DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim(s) 1-13, 15-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prud’homme et al., US 2010/0125241, in view of Chen, WO 2019/125932 A1.
Regarding Claim 1
Prud’homme discloses a method for determining a bolus dose for a user (Prud’homme, Abstract), comprising:
receiving a macronutrient profile for a meal (meal composition) (Prud’homme, [0005] and [0021]);
determining an initial bolus dose (Prud’homme, [0029]);
predicting, based on the macronutrient profile, a post-prandial blood glucose trace comprising one or more blood glucose readings of the user at one or more time points in a post-prandial window, given the initial bolus dose (Prud’homme, [0029]-[0031], Figure 1);
iteratively evaluating the predicted post-prandial blood glucose trace, adjusting the bolus dose (Prud’homme, [0029]-[0030], Figure 1), and re-predicting the post-prandial blood glucose trace until the prediction shows desired blood glucose readings (Prud’homme, [0030]); and
providing an indication of the bolus dose that produced the prediction of the desired blood glucose readings (Prud’homme, [0031]).
However, Prud’homme does not explicitly disclose that the macronutrient profile comprises an estimated total quantity of carbohydrates, fat, and protein from the meal.
Chen teaches determining a bolus dose for a user by receiving a macronutrient profile for a meal, the macronutrient profile comprising an estimated total quantity of carbohydrates, fat, and protein from the meal (Chen, [0197] and [0070]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to substitute the macronutrient profile comprising an estimated total quantity of carbohydrates, fat, and protein from the meal as taught by Chen with the macronutrient profile determination taught by Prud’homme since this would provide the advantage of covering meals of varying macronutrient composition to accurately determine bolus dosing.
Regarding Claim 2
Prud’homme and Chen teach the method as rejected in Claim 1 above. Prud’homme further discloses that the bolus dose comprises a bolus quantity and a bolus split (Prud’homme, [0030], Figures 3, 6B, 7B, 8B), the bolus split comprising a first portion of the bolus quantity to be administered at the start of the post-prandial window, and a second portion of the bolus quantity to be administered later in the post-prandial window (Prud’homme, [0030], Figures 3, 6B, 7B, 8B).
Regarding Claim 3
Prud’homme and Chen teach the method as rejected in Claims 1-2 above. Prud’homme further discloses that the predictions of the post-prandial blood glucose trace are provided by a trained machine-learning model (neural network model) (Prud’homme, [0027).
Regarding Claim 4
Prud’homme and Chen teach the method as rejected in Claims 1-3 above. Prud’homme further discloses deriving a set of one or more metrics (objective function) (Prud’homme, [0030]) from the predicted post-prandial blood glucose trace; and using the set of one or more derived metrics (objective function) to evaluate the predicted post-prandial blood glucose trace (Prud’homme, [0030]).
Regarding Claim 5
Prud’homme and Chen teach the method as rejected in Claim 3 above. Prud’homme further discloses receiving a blood glucose trace of the user from the post-prandial window (Prud’homme, [0031]); and updating the machine learning model using the received blood glucose trace or one or more metrics derived from the actual blood glucose trace all (Prud’homme, [0029]-[0031] and [0052]).
Regarding Claim 6
Prud’homme and Chen teach the method as rejected in Claims 1-3 and 5 above. Prud’homme further discloses that the blood glucose trace of the user is received from a continuous glucose monitor (212) (Prud’homme, [0071], Figure 4) worn by the user (Prud’homme, [0071]-[0074], Figure 4).
Regarding Claim 7
Prud’homme and Chen teach the method as rejected in Claims 1-3 above. Prud’homme further discloses that the input to the machine-learning model comprises insulin on board for the user, a basal insulin rate and one or more current and recent blood glucose readings (Prud’homme, [0031]).
