant).
Regarding claim 1, Rack-Gomer teaches a method for alerting a user of an automated drug delivery system having a of a risk of a hypoglycemic or hyperglycemic condition comprising:
receiving at the processor an indication of a current blood glucose level of the user (Paragraph 0007, “Systems and methods are disclosed that employ numerous variables or parameters in the determination and/or calculation of a glycemic urgency index (GUI), which may be based in part on a measured blood glucose level and which generally includes consideration of other factors”);
with the processor, calculating one or more factors based on the current blood glucose level of the user indicative of a risk of a hypoglycemic or hyperglycemic condition (Table II, wherein predictors are calculated, some of which are calculated based on glucose levels, along with their respective thresholds for decisions), wherein each factor is a probability of the hypoglycemic condition or the hyperglycemic condition for the user (Paragraph 0265, wherein each decision is converted to a likelihood value);
determining, with the processor, for each of the factors, whether the factor exceeds a probability threshold for that factor (Paragraph 0262, wherein each parameter is compared to its threshold and a yes/no decision is made about whether or not a hypoglycemic event is about to occur”) and thus indicates that the hypoglycemic condition or the hyperglycemic condition is likely for the user (Paragraph 0259, “Decision fusion uses a statistical model to optimally combine risk information from multiple inputs and produces a likelihood value that some event will occur, like hypoglycemia”); Paragraph 0265, “This weighting of each decision based on test performance thus enters the calculation. Once each decision is converted to a likelihood value, all the likelihood values may be simply multiplied together);
with the processor, outputting an alert to the user via a user interface of the automatic drug delivery system (Paragraph 0312, wherein various means of alerting/outputting an alarm are discussed) when more than a threshold number of factors indicate that the hypoglycemic condition or the hyperglycemic condition for the user is likely and wherein the threshold number is more than two (Paragraph 0265, “The final likelihood value is then a range where low numbers mean that hypoglycemia is very unlikely to happen and high numbers mean that hypoglycemia is very likely to happen”). In summary, all of the parameters in Table II are compared to their respective thresholds to determine a yes/no decision, and the yes/no decisions are combined to compute a likelihood of hypoglycemia. Thus, the final likelihood contains multiple decisions that have crossed a threshold to determine the hypoglycemic condition.
Regarding claim 2, Rack-Gomer further teaches receiving glucose levels of the user over time (Paragraph 0013, “The data of the first type may be analyte, e.g. glucose, data from a continuous analyte sensor implanted in the body”) and wherein the one or more risk factors include a factor indicating a linear relationship between a probability of hypoglycemia and a drop in the blood glucose level of the user or the probability of hyperglycemia and a rise in the blood glucose level of the user (Paragraph 0146, “In the GUI determination, all other factors being equal, a high glucose level tends to move the value of the GUI towards a greater value of urgency, that indicating a hyperglycemic state. Conversely, a low glucose level tends to move the value of the GUI again towards a greater value of urgency, that indicating a hypoglycemic state … Other quantitative or qualitative risk indexes may be envisioned as is appreciated by one skilled in the art, understanding that the risk index is not necessarily correlated with glycemic state, but rather urgency of clinical action to avoid a dangerous glycemic status”; Paragraph 0258, wherein probability algorithms may be employed to determine risk index therefrom).
Regarding claim 3, Rack-Gomer further teaches wherein the one or more risk factors include a factor indicating a linear relationship between a probability of hypoglycemia and a rate of change of the blood glucose level of the user below a predetermined threshold per unit time or a probability of hyperglycemia and a rate of change of the blood glucose level of the user above a predetermined threshold per unit time (Paragraph 0147, “Other types of data may be based on this first type of data, e.g., the first derivative of the glucose values with respect to time can be employed to determine a time rate of change of glucose value, i.e., a “velocity” of the glucose value, i.e., if the glucose value is increasing or decreasing, as well as how fast such changes are occurring. Thus, a data value representing the first derivative can be employed in an initial estimate of a prediction of future glucose values, and also in the determination of the GUI”; Paragraph 0258, wherein probability algorithms may be employed to determine risk index therefrom).
