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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/16/2026 has been entered.
Claim Objections
Claim 1, 14, and 25 are objected to because of the following informalities:
Regarding Claim 1
Line 9 recites the language “the meal”. For clarity within the claims, the claim language should be amended such that it reads –a
Line 11 recites the language “the macronutrient information”. For consistency and clarity within the claims, the claim language should be amended such that it reads –the macronutrient profile—
Regarding Claim 14
Lines 4-5 recite the language “the blood glucose trace”. For consistency and clarity within the claims, the claim language should be amended such that it reads –the post-prandial blood glucose trace—
Regarding Claim 25
Line 3 recites the language “a post-prandial blood glucose trace; ;and”. The claim language should be amended such that it reads –a post-prandial blood glucose trace; and—
Appropriate correction is required.
Claim Rejections - 35 USC § 112
4. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 4
Line 1 recites the limitation “the meal information”. There is insufficient antecedent basis for this limitation in the claim.
Lines 2, 3, and 5-6 recites the limitation “the one or more food item”. There is insufficient antecedent basis for this limitation in the claim.
Lines 3-4 recite the limitation “a meal selection interface”. However, claim 1 previously claimed “a meal selection interface”. Therefore, it is unclear if the meal selection interface of claim 4 is the same as claimed in claim 1 or if it is a new limitation.
Lines 5-6 recite the limitation “a portion size of each food item of the one or more food items”. However, claim 1 previously disclosed “receiving a portion size for each selected food item”. Therefore, it is unclear if the limitation of claim 4 is different from that recited in claim 1.
Regarding Claim 5
Line 3 recites the limitation “an interface”. However, line 1 previously disclosed “the meal selection interface”. Therefore, it is unclear if the interface of line 3 is the same interface as “the meal selection interface” or what is different from the “meal selection interface”.
Regarding Claim 6
Line 2 recites the limitation “a portion size of each food item”. However, claim 1 previously disclosed “receiving a portion size for each selected food item”. Therefore, it is unclear if the limitation of claim 6 is different from that recited in claim 1.
Regarding Claim 13
Lines 1-3 recite the limitation “a quantity of the carbohydrate, fat and protein constituents of each of the one or more selected food items”. However, claim 1 previously disclosed “quantities of carbohydrates, fat, and protein for the meal”. Therefore, it is unclear if the limitation of claim 13 is different from that recited in claim 1.
Regarding Claim 14
Line 2 recites the limitation “a user”. However, claim 1 previously disclosed “a user”. Therefore, it is unclear if the user of claim 14 is the same user of claim 1.
Line 6 recites the limitation “the accuracy information”. There is insufficient antecedent basis for this limitation in the claim. There is antecedent basis for “the accuracy of the macronutrient profile of the meal”.
Regarding Claim 15
Lines 9 and 10 recite the limitation “the user”. There is insufficient antecedent basis for this limitation in the claim.
Line 12 recites the limitation "each selected food item". There is insufficient antecedent basis for this limitation in the claim.
Line 14 recites the limitation “the meal”. There is insufficient antecedent basis for this limitation in the claim.
Line 18 recites the limitation “a user”. However, line 9 previously disclosed “the user”. Therefore, it is unclear if the user of claim 18 is the same user of lines 9 and 10.
Regarding Claim 16
Lines 3-5 recite the limitation “a meal selection component for: providing an interface presenting graphical representations of suggested food items”. However, claim 15 previously claimed “presents food items to the user via a meal selection interface”. Therefore, it is unclear if the interface of claim 16 is the same interface as recited in claim 15 or if it is a new limitation.
Line 7 recites the limitation “receiving information regarding a portion size of each selected food item”. However, claim 15 previously disclosed “receives a portion size for each selected food item”. Therefore, it is unclear if the limitation of claim 16 is different from that recited in claim 15.
Regarding Claims 7-12 and 17-20
Claims 7-12 and 17-20 are rejected insofar as they are dependent upon a rejected claim.
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) 1-6, 13, 15-16, and 21-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen, WO 2019/125932 A1, in view of Saint et al., US 2018/0353698.
