DETAILED ACTION
Notice to Applicant
This communication is in response to the Request for Continued Examination (RCE) submitted June 3, 2026. Claims 1 is amended. Claim 14 was previously cancelled. Claims 1 – 13 and 15 – 19 are pending.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 3, 2026 has been entered.
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 – 13 and 16 – 19 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.
Step One
Claims 1 – 13 and 16 – 19 are drawn to a system, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1 and 15 recite ensuring the safety of a quantity of insulin to be injected to a person, comprising: determining an initial quantity of insulin at a current instant, said initial quantity of insulin being determined at least based on: a value of glycemia; and/or an Insulin On Board (lOB) value; determining a quantity of insulin to be injected by: performing a testing of the initial quantity of insulin by simulating an occurrence of a potential event, wherein said potential event presents an impact on glycemia, determining a predicted value of glycemia corresponding to a value of glycemia determined for a future instant if the potential event had occurred and if the initial quantity of insulin had been injected at the current instant, performing a determination of whether the predicted value of glycemia is comprised within a predetermined range and if no, adjust the initial quantity of insulin to obtain a adjusted quantity of insulin, said adjusted quantity of insulin being the quantity of insulin to be injected, if yes, keep the initial quantity of insulin, said initial quantity of insulin being the quantity of insulin to be injected.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover mental processes, as reflected in the specification, which states that “present invention relates to a system for ensuring the safety of a quantity of insulin to be injected and to a medical system for regulating a glycemia of a person” (paragraph 2 of the published specification). If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the human mind, including an observation, evaluation, judgment, or opinion, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including:
Claims 1, 6, 14: “system”, “an insulin determination module”, “safety module”, “medical device comprising a dispenser ….. determined by the safety module”
Claim 2: “system”, “glycemia determination module”, “an insulin determination module”
Claims 3 – 5, 7 – 8, 10 – 11: “system”, “safety module”
Claim 9: “system”, “an insulin determination module”, “a model chosen between: a PID controller, a deep learning model, a predictive control model, LQR/LQG controller, Ho robust controller, Sliding-mode based controller, Adaptive controller, and Reinforcement Learning-based controller”
Claims 12 – 13: “system”
Claim 15: “system”, “an insulin determination module”, “safety module”, “medical device”
Claim 16: “system”, “an insulin determination module”
Claims 17 – 18: “system”, “safety module”
Claim 19: “system”, “safety module”, “an insulin determination module”
These features are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The published specification supports this conclusion as follows:
[0111] The medical system 1 is a computerized medical system. In particular, the data processing unit 3 is a computerized data processing unit. Typically, the data acquisition system 2 includes a computerized data acquisition system. The insulin dispensing system 4 may be a computerized insulin dispensing system, including a processor which performs a process based on input parameters.
[0112] Any computerized system may include a processing subsystem, a storage subsystem, a user interface, a communication subsystem and a power subsystem. A computerized system may also include other components such as power battery, connectors and so on (not explicitly shown).
[0120] The data processing unit 3 of the medical system 1 may include a computerized system 31 for communicating treatment information to a user. Said computerized system 31, as shown on FIG. 2, includes a processor 32 and a memory 33. The data processing unit 3 includes a data communication module 34 adapted to communicate data, i.e. to receive and transmit data. The data communication module 34 might be adapted to communicate wirelessly, for example using a short-range radio communication corresponding to the Bluetooth® standard. For example, the data communication module 34 of the terminal 3 and the data communication module 21 of the data acquisition system 2 are compatible with one another, to form a communication channel, by which the data processing unit 3 and the data acquisition system 2 communicate with one another. In particular, time-based data is communicated from the sensing unit 2 to the data processing unit 3. The time-based data may be stored in the memory 22.
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2 – 13 and 16 – 19 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
The rejection of Claim 3, 4, and 5 under 35 U.S.C. 101 because the claimed invention is not supported by either a specific and substantial asserted utility or a well-established utility are withdrawn based upon the amendment submitted
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 – 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Constantin et al., herein after Constantin (U.S. Publication Number 2019/0246914 A1) in view of Ruchti et al., herein after Ruchti (U.S. Publication Number 2013/0165901 A1).