Regarding claim 4, Rack-Gomer further teaches wherein the one or more risk factors include a factor indicating a linear relationship between a probability of hypoglycemia and an amount of insulin-on-board the user necessary to bring the blood glucose level of the user to zero or a probability of hyperglycemia and an amount of insulin-on-board the user necessary to bring the blood glucose level of the user to a predetermined threshold (Paragraphs 0201 and 0258, wherein insulin on board is used for quantifying risk of an event and wherein probability algorithms may be employed to determine risk index therefrom; Paragraphs 0259-0261, wherein the method of using multiple inputs determines whether a user is likely to be below a threshold blood glucose level (e.g., 55 mg/dL)).
Regarding claim 5, Rack-Gomer further teaches calculating an additional risk factor based on accuracy of previous alerts issued to the user (Paragraph 0154).
Regarding claim 7, Rack-Gomer further teaches wherein the alert comprises a text flag displayed on a screen of a user application running on a user device portion of the automatic drug delivery system (Fig. 24, textual indication 612; Figs. 16-29, various alert types).
Regarding claim 8, Rack-Gomer further teaches wherein the alert comprises a modal window displayed on a screen of a user application running on a user device portion of the automatic drug delivery system (Paragraph 0056, wherein an alert may override other applications on the mobile device to indicate the alert; Figs. 16-29, various alert types).
Regarding claim 9, Rack-Gomer further teaches wherein the alert comprises a visual (Figs. 16-29), audible or haptic feedback (Paragraphs 0269-0271) provided via a user interface of a wearable drug delivery device portion of the automatic drug delivery system.
Regarding claim 10, Rack-Gomer further teaches wherein a machine learning model providing weighting factors applicable to the risk factors, the weighting factors being dependent upon the accuracy of previous alerts (Paragraph 0211 and Table I, wherein weighting of inputs based on calculated accuracy determined by the assessment module; Paragraph 0168, wherein the assessment module uses machine learning).
Regarding claim 12, Rack-Gomer further teaches wherein the method is executed periodically or each time a new blood glucose reading for the user is received (Paragraphs 0104-0105).
Regarding claim 13, Rack-Gomer further teaches wherein the risk of hypoglycemia is determined based on the assumption that the user intends on engaging in a specific activity (Paragraphs 0168-0171, regarding tracking user patterns such as exercise; Paragraph 0178, wherein exercise is useful information for the monitoring hyper- and hypoglycemia and can be used to calculate risk).
Regarding claim 15, Rack-Gomer teaches a system comprising:
a drug delivery device (Paragraph 0009, “Automatic medicament delivery devices may be interfaced with the system as well, e.g., to indicate insulin pump actions for consideration in the GUI determination”; Paragraph 0072);
a blood glucose sensor (Fig. 3, continuous analyte sensor 10; Paragraph 0115, “The sensor electronics module 12 may include hardware, firmware, software, or a combination thereof, to provide measurement of levels of the analyte via a continuous analyte sensor, such as a continuous glucose sensor”) in communication with the drug delivery device (Paragraph 0072);
a user device in communication with the drug delivery device (Figs. 3-4 and Paragraphs 0117/0122); and
software executing on a processor of the user device or of the drug delivery device (Figs. 3-4 and Paragraphs 0117/0122) which causes the system to:
receive at the processor an indication of a current blood glucose level of the user (Paragraph 0007, “Systems and methods are disclosed that employ numerous variables or parameters in the determination and/or calculation of a glycemic urgency index (GUI), which may be based in part on a measured blood glucose level and which generally includes consideration of other factors”);
calculate one or more factors based on the current blood glucose level of the user indicative of a risk of a hypoglycemic or hyperglycemic condition (Table II, wherein predictors are calculated, some of which are calculated based on glucose levels, along with their respective thresholds for decisions), wherein each factor is a probability of the hypoglycemic condition or the hyperglycemic condition for the user (Paragraph 0265, wherein each decision is converted to a likelihood value);
determine, with the processor, for each of the factors, whether the factor exceeds a probability threshold for that factor (Paragraph 0262, wherein each parameter is compared to its threshold and a yes/no decision is made about whether or not a hypoglycemic event is about to occur”) and thus indicates that the hypoglycemic condition or the hyperglycemic condition is likely for the user (Paragraph 0259, “Decision fusion uses a statistical model to optimally combine risk information from multiple inputs and produces a likelihood value that some event will occur, like hypoglycemia”); Paragraph 0265, “This weighting of each decision based on test performance thus enters the calculation. Once each decision is converted to a likelihood value, all the likelihood values may be simply multiplied together); and
with the processor, output an alert to the user via a user interface of the automatic drug delivery system (Paragraph 0312, wherein various means of alerting/outputting an alarm are discussed) when more than a threshold number of factors indicate that the hypoglycemic condition or the hyperglycemic condition for the user is likely and wherein the threshold number is more than two (Paragraph 0265, “The final likelihood value is then a range where low numbers mean that hypoglycemia is very unlikely to happen and high numbers mean that hypoglycemia is very likely to happen”). In summary, all of the parameters in Table II are compared to their respective thresholds to determine a yes/no decision, and the yes/no decisions are combined to compute a likelihood of hypoglycemia. Thus, the final likelihood contains multiple decisions that have crossed a threshold to determine the hypoglycemic condition.
