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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6, 13, and 15-16 is/are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Chen, WO 2019/125932 A1.
Regarding Claim 1
Chen discloses a method for providing information to a drug delivery device (12) to enable post-prandial bolus dosing of insulin to a user (Chen, [0063]), comprising:
receiving meal information comprising one or more food items in a meal (Chen, [0066] [0070] [0168] and [0197]);
determining a macronutrient profile (amount of carbohydrates, fat, and/or protein, [0197]) for the meal (Chen, [0197]); and
using the macronutrient information (amount of carbohydrates, fat, and/or protein, [0197]) to enable the drug delivery device to deliver a bolus dose as a bolus split of insulin to the user (Chen, [0197]).
Regarding Claim 2
Chen discloses the method as rejected in Claim 1, and further discloses providing the macronutrient profile (amount of carbohydrates, fat, and/or protein, [0197]) of the meal to the drug delivery device (12); wherein the drug delivery device (12) determines the bolus dose and the bolus split based on the macronutrient profile of the meal (Chen, [0181] and [0197]).
Regarding Claim 3
Chen discloses the method as rejected in Claim 1, further determining the bolus dose and the bolus split based on the macronutrient profile (amount of carbohydrates, fat, and/or protein, [0197]); and providing the bolus dose and the bolus split information to the drug delivery device (Chen, [0197]).
Regarding Claim 4
Chen discloses the method as rejected in Claim 2 above. Chen further discloses that the meal information comprises one or more food items selected as part of the meal further comprising: receiving user selections of the one or more food items via a meal selection interface (20) (Chen, [0070] and [0168]); receiving information regarding a portion size of each food item of the one or more food items via the meal selection interface (Chen, [0168]); and storing the meal information in a meal catalog (Chen, [0168]).
Regarding Claim 5
Chen discloses the method as rejected in Claim 4 above. Chen further discloses that the meal selection interface (20): provides an interface presenting graphical representations of suggested food items (Chen, [0066]); and accepts selections of food items via a user selection of the graphical representations of the food items (Chen, [0066]).
Regarding Claim 6
Chen discloses the method of claim 5, wherein receiving information regarding a portion size of each food item comprises: displaying graphical representations of small, medium and large portions of the food item (Chen, [0066] and [0168]); and receiving a user selection of one of the graphical representations of a portion of the food item (Chen, [0066] and [0168]).
Regarding Claim 13
Chen discloses the method of claim 3, wherein the macronutrient profile of the meal comprises a quantity of the carbohydrate, fat, and protein constituents of each of the one or more selected food items (Chen, [0197]).
Regarding Claim 15
Chen discloses an automatic insulin delivery system (10, [0063]), comprising:
a personal computing device (20, [0066]);
a macronutrient management application executing on the personal computing device (20) (Chen, [0197], Figure 1); and
a drug delivery device (12, [0063]-[0064]), in wireless communication with the personal computing device (20) (Chen, [0065], Figure 1);
wherein the macronutrient management application:
receives meal information comprising food items in a meal (Chen, [0070] and [0197]);
determines a macronutrient profile (amount of carbohydrates, fat, and/or protein, [0197]) for the meal (Chen, [0197]); and
provides the macronutrient profile (amount of carbohydrates, fat, and/or protein, [0197]) to the drug delivery device (12) to enable the drug delivery device (12) to calculate and deliver a bolus dose as a bolus split of insulin to a user (Chen, [0197]).
Regarding Claim 16
Chen discloses the system as rejected in Claim 15. Chen further discloses that the macronutrient management application comprises: a meal selection component for: providing an interface presenting graphical representations of suggested food items (Chen, [0066]); receiving user selections of one or more of the food items (Chen, [0070] and [0168]; and receiving information regarding a portion size of each selected food item (Chen, [0168]).
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) 7-12, 14, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen, WO 2019/125932 A1, in view of Leifer et al., US 2019/0228856.
Regarding Claim 7
Chen discloses the method as rejected in Claim 6 above. However, Chen does not disclose selections of food items presented in the meal selection interface are provided by a suggestion filter comprising: a machine leaning model trained to suggest one or more food items in the meal selection interface based on meal history information.
