Dietary plan generation method and device and computer-implemented algorithm thereof

Abstract

A method executed by an electronic device for suggesting a diet plan includes: obtaining diet information of a user; forming a first diet plan subset from a predetermined diet plan set according to the diet information; generating a second subset of dietary plans from the first subset of dietary plans via an evolutionary algorithm; calculating a score of each dietary plan in the second subset of dietary plans; dietary plans in the second subset of dietary plans are ranked, where a score for each dietary plan is calculated based on the dietary information and one or more elements included in each dietary plan.

Description

Technical Field

[0001] The present invention relates to a method and apparatus for generating a meal plan and a computer-implemented algorithm therefor. Background Art

[0002] Nutrition is a key driver of human health and well-being, and nutrition is typically obtained by a person through the intake of food (including beverages). For specific populations, nutritional intake is very crucial. For example, for pregnant or lactating women, people suffering from nutrition-related diseases (such as diabetes), and specific groups such as the elderly / older people with special nutritional intake requirements. Therefore, it is very important to control / personalize nutritional intake according to the different situations of each target person.

[0003] The present invention provides a method and apparatus for generating a meal plan and a computer-implemented algorithm therefor, which aims to optimize and personalize nutritional intake and at the same time take into account the user's preferences. Summary of the Invention

[0005] The present invention relates to a method and apparatus for generating a meal plan and a computer-implemented algorithm / method therefor.

[0006] The present invention aims to provide a personalized meal plan for users.

[0007] The present invention is defined according to the claims. Brief Description of the Drawings

[0008] The present invention will be discussed in more detail below with reference to the accompanying drawings, in which:

[0009] Figure 1 A method for generating a meal plan is shown.

[0010] Figure 2 An example of an evolutionary algorithm when generating a second subset is shown.

[0011] Figure 3 An example of determining a weight value is shown.

[0012] Figure 4 An apparatus configured to perform some or all of the steps in the present invention is shown. Detailed Description of the Embodiments

[0013] Embodiments of the present disclosure will be described below with reference to the accompanying drawings. However, the embodiments of the present disclosure are not limited to specific embodiments, but should be understood to include all modifications, variations, equivalent devices and methods, and / or alternative embodiments of the present disclosure.

[0014] As used herein, the terms "comprises," "may comprise," "includes," and "may include" indicate the presence of corresponding features (e.g., elements such as numerical values, functions, operations, or components), and do not preclude the presence of additional features.

[0015] As used herein, the terms "A or B," "at least one of A or / and B," or "one or more of A or / and B" include all possible combinations of the items listed therewith. For example, "A or B," "at least one of A and B," or "at least one of A or B" means (1) including at least one A, (2) including at least one B, or (3) including at least one A and at least one B.

[0016] Terms such as "first" and "second" used herein may modify various elements regardless of the order and / or importance of the corresponding elements, and do not limit the corresponding elements. These terms may be used to distinguish one element from another. For example, a first printing form and a second printing form may represent different printing forms regardless of the order or importance. For example, without departing from the scope of the present invention, the first element may be referred to as the second element, and similarly, the second element may be referred to as the first element.

[0017] It should be understood that when an element (e.g., a first element) is "(operatively or communicatively) coupled / couples to" or "connected to" another element (e.g., a second element), the element may be directly coupled to the other element, and there may be an intermediate element (e.g., a third element) between the element and the other element. In contrast, it should be understood that when an element (e.g., a first element) "directly couples / couples to" or "directly connects to" another element (e.g., a second element), there is no intermediate element (e.g., a third element) between the element and the other element.

[0018] Depending on the context, the expression "configured to (or set to)" used herein may be interchangeably used with "suitable for," "capable of," "designed to," "adapted to," "manufactured to," or "able to." The term "configured to (set to)" does not necessarily mean "specially designed at the hardware level." Rather, the expression "the device is configured to..." may mean that the device "is able to..." together with other devices or components in a specific context.