Claim 1 (Currently Amended): Constantin teaches a system for ensuring the safety of a quantity of insulin to be injected to a person, said system comprising:
an insulin determination module, said insulin determination module being configured to determine an initial quantity of insulin at a current instant (Figure 35; paragraph 300 discloses a system providing therapy optimization tools “that learn a patient's physiology and behavior and calculate guidance to help the patient identify optimal or desirable therapy parameters, such as basal insulin requirements, insulin to carb ratios, correction factors, or changes to insulin sensitivity due to exercise”; paragraph 327 discloses “Input to the decision support engine 104 may also include insulin data 122. Insulin data may, for example, be captured from an insulin delivery device such as a pump or may be received from a smart pen that is configured to track insulin delivery and communicate insulin delivery information to the system, e.g. via wireless communication to a smart device that may relay the information to the decision support engine. Insulin data may also be received from a user, e.g. from a patient or caregiver through a user interface on a smartphone or other computer device”), said initial quantity of insulin being determined at least based on:
a value of glycemia (paragraph 300 discloses delivered guidance to assist patients, caregivers, or health providers by meeting a variety of challenges such as addressing hypoglycemic events); and/or
an Insulin On Board (lOB) value (Figure 34; paragraph 302 discloses data gathered (both current and historic) and information such as insulin on board is considered);
a safety module, said safety module configured to determine a quantity of insulin to be injected (Figure 35 discloses a bolus calculator; paragraph 354 discloses “… manual inputs and learned or planned inputs (e.g. meal information) may be fed into a bolus calculator. The bolus calculator may be a general one, or may be personalized, e.g. specified to the user's current diabetic state. The bolus calculator may output a dose recommendation, and the system may then observe glucose levels after the user administers the recommended dose. Based on historical data, a typical glycemic response may be known for a particular situation, which allows for a comparison of real-time data with historical data or a pattern derived from historical data” where the bolus calculator determines the amount of insulin to be injected, which correlates with the safety module claimed) by:
performing a testing of the initial quantity of insulin by simulating an occurrence of a potential event (paragraph 414 discloses information determined from the physiology model or the behavior model may be used to propose alternate approaches to common patterns, the system may simulate what would happen with alternate therapy approaches or compare historic approaches),
wherein said potential event presents an impact on glycemia (paragraph 304 discloses a decision support system may use a variety of sources of information to determine guidance, such as patterns or other information related to eating behavior (e.g. number of meals per day, meal size distribution across meals and snacks, size of treatments for hypoglycemic excursions, or the number of repeat treatments for a hypoglycemic event), exercise timing, exercise duration, and exercise intensity, physiological response to activity), physiological factors (patterns in response to insulin or carbohydrates or other foods, tendency to "rebound" to a hyperglycemic state after a low glucose event)),
determining a predicted value of glycemia corresponding to a value of glycemia determined for a future instant if the potential event had occurred and if the initial quantity of insulin had been injected at the current instant (paragraph 300 discloses decision support tools may, for example, help a patient respond to a problem in real time by predicting hypoglycemia or hyperglycemia events or trends, providing treatment recommendations to address occurring or potential hypoglycemia or hyperglycemia events or trends, and monitor how the glycemic, physiologic, or behavioral response in real time; paragraph 347 discloses guidance output from the decision support engine which may include a predicted or actual insulin graph, or probability cone, which may show the range of possible outcomes from an action, such as exercise, food consumption, or insulin delivery),
performing a determination of whether the predicted value of glycemia is comprised within a predetermined range (paragraph 45 discloses the guidance message includes an actionable prompt that is calculated to cause the patient's glucose concentration level to move towards a target level or a target range) and
if no, adjust the initial quantity of insulin to obtain an adjusted quantity of insulin, said adjusted quantity of insulin being the quantity of insulin to be injected (paragraph 338 discloses the system may determine that an excursion (e.g., high-glucose trend) is likely to occur and determine a time to deliver guidance that allows the user to deliver take corrective action (e.g., deliver a correction bolus, or exercise) to counter-act the likely excursion),
if yes, keep the initial quantity of insulin, said initial quantity of insulin being the quantity of insulin to be injected (paragraph 338 discloses the system may periodically notify the user that no intervention is required (e.g., "Looking good: Glucose levels are well controlled".));
