Office Action 1: Notice of Allowance (2025-02-10)

CLAIMS:
What is claimed is:
1\. A processor-implemented method comprising: obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval; determining one or more glucose measurement statistics based on the plurality of glucose measurements; determining a likelihood of the patient having consumed a meal during the analysis interval based on the one or more glucose measurement statistics that are correlative to an occurrence of a meal for the patient; and detecting an occurrence of the meal during the analysis interval based on the likelihood of the patient having consumed the meal during the analysis interval.
2\. The method of claim 1, wherein determining the likelihood of the patient having consumed the meal during the analysis interval is based on historical event data associated with the patient.
3\. The method of claim 2, wherein the historical event data comprises meal bolus data associated with the patient, and wherein determining the likelihood is based on the meal bolus data associated with the patient.
4\. The method of claim 1, further comprising: determining respective correlation coefficient values for the one or more glucose measurement statistics using a patient-specific meal detection model, wherein determining the likelihood of the patient having consumed the meal during the analysis interval is based on the values for the one or more glucose measurement statistics and the respective correlation coefficient values for the one or more glucose measurements statistics.
5\. The method of claim 1, further comprising determining whether a meal bolus was identified and/or delivered during the analysis interval.
6\. The method of claim 1, further comprising providing an indication of the occurrence of the meal during the analysis interval.
7\. The method of claim 1, wherein the detected occurrence of the meal corresponds to detection of an unannounced meal.
8\. The method of claim 1, further comprising updating historical event data associated with the patient to indicate the occurrence of the meal associated with the analysis interval.
9\. The method of claim 1, wherein the one or more glucose measurement statistics are particular to the patient and have been identified as correlative of meal consumption for the patient.
10\. The method of claim 1, wherein the one or more glucose measurement statistics comprise at least a mean or average rate of change of the plurality of glucose measurements over at least a portion of the analysis interval.
11\. A system comprising: one or more processors; and one or more processor-readable storage media storing instructions which, when executed by the one or more processors, cause performance of: obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval; determining one or more glucose measurement statistics based on the plurality of glucose measurements; determining a likelihood of the patient having consumed a meal during the analysis interval based on the one or more glucose measurement statistics that are correlative to an occurrence of a meal for the patient; and detecting an occurrence of the meal during the analysis interval based on the likelihood of the patient having consumed the meal during the analysis interval.
12\. The system of claim 11, wherein determining the likelihood of the patient having consumed the meal during the analysis interval is based on historical event data associated with the patient.
13\. The system of claim 12, wherein the historical event data comprises meal bolus data associated with the patient, and wherein determining the likelihood is based on the meal bolus data associated with the patient.
14\. The system of claim 11, wherein the instructions further cause performance of: determining respective correlation coefficient values for the one or more glucose measurement statistics using a patient-specific meal detection model, wherein determining the likelihood of the patient having consumed the meal during the analysis interval is based on the values for the one or more glucose measurement statistics and the respective correlation coefficient values for the one or more glucose measurements statistics.
15\. The system of claim 11, wherein the instructions further cause performance of determining whether a meal bolus was identified and/or delivered during the analysis interval.
16\. The system of claim 11, wherein the instructions further cause performance of providing an indication of the occurrence of the meal during the analysis interval.
17\. The system of claim 11, wherein the detected occurrence of the meal corresponds to detection of an unannounced meal.
18\. The system of claim 11, wherein the instructions further cause performance of updating historical event data associated with the patient to indicate the occurrence of the meal associated with the analysis interval.
19\. A processor-implemented method comprising: obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval; determining one or more glucose measurement statistics based on the plurality of glucose measurements; determining a likelihood of the patient having consumed a meal during the analysis interval using a patient-specific meal detection model and the one or more glucose measurement statistics; and detecting an occurrence of the meal during the analysis interval based on the likelihood of the patient having consumed the meal during the analysis interval.
20\. The method of claim 19, wherein the patient-specific meal detection model is generated using time segments of historical data classified as corresponding to meal consumption or no meal consumption and corresponding glucose statistics for the time segments.

OFFICE 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 .
Reasons for Allowance
The following is an examiner’s statement of reasons for allowance: Claims 1-20 are allowable.