[0019] The terms used to describe the various embodiments of this disclosure are for the purpose of describing particular embodiments and are not intended to limit the disclosure. Unless the context clearly dictates otherwise, the singular forms used herein are also intended to include the plural forms. All terms used herein (including technical or scientific terms), unless otherwise defined, have the same meaning as commonly understood by one of ordinary skill in the relevant art. Terms defined in commonly used dictionaries, unless explicitly defined herein, should be construed to have a meaning consistent with the context of the relevant art and should not be construed to have an ideal or exaggerated meaning. Depending on the circumstances, even terms defined in this disclosure should not be construed to exclude embodiments of this disclosure.

[0020] The present invention provides a meal plan generation method, apparatus, and computer-implemented algorithm thereof, which aim to optimize and personalize nutritional intake and at the same time take into account the user's preferences. The target user / person may be a pregnant woman, a pregnant woman with diabetes, a man, a baby, a child, an elderly person, or any other person. For example, the meal plan generation method may be for women in different stages, such as, for example, a woman planning pregnancy, pregnant, recovering from childbirth, or breastfeeding, especially because women in different stages may have different lifestyles, personal food preferences, and health conditions, all of which require personalized nutrients.

[0021] Figure 1 The meal plan generation method is shown.

[0022] In step 101, the user's dietary information is obtained. The dietary information may include any information related to the user's diet, such as, for example, at least one of the following: allergens, restricted ingredients, genetic / DNA information, number of meals per day, height, weight, gender, activity level, location information, dietary preferences, user pattern data, dietary history, family history information, and the stage the user is in, such as the stage being pre-pregnancy, pregnancy, puerperium, or lactation. The dietary information may further include other information, such as, for example, health conditions, diseases, body mass index (BMI), age, etc.

[0023] User pattern data may include data on at least one of user behavior information regarding meal plans (such as, for example, a recently selected (or saved, searched, commented on, viewed for more than a predetermined period, etc.) meal plan) or ingredients. User pattern data may further include other user data when the user uses this method to obtain meal plan suggestions (such as, for example, when using an application / software implementing this method).

[0024] Location information may be the user's current location or previous location, such as, for example, the birth location, the location where the user stayed the longest in the past, etc.

[0025] Family history information may be information related to the user's family members. For example, family member disease records, the weights of family members, the dietary preferences of family members, etc.

[0026] The user's user weight data may include the user's current weight, the user's weight change data over a past period, the user's weight change data related to a dietary plan, etc. The user weight change data can be very important because it gives hints about the dynamic changes in the body / weight, enabling the dietary plan to be recommended / scored accordingly. For example, a user with a rapid weight loss may be unhealthy, so the recommended diet may temporarily include more energy / calories; or a user with a rapid weight gain should slowly reduce the energy in the recommended diet rather than immediately reducing calories.

[0027] Dietary information can be obtained via at least one of the following means: for example, a questionnaire (e.g., on a smartphone / tablet), from an existing database (e.g., authorized medical records, recorded dietary history, etc.), via certain devices (e.g., via a smart scale for obtaining the user's weight, via a device with a camera for estimating the user's BMI), or any other means.

[0028] In step 102, a first subset of dietary plans is formed / generated based on the obtained dietary information of the user. The first subset of dietary plans includes at least one dietary plan.

[0029] A dietary plan can be a plan that includes at least one element. For example, an element can be a set of different foods (the term "food" in this document includes solid or paste foods, colloids, liquids / solutions, and beverages), for example, based on one or more food recipes (e.g., one or more dishes). As another example, one or more elements in each dietary plan may include at least one of the following items: a breakfast menu, one or more lunch main courses, one or more dinner main courses, an extra meal menu, one or more lunch dishes, and one or more dinner dishes.