Leifer teaches selections of food items presented in the meal selection interface are provided by a suggestion filter (Leifer, [0019]) comprising: a machine leaning model trained to suggest one or more food items in the meal selection interface based on meal history information (Leifer, [0033]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to teach selections of food items presented in the meal selection interface are provided by a suggestion filter comprising: a machine leaning model trained to suggest one or more food items in the meal selection interface based on meal history information as taught by Leifer with that taught by Chen since this would provide the advantage of improving food related personalizations (e.g. determination of personalized meal options) (Leifer, [0019]).
Regarding Claim 8
Chen and Leifer teach the method of claim 7. Leifer further teaches the meal history information is selected from a group comprising past meals, locations associated with past meals (Leifer, [0034], and time-of-day associated with past meals (Leifer, [0032]).
Regarding Claim 9
Chen and Leifer teach the method of claim 7. Leifer further teaches that food item suggestions of the machine learning model are further based on location (Leifer, [0034]) and time-of-day (Leifer, [0032]).
Regarding Claim 10
Chen and Leifer teach the method of claim 7. Leifer further teaches receiving information regarding food items complementary to food items (alternative meal options, Leifer, [0042]) selected by the user (Leifer, [0042]); and including the complementary food items (alternative meal options) as food items suggested by the machine learning model (Leifer, [0042]).
Regarding Claim 11
Chen and Leifer teach the method of claim 10, wherein the determination of complementary food items is based on food items selected together by a large population of users (Leifer, [0008]).
Regarding Claim 12
Chen and Leifer teach the method of claim 7. Leifer further teaches food item suggestions of the machine learning model are tailored based on one or more user preferences (Leifer, [0032]).
Regarding Claim 14
Chen and Leifer teach the method of claim 7, wherein Chen further discloses:
receiving a post-prandial blood glucose trace of a user for a predetermined period of time after ingestion of the meal (Chen, [0197]);
determining accuracy of the macronutrient profile of the meal based on the blood glucose trace (Chen, [0197]); and
providing the accuracy information to the suggestion filter such that certain food items or combinations of food items may be promoted or avoided in future suggestions (Chen, [0197]).
Regarding Claim 17
Chen discloses the system as rejected in Claim 16. However, Chen does not disclose a suggestion filter component for providing food item suggestions to the meal selection interface component, wherein the suggestion filter component uses a machine learning model trained to provide the food item suggestions based on meal history information.
Leifer teaches a suggestion filter component for providing food item suggestions to the meal selection interface component (Leifer, [0019]); wherein the suggestion filter component uses a machine learning model trained to provide the food item suggestions based on meal history information (Leifer, [0033]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to include a suggestion filter component for providing food item suggestions to the meal selection interface component, wherein the suggestion filter component uses a machine learning model trained to provide the food item suggestions based on meal history information as taught by Leifer with that taught by Chen since this would provide the advantage of improving food related personalizations (e.g. determination of personalized meal options) (Leifer, [0019]).
Regarding Claim 18
Chen and Leifer teach the system claim 17. Leifer further teaches that food item suggestions of the machine learning model are further based on location (Leifer, [0034]) and time-of-day (Leifer, [0032]).
Regarding Claim 19
Chen discloses the system of claim 16, however, Chen does not disclose that the macronutrient management application further comprises: a complementary association filter component for providing suggestions of food items complementary to user-selected food items to the meal selection interface; wherein determination of complementary food items is based on food items selected together by a large population of users.
Leifer teaches a complementary association filter component for providing suggestions of food items complementary to user-selected food items to the meal selection interface (alternative meal options, Leifer, [0042]); wherein determination of complementary food items (alternative meal options) is based on food items selected together by a large population of users (Leifer, [0008] and [0042]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to include a complementary association filter component for providing suggestions of food items complementary to user-selected food items to the meal selection interface; wherein determination of complementary food items is based on food items selected together by a large population of users as taught by Leifer with that taught by Chen since this would provide the advantage of improving food related personalizations (e.g. determination of personalized meal options) (Leifer, [0019] and [0042]).
