Application US20190252058

GENERATING PERSONALIZED NUTRITIONAL RECOMMENDATIONS USING PREDICTED VALUES OF BIOMARKERS
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US20190252058

1. A non-transitory computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by a computer, cause the computer to: (6) (3)

generate prediction data indicating a first value of a first target biomarker and a second value of a second target biomarker predicted after eating one or more foods, wherein the prediction data is based at least in part on health data associated with an individual, wherein the first biomarker is a first indicator associated with at least one of a particular disease state or some other physiological state of an organism, and wherein the second biomarker is a second indicator associated with at least one of a different particular disease state or some other different physiological state of an organism;

generate, based at least in part on the first prediction data and the second prediction data, personalized nutritional recommendation data that indicates one or more meals for the individual to eat; and

provide at least a portion of the personalized nutritional recommendation data to a computing device associated with the individual.

2. The non-transitory computer-readable storage medium of claim 1, wherein generating the personalized nutritional recommendation data comprises determining that the one or more meals satisfy one or more objectives and one or more constraints, wherein the constraints identify one or more of a calorie constraint, a type of food constraint, a food variety constraint, or a favorite type of food constraint. (1) (0)

4. The non-transitory computer-readable storage medium of claim 1, wherein the health data includes one or more of microbiome data, long-term user data, or slow changing user data. (0)

5. The non-transitory computer-readable storage medium of claim 1, wherein generating the personalized nutritional recommendation data includes causing prediction data for one or more foods indicated to be preferred by the individual to be generated. (0)

6. The non-transitory computer-readable storage medium of claim 1, wherein generating the personalized nutritional recommendation data includes causing prediction data to be generated for a plurality of foods associated with a plurality of meals. (0)

7. The non-transitory computer-readable storage medium of claim 1, wherein generating the first prediction data, and the second prediction data, includes utilizing one or more machine learning mechanisms and wherein the machine learning mechanism is based at least in part on training data that includes first health data obtained in a structured setting and second health data obtained in a non-structured setting. (0)

8. The non-transitory computer-readable storage medium of claim 1, wherein the computer-executable instructions further cause the computer to rank foods indicated within the personal nutritional recommendation data and wherein providing at least the portion of the personalized nutritional recommendation data comprises causing a graphical user interface to be generated that includes a list of the ranked foods. (0)

9. A system, comprising: (6) (1)

one or more processors, configured to generate prediction data indicating a first value of a first target biomarker and a second value of a second target biomarker predicted after eating one or more foods, wherein the prediction data is based at least in part on health data associated with an individual;

provide at least a portion of the personalized nutritional recommendation data to a computing device associated with the individual.

10. The system of claim 9, wherein the health data includes one or more of microbiome data, long-term user data, or slow changing user data. (0)

11. The system of claim 9, wherein generating the personalized nutritional recommendation data comprises determining that the one or more meals satisfy one or more objectives and one or more constraints, wherein the constraints indicate one or more of a calorie constraint, a type of food constraint, or a favorite type of food constraint. (1) (0)

13. The system of claim 9, wherein generating the personalized nutritional recommendation data includes causing prediction data for one or more foods indicated to be preferred by the individual to be generated. (0)

14. The system of claim 9, wherein generating the personalized nutritional recommendation data includes causing predication data to be generated for a plurality of foods associated with a plurality of meals. (0)

15. The system of claim 9, wherein generating the first prediction data, and the second prediction data, includes utilizing one or more machine learning mechanisms, and wherein at least one of the machine learning mechanisms is based at least in part on training data that includes first health data obtained in a structured setting and second health data obtained in a non-structured setting. (0)

16. The system of claim 9, wherein the one or more processors are further configured to rank foods indicated within the personal nutritional recommendation data and wherein providing at least the portion of the personalized nutritional recommendation data comprises causing a graphical user interface to be generated that includes a list of the ranked foods. (0)

17. A method, comprising: (6) (3)

generating prediction data indicating a first value of a first target biomarker and a second value of a second target biomarker predicted after eating one or more foods, wherein the prediction data is based at least in part on health data associated with an individual;

generating, based at least in part on the first prediction data and the second prediction data, personalized nutritional recommendation data that indicates one or more meals for the individual to eat; and

providing at least a portion of the personalized nutritional recommendation data to a computing device associated with the individual.

18. The method of claim 17, wherein the health data includes one or more of microbiome data, long-term user data, or slow changing user data. (0)

19. The method of claim 17, wherein generating the personalized nutritional recommendation data comprises determining that the one or more meals satisfy one or more objectives and one or more constraints, wherein the constraints indicate one or more of a calorie constraint, a type of food constraint, or a favorite type of food constraint. (1) (0)

21. The method of claim 17, wherein generating the personalized nutritional recommendation data comprises includes causing predication data for one or more foods indicated to be preferred by the individual to be generated. (0)

22. The method of claim 17, wherein generating the personalized nutritional recommendation data includes causing prediction data to be generated for a plurality of foods associated with a plurality of meals. (0)

23. The method of claim 17, wherein generating the first prediction data, and the second prediction data, includes utilizing one or more machine learning mechanisms, and wherein at least one of the machine learning mechanisms is based at least in part on training data that includes first health data obtained in a structured setting and second health data obtained in a non-structured setting. (0)

24. The method of claim 17, wherein the method further ranks foods indicated within the personal nutritional recommendation data and wherein providing at least the portion of the personalized nutritional recommendation data comprises causing a graphical user interface to be generated that includes a list of the ranked foods. (0)

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Application US20190252058

GENERATING PERSONALIZED NUTRITIONAL RECOMMENDATIONS USING PREDICTED VALUES OF BIOMARKERS

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