Constantin fails to explicitly teach the following limitations met by Ruchti as cited:
wherein the model implemented by the insulin determination module to determine the initial quantity of insulin is different and distinct from the model implemented by the safety module to determine the quantity of insulin to be injected (paragraph 123 discloses processing modules and information flow of a glucose management system that includes a safety component to estimate insulin-on-board determinations and adjustments to insulin recommendations where control information may include methods including insulin dosing, glucose measurement interval, glucose prediction, and glucose target assessments. Safety information may include module including risk assessment, insulin-onboard, hypoglycemia prediction, an advisory rule base, and dosing limits);
a medical device comprising a dispenser of insulin configured to inject to the person the quantity of insulin to be injected, said quantity of insulin to be injected being determined by the safety module (paragraph 33 discloses medical personnel may administer insulin to the patient subcutaneously using a syringe, or using an infusion pump, where the infusion pump may be programmed with software, such as ENDOTOOL™, where the software may include instructions for calculating appropriate insulin doses/rates and glucose measurement intervals to provide intensive insulin therapy. The software may provide clinicians with insulin dosage, D50W dosage (Dextrose 50% in water) or other carbohydrate amount, and testing frequency (time of next glucose test) recommendations through a pump interface; paragraph 123 discloses processing modules and information flow of a glucose management system that includes a safety component to estimate insulin-on-board determinations and adjustments to insulin recommendations where control information may include methods including insulin dosing, glucose measurement interval, glucose prediction, and glucose target assessments. Safety information may include module including risk assessment, insulin-onboard, hypoglycemia prediction, an advisory rule base, and dosing limits).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Constantin to further include therapeutic decision support systems involving periodic delivery of medications to a patient to achieve a particular physiological objective including systems and methods for insulin therapy for a patient as disclosed by Ruchti.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Constantin in this way to operate and insulin dosing method based on a proactive system of glucose control to predict preprandial and basal insulin doses based on glucometer readings preprandially, and on the history of glucometer readings as well as previous insulin doses and other status of the patient (Ruchti: paragraph 43).
Claim 2 (Previously Presented): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system further comprising a glycemia determination module (paragraph 300 discloses decision support tools may, for example, help a patient respond to a problem in real time by predicting hypoglycemia or hyperglycemia events or trends, providing treatment recommendations to address occurring or potential hypoglycemia or hyperglycemia events or trends, and monitor how the glycemic, physiologic, or behavioral response in real time), wherein:
said insulin determination module is configured to determine the value of glycemia (paragraph 114 discloses providing decision support functionality for a user, comprising: loading a model into a memory of a computing environment, receiving a datum indicative of a glucose concentration value of the user, and causing a display of a calculated insight on a user interface of the computing environment, the insight calculated using at least the model and the datum indicative of the glucose concentration value; paragraph 454 discloses real-time data associated with a patient is measured, determined and received from a glucose sensor), and
wherein the insulin determination module is configured to determine the initial quantity of insulin if the determined value of glycemia satisfies a criterion (paragraph 338 discloses the system may determine that an excursion (e.g., high-glucose trend) is likely to occur and determine a time to deliver guidance that allows the user to deliver take corrective action (e.g., deliver a correction bolus, or exercise) to counter-act the likely excursion and may periodically notify the user that no intervention is required (e.g., "Looking good: Glucose levels are well controlled".) indicating the patient’s glucose levels are stable and within an acceptable range).
Claim 3 (Previously Presented): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system wherein the safety module is configured to adjust the initial quantity of insulin by dividing or multiplying the initial quantity of insulin with a number greater than 1 (paragraph 362 discloses a modification to an insulin dosage change based on a predefined blood glucose range; paragraph 400 discloses if there are periods of time when the blood glucose (BG) is increasing or decreasing, then basal rates affecting that period of time may be modified by small, safe increments, until the blood glucose is stable over the whole period).