[0030] The first subset of dietary plans can be formed from dietary plans selected from the original set of dietary plans (i.e., the original dietary plans or the set of predefined dietary plans used interchangeably in this document). The original set of dietary plans can be the original collection of all dietary plans stored in a database (either inside or outside the device including the application / software implementing this method). For example, the first subset of dietary plans can be directly selected from the original set, e.g., via an artificial intelligence model based on dietary information. Alternatively, the first subset of dietary plans can be selected based on certain conditions. For example, if the dietary information indicates that the user is elderly, a dietary plan with liquid / soft foods is more likely to be selected; if the dietary information indicates that the user prefers spicy foods, a dietary plan with a strong spicy flavor is more likely to be selected; if the dietary information indicates that the user has not consumed any dairy products in the past week, a dietary plan with cheese and milk is more likely to be selected; if the dietary information indicates that the user is allergic to seafood, a dietary plan with seafood will not be selected. Note that the above conditions are merely examples and they do not limit the scope of the present invention.

[0031] The first subset of dietary plans can be formed from a screened set of original dietary plans (i.e., from the original set of dietary plans), i.e., by first screening the original dietary plans by removing all dietary plans that include allergens and / or restricted ingredients (based on dietary information) and / or removing dietary plans that include ingredients other than the existing dietary ingredients (i.e., the proposed dietary plans can be restricted to those that can be prepared based on the existing food ingredients in the user's home); then selecting dietary plans from the screened original set when forming the first subset of dietary plans (i.e., by any of the methods disclosed above). For example, the first subset of dietary plans can be formed by removing zero, one, or more dietary plans that include allergens and / or restricted ingredients and / or removing dietary plans that include ingredients other than the existing dietary ingredients from the predefined / original set of dietary plans according to the dietary information, i.e., the first subset can be the screened set of original dietary plans.

[0032] The first dietary plan subset can be formed based on the estimated at least one intake target, where the intake target can be the standard or recommended intake (e.g., mass, volume, or according to any other measurement method) during a specific period (e.g., one day, one week, one month, the entire pregnancy, etc.). For example, before step 102, at least one intake target for each ingredient (e.g., each food category, each macronutrient, and each micronutrient) can be determined / estimated based on dietary information, where each food category, each macronutrient, and each micronutrient can be predefined. The food categories can include at least one of the following: grains / cereal foods, dairy products, fruits, vegetables, soy products / nuts, sweets / sugars, water, meat, poultry, fish, and other alternatives. The macronutrients can include at least one of the following: energy, fat, protein, carbohydrates, and other macronutrients. The micronutrients can include at least one of the following: calcium, Fe (iron / ferrum), zinc, folic acid, vitamin D, vitamin B12, vitamin B6, vitamin C, vitamin B2, vitamin E, DHA (docosahexaenoic acid), and other micronutrients. For example, the determination of the intake target can be further based on the standard nutrient recommendations according to the user's dietary information. The determination of the intake target can be further based on other information, such as the user's biometric information, microbiome information, and / or blood glucose information.

[0033] Each intake target for each food category, each macronutrient, or each micronutrient can be determined based on the obtained dietary information. For example, if the dietary information indicates that the user is pregnant (which may request a total daily energy intake within a specific range), then the dietary plan within this range has a higher chance of being selected when forming the first subset; if the dietary information indicates that the user is breastfeeding (which may request a certain range of daily folic acid intake), then the dietary plan within this folic acid intake range has a higher chance of being selected when forming the first subset; and so on. The database can be formed as at least one lookup table that provides a cross-reference between different dietary information and the standard intake targets for different ingredients.

[0034] For example, each intake target can be determined by comparing the dietary information with a standard reference database (e.g., a lookup table). For example, the standard reference database associates the average / standard intake for each ingredient (e.g., each food category, each macronutrient, and / or each micronutrient) with specific dietary information. For example, the database can indicate what the average / standard daily energy intake is for a pregnant woman with a specific weight, age, and health condition; what the average / standard vitamin D intake is within a day for the elderly of a specific age.

[0035] The above determination of the intake target may be omitted in this method, or alternatively, may be performed before calculating the score of each dietary plan in step 104 or when using an evolutionary algorithm during step 103, which will be discussed later in this document.

