| CPC B29C 64/393 (2017.08) [A23L 33/40 (2016.08); A23P 30/10 (2016.08); B29C 64/153 (2017.08); B29C 64/268 (2017.08); B33Y 10/00 (2014.12); B33Y 30/00 (2014.12); B33Y 50/02 (2014.12); G05B 13/0265 (2013.01); A23V 2002/00 (2013.01)] | 16 Claims |
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1. A system for additive manufacturing of nutritional supplement servings, the system comprising a computing device, wherein the computing device comprises a processor, the processor is designed and configured to:
receive a nutritional need of a user;
receive a user texture preference;
determine a nutritional input of the user;
determine a user misreporting factor, wherein the user misreporting factor is a numerical quantity representing a degree of likelihood that a user is under-reporting a user-reported nutritional input, wherein determining the user misreporting factor comprises determining the user misreporting factor based on accuracy of past user-reported nutritional inputs retrieved from a database, wherein the user misreporting factor is used to weigh user-reported data to generate a nutrition input, and wherein the user misreporting factor modifies the user-reported data as it relates to the nutritional input;
adjust the nutritional input of the user as a function of the user misreporting factor;
detect a deficit between the nutritional need of the user and the nutritional input of the user;
calculate a supplement dose as a function of the nutritional need of the user and the deficit, wherein the supplement dose is an amount of a supplement intended to address a nutritional deficiency, wherein the nutritional deficiency comprises a chronic deficiency and an acute deficiency, and wherein calculating the supplement dose further comprises:
detecting a chronic deficiency using a first supervised machine learning process, wherein the chronic deficiency is detected using a long-term nutritional input pattern of a plurality of long-term nutritional input patterns to indicate a nutritional deficiency;
detecting an acute deficiency using a second supervised machine learning process, wherein the acute deficiency is detected using a current nutritional input and a user-reported nutritional intake that indicate a nutritional deficiency;
determining the deficit as a function of a difference between the input quantity and the nutritional need of the user and the difference between the chronic deficiency and the acute nutritional deficiency; and
determining the supplement dose mapped to the deficit;
generate a nutrient supplementation plan, wherein generation of the nutrient supplementation plan includes calculating the supplement dose from the nutritional need and the nutritional deficiency, wherein the nutrient supplementation plan comprises a supplement regimen, wherein the supplement regimen refers to the supplement dose and frequency of use of the supplement;
select an ingredient combination as a function of the supplement dose and the user texture preference, wherein selecting the ingredient combination further comprises:
receiving a plurality of ingredients stored at an additive manufacturing device, said additive manufacturing device comprising an applicator configured to deposit at least a portion of the ingredient combination, and wherein the plurality of ingredients include a plurality of supplement ingredients and at least a substrate ingredient; and
selecting the ingredient combination including the at least a substrate ingredient and the at least a supplement ingredient as a function of the supplement dose and the user texture; and
initiate the applicator to deposit successive layers, wherein the successive layers include a portion of the at least a substrate ingredient and the at least a supplement ingredient, wherein depositing the successive layers comprises sintering the successive layers together using a laser.
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