Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claim(s)
Claims 1-17 have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1, 7, 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 7, 11 of U.S. Patent No.12283381. Although the claims at issue are not identical, they are not patentably distinct from each other because both recite sensing a user’s glucose levels using a continuous monitoring device, determining a first glycemia risk index value based on a first amount of time, determining a time in range value of the user’s glucose level, input the user’s glucose levels and the engagement data into a machine learning model, etc...
Allowable Subject Matter Over the Prior Art
The primary reason for indicating allowability over the prior art is the inclusions of the following limitations in the combination as recited.
A computer-implemented method for predicting health and engagement levels for a user, the method comprising:
sensing a user's glucose levels using a continuous glucose monitoring (CGM) device over a time period;
receiving the user's glucose levels collected by the CGM device;
receiving engagement data associated with the user, the engagement data collected by a computing device over the time period, wherein at least some of the engagement data is collected using one or more sensors associated with the user;
determining a first glycemia risk index (GRI) value based on a first amount of time the user is hypoglycemic during the time period and a second amount of time the user is hyperglycemic during the time period;
determining a time in range (TIR) value of the user's glucose level, wherein the determined TIR value is based on an amount of time the user's glucose level is within a threshold band over the time period;
inputting the user's glucose levels and the engagement data into a machine learning model;
outputting, by the machine learning model and responsive to the user's glucose levels and the engagement data collected over the time period, one or more predictions for future glucose levels for the user including a prediction that a future GRI value is greater than or less than the first GRI value, wherein the one or more predictions for future glucose levels for the user further comprise a prediction that a future TIR value is one of within a threshold value of the determined TIR value, greater by more than threshold value, or less by more than the threshold value, wherein the one or more predictions for future glucose levels for the user further comprise a prediction that a future GRI value is in a higher GRI zone than the first GRI zone or in a lower GRI zone than the first GRI zone, wherein the one or more predictions for future glucose levels for the user further comprise a prediction that the future GRI value is in the first GRI zone, is in a second GRI zone higher than the first GRI zone, or is in a third GRI zone lower than the first GRI zone, wherein the one or more predictions for future glucose levels for the user further comprise a prediction that the future GRI value is greater than a threshold GRI value or less than the threshold GRI value;
outputting, by the machine learning model and responsive to the user's engagement data collected over the time period, one or more predictions for future engagement levels, wherein the one or more predictions for future engagement levels comprises a prediction that a future CGM device engagement level is above a threshold amount of CGM device engagement or below the threshold amount of CGM device engagement, wherein CGM device engagement includes a measure of CGM device use by the user, wherein the one or more predictions for future engagement levels further comprise a prediction that a future engagement level is a high engagement state or a low engagement state, and wherein the one or more predictions for future engagement levels further comprise a prediction that a future manual engagement level is a first manual engagement state or a second manual engagement state;
calculating, by the machine learning model, a timing and a dosing amount of basal insulin for the user at regular intervals;
administering, via an insulin pump, in response to an output of the machine learning model, at the timing of basal insulin at the regular intervals, the dosing amount of basal insulin to the user;
calculating, by the machine learning model, a timing and a dosing amount of bolus insulin for the user, the timing corresponding to mealtimes;
administering, via the insulin pump, in response to an output of the machine learning model, at the timing of bolus insulin at the mealtimes, the dosing amount of bolus insulin to the user;
providing, by the machine learning model, notifications reminders to the user in response to calculating the timing and the dosing amount of basal insulin and the timing and the dosing amount of bolus insulin, wherein the notifications reminders are provided based on the one or more predictions of future engagement levels; and
synchronizing administration, via the insulin pump, in response to an output of the machine learning model, at the timing, of the dosing amount of basal insulin and the dosing amount of bolus insulin to the user.