Claim 4 (Previously Presented): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system wherein the safety module is configured to adjust the initial quantity of insulin by deducting a quantity of insulin from said initial quantity of insulin or by adding a quantity of insulin to said initial quantity of insulin (paragraph 362 discloses a modification to an insulin dosage change based on a predefined blood glucose range; paragraph 400 discloses if there are periods of time when the blood glucose (BG) is increasing or decreasing, then basal rates affecting that period of time may be modified by small, safe increments, until the blood glucose is stable over the whole period).
Claim 5 (Previously Presented): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system wherein the safety module is configured to adjust the initial quantity of insulin by forcing the value of said initial quantity of insulin to 0 (paragraph 338 discloses the system may determine that an excursion (e.g., high-glucose trend) is likely to occur and determine a time to deliver guidance that allows the user to deliver take corrective action (e.g., deliver a correction bolus, or exercise) to counter-act the likely excursion and may periodically notify the user that no intervention is required (e.g., "Looking good: Glucose levels are well controlled".) indicating the patient’s glucose levels are stable and within an acceptable range).
Claim 6 (Previously Presented): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system wherein the safety module is further configured to iteratively adjust the initial quantity of insulin, to obtain the quantity of insulin to be injected, until the predicted value of glycemia is within the predetermined range (paragraph 400 discloses if there are periods of time when the blood glucose (BG) is increasing or decreasing, then basal rates affecting that period of time may be modified by small, safe increments, until the blood glucose is stable over the whole period), wherein:
the initial quantity of insulin determined by the insulin determination module is the entry of the first iteration (Figure 35; paragraph 300 discloses a system providing therapy optimization tools “that learn a patient's physiology and behavior and calculate guidance to help the patient identify optimal or desirable therapy parameters, such as basal insulin requirements, insulin to carb ratios, correction factors, or changes to insulin sensitivity due to exercise”; paragraph 327 discloses “Input to the decision support engine 104 may also include insulin data 122. Insulin data may, for example, be captured from an insulin delivery device such as a pump or may be received from a smart pen that is configured to track insulin delivery and communicate insulin delivery information to the system, e.g. via wireless communication to a smart device that may relay the information to the decision support engine. Insulin data may also be received from a user, e.g. from a patient or caregiver through a user interface on a smartphone or other computer device”);
the entry of each next iteration is the adjusted quantity of insulin determined at the respective previous iteration, the quantity of insulin to be injected is the quantity of insulin considered at the current iteration for which the predicted glycemia is within the predetermined range (paragraph 399 discloses tracking factors such as ingestion of carbs and insulin administration; paragraph 496 discloses doses could be calculated based on both sensor values in parallel and then the more conservative dose could be recommended, also, a glucose rate of change estimate based on either a trend calculated in the CGM algorithm or as a two-point difference of the most recent pair of glucose values can be used, and the rate of change that provides the more conservative dose would be used).
Claim 7 (Previously Presented): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system wherein the safety module is further configured to iteratively determine the quantity of insulin to be injected until the predicted value of glycemia is within the predetermined range (paragraph 338 discloses the system may determine that an excursion (e.g., high-glucose trend) is likely to occur and determine a time to deliver guidance that allows the user to deliver take corrective action (e.g., deliver a correction bolus, or exercise) to counter-act the likely excursion and may periodically notify the user that no intervention is required (e.g., "Looking good: Glucose levels are well controlled".) indicating the patient’s glucose levels are stable and within an acceptable range), wherein:
the entry of each iteration is the initial quantity of insulin determined by the insulin determination (Figure 35; paragraph 300 discloses a system providing therapy optimization tools “that learn a patient's physiology and behavior and calculate guidance to help the patient identify optimal or desirable therapy parameters, such as basal insulin requirements, insulin to carb ratios, correction factors, or changes to insulin sensitivity due to exercise”; paragraph 327 discloses “Input to the decision support engine 104 may also include insulin data 122. Insulin data may, for example, be captured from an insulin delivery device such as a pump or may be received from a smart pen that is configured to track insulin delivery and communicate insulin delivery information to the system, e.g. via wireless communication to a smart device that may relay the information to the decision support engine. Insulin data may also be received from a user, e.g. from a patient or caregiver through a user interface on a smartphone or other computer device”),
wherein at each iteration, the safety module is configured to adjust the initial quantity of insulin by more reducing/increasing said initial quantity of insulin than at the previous iteration, and wherein the quantity of insulin to be injected is the quantity of insulin considered at the current iteration for which the predicted glycemia is within the predetermined range (paragraph 400 discloses if there are periods of time when the blood glucose (BG) is increasing or decreasing, then basal rates affecting that period of time may be modified by small, safe increments, until the blood glucose is stable over the whole period).
Claim 8 (Previously Presented): Constantin and Ruchti teach the system according to claim 6. Constantin teaches a system wherein the safety module is further configured to stop the iterations when the quantity of insulin of the current iteration is equal to zero (paragraph 400 discloses if there are periods of time when the blood glucose (BG) is increasing or decreasing, then basal rates affecting that period of time may be modified by small, safe increments, until the blood glucose is stable over the whole period).
Claim 9 (Previously Presented): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system wherein the insulin determination module is configured to determine the initial quantity of insulin by implementing a model chosen between:
a PID controller,
a deep learning model (paragraph 322 discloses a machine learning method may be used a-priori to identify possible states, a multitude of possible states may be deduced from a set of data),
a predictive control model (paragraph 302 discloses an algorithm or model used to determine whether a hyperglycemia or hypoglycemia event is possible or likely and develop guidance, indicating a predictive model),
LQR/LQG controller,
H∞ robust controller,
Sliding-mode based controller,
Adaptive controller, and
Reinforcement Learning-based controller.
Claim 10 (Previously Presented): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system wherein the safety module is configured to determine the predicted glycemia according to a sensitivity to insulin factor (paragraph 191 discloses the learning the model includes learning a pattern of insulin sensitivity as a function of time).
Claim 11 (Previously Presented): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system wherein the safety module is configured to determine the predicted glycemia according to a parameter indicating the importance of the glycemia impact of the potential event. (paragraph 300 discloses delivered guidance to assist patients, caregivers, or health providers by meeting a variety of challenges such as addressing hypoglycemic events; paragraph 302 discloses an algorithm or model used to determine whether a hyperglycemia or hypoglycemia event is possible or likely and develop guidance, indicating a predictive model).
Claim 12 (Previously Presented): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system wherein the potential event includes:
a physical activity (paragraph 441),
an undeclared injection of a bolus of insulin (paragraph 304),
a change in insulin sensitivity (paragraph 77),
a menstrual cycle (paragraph 504),
an inaccurate value of current glycemia (paragraph 371),
an untaken meal (paragraph 400),
unannounced meal (paragraph 304), and
stress (Figure 16 discloses user entered data including stress, emotion, feelings).
Claim 13 (Original): Constantin and Ruchti teach the system according to claim 1. Constantin teaches a system wherein the quantity of insulin to be injected is a basal (paragraph 400 discloses if there are periods of time when the blood glucose (BG) is increasing or decreasing, then basal rates affecting that period of time may be modified by small, safe increments, until the blood glucose is stable over the whole period).