Regarding Claim 20
Chen discloses the system as rejected in Claim 16. However, Chen does not disclose that the macronutrient management application further comprises: a personalization filter component for providing user preferences to the suggestion filter, the user preferences comprising diet style preferences, cuisine preferences, individual food item preferences and allergy information; and a meal catalog component for storing and analyzing macronutrient profiles of previous meals.
Leifer teaches a macronutrient management application comprising: a personalization filter component for providing user preferences to the suggestion filter, the user preferences comprising diet style preferences (dietary preferences e.g. vegan, keto, gluten-free, [0019]), cuisine preferences (taste preferences e.g. types of cuisines, [0019]), individual food item preferences (taste preferences e.g. textures, types of tastes, preferences for sweetness, sourness, saltiness, bitterness, umami, [0019]), and allergy information (dietary preferences e.g. allergies, [0019]); and a meal catalog component for storing and analyzing macronutrient profiles of previous meals (Leifer, [0020]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to combine a personalization filter component for providing user preferences to the suggestion filter, the user preferences comprising diet style preferences, cuisine preferences, individual food item preferences and allergy information; and a meal catalog component for storing and analyzing macronutrient profiles of previous meals as taught by Leifer with that taught by Chen since this would provide the advantage of improving food related personalizations (e.g. determination of personalized meal options) (Leifer, [0019]).
Response to Arguments
Applicant’s arguments, with respect to the claim objections have been fully considered and are persuasive. The claim objections have been withdrawn.
Applicant's arguments, with respect to the rejection of claims 1-6, 13, and 15-16 under 35 U.S.C. 102(a)(1) and claims 7-12, 14, and 17-20 under 35 U.S.C. 103, have been fully considered but they are not persuasive. With regards to the Applicant’s argument that Chen does not allow a user to enter meal information including the food items in the meal and then determine the macronutrient profile from the meal, the Examiner is unconvinced. Chen discloses a user interface (UI) device (20) to input user data to the system (10), modify values, and receive information, prompts, data, etc., generated by the system (10) (Chen, [0066]). Chen discloses that the user data may include but is not limited to insulin/carbohydrate ratio, meal size, carbohydrate ratio of meal, and exercise, and insulin need data (Chen, [0070]). Chen further discloses that the carbohydrate content of the meal can be explicitly entered at the UI (20) or may be inferred by the controller (24) from meal data supplied at the UI (20) (Chen, [0168]). Therefore, Chen discloses a system in which a user enters meal information (to a user interface (20)) including one or more food items in the meal (meal data) and then determined the macronutrient profile (amount of carbohydrates, fat, and/or protein) from the meal.
The Applicant further argues that Chen relies on the user to estimate their own carbohydrate ratios. Chen discloses that the carbohydrate content of the meal can be explicitly entered at the UI (20) or may be inferred by the controller (24) from meal data supplied at the UI (20) (Chen, [0168]). Therefore, the Examiner is unconvinced.
In reference to calculating a macronutrient profile, the Applicant states that it is reasonable to assume that the amounts of fat and protein are received in the same way as the carbohydrates, but as carbohydrates are entered directly, Chen does not disclose entering the food items in the meal as claimed. The Examiner is unconvinced. Chen discloses that the carbohydrate content of the meal can be explicitly entered at the UI (20) or may be inferred by the controller (24) from meal data supplied at the UI (20) (Chen, [0168]). Therefore, the macronutrient profile (amount of carbohydrates, fat, and/or protein) of a meal can be inferred by the controller (24) from one or more food items in a meal (meal data).
Claims 1-6, 13, and 15-16 remain rejected under 35 U.S.C. 102(a)(1) and claims 7-12, 14, and 17-20 remain rejected under 35 U.S.C. 103.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KELSEY L STANEK whose telephone number is (571)272-3565. The examiner can normally be reached Mon - Fri 8:30am-3:00pm.
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/K.L.S/Examiner, Art Unit 3746 12/5/2025
/MARK A LAURENZI/Supervisory Patent Examiner, Art Unit 3746 1/13/2026