Regarding Claim 8
Prud’homme and Chen teach the method as rejected in Claim 3 above. Prud’homme further discloses that the initial bolus dose is determined by: identifying, in a catalog of past meals consumed by the user (Prud’homme, [0078]), a closely-matched meal having a macronutrient profile that is a closest match to the macronutrient profile of the current meal (Prud’homme, [0079]); retrieving the bolus dose and a blood glucose trace for the closely-matched meal (Prud’homme, [0079]); adjusting the bolus dose to compensate for any undesirable blood glucose readings in the blood glucose trace for the closely-matched meal or for differences between the closely-matched meal and the meal; and using the adjusted bolus dose as the initial bolus dose (Prud’homme, [0081]).
Regarding Claim 9
Prud’homme and Chen teach the method as rejected in Claim 3 above. Prud’homme further discloses that the bolus dose that produced the prediction of the desired blood glucose trace is provided to an automatic drug delivery device (210) (Prud’homme, [0031]) that administers the first and second portions of the bolus dose to the user (Prud’homme, [0031] and [0071]-[0074], Figure 4).
Regarding Claim 10
Prud’homme and Chen teach the method as rejected in Claim 9 above. Prud’homme further discloses that the automatic drug delivery device receives information regarding the bolus dose that produced the prediction of the desired blood glucose trace via a wireless interface (Prud’homme, [0071]-[0074], Figure 4).
Regarding Claim 11
Prud’homme and Chen teach the method as rejected in Claim 3 above. Prud’homme further discloses that the second portion of the bolus quantity is delivered at a predetermined time after the start of the post-prandial window (Prud’homme, [0030]-[0031], Figures 3, 6B, 7B, 8B).
Regarding Claim 12
Prud’homme and Chen teach the method as rejected in Claim 3 above. Prud’homme further discloses that the second portion of the bolus quantity is delivered in one or more timed doses after the start of the post-prandial window (Prud’homme, [0030]-[0031], Figures 3, 6B, 7B, 8B).
Regarding Claim 13
Prud’homme and Chen teach the method as rejected in Claim 3 above. Prud’homme further discloses utilizing not only meal carbohydrate content to distinguish between different meal types (Prud’homme, [0005] and [0036]), while Chen teaches a macronutrient profile comprising an estimated total quantity of carbohydrates, fat, and protein from the meal (Chen, [0197] and [0070]).
Chen further teaches that the timing of the delivery of the second portion of the bolus quantity is based on a characterization of the fat and protein concentrations in the macronutrient profile of the meal (Chen, [0197]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to have the bolus quantity based on fat and protein concentrations in the macronutrient profile of the current meal as taught by Chen with that taught by Prud’homme since this would provide the advantage of covering meals of varying macronutrient composition.
Regarding Claim 15
Prud’homme and Chen teach the method as rejected in Claim 3 above. Prud’homme further discloses that the macronutrient profile of the meal is provided by the user (Prud’homme, [0058] and [0071]-[0074], Figure 4).
Regarding Claim 16
Prud’homme and Chen teach the method as rejected in Claim 15 above. Prud’homme further discloses that information regarding the meal is entered on an application running on a personal computing device (214) (Prud’homme, [0071], Figure 4) of the user (Prud’homme, [0027] and [0071]-[0074], Figure 4).
Regarding Claim 17
Prud’homme and Chen teach the method as rejected in Claim 15 above. Prud’homme further discloses that the machine-learning model executes on a personal computing device (214) (Prud’homme, [0071], Figure 4) of the user or is provided as a cloud-based service (Prud’homme, [0027] and [0071]-[0074], Figure 4).
Regarding Claim 19
Prud’homme discloses a system comprising:
a personal computing device (214) (Prud’homme, [0071]) of a user running an application (216) (Prud’homme, [0071]) enabling the user to input a macronutrient profile for a meal (Prud’homme, [0058] and [0071]-[0074], Figure 4);
a machine-learning model (neural network model) that predicts a post-prandial blood glucose trace given the macronutrient profile for the meal and an initial bolus dose (Prud’homme, [0027]); and
an automatic drug delivery device (210) (Prud’homme, [0071], Figure 4) in wireless communication (Prud’homme, [0072], Figure 4) with a personal computing device (214) (Prud’homme, [0071]-[0074], Figure 4);
wherein the initial bolus dose is determined based on the macronutrient profile of the meal (Prud’homme, [0021] and [0079]) and further wherein the initial bolus dose is iteratively adjusted until the machine-learning model predicts a post-prandial blood glucose trace having desired blood glucose readings (Prud’homme, [0027] and [0030]).