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 a selection from the user of food items from a meal selection interface (Chen, [0066] [0168] and [0197]);
receiving a portion size for each selected food item (Chen, [0168]);
determining a macronutrient profile comprising quantities of carbohydrates, fat, and protein for the meal (Chen, [0197]) from the selected food items and their respective portion sizes (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]).
However, Chen does not explicitly disclose presenting food items to the user via the meal selection interface. Saint teaches a method for providing information to a drug delivery device, comprising presenting food items to a user via a meal selection interface (5) (Saint, [0091]-[0092]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to modify the method of Chen to include presenting food items to a user via the meal selection interface as taught by Saint to provide ease of food/meal selection.
Regarding Claim 2
Chen and Saint teach 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 and Saint teach 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 and Saint teach 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 and Saint teach 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 and Saint teach 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 and Saint teach 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 a selection from the user of food items from a meal selection interface (Chen, [0066] [0070] [0168] and [0197]);
receives a portion size for each selected food item (Chen, [0168]);
determines a macronutrient profile comprising quantities of carbohydrates, fat, and protein for the meal (Chen, [0197]) from the selected food items and their respective portion sizes (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]).
However, Chen does not explicitly disclose presenting food items to the user via the meal selection interface. Saint teaches a macronutrient management application on a personal computing device, comprising presenting food items to a user via a meal selection interface (5) (Saint, [0091]-[0092]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to modify the system of Chen to include presenting food items to a user via the meal selection interface as taught by Saint to provide ease of food/meal selection.
Regarding Claim 16
Chen and Saint teach 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]).
Regarding Claim 21
Chen and Saint teach the method of claim 1. Chen further discloses that the meal selection interface presents graphical representations of the food items and accepts selections via user interaction with the graphical representations [Chen discloses that the user interface device (20) is a graphical user interface (GUI) with a display, where the user interacts with presented information, menus, buttons, etc., to receive and provide information to the system] (Chen, [0066]).
Regarding Claim 22
Chen and Saint teach the method of Claim 1. Saint further teaches determining the macronutrient profile comprises querying a local or third-party nutritional database (Saint, [0089]-[0090]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to modify the method determining the macronutrient profile of Chen to include querying a local or third-party nutritional database as taught by Saint to provide averaged/conservative macronutrient profile values. Therefore, the combination of Chen and Satin teaches querying a local or third-party nutritional database based on the selected food items.
Regarding Claim 23
Chen and Saint teach the method of claim 1. Saint further teaches applying a suggestion filter that uses a machine learning model trained on a meal history of the user (Saint, [0005] and [0071]).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to modify the method determining the macronutrient profile of Chen to include apply a suggestion filter that uses a machine learning model trained on a meal history of the user as taught by Saint to provide relevant suggestions.
Regarding Claim 24
Chen and Saint teach the method of claim 23. Saint further teaches that the machine learning model correlates past meals, blood glucose outcomes, location, and time of day to generate food item suggestions (Saint, [0005] and [0071]-[0072]).
Regarding Claim 25
Chen and Saint teach the method of claim 23. Saint further teaches: evaluating an accuracy of the macronutrient profile based on a post-prandial blood glucose trace (Saint, [0112]); and updating the suggestion filter to promote or suppress food items in future suggestions based on the accuracy (Saint, [0116]).
Claim(s) 7-9, 12, 14, 17-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen, WO 2019/125932 A1, in view of Saint et al., US 2018/0353698, and further in view of Leifer et al., US 2019/0228856.
Regarding Claim 7
Chen and Saint teach the method as rejected in Claim 6 above. However, Chen and Saint do not explicitly 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 modify the meal selection interface of Chen/Saint 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 in order to improve food related personalizations (e.g. determination of personalized meal options) (Leifer, [0019]).
Regarding Claim 8
Chen, Saint, 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, Saint, 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 12
Chen, Saint, 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, Saint, and Leifer teach the method of claim 7, wherein Saint further discloses:
receiving a post-prandial blood glucose trace of a user for a predetermined period of time after ingestion of the meal (Saint, [0112]);
determining accuracy of the macronutrient profile of the meal based on the blood glucose trace (Saint, [0112] and [0116]]); 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 (Saint, [0116]).
Regarding Claim 17
Chen and Saint teach the system as rejected in Claim 16. However, Chen and Saint do not explicitly 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 modify the meal selection interface component of Chen/Saint 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 in order to improve food related personalizations (e.g. determination of personalized meal options) (Leifer, [0019]).