Regarding claim 16, Rack-Gomer further teaches wherein the system receives glucose levels of the user over time (Paragraph 0013, “The data of the first type may be analyte, e.g. glucose, data from a continuous analyte sensor implanted in the body”) and wherein the one or more risk factors includes:
a factor indicating a linear relationship between a probability of hypoglycemia and a drop in the blood glucose level of the user or the probability of hyperglycemia and a rise in the blood glucose level of the user (Paragraph 0146, “In the GUI determination, all other factors being equal, a high glucose level tends to move the value of the GUI towards a greater value of urgency, that indicating a hyperglycemic state. Conversely, a low glucose level tends to move the value of the GUI again towards a greater value of urgency, that indicating a hypoglycemic state … Other quantitative or qualitative risk indexes may be envisioned as is appreciated by one skilled in the art, understanding that the risk index is not necessarily correlated with glycemic state, but rather urgency of clinical action to avoid a dangerous glycemic status”; Paragraph 0258, wherein probability algorithms may be employed to determine risk index therefrom);
a factor indicating a linear relationship between a probability of hypoglycemia and a rate of change of the blood glucose level of the user below a predetermined threshold per unit time or a probability of hyperglycemia and a rate of change of the blood glucose level of the user above a predetermined threshold per unit time (Paragraph 0147, “Other types of data may be based on this first type of data, e.g., the first derivative of the glucose values with respect to time can be employed to determine a time rate of change of glucose value, i.e., a “velocity” of the glucose value, i.e., if the glucose value is increasing or decreasing, as well as how fast such changes are occurring. Thus, a data value representing the first derivative can be employed in an initial estimate of a prediction of future glucose values, and also in the determination of the GUI”; Paragraph 0258, wherein probability algorithms may be employed to determine risk index therefrom); and
a factor indicating a linear relationship between a probability of hypoglycemia and an amount of insulin-on-board the user necessary to bring the blood glucose level of the user to zero or a probability of hyperglycemia and an amount of insulin-on-board the user necessary to bring the blood glucose level of the user to a predetermined threshold (Paragraphs 0201 and 0258, wherein insulin on board is used for quantifying risk of an event and wherein probability algorithms may be employed to determine risk index therefrom; Paragraphs 0259-0261, wherein the method of using multiple inputs determines whether a user is likely to be below a threshold blood glucose level (e.g., 55 mg/dL)).
Regarding claim 17, Rack-Gomer further teaches wherein the software calculates an additional risk factor based on the accuracy of previous alerts issued to the user (Paragraph 0154).
Regarding claim 19, Rack-Gomer further teaches wherein the alert comprises a text flag or modal window displayed on a screen of the user device (Fig. 24, textual indication 612; Paragraph 0056, wherein an alert may override other applications on the mobile device to indicate the alert; Figs. 16-29, various alert types).
Regarding claim 20, Rack-Gomer further teaches wherein the alert comprises a visual (Figs. 16-29), audible or haptic feedback (Paragraphs 0269-0271) provided via the drug delivery device.
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.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Rack-Gomer as applied to claim 10 above, and further in view of Dowd (US 20230134919).
Regarding claim 11, Rack-Gomer discloses using a machine learning model to provide weighting factors to calculate risk as described above. Rack-Gomer fails to explicitly disclose how the machine learning model was trained.
However, Dowd teaches an analogous glucose level measurement system wherein a trained machine learning system uses weights to predict a risk of hypoglycemia and hyperglycemia (Paragraphs 0067-0068), and the machine learning system was trained using data from other users (Paragraph 0069). Dowd discusses this is useful to customize the system to the particular user (Paragraph 0070). Thus, 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 system of Rack-Gomer to incorporate the machine learning system training of Dowd to customize the system to a particular user.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Rack-Gomer as applied to claim 13 above, and further in view of Aykroyd (US 20150095042).