The claims in this application have been allowed because the prior art of record fails to disclose either singly or in combination the claimed method or apparatus with determining a likelihood of the patient having consumed a meal during the analysis interval based on the one or more glucose measurement statistics that are correlative to an occurrence of a meal for the patient; and detecting an occurrence of the meal during the analysis interval based on the likelihood of the patient having consumed the meal during the analysis interval..
The system including these specifics have not been found in the prior art of record.
The closest prior art reference is Patek WO 2013/032965, however this reference fails to disclose this feature above and further the configuration as specified in the applicants claims.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion



CITED REFERENCE SUMMARIES: Ref WO2013032965: Claims:
A processor-based method for providing posterior assessment of the risk of hypoglycemic of a patient, said method comprises: providing an algorithm to compute a statistic, R^ypo (record), for the risk of hypoglycemia based on the absolute BG levels, BG variability, and insulin delivery that is highly correlated to the posterior (conditional) probability of hypoglycemia, P(Ehypo where Ehypo denotes the event of hypoglycemia in the next day and record refers to the subject's historical BG, insulin delivery, and activities record; and providing the computed statistic, Rhypo (record), whereby actionable prior warning of the possibility of hypoglycemia about the patient is so provided to patient or user.
The method of claim 1, wherein the absolute BG levels and BG variability may be data derived from a CGM device and the absolute insulin delivery may be data obtained from an insulin pump device.
The method of claim 1, wherein the absolute BG levels and BG variability may be data derived from a CGM device and the absolute insulin delivery may be data obtained from a manual insulin injection device.
The method of claim 1, wherein the absolute BG levels and BG variability may be data derived from an SMBG device and/or the absolute insulin delivery may be data obtained from an insulin pump device.
The method of claim 1, wherein the absolute BG levels and BG variability may be data derived from an SMBG device and/or the absolute insulin delivery may be data obtained from a manual insulin injection device.
A processor-based method for retroactively providing a safe level of insulin for the patient, said method comprises: providing an algorithm to retroactively compute a risk-based insulation attenuation factor to the subject's record of insulin delivery; and providing the computed risk-based insulation attenuation factor and applying the risk- based attenuation factor so that any internal threshold is provided to the patient or user for deciding on reduced temporary basal rates before meals and/or following exercise in the future that may be implemented.
The method of claim 6, wherein the record of the insulin delivery may be data obtained from an insulin pump device.
The method of claim 6, wherein the record of the insulin delivery may be data obtained from a manual insulin injection device.
The method of claim 6, wherein the risk-based attenuation factor would be computed as follows: 1 ^-patient' where R(t, τ) is a measure of the risk of hypoglycemia between time t and t + τ based on the historical record of BG and insulin data up to time t, based on the BG symmetrization of function and kpatient is a patient-specific "aggressiveness" factor.
A processor-based method for providing a "net effect" based patient adoptive model, said method comprises: providing an algorithm to compute: a dynamic model of the patient's metabolic system, wherein said dynamic model includes descriptive parameters of an individual physiology of the model patient; a corresponding inferred history of behavioral "net effect" model that explains the glucose variability in the historical record through the dynamic model; wherein said "net effect" model includes a mathematical representation perturbations of the model patient; and an update of the patient's physiological parameters based on both (i) the ability of the dynamic model to predict future BG based on known inputs and (ii) the ability of the model to produce net effect curves that are consistent with the patient's record of the perturbations; and providing said update to the patient or user whereby patient or user can use the update for future course of action.
The method of claim 10, wherein said descriptive parameters include a representation of the dynamic relationship between oral carbs d (g/min), physical activity e (cal/min), subcutaneous insulin u (U/hr), and the model patient's metabolic state vector χ whose elements include glucose and insulin concentrations (mg/dl) in various compartments of the body and carbohydrate mass (mg) in the gut.
The method of claim 11, wherein the glucose concentration (mg/dl) may be data derived from a CGM device and the subcutaneous insulin u and the insulin concentration (mg/dl) may be data obtained from an insulin pump device.
The method of claim 11, wherein the glucose concentration (mg/dl) may be data derived from a CGM device and the subcutaneous insulin u and the insulin concentration (mg/dl) may be data obtained from a manual insulin injection device.
The method of claim 11, wherein the glucose concentration (mg/dl) may be data derived from a SMBG device and/or the subcutaneous insulin u and the insulin concentration (mg/dl) may be data obtained from an insulin pump device.
The method of claim 11, wherein the glucose concentration (mg/dl) may be data derived from a SMBG device and/or the subcutaneous insulin u and the insulin concentration (mg/dl) may be data obtained from a manual insulin injection device.
The method of claim 11, wherein relationship of said descriptive parameters can be described as a set of discrete-time nonlinear difference equations: X(k + 1) = F(x(k) k), d(k), e(k); 0(fc)) BGmodel (k) = G (k) k), dik), e(k); 0 (fc)) where F and G are nonlinear system equations and 6>(/c) is a vector of parameter values that are characteristic of the patient, such as body weight, volumes of distribution in various compartments, various time constant that describe the rates of absorption and clearance between various compartments, some of which are prone to varying as a function of time k.
The method of claim 11, wherein relationship of said of descriptive parameters can be described as a set of continuous-time nonlinear differential equations: = F(X{t), u{t), d(t), e(t , 0 (t)) BGmodel (t) = G (t) t), d _t), e _t); 6> (t)).
The method of claim 17, wherein nonlinear representation can be linearized around any desired operating point (e.g. steady state glucose concentration) to yield a linear dynamic model: x(k + 1) = Ax(k) + Buus(k) + Bdd{k) + Bee{k) y{k) = Cx(k) where x is the vector of metabolic state differentials (away from the operating point), u$ (U/hr) is the deviation in insulin delivery from the patient's steady state (basal) insulin delivery rate, A, Bu Bd Be are the state space matrices of the linear model, and y (/c) represents BG deviation away from the desired operating point, and the dependence on Θ (/c) is embedded within the state space matrices A, Bu Bd Be .
The method of claim 10, wherein said perturbations include meal profiles, physical activity, and sleep/awake periods.
The method of claim 10, wherein said "net effect" model provides a "history" of virtual system inputs that reconciles the patient's historical record of BG and historical record of insulin delivery.
The method of claim 20, wherein the patient's historical record of BG concentration, { BG (k) }keday > and historical record of insulin delivery, { u(k) }keday > me net effect that reconciles the historical information is the vector of virtual carbohydrate inputs { ^n.e. (k) }keday that minimizes the error function: dist({BG (k) }ileday { where dist measures the distance between two vectors of BG concentration (in this case actual BG versus model-predicted BG) given the fixed record of insulin delivery { u(k) }keday and the candidate net effect vector { dn e (k) }keday
The method of claim 21 , wherein the resulting optimal net effect vector (aka. net effect curve), { dn e (k) }keday^ optimally reconciles the BG and insulin data collected by the patient through a virtual carbohydrate signal, which captures all external influences on the patient as a single external disturbance signal measured in (mg/min).
The method of claim 22, wherein: when the net effect curve is positive this shall correspond to the patient actually eating, or it may correspond a period of the day in which the patient is experiencing enhanced insulin sensitivity; and when the net effect curve is negative then this shall correspond to the patient engaging in intense physical activity or exercise.
The method of claim 10, wherein: the patients physiological model parameters, { B (k) }keday , includes daily variability due to the patients circadian rhythm; and the model updater, includes a formula that takes the form having the following: θ ·= Θ + U (BGres NEres) Θ), where U is the recursive parameter update function, which could be gradient-based, BGres is a vector of BG model prediction errors (residuals) and NEres is a vector of errors between the computed net effect curve and the patient's record of actual (verified) behavioral inputs.
The method of claim 24, wherein the dynamic model is adjusted on multiple time scales, whereby parameter updates can be computed daily based on BG residuals: θ ·= Θ + U-i(BGres ; 0), and updates based on net effect mismatch can be computed on a longer time scale, such as every week or month: θ := θ + υ2 {ΝΕτβε ; θ).