Regarding Claim 18
Chen, Saint, 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 20
Chen and Saint teach the system as rejected in Claim 16. However, Chen and Saint do not explicitly 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 modify the macronutrient management application of Chen/Saint to include 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 in order to improve food related personalizations (e.g. determination of personalized meal options) (Leifer, [0019]).
Claim(s) 10-11, 19, and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen, WO 2019/125932 A1, in view of Saint et al., US 2018/0353698, in view of Leifer et al., US 2019/0228856, and further in view of Kolawa et al., US 8,429,026.
Regarding Claim 10
Chen, Saint, and Leifer teach the method of claim 7. However, Chen, Saint, and Leifer do not explicitly disclose receiving information regarding food items complementary to food items selected by the user; and including the complementary food items as food items suggested by the machine learning model.
Kolawa teaches receiving information regarding food items complementary to food items selected by the user (Kolawa, Column 29, Lines 56-65). At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to modify the meal selection interface of Chen/Saint to include receiving information regarding food items complementary to food items selected by the user as is taught by Kolawa in order to provide alternative meal options to the user. Therefore, it would have been obvious to one of ordinary skill in the art to include the complementary food items as food items suggested by the machine learning model in order to improve food related personalizations.
Regarding Claim 11
Chen, Saint, Leifer, and Kolawa teach the method of claim 10. Leifer further discloses that the determination of food items is based on food items selected together by a large population of users (Leifer, [0008]). Therefore, the combination of Chen, Saint, Leifer, and Kolawa teaches that the determination of complementary food items is based on food items selected together by a large population of users.
Regarding Claim 19
Chen and Saint teach the system of claim 16, however, Chen and Saint do not explicitly 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 filter component for providing user-selected food items to the meal selection interface (Leifer, [0042]); wherein the food items are compiled 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 modify the meal selection interface of Chen/Saint to include a filter component for providing user-selected food items to the meal selection interface; wherein the food items selected are compiled by a large population of users as taught by Leifer in order to improve food related personalizations (e.g. determination of personalized meal options) (Leifer, [0019] and [0042]).
However, Chen, Saint, and Leifer do not explicitly disclose that the filter is a complementary association filter that provides suggestions of food items complementary to the user-selected food items. Kolawa teaches receiving information regarding food items complementary to food items selected by the user (Kolawa, Column 29, Lines 56-65).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to modify the filter component of Chen/Saint/Leifer to include receiving information regarding food items complementary to food items selected by the user as is taught by Kolawa in order to provide alternative meal options to the user. Therefore, it would have been obvious to one of ordinary skill in the art that the determination of complementary food items is based on food items selected together by a large population of users.
Regarding Claim 26
Chen and Saint teach the method of claim 1. However, Chen and Saint do not explicitly disclose that presenting the food items to the user comprises applying a complementary association filter configured to identify foods that are paired together in population level co-selection data.
Leifer teaches a filter component for providing user-selected food items to the meal selection interface (Leifer, [0042]); wherein the food items are compiled 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 modify the meal selection interface of Chen/Saint to include a filter component that utilizes population level co-selection data as taught by Leifer in order to improve food related personalizations (e.g. determination of personalized meal options) (Leifer, [0019] and [0042]).
However, Chen, Saint, and Leifer do not explicitly disclose that the filter configured to identify foods that are paired together in population level co-selection data. Kolawa teaches receiving information regarding food items complementary to food items selected by the user (Kolawa, Column 29, Lines 56-65).
At the time the claimed invention was filed it would have been obvious to one of ordinary skill in the art to modify the filter component of Chen/Saint/Leifer to include receiving information regarding food items complementary to food items selected by the user as is taught by Kolawa in order to provide alternative meal options to the user.
Response to Arguments
Applicant’s arguments, with respect to the rejection(s) of claim(s) 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 and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made under 35 U.S.C. 103.
Applicant’s arguments, with respect to new claims 21-26 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DEVON KRAMER can be reached at (571) 272-7118. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.L.S/Examiner, Art Unit 3741 /DEVON C KRAMER/Supervisory Patent Examiner, Art Unit 3741