Claim 15 (Previously Presented): Constantin and Ruchti teach a medical system for regulating a glycemia of a person including:
a system for ensuring the safety of a quantity of insulin to be injected, said system comprising:
an insulin determination module, said insulin determination module being configured to determine an initial quantity of insulin at a current instant (Figure 35; paragraph 300 discloses a system providing therapy optimization tools “that learn a patient's physiology and behavior and calculate guidance to help the patient identify optimal or desirable therapy parameters, such as basal insulin requirements, insulin to carb ratios, correction factors, or changes to insulin sensitivity due to exercise”; paragraph 327 discloses “Input to the decision support engine 104 may also include insulin data 122. Insulin data may, for example, be captured from an insulin delivery device such as a pump or may be received from a smart pen that is configured to track insulin delivery and communicate insulin delivery information to the system, e.g. via wireless communication to a smart device that may relay the information to the decision support engine. Insulin data may also be received from a user, e.g. from a patient or caregiver through a user interface on a smartphone or other computer device”), said initial quantity of insulin being determined at least based on:
a value of glycemia (paragraph 300 discloses delivered guidance to assist patients, caregivers, or health providers by meeting a variety of challenges such as addressing hypoglycemic events); and/or
an Insulin On Board (IOB) value (Figure 34; paragraph 302 discloses data gathered (both current and historic) and information such as insulin on board is considered);
a safety module, said safety module configured to determine a quantity of insulin to be injected (Figure 35 discloses a bolus calculator; paragraph 354 discloses “… manual inputs and learned or planned inputs (e.g. meal information) may be fed into a bolus calculator. The bolus calculator may be a general one, or may be personalized, e.g. specified to the user's current diabetic state. The bolus calculator may output a dose recommendation, and the system may then observe glucose levels after the user administers the recommended dose. Based on historical data, a typical glycemic response may be known for a particular situation, which allows for a comparison of real-time data with historical data or a pattern derived from historical data” where the bolus calculator determines the amount of insulin to be injected, which correlates with the safety module claimed) by:
performing a testing of the initial quantity of insulin by simulating an occurrence of a potential event (paragraph 414 discloses information determined from the physiology model or the behavior model may be used to propose alternate approaches to common patterns, the system may simulate what would happen with alternate therapy approaches or compare historic approaches),
wherein said potential event presents an impact on glycemia (paragraph 304 discloses a decision support system may use a variety of sources of information to determine guidance, such as patterns or other information related to eating behavior (e.g. number of meals per day, meal size distribution across meals and snacks, size of treatments for hypoglycemic excursions, or the number of repeat treatments for a hypoglycemic event), exercise timing, exercise duration, and exercise intensity, physiological response to activity), physiological factors (patterns in response to insulin or carbohydrates or other foods, tendency to "rebound" to a hyperglycemic state after a low glucose event)),
determining a predicted value of glycemia corresponding to a value of glycemia determined for a future instant if the potential event had occurred and if the initial quantity of insulin had been injected at the current instant (paragraph 300 discloses decision support tools may, for example, help a patient respond to a problem in real time by predicting hypoglycemia or hyperglycemia events or trends, providing treatment recommendations to address occurring or potential hypoglycemia or hyperglycemia events or trends, and monitor how the glycemic, physiologic, or behavioral response in real time; paragraph 347 discloses guidance output from the decision support engine which may include a predicted or actual insulin graph, or probability cone, which may show the range of possible outcomes from an action, such as exercise, food consumption, or insulin delivery),
performing a determination of whether the predicted value of glycemia is comprised within a predetermined range (paragraph 45 discloses the guidance message includes an actionable prompt that is calculated to cause the patient's glucose concentration level to move towards a target level or a target range) and
if no, adjust the initial quantity of insulin to obtain an adjusted quantity of insulin, said adjusted quantity of insulin being the quantity of insulin to be injected (paragraph 338 discloses the system may determine that an excursion (e.g., high-glucose trend) is likely to occur and determine a time to deliver guidance that allows the user to deliver take corrective action (e.g., deliver a correction bolus, or exercise) to counter-act the likely excursion),
if yes, keep the initial quantity of insulin, said initial quantity of insulin being the quantity of insulin to be injected (paragraph 338 discloses the system may periodically notify the user that no intervention is required (e.g., "Looking good: Glucose levels are well controlled".)).
Constantin fails to explicitly teach the following limitations met by Ruchti as cited:
a medical device (paragraph 33 discloses a syringe, or an infusion pump) comprising:
a dispenser of insulin configured to inject to the person the quantity of insulin to be injected, said quantity of insulin to be injected being determined by the safety module (paragraph 33 discloses medical personnel may administer insulin to the patient subcutaneously using a syringe, or using an infusion pump, where the infusion pump may be programmed with software, such as ENDOTOOL™, where the software may include instructions for calculating appropriate insulin doses/rates and glucose measurement intervals to provide intensive insulin therapy. The software may provide clinicians with insulin dosage, D50W dosage (Dextrose 50% in water) or other carbohydrate amount, and testing frequency (time of next glucose test) recommendations through a pump interface; paragraph 123 discloses processing modules and information flow of a glucose management system that includes a safety component to estimate insulin-on-board determinations and adjustments to insulin recommendations where control information may include methods including insulin dosing, glucose measurement interval, glucose prediction, and glucose target assessments. Safety information may include module including risk assessment, insulin-onboard, hypoglycemia prediction, an advisory rule base, and dosing limits)).