The method of claim 10, further comprising providing a retroactive assessment of the patient's optimal rate of insulin delivery, wherein said algorithm: retroactively computes what the patient's optimal rate of insulin delivery would have been over a predetermined period of historical time given that the disturbances to the system are exactly the historical of net effect curves computed for the patient over that interval of time, wherein for each "history" of net effect curves there is a corresponding "history" of insulin delivery rates that account for meals, exercise, and corrections for each day in the considered interval of time; maps between the net effect curve for a given day and the model-based response of an optimal controller, wherein these vectors of optimal responses are collected and analyzed, and presented to the patient or user for a day-by- day review of insulin treatment; extracts features from the optimal responses that correspond to important but random events by subtracting discrete amounts of insulin associated with meals or accounting for discrete insulin deficits associated with temporary basal rates around exercise, whereby the remaining schedule of insulin delivery corresponds to a representation of the patient's "optimal" basal pattern each day in the historical record; and identifies consistency in the retroactively computed optimal basal rates, such optimal basal rates in a plurality of duration segments representing the patient's treatment duration; and said method further comprising: providing to the patient or user the median level of basal insulin that would have been applied in each segment, wherein the patient or user could use this information to (i) decide upon on reduced temporary basal rates before meals and/or following exercise in the future or (ii) adjust the patient's long-term basal rate profile.
The method of claim 10, further comprising providing an on-demand adaptive correction of insulin advice model, said method comprises: providing an algorithm to include the following computations: retrospective detecting for meal and exercise activities; stochastic modeling to provide a description about the timing and content of meals and exercise; and providing insulin correction advice to a patient or user that would be in response to a patient and user request.
The method of claim 27, wherein: said retrospective detection for meal and exercise activities includes the algorithm for reconciling current history of said patient "net effect curves" with the historical record of patient-acknowledged meals and exercise events to produce a validated (high-confidence) record of relevant patient behaviors, wherein the reconciling includes identifying discrepancies between (i) the net effect curves computed from the available BG and insulin data for the patient and (ii) the meal and exercise events that are acknowledged by the patient or user through the systems user interface; and said method comprises: providing suggestions from said discrepancies, wherein suggestions are communicated to patient or user; and receiving any responses resultant from user or patient to form the final, validated record of relevant patient activities.
The method of claim 28, wherein: said stochastic modeling includes the algorithm for receiving said final, validated record of relevant patient activities and stochastically modeling to represent the timing and content of meals and exercise of the patient's behavior.
The method of claim 29, wherein: said insulin correction includes the algorithm for monitoring the patient's status and to provide insulin correction advice in the moment the patient or user asks for it, based on (i) the stochastic modeling for upcoming behavioral disturbances and (ii) the current dynamic model of the patient's metabolic system that allows for the prediction of the impact of various alternative correction insulin amounts.
A system for providing posterior assessment of the risk of hypoglycemic of a patient, said system comprises: a retroactive risk-based safety module having a processor to compute a statistic, Rhypo (record), for the risk of hypoglycemia based on the absolute BG levels, BG variability, and insulin delivery that is highly correlated to the posterior (conditional) probability of hypoglycemia, P(Ehypo where Ehypo denotes the event of hypoglycemia in the next day and record refers to the subject's historical BG, insulin delivery, and activities record; and said processor outputs the computed statistic, Rhypo (record), whereby actionable prior warning of the possibility of hypoglycemia about the patient is so provided to patient or user.
The system of claim 31 , wherein the absolute BG levels and BG variability may be data derived from a CGM device and the absolute insulin delivery may be data obtained from an insulin pump device.
The system of claim 31 , wherein the absolute BG levels and BG variability may be data derived from a CGM device and the absolute insulin delivery may be data obtained from a manual insulin injection device.
The system of claim 31 , wherein the absolute BG levels and BG variability may be data derived from an SMBG device and the absolute insulin delivery may be data obtained from an insulin pump device.
The system of claim 31 , wherein the absolute BG levels and BG variability may be data derived from an SMBG device and the absolute insulin delivery may be data obtained from a manual insulin injection device.
The system of claim 31 , further comprising: a CGM device, wherein the absolute BG levels and BG variability may be data derived from said CGM device; and an insulin pump device, wherein the absolute insulin delivery may be data obtained from said insulin pump device.