[0036] In step 103, a second subset of dietary plans is generated from the first subset of dietary plans, for example, via an evolutionary algorithm or a trained artificial intelligence model. Examples of using an evolutionary algorithm will be presented later in Figure 2 this document.

[0037] In step 104, the score of each dietary plan in the second subset is calculated. The score of a dietary plan can be calculated based on at least two scoring items, where the scoring items can be ingredient score, weekly score, daily score, gene score, or preference score. A higher score can be defined as more desirable, or a lower score can be defined as more desirable. For the sake of illustration, in the remainder of this document, it is assumed that a higher score is more desirable; however, the option that a lower score is more desirable is also implicitly disclosed.

[0038] The ingredient score can be calculated based on at least one of the total food category score, the total macronutrient score, and the total micronutrient score. The total food category score can be the sum of the individual food category scores, and the individual food category scores can be calculated based on the amount (mass, volume, or other measurement method) of the food category included in the dietary plan and the intake target for that food category. For example, the food category is at least one of grains, cereal foods, dairy products, fruits, vegetables, soy products, nuts, sweets, water, meat, fish, and substitutes. The total macronutrient score is the sum of the individual macronutrient scores, and the individual macronutrient scores can be obtained based on the amount (mass, volume, or other measurement method) of the macronutrients included in the dietary plan and the intake target for the macronutrients. The total micronutrient score can be the sum of the individual micronutrient scores, and the individual micronutrient scores can be obtained based on the amount (mass, volume, or other measurement method) of the micronutrients included in the dietary plan and the intake target for the micronutrients.

[0039] Intake targets for each food category, each macronutrient, and each micronutrient can be determined based on the dietary information presented above and not repeated here. Separate food category scores can be based on the determined intake targets for that separate food category; separate macronutrient scores can be based on the determined intake targets for that separate macronutrient; separate micronutrient scores can be based on the determined intake targets for that separate micronutrient. If the amount of the ingredient (e.g., mass, volume, or according to any other measurement method) contained in the dietary plan is closer to the intake target for that ingredient, a higher (e.g., more desirable) separate ingredient score (i.e., for any one of food category, macronutrient, and micronutrient) can be given to the separate ingredient (i.e., for any one of food category, macronutrient, and micronutrient). When the amount of the ingredient contained in the dietary plan is further from the intake target for that ingredient, the separate ingredient score is lower (e.g., less desirable). Some examples of how to determine separate ingredient scores are shown below.

[0040] An example of a separate food category score table for cereal and grain food intake (grams per day) is shown in Table 1. Table 1. Example of cereal and grain food scores

[0041] As shown in Table 1, for women in the first trimester of pregnancy, if the intake of cereal and grain foods during a day is less than 200 grams, the cereal and grain food score (as an example of one of the individual food category scores) is 1; if the intake is 200 to 249 grams, the cereal and grain food score is 2; if the intake is 250 to 300 grams, the cereal and grain food score is 3 (which is the most desirable / healthiest according to the determined intake target for cereal and grain foods); if the intake is 301 to 350 grams, the cereal and grain food score drops to 2 (already too much); if the intake is more than 350, the cereal and grain food score is 1. Such scores can be calculated for each food category. In this example table, when the cereal and grain food score is the highest (3), the cereal and grain food intake targets (i.e., the standard or recommended daily intake of cereal and grain foods) according to different dietary information (first trimester, second trimester, third trimester, lactation stage, and other adults) are given in the column.