The motivation to combine the teachings of Constantin and Ruchti is discussed in the rejection of claim 1, and incorporated herein.
Claim 16 (Previously Presented): Constantin and Ruchti teach the system of claim 15.
Constantin fails to explicitly teach the following limitations met by Ruchti as cited:
wherein the model implemented by the insulin determination module to determine the initial quantity of insulin is different and distinct from the model implemented by the safety module to determine the quantity of insulin to be injected (paragraph 123 discloses processing modules and information flow of a glucose management system that includes a safety component to estimate insulin-on-board determinations and adjustments to insulin recommendations where control information may include methods including insulin dosing, glucose measurement interval, glucose prediction, and glucose target assessments. Safety information may include module including risk assessment, insulin-onboard, hypoglycemia prediction, an advisory rule base, and dosing limits).
The motivation to combine the teachings of Constantin and Ruchti is discussed in the rejection of claim 1, and incorporated herein.
Claim 17 (Previously Presented): Constantin and Ruchti teach the system of claim 6. Constantin discloses wherein the model implemented by the safety module is a predictive control model (paragraph 300 discloses decision support tools may, for example, help a patient respond to a problem in real time by predicting hypoglycemia or hyperglycemia events or trends, providing treatment recommendations to address occurring or potential hypoglycemia or hyperglycemia events or trends, and monitor how the glycemic, physiologic, or behavioral response in real time; paragraph 522 discloses safety rules may be employed to provide decision-support to various levels of aggressiveness).
Claim 18 (Previously Presented): Constantin and Ruchti teach the system of claim 1. Constantin discloses wherein the testing of the initial quantity of insulin, the determining of the predicted value of glycemia, and the conditional adjustment of the initial quantity of insulin are performed automatically by the safety module, without user intervention, before injection of the insulin (Figure 35; paragraph 300 discloses a system providing therapy optimization tools “that learn a patient's physiology and behavior and calculate guidance to help the patient identify optimal or desirable therapy parameters, such as basal insulin requirements, insulin to carb ratios, correction factors, or changes to insulin sensitivity due to exercise”; paragraph 327 discloses “Input to the decision support engine 104 may also include insulin data 122. Insulin data may, for example, be captured from an insulin delivery device such as a pump or may be received from a smart pen that is configured to track insulin delivery and communicate insulin delivery information to the system, e.g. via wireless communication to a smart device that may relay the information to the decision support engine. Insulin data may also be received from a user, e.g. from a patient or caregiver through a user interface on a smartphone or other computer device”).
Claim 19 (Previously Presented): Constantin and Ruchti teach the system of claim 1. Constantin discloses wherein the safety module implements a predictive simulation model that does not compute the initial quantity of insulin but validates the initial quantity determined by the insulin determination module by simulating the occurrence of the potential event to determine whether an adjustment is required (paragraph 414 discloses the system may simulate what would happen with alternate therapy approaches or compare historic approaches; paragraph 430 discloses forms of long-acting insulin or intermediate-acting insulin can be combined to simulate the body's basal insulin secretion patterns).
Response to Arguments
Applicant's arguments filed June 3, 2026 have been fully considered but they are not persuasive. The Applicant’s arguments have been addressed in the order in which they were presented.