The system of claim 31 , further comprising: a CGM device, wherein the absolute BG levels and BG variability may be data derived from said CGM device; and a manual insulin injection device, wherein the absolute insulin delivery may be data obtained from said manual insulin injection device.
The system of claim 31 , further comprising: an SMBG device, wherein the absolute BG levels and BG variability may be data derived from said SMBG device; and/or an insulin pump device, wherein the absolute insulin delivery may be data obtained from said insulin pump device .
The system of claim 31 , further comprising: an SMBG device, wherein the absolute BG levels and BG variability may be data derived from said SMBG device; and/or a manual insulin injection device, wherein the absolute insulin delivery may be data obtained from said manual insulin injection device.
system for retroactively providing a safe level of insulin for the patient, said system comprises: a retroactive risk-based safety module having a processor to retroactively compute a risk-based insulation attenuation factor to the subject's record of insulin delivery; and said processor outputs the computed risk-based insulation attenuation factor and applying the risk-based attenuation factor so that any internal threshold is provided to the patient or user for deciding on reduced temporary basal rates before meals and/or following exercise in the future that may be implemented.
The system of claim 40, wherein the insulin delivery may be data obtained from an insulin pump device.
The system of claim 40, wherein the insulin delivery may be data obtained from a manual insulin injection device.
The system of claim 40, further comprising: an insulin pump device, wherein the insulin delivery may be data obtained from said insulin pump device.
The system of claim 40, further comprising: a manual insulin injection device, wherein the insulin delivery may be data obtained from said manual insulin injection device.
The system of claim 40, wherein the risk-based attenuation factor would be computed as follows: ^ ^ 1 + kpatientR (t> T) where R (t, τ) is a measure of the risk of hypoglycemia between time t and t + τ based on the historical record of BG and insulin data up to time t, based on the BG symmetrization of function and kpatient is a patient-specific "aggressiveness" factor.
A system for providing a "net effect" based patient adoptive model, said system comprises: a net effect estimator module having a processor to compute: a dynamic model of the patient's metabolic system, wherein said dynamic model includes descriptive parameters of an individual physiology of the model patient; and a corresponding inferred history of behavioral "net effect" model that explains the glucose variability in the historical record through the dynamic model; wherein said "net effect" model includes a mathematical representation perturbations of the model patient; and a model updater module having a processor to compute: an update of the patient's physiological parameters based on both (i) the ability of the dynamic model to predict future BG based on known inputs and (ii) the ability of the model to produce net effect curves that are consistent with the patient's record of the perturbations; and said system outputs said update to the patient or user whereby patient or user can use the update for future course of action.
The system of claim 46, wherein said descriptive parameters include a representation of the dynamic relationship between oral carbs d (g/min), physical activity e (cal/min), subcutaneous insulin u (U/hr), and the model patient's metabolic state vector χ whose elements include glucose and insulin concentrations (mg/dl) in various compartments of the body and carbohydrate mass (mg) in the gut.
The system of claim 47, wherein the glucose concentration (mg/dl) may be data derived from a CGM device and the subcutaneous insulin u and the insulin concentration (mg/dl) may be data obtained from an insulin pump device.
The system of claim 47, wherein the glucose concentration (mg/dl) may be data derived from a CGM device and the subcutaneous insulin u and the insulin concentration (mg/dl) may be data obtained from a manual insulin injection device.
The system of claim 47, wherein the glucose concentration (mg/dl) may be data derived from a SMBG device and the subcutaneous insulin u and the insulin concentration (mg/dl) may be data obtained from an insulin pump device.
The system of claim 47, wherein the glucose concentration (mg/dl) may be data derived from a SMBG device and the subcutaneous insulin u and the insulin concentration (mg/dl) may be data obtained from a manual insulin injection.
The system of claim 47, further comprising: an CGM device, wherein the glucose concentration (mg/dl) may be data derived from said CGM device; and an insulin pump, wherein the subcutaneous insulin u and the insulin concentration (mg/dl) may be data obtained from an insulin pump device.
The system of claim 47, further comprising: an SMBG device, wherein the glucose concentration (mg/dl) may be data derived from said SMBG device; and an insulin pump device or an insulin injection device, wherein the subcutaneous insulin u and the insulin concentration (mg/dl) may b
[Truncated to input limit]