[0042] An example of a separate macronutrient score table for fat intake (grams per day) is shown in Table 2. Table 2. Example of fat scores Dietary Information and Fat Score 1 2 3 2 1 Pre-pregnancy <40 40-44 45-55 56-60 >60 First Trimester <40 40-44 45-55 56-60 >60 Second Trimester <47.2 47.2-52.1 53.1-64.9 65.9-70.8 >70.8 Third Trimester <50.4 50.4-55.7 56.7-69.3 70.3-75.6 >75.6 Lactation Period <51.2 51.2-56.6 57.6-70.4 71.4-76.8 >76.8 Other Adults <40 40-44 45-55 56-60 >60

[0043] As shown in Table 2, for women in the early pregnancy stage, if the fat intake during a day is less than 40 grams, the fat score (as an example of one of the scores for each macronutrient) is 1; if the intake is 40 to 44 grams, the fat score is 2; if the intake is 45 to 55 grams, the fat score is 3 (the most desirable / healthiest); if the intake is 56 to 60 grams, the fat score drops to 2 (already too much); if the intake is more than 60, the fat score is 1. Such scores can be calculated for each macronutrient. In this example table, when the fat score is the highest (3), the fat intake targets (i.e., the standard or recommended daily fat intake) are given in the column according to different dietary information (pre-pregnancy, early pregnancy, mid-pregnancy, late pregnancy, lactation stage, and other adults).

[0044] Separate micronutrient scores can be determined in a manner similar to that in Tables 1 and 2 for food categories and macronutrients (which will not be repeated here).

[0045] The total food category score can be defined as the average score of at least one of the individual food category scores. For example, if eight different food categories are considered, the food category score = (grain and cereal food score + dairy product score + fruit score + vegetable score + soy product and nut score + sweet score + water score + meat, poultry, fish, and substitutes) / 8. Alternatively, the total food category score can be defined in other ways, such as the lowest within each food category score or the highest within each food category score or any other way.

[0046] Similarly, the total macronutrient score can be defined as the average score of at least one of the individual macronutrient scores; and the total micronutrient score can be defined as the average score of at least one of the individual micronutrient scores. Alternatively, the total macronutrient (or micronutrient) score can be defined in other ways, such as the lowest within each score or the highest within each score or any other way.

[0047] The ingredient score (i.e., the total ingredient score) can be calculated based on at least one of the total food category score, the total macronutrient score, and the total micronutrient score. The ingredient score can be further adjusted to fit a specific metric, for example, with a maximum value of 10. For example, the ingredient score can be the sum of at least one of the total food category score, the total macronutrient score, and the total micronutrient score. Or after this sum, the value can be further adjusted. For example, if the maximum possible scores for the total food category score, the total macronutrient score, and the total micronutrient score are X, Y, and Z respectively, and their total score (i.e., the true score) is x, y, and z. Then the ingredient score can be calculated as Wi*(x / X + y / Y + z / Z) / 3 or (x / X + y / Y + z / Z) / 3, where Wi is the weight given to the ingredient score when calculating the score of the dietary plan. Other alternatives are also possible.

[0048] The weekly score can be determined based on at least one predetermined weekly rule. The at least one predetermined weekly rule can be determined based on dietary information, that is, different dietary information can correspond to different weekly rules. If the rule (from the at least one predetermined weekly rule) is satisfied, then a separate rule score (e.g., an integer greater than 0) is assigned, otherwise, another separate rule score (e.g., 0) is assigned. The separate rule score can be further based on the predetermined weight corresponding to the rule score. An example of the weekly rules and weights for the first trimester stage is shown in Table 3 (as an example of dietary information). Table 3. Example of weekly rules and weights for the first trimester stage. Weekly Rule Index Weekly Rule Weight 1 Consume seafood 2 to 3 times a week 2 2 Consume dishes high in Fe 1 to 2 times a week 3 3 Consume high-iodine dishes twice a week 4 4 Consume dishes high in DHA twice a week 5

[0049] In the above example in Table 3, there are four weekly rules, and each rule has a different rule weight. If only Rule 1 is satisfied, the weekly score (i.e., the total) can be calculated as 4*2 / (2 + 3 + 4 + 5) = 0.57. For example, if Rw(i) indicates whether the dietary plan satisfies Rule i, then when Rw(i) = 1, Rule i is satisfied; when Rw(i) = 0, Rule i is not satisfