Patent ID: 11862322
Assignee: KPN INNOVATIONS LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A system for generating a dynamic weighted combination, the system comprising a computing device, the computing device configured to:
gain at least a nourishment element from at least a nourishment directory;
receive at least a nourishment metric from a user, the at least a nourishment metric comprising a biological extraction, wherein receiving the at least a nourishment metric comprises:
receiving user responses from the user using questionnaire, the user responses comprising user nutritional deficiencies;
receiving the at least a nourishment metric from a wearable device configured to detect, analyze and transmit the at least a nourishment metric relating to the user;

determine at least a nourishment vector from the biological extraction of the user, wherein determining the at least a nourishment vector comprises:
receiving a vector training set, wherein the vector training set correlates nourishment element data comprising macronutrients and micronutrients and nourishment metric data to at least a nourishment vector value;
training a vector machine-learning model as a function of the vector training set wherein the vector machine learning model determines a fatty acid deficiency using a deep deterministic policy gradient; and
determining the at least a nourishment vector as a function of the at least a nourishment element, at least a nourishment metric and the vector machine-learning model, wherein the vector machine learning model inputs the at least a nourishment element and the at least a nourishment metric and outputs the at least a nourishment vector;
wherein the at least a nourishment vector describes a nourishment value of esterified fatty acids that provide a threshold level of nourishment generate a plurality of nourishment programs relating to a plurality of aliments as a function of the at least a nourishment vector, wherein generating the plurality of nourishment programs comprises:

receiving a nutrient training set, wherein the nutrient training set correlates nutrient deficiency data and nourishment vector data to aliment data;
training a nutrient machine-learning model as a function of the nutrient training set;
generating the plurality of nourishment programs as a function of the nutrient machine-learning model, wherein the nutrient machine-learning model produces a nourishment program output given nutrient deficiencies and the at least a nourishment vector provided as inputs;

determine a quantitative signature as a function of the plurality of nourishment programs, wherein determining the quantitative signature further comprises:
selecting a first set of aliments as a function of a first nourishment program;
determining a user geographical location by cell-tower triangulation of a mobile phone, wherein the cell-tower triangulation comprises identifying an alpha sector, a beta sector, and a gamma sector;
generating a plurality of elements of cost relating to user travel expenses in procuring each aliment of the first set of aliments as a function of the user geographical location; and
determining a first quantitative signature for the first nourishment program as a function of the first set of aliments and the plurality of elements of cost; and

generate a dynamic weighted combination of a plurality of dynamic weighted combinations as a function of the quantitative signature, wherein generating the dynamic weighted combination further comprises:
identifying, for each dynamic weighted combination of the plurality of dynamic weighted combinations, a degree of refinement according to at least a refinement criterion, the degree of refinement indicating at least one degree of freedom of each of the plurality of dynamic weighted combinations based on the refinement criterion;
comparing the degree of refinement for each dynamic weighted combination of the plurality of dynamic weighted combinations to the degree of refinement for at least one other dynamic weighted combination of the plurality of dynamic weighted combinations; and
generating the dynamic weighted combination as a function of the comparison.