Regarding claim 14, Rack-Gomer discloses calculating the risk of hypoglycemia in the presence of the activity (Paragraphs 0168-0171, regarding tracking user patterns such as exercise; Paragraph 0178, wherein exercise is useful information for the monitoring hyper- and hypoglycemia and can be used to calculate risk). Additionally, Rack-Gomer discloses wherein the user may input data and otherwise interact with the system (Paragraphs 0051 and 0316). Rack-Gomer fails to explicitly disclose wherein the risk is calculated at the request of the user.
However, Aykroyd teaches a blood glucose measurement system wherein a user may request an assessment of their hypo- and/or hyperglycemic risk (Paragraphs 0113, 0127, 0129, 0143, and 0151; Figs. 15-17). One of ordinary skill in the art would have been motivated in applying the step of a user requesting a device to calculating a risk score taught by Aykroyd to the system/method disclosed in Rack-Gomer, and the results of calculating a user-requested risk score would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rack-Gomer to incorporate the teachings of Aykroyd.
Response to Arguments
Applicant’s arguments, see page 8, filed 02/19/2026, with respect to the specification objection have been fully considered and are persuasive. Applicant amended the specification per the suggestion of the Examiner. The objection of the specification has been withdrawn.
Applicant’s arguments, see page 8, filed 02/19/2026, with respect to the claim objection have been fully considered and are persuasive. Applicant has amended the claim per the suggestion of the Examiner. The objection of the claim has been withdrawn.
Applicant's arguments, see page 8, filed 02/19/2026, with respect to the 35 U.S.C. §101 rejection have been fully considered but they are not persuasive. Applicant argues that the implementation of a multi-factor voting system to trigger an alert of a hypoglycemic or hyperglycemic condition integrates the judicial exception into a practical application. Examiner respectfully disagrees.
The abstract idea of calculating probabilities based on glucose level measurements and determining whether the calculated probabilities cross a threshold to alert a user does not integrate the judicial exception into a practical application. The judicial exception is not directed towards mathematical equations as argued by the Applicant. Rather, the judicial exception is directed towards mental processes that can be performed in the human mind with the aid of pen and paper or a generic computer.
Applicant further points to the “voting scheme” being a critical safety function of an automatic drug delivery system; however, Examiner notes that the claims are directed to the generic post-solution activity of alerting a user and, therefore, remain in the abstract realm. There is no claim of dispensing a drug or performing some other action with the automatic drug delivery system. The claims are directed to alerting a user of a condition based on the mental processes of calculating probabilities and determining when the probabilities cross a threshold.
Furthermore, Applicant asserts that the additional elements are not well-understood, routine, and conventional due to their unconventional arrangement. However, as recited above, the additional elements are well-understood, routine, and conventional as disclosed by the Applicant. Applicant explicitly states that drug delivery devices are “well-known”, and a generic blood glucose sensor is also well-known in the art. In combination with well-understood, routine, and conventional elements such as a processor, software, and a user device, judicial exception is not integrated into a practical application because the steps merely apply conventional computer implementation to the judicial exception.
Examiner notes that claim 11 was previously not rejected under 35 U.S.C. §101, but, upon further consideration, has been included in the rejection. The rejection above as been modified to reflect the amendment to the claims.
Applicant’s arguments, see page 8, filed 02/19/2026, with respect to the 35 U.S.C. §112(b) rejections have been fully considered and are persuasive. Applicant has amended the claims to recite that glucose levels are received over time and fixed the lack of antecedent basis issue. The rejection of the claims has been withdrawn.
Applicant’s arguments, see page 10, filed 02/19/2026, with respect to the rejection(s) of claim(s) 1-10, 12-13, and 15-20 under 35 U.S.C. §102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration of the amended claims, a new ground(s) of rejection is made in view of Rack-Gomer. The rejections of Rack-Gomer above have been modified to reflect the amendment to the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Malecha (US 20090105573 – cited by Applicant) teaches a device for predicting a user’s future glycemic state wherein a set of probabilities (Figs. 2A-B; Paragraphs 0048-0051) determine a user’s glycemic state (Fig. 3).
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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/NOAH M HEALY/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791