Section 103
The Applicant argues Constantin does teach or suggest a safety module that performs a testing of an initial quantity of insulin by simulating an occurrence of a potential event, that Constantin describes a bolus calculator that outputs a dose recommendation. The Examiner respectfully disagrees. The Examiner submits Constantin discloses a system may use an algorithm or model to determine whether a hyperglycemia or hypoglycemia event (potential event) is possible or likely and develop guidance, which may for example include a pre-sleep action item such as insulin delivery, eating a food item (e.g., fast carbs, slow carbs, or carbs in combination with protein), or setting an alarm to check a status or check for guidance at a particular time (paragraph 302). A guidance may include an amount of insulin on board, which may be the amount of insulin determined to be active in the body as determined by predefined algorithm (e.g. based on population-based or theoretical data), or as determined by a model (in which case the insulin on board may be patient-specific). Insulin on board guidance may be provided in response to a user request, or in response to an event, such as a determination that the insulin on board is likely to cause a low glucose level, or a determination that the amount of insulin on board is likely to be insufficient to control glucose levels (i.e., a high glucose level may occur), and an insulin dosage change may be suggested (paragraph 362). Constantin discloses a decision support system may predict glucose levels in real-time to provide advance notice of potential hypoglycemic or hyperglycemic events before they happen, where the predicted glucose levels may be used to generate an alert or alarm to the user. The predicted glucose level may also be used to deliver early guidance to deliver a correction bolus before a hyperglycemic event occurs or before the patient becomes insulin resistant due to high glucose levels (paragraph 437). Thus, Applicant’s argument is not persuasive and the rejection is maintained.
Section 101
The Applicant argues the amended claims integrate the recited operations into a practical application of any alleged judicial exception. The Examiner respectfully disagrees. The additional elements of the present claims fail to integrate the exception into a practical application of the exception. The MPEP 2106.04(d) defines the phrase “integration into a practical application” to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. For example, 2106.04(d) recite limitations that are indicative of integration into a practical application when recited in a claim with a judicial exception include:
Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
The present claims fail to demonstrate an improvement to the functioning of a computer or to any other technology or technical field. Thus, Applicant’s argument is not persuasive, and the rejection is maintained.
The Applicant argues the recited claims reflect an improvement to the technology of glycemia regulation. The Examiner respectfully disagrees. The Examiner submits in Enfish, for example, the Court found that "the claims at issue focused not on asserted advances in uses to which existing computer capabilities could be put, but on a specific improvement in how computer could carry out one of their basic functions of storage and retrieval of data”. Here, the focus of the claims is not on such an improvement in computers as tools, but on abstract ideas that use computers as tools. The claims here do not require any nonconventional computer, network or display components, or even a "non-conventional and non-generic arrangement of known, conventional pieces”. This conclusion is supported by the Applicant’s published specification supports this conclusion as follows:
[0111] The medical system 1 is a computerized medical system. In particular, the data processing unit 3 is a computerized data processing unit. Typically, the data acquisition system 2 includes a computerized data acquisition system. The insulin dispensing system 4 may be a computerized insulin dispensing system, including a processor which performs a process based on input parameters.
[0112] Any computerized system may include a processing subsystem, a storage subsystem, a user interface, a communication subsystem and a power subsystem. A computerized system may also include other components such as power battery, connectors and so on (not explicitly shown).
[0120] The data processing unit 3 of the medical system 1 may include a computerized system 31 for communicating treatment information to a user. Said computerized system 31, as shown on FIG. 2, includes a processor 32 and a memory 33. The data processing unit 3 includes a data communication module 34 adapted to communicate data, i.e. to receive and transmit data. The data communication module 34 might be adapted to communicate wirelessly, for example using a short-range radio communication corresponding to the Bluetooth® standard. For example, the data communication module 34 of the terminal 3 and the data communication module 21 of the data acquisition system 2 are compatible with one another, to form a communication channel, by which the data processing unit 3 and the data acquisition system 2 communicate with one another. In particular, time-based data is communicated from the sensing unit 2 to the data processing unit 3. The time-based data may be stored in the memory 22.
Further the claims, unlike Enfish, use existing computers as tools in aid of processes focused on abstract ideas. Thus, Applicant’s argument is not persuasive and the rejection is maintained.
Conclusion
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KRISTINE K. RAPILLO
Examiner
Art Unit 3626
/KRISTINE K RAPILLO/Examiner, Art Unit 3682
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