For claim rejection under 35USC 101, the current invention recites “the processor of the injection pen device configured to generate dose data associated with a dispensing event of a dose of the medicine dispensed from the injection pen device and time data associated with the dispensing event, and to wirelessly transmit the dose data”. Under the 2019 Revised Patent Subject Matter Eligibility Guidance (the “2019 Revised PEG”), the combination of recited additional elements in the recited claims is patent eligible because the claims as a whole integrate an abstract idea into practical application under Prong Two of Step 2A of the Alice/Mayo Test as described in the 2019 Revised PEG. The claims are eligible because it is not directed to an abstract idea or any other judicial exception.
For claim rejection under 35 USC 103, Claim 21 closely relates to Dobbles (US20180043096A1 in view of Mault (US 20030208113A1) and further in view of Dalal (US 20200176121A1) and further in view of Hayter (US 20210050085A1). Dobble discloses systems and methods for integrating a continuous glucose sensor 12, including a receiver 14, a medicament delivery device 16, a controller module, and optionally a single point glucose monitor 18 are provided. Mault discloses a system for assisting a person to maintain a blood glucose level between predetermined limits comprises: an electronic device, comprising a display, a clock, a memory, and a processor; and a software program executable by the processor of the electronic device, adapted to receive nutritional data of food consumed by the person, adapted to calculate the blood glucose level for the person using the nutritional data and a glycemic response model for the person, and further adapted to present the blood glucose level to the person on the display of the electronic device. Dalal discloses using machine learning algorithms to predict biophysical responses from biophysical data, such as heart rate monitor data, food logs, or glucose measurements. Biophysical responses may include behavioral responses. Hayter discloses determining a medication dose for a patient or user. The dose determination can account for recent and/or historical analyte levels of the patient or user. The dose determination can also take into account other information about the patient or user, such as physiological information, dietary information, activity, and/or behavior.
However, the combined art fails to disclose calculating, by the machine learning model, a timing and a dosing amount of insulin for the user; providing, by the machine learning model, reminders to the user in response to calculating the timing and the dosing amount of insulin, wherein the reminders are provided based on the one or more predictions of future engagement levels; and synchronizing administration, via an insulin pump, in response to an output of the machine learning model, at the timing, of the dosing amount of insulin to the user.
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The foreign reference WO2009048462A1 The system has a continuous glucose sensor continuously measuring a glucose concentration in a host, and providing sensor data associated with the glucose concentration in the host. An electronics module comprises an on/off controller module iteratively determining an insulin therapy instruction in response to an evaluation of a relationship of internally derived data i.e. sensor data, and a glucose boundary. An insulin delivery device is physically and operably connected to a receiver, where the delivery device receives the therapy instruction from the controller.
However, the foreign reference does not disclose calculating, by the machine learning model, a timing and a dosing amount of insulin for the user; providing, by the machine learning model, reminders to the user in response to calculating the timing and the dosing amount of insulin, wherein the reminders are provided based on the one or more predictions of future engagement levels; and synchronizing administration, via an insulin pump, in response to an output of the machine learning model, at the timing, of the dosing amount of insulin to the user.
The NPL “Prediction of Daytime Hypoglycemic Events Using Continuous Glucose Monitoring Data and Classification Technique “ describes daytime hypoglycemia should be accurately predicted to achieve normoglycemia and to avoid disastrous situations. Hypoglycemia, an abnormally low blood glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia. Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In this paper, the author proposes new predictor variables to predict daytime hypoglycemia using continuous glucose monitoring (CGM) data and to apply classification and regression tree (CART) as a prediction method. The independent variables of our prediction model are the rate of decrease from a peak and absolute level of the BG at the decision point. The evaluation results showed that our model was able to detect almost 80% of hypoglycemic events 15 min in advance, which was higher than the existing methods with similar conditions. The proposed method might achieve a real-time prediction as well as can be embedded into BG monitoring device.
Claims 1-17 would be allowable if rewritten to overcome the rejection(s) under Double Patenting, as set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
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/HIEP V NGUYEN/Primary Examiner, Art Unit 3686
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