A system for generating an immune protocol for identifying and reversing immune disease is presented. The system comprising a computing device configured to receive at least an immune biomarker from a graphical user interface, determine a current immunological state of the user including an immune dysfunction as a function of the immune biomarker and an immune profile, assign an immune category to the immune profile as a function of the current immunological state, identify an effect on the immune profile for each nutritional element of a plurality of nutritional elements, determine at least a nutritional element that contributes to the immune category as a function of an immune machine-learning model and the nutritional input, identify a plurality of protocol elements, wherein each protocol element contains at least a nutrient amount intended to address the immunological dysfunction, and generate an immune protocol as a function of the plurality of protocol elements.

A system for generating an immune protocol for identifying and reversing immune disease is presented. The system comprising a computing device configured to receive at least an immune biomarker from a graphical user interface, determine a current immunological state of the user including an immune dysfunction as a function of the immune biomarker and an immune profile, assign an immune category to the immune profile as a function of the current immunological state, identify an effect on the immune profile for each nutritional element of a plurality of nutritional elements, determine at least a nutritional element that contributes to the immune category as a function of an immune machine-learning model and the nutritional input, identify a plurality of protocol elements, wherein each protocol element contains at least a nutrient amount intended to address the immunological dysfunction, and generate an immune protocol as a function of the plurality of protocol elements.

A health condition estimation apparatus 10 includes an obtaining unit 40 and a controller 42. The obtaining unit 40 obtains gas information. The gas information is based on a signal output by a sensor unit in a period in which a first gas is supplied to the sensor unit and a signal output by the sensor unit in a period in which a second gas is supplied to the sensor unit. The sensor unit outputs a signal with a signal value in accordance with the concentration of a specific gas. The first gas and the second gas are different in at least either of obtaining position and obtaining time. A controller 39 generates health information based on the gas information.

The present invention relates to systems and methods for providing individualized microbiome recommendations to improve or maintain microbiome health. In several embodiments of the invention, the individualized microbiome health recommendations are diets, menus and recipes for improving or maintaining a healthy microbiome. In several embodiments, the microbiome-healthy recommendations are delivered by a computer-implemented system.

The present invention relates to systems and methods for providing individualized microbiome recommendations to improve or maintain microbiome health. In several embodiments of the invention, the individualized microbiome health recommendations are diets, menus and recipes for improving or maintaining a healthy microbiome. In several embodiments, the microbiome-healthy recommendations are delivered by a computer-implemented system.

The present invention pertains to a method for determining the composition of a food product, a method for preparing a food product, a food product prepared by the method according to the present invention and the use thereof.

The present invention pertains to a method for determining the composition of a food product, a method for preparing a food product, a food product prepared by the method according to the present invention and the use thereof.

The techniques described herein relate to computerized methods and apparatus for grouping images of objects based on semantic and visual information associated with the objects. The techniques described herein further relate to computerized methods and apparatus for training a machine learning model for object recognition.

Method of predicting a response of a subject to food is disclosed. The method comprises: selecting a food to which a response of the subject is unknown; accessing a first database having data describing the subject but not a response of the subject to the selected food; accessing a second database having data pertaining to responses of other subjects to foods, the responses of the other subjects including responses of at least one other subject to the selected food or a food similar to said selected food; and analyzing the databases based on the selected food to estimate the response of the subject to the selected food.

Method of predicting a response of a subject to food is disclosed. The method comprises: selecting a food to which a response of the subject is unknown; accessing a first database having data describing the subject but not a response of the subject to the selected food; accessing a second database having data pertaining to responses of other subjects to foods, the responses of the other subjects including responses of at least one other subject to the selected food or a food similar to said selected food; and analyzing the databases based on the selected food to estimate the response of the subject to the selected food.