Methods and systems for informing food element decisions in the acquisition of edible materials from any source

1. A system for informing food element decisions in the acquisition of edible materials from any source, the system comprising a processor coupled to a memory, the processor is further configured to:

receive, from a user client device operated by a user, a food element descriptor wherein the food element descriptor further comprises a sequence uniquely identifying a particular food element; wherein the particular food element further comprises one or more ingredients; and wherein the one or more ingredients is selected based on a threshold importance score of ingredients based on user body information;

retrieve, by the processor from a physiological database, at least an element of user physiological data, said at least an element of user physiological data comprising a user nutrient body measurement;

identify, using the user nutrient body measurement and a machine-learning algorithm, a plurality of constitutional effect labels, each of the plurality of constitutional effect labels indicating one of a positive or negative effect on the user, wherein identifying the plurality of constitutional effect labels comprises:

receiving physiological training data wherein physiological training data further comprises physiological data and correlated to constitutional effect labels; and

training, using the machine-learning algorithm and the physiological training data, a physiological model correlating physiological data with constitutional effect labels;

inputting the at least an element of user physiological data comprising the user nutrient body measurement into the physiological machine-learning model; and

outputting, from the physiological machine-learning model, the plurality of constitutional effect labels as a function of the physiological machine-learning and the at least an element of user physiological data comprising the user nutrient body measurement;

generate a food element classifier, wherein the food element classifier comprises a classification algorithm, wherein the classification algorithm is trained by using training data comprising user physiological data sets of a plurality of users, the user physiological data sets comprising at least a nutrient body measurement of a user of the plurality of users, wherein the training data correlates the food element descriptor to the plurality of identified constitutional effect labels, and wherein responsive to training, the food element classifier is configured to provide an output of one or more constitutional effect labels of the food element description for a specific user responsive to an input of the nutrient body measurement of the specific user and the food element descriptor;

classify, using the food element classifier based at least on the nutrient body measurement of the specific user, the food element descriptor as a function of the plurality of constitutional effect labels; and

display, by the processor on a graphical user interface for the specific user, the food element descriptor and a constitutional effect label for the food element descriptor, wherein the food element descriptor is displayed according to a food element category of the food element descriptor.

2. The system of claim 1 , wherein the processor is further configured to receive, by the processor from an image capture device, a wireless transmission from the user client device containing a picture of a food element.

3. The system of claim 1 , wherein the processor is further configured to receive, by the processor from an image capture device, a wireless transmission from the user client device containing a picture of a uniform code commission barcode.

4. The system of claim 1 , wherein the machine-learning model further comprises executing a supervised machine-learning algorithm.

5. The system of claim 1 , wherein the machine-learning model further comprises executing an unsupervised machine-learning algorithm.

6. The system of claim 1 , wherein the machine-learning model further comprises executing a lazy-learning algorithm.

7. The system of claim 1 , wherein the classification algorithm further comprises a Naive-Bayes classification algorithm.

8. The system of claim 1 , wherein the classification algorithm further comprises a K-nearest neighbor algorithm.

9. A method of informing food element decisions in the acquisition of edible materials from any source, the method comprising:

receiving, by a processor from a user client device operated by a user, a food element descriptor wherein the food element descriptor further comprises a sequence uniquely identifying a particular food element; wherein the particular food element further comprises one or more ingredients; and wherein the one or more ingredients is selected based on a threshold importance score of ingredients based on user body information;

retrieving, by the processor from a physiological database at least an element of user physiological data, said at least an element of user physiological data comprising a user nutrient body measurement;

identifying, by the processor using the user nutrient body measurement and a machine-learning algorithm, a plurality of constitutional effect labels, each of the plurality of constitutional effect labels indicating one of a positive or negative effect on the user, wherein identifying the plurality of constitutional effect labels comprises:

receiving physiological training data wherein physiological training data further comprises physiological data and correlated to constitutional effect labels; and

training, using the machine-learning algorithm and the physiological training data, a physiological model correlating physiological data with constitutional effect labels;

inputting the at least an element of user physiological data comprising the user gut-wall body characteristic measurement into the physiological machine-learning model; and

outputting, from the physiological machine-learning model, the plurality of constitutional effect labels as a function of the physiological machine-learning and the at least an element of user physiological data comprising the user nutrient body measurement;

generating, by the processor, a food element classifier, wherein the food element classifier comprises a classification algorithm, wherein the classification algorithm is trained by using training data comprising user physiological data sets of a plurality of users, the user physiological data sets comprising at least a nutrient body measurement of a user of the plurality of users, wherein the training data correlates the food element descriptor to the plurality of identified constitutional effect labels, and wherein responsive to training, the food element classifier is configured to provide an output of one or more constitutional effect labels of the food element description for a specific user responsive to an input of the nutrient body measurement of the specific user and the food element descriptor;

classifying, by the processor using the food element classifier based at least on the nutrient body measurement of the specific user, the food element descriptor as a function of the plurality of constitutional effect labels; and

displaying, by the processor on a graphical user interface for the specific user, the food element descriptor and a constitutional effect label for the food element descriptor, wherein the food element descriptor is displayed according to a food element category of the food element descriptor.

10. The method of claim 9 , wherein receiving the food element descriptor further comprises receiving a wireless transmission from the user client device containing a picture of a food element.

11. The method of claim 9 , wherein receiving the food element descriptor further comprises receiving a wireless transmission from the user client device containing a picture of a uniform code commission barcode.

12. The method of claim 9 , wherein generating the machine-learning algorithm further comprises executing a supervised machine-learning algorithm.

13. The method of claim 9 , wherein generating the machine-learning algorithm further comprises executing an unsupervised machine-learning algorithm.

14. The method of claim 9 , wherein generating the machine-learning algorithm further comprises executing a lazy-learning algorithm.

15. The method of claim 9 , wherein generating the classification algorithm further comprises a Naïve-Bayes classification algorithm.

16. The method of claim 9 , wherein generating the classification algorithm further comprises a K-nearest neighbor algorithm.

17. The system of claim 1 , wherein the processor is further configured to display, by the processor on the graphical user interface for the specific user, at least a previous user behavior, wherein the at least a previous user behavior comprises: a previous food element descriptor that had been received from the user;

and a constitutional effect label for the previous food element descriptor.

18. The method of claim 9 , further comprising displaying by the processor on the graphical user interface for the specific user, at least a previous user behavior, wherein the at least a previous user behavior comprises:

a previous food element descriptor that had been received from the user; and

a constitutional effect label for the previous food element descriptor.