Abstract: Conventionally, it has been necessary to collect blood of a subject and perform biochemical analysis to measure LDL cholesterol and HDL cholesterol. However, in this method, a needle or the like needs to be invasively inserted into the skin of the subject, which causes a psychological or physical burden on the subject. According to the present invention, it is possible to non-invasively estimate cholesterol based on attribute information and/or non-invasive biological information of a predetermined user by generating a cholesterol estimation model by machine learning based on attribute information, non-invasive biological information, and blood examination data acquired from a plurality of subjects in advance.

Abstract: A method for producing nutrition-added food, includes adding nutrient additives consisting of at least three groups: a group including at least one or more types of vitamins having an upper limit of intake in public nutrient reference intakes, a group including at least one or more types of minerals having an upper limit of intake in public nutrient reference intakes, and a group including at least one or more types of vitamins or/and minerals having no upper limit of intake in public nutrient reference intakes to at least one or more types of single items constituting a diet having one meal amount, and thereby preparing nutrition-added food that satisfies a specific nutrient reference designed on the basis of the public nutrient reference intakes.

Abstract: The present invention makes a more appropriate suggestion concerning a meal of a user. A food providing system is provided with: an information acquisition unit for acquiring user information relating to a user as a food providing subject; a recipe suggestion unit for suggesting a plurality of recipes of dishes to the user on the basis of the user information; a recipe selection result acquisition unit for acquiring a result of a selection by the user for the plurality of recipes suggested by the recipe suggestion unit; and a providing means selection result acquisition unit for acquiring a result of a selection by the user concerning providing means of food provided on the basis of the recipe.

Abstract: A food is provided that includes cheese and/or imitation cheese and sauce, that meets a predetermined nutritional standard, and that has excellent sensory quality. In the food, the cheese and/or imitation cheese includes 5% by mass or less (including 0% by mass) of a total amount of choline, and/or the sauce includes 40% by mass or more of a total amount of choline, the cheese and/or imitation cheese includes 10% by mass or less (including 0% by mass) of a total amount of magnesium, and/or the sauce includes 40% by mass or more of a total amount of magnesium, and the cheese and/or imitation cheese includes 5% by mass or less (including 0% by mass) of a total amount of calcium, and/or the sauce includes 60% by mass or more of a total amount of calcium.

Abstract: Heretofore, for the measurement of creatinine, it is required to collect blood from a subject and perform a biochemical analysis of the blood. However, such a method has the problem that it is required to prick the skin of a subject with a needle or the like in an invasive manner, which places psychological and physical burdens on the subject. [Solution] The present invention generates a creatinine risk estimation model by machine learning on the basis of attribute information, non-invasive biological information, and blood test data of a plurality of subjects which have been acquired in advance, and thus makes it possible to estimate a creatinine risk in a non-invasive manner from attribute information and non-invasive biological information of a specific user.

Abstract: Conventionally, it has been necessary to collect blood of a subject and perform biochemical analysis to measure LDL cholesterol and HDL cholesterol. However, in this method, a needle or the like needs to be invasively inserted into the skin of the subject, which causes a psychological or physical burden on the subject. According to the present invention, it is possible to non-invasively estimate cholesterol based on attribute information and/or non-invasive biological information of a predetermined user by generating a cholesterol estimation model by machine learning based on attribute information, non-invasive biological information, and blood examination data acquired from a plurality of subjects in advance.

Abstract: Conventionally, a needle or the like needs to inserted into skin of a subject to invasively collect blood in ?GT measurement, which causes a psychological or physical burden on a subject. According to the present invention, it is possible to invasively estimate ?GT based on attribute information and non-invasive biological information of a predetermined user by generating a ?GT estimation model by machine learning based on attribute information, non-invasive biological information, and blood examination data acquired from a plurality of subjects in advance.

Abstract: Conventionally, a needle or the like needs to be inserted into skin of a subject to invasively collect blood in blood neutral fat measurement, which provides a psychological physical load on a subject. According to the present invention, it is possible to non-invasively estimate the blood neutral fat based on attribute information and/or non-invasive biological information of a predetermined user by generating a blood neutral fat estimation model by machine learning based on attribute information, non-invasive biological information, and blood examination data acquired from a plurality of subjects in advance.

Abstract: Conventionally, it has been necessary to collect blood of a subject and perform biochemical analysis to measure HbA1c. However, in this method, a needle or the like needs to be invasively inserted into the skin of the subject, which causes a psychological or physical burden on the subject. According to the present invention, it is possible to non-invasively estimate an HbA1c risk based on attribute information and/or non-invasive biological information of a predetermined user by generating an HbA1c risk estimation model by machine learning based on attribute information and non-invasive biological information acquired from a plurality of subjects in advance and examination data of a blood examination.

Abstract: A method for improving health conditions is developed by studying the way meals containing adjusted nutritional components in terms of not only the content of the meal, but also time, period, and the like when the meals are taken. The present invention improves the health conditions by continually taking at least one meal selected from daily three meals, i.e., breakfast, lunch, and evening meal, which contains 13% to 20% of proteins, 20% to 30% of fats, and 50% to 65% of carbohydrates in terms of energy percentage and in which proteins, fats, carbohydrates, dietary fiber, vitamins, and minerals are adjusted to predetermined amounts.

Abstract: Conventionally, it has been necessary to collect blood of a subject and perform biochemical analysis to measure the uric acid level. However, in this method, a needle or the like needs to be invasively inserted into the skin of the subject, which provides a psychological or physical load on the subject. According to the present invention, it is possible to estimate the uric acid level based on attribute information and non-invasive biological information of a predetermined user by generating a uric acid level estimation model by machine learning based on attribute information, non-invasive biological information, and blood examination data acquired from a plurality of subjects in advance.

Abstract: In conventionally known methods, the blood sugar level is measured by collecting blood of a subject or predicted based on interstitial fluid. However, in any method, a needle or the like needs to be invasively inserted into the skin of the subject, which causes a psychological or physical burden on the subject. According to the present invention, it is possible to invasively estimate the blood sugar level based on attribute information and/or non-invasive biological information of a predetermined user by generating a blood sugar level estimation model by machine learning based on attribute information, non-invasive biological information, and blood examination data acquired from a plurality of subjects in advance.

Abstract: The present embodiment relates a complete nutritious food product that eliminates the need for adjustment of nutrients according to each category of age, gender, and physical activity level, and enables eating of a complete nutritious food product satisfying the DRIs only by adjusting the amount (calories) of foods, and a providing system thereof. As a result, users who receive meals can easily take the complete nutritious food product, are freed from the trouble of nutrition management, and can improve their health. The invention also relates to a complete nutritious food product-providing system and a nutrition calculation device for a complete nutritious food product.

Abstract: The present invention makes a more appropriate suggestion concerning a meal of a user. A food providing system is provided with: an information acquisition unit for acquiring user information relating to a user as a food providing subject; a recipe suggestion unit for suggesting a plurality of recipes of dishes to the user on the basis of the user information; a recipe selection result acquisition unit for acquiring a result of a selection by the user for the plurality of recipes suggested by the recipe suggestion unit; and a providing means selection result acquisition unit for acquiring a result of a selection by the user concerning providing means of food provided on the basis of the recipe.

Abstract: Provided is a food product information providing system that creates indices for health risks based on genetic information such as the results of genetic testing, biological information such as the results of a medical checkup, and lifestyle information such as the results of a lifestyle questionnaire from a user, and then selects one or more functional materials according to each risk index and provides the user with a food product or recipe that includes the selected functional material(s) and matches the preference of the user.