However, Prud’homme does not explicitly disclose that the macronutrient profile comprises an estimated total quantity of carbohydrates, fat, and protein from the meal.
Chen teaches a macronutrient profile comprising an estimated total quantity of carbohydrates, fat, and protein from the meal (Chen, [0197] and [0070]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to substitute the macronutrient profile comprising an estimated total quantity of carbohydrates, fat, and protein from the meal as taught by Chen with the macronutrient profile determination taught by Prud’homme since this would provide the advantage of covering meals of varying macronutrient composition to accurately determine bolus dosing.
Regarding Claim 20
Prud’homme and Chen teach the system as rejected in Claim 19 above. Prud’homme further discloses a continuous glucose monitor (212) worn by the user (Prud’homme, [0071]) and in wireless communication (Prud’homme, [0072]) with the personal computing device (214) (Prud’homme, [0071], Figure 4); wherein the machine-learning model is updated using actual blood glucose readings from the continuous glucose monitor (212) or using one or more metrics derived from the actual blood glucose readings (Prud’homme, [0026]-[0027] and [0071]-[0072], Figure 4).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prud’homme et al., US 2010/0125241, in view of Chen, WO 2019/125932 A1, and further in view of Agrawal et al., US 2021/0038163.
Regarding Claim 14
Prud’homme and Chen teach the method as rejected in Claim 3 above. However, Prud’homme does not disclose that the machine-learning model comprises: a convolutional neural network that extracts one or more features from data input to the model; and a recurrent neural network that uses the features identified by the convolutional neural network to provide the prediction of the post-prandial blood glucose trace (taught by the combination of Prud’homme and Agrawal).
Agrawal teaches a machine learning-based model for an insulin infusion management system that comprises: a convolution neural network (CNN); and a recurrent neural network (RNN) (Agrawal, [0112]-[0113] and [0116]).
It would have been obvious to one of ordinary skill in the art to substitute the machine learning-based model as taught by Agrawal for the machine-learning model as taught by Prud’homme/Chen, Since an express suggestion to substitute one equivalent component or process for another is not necessary to render such substitution obvious (MPEP 2144.06).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prud’homme et al., US 2010/0125241, in view of Chen, WO 2019/125932 A1, and further in view of Finan, US 2014/0005633.
Regarding Claim 18
Prud’homme and Chen teach the method as rejected in Claim 5 above. Prud’homme further discloses that the machine-learning model is updated based on subsequent meals entered by the user and the resulting post-prandial blood glucose traces (Prud’homme, [0078]-[0079]).
However, Prud’homme does not explicitly disclose that the machine-learning model is initially trained on a wide population of users or a cluster of users similar to the user.
Finan teaches a model that is trained on a wide population of users (Finan, [0039]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to have the machine-learning model is initially trained on a wide population of users or a cluster of users similar to the user as taught by Finan with that taught by Prud’homme/Chen since this would provide the advantage of desired insulin infusion to an average user.
Response to Arguments
Applicant’s arguments, with respect to the Objections to claims 1-20 have been fully considered and are persuasive. The Objection to Claims 1-20 have been withdrawn.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 1-12, 15-17, and 19-20 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, the Applicant’s amendments to claims 1 and 19 have initiated a new ground(s) of rejection is made under 35 U.S.C. 103 in view of Prud’homme et al., US 2010/0125241, in view of Chen, WO 2019/125932 A1.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 13-14 and 18 under 35 U.S.C. 103 have been fully considered. However, upon further consideration, the Applicant’s amendments to claims 1 and 19 have initiated a new ground(s) of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.L.S/Examiner, Art Unit 3746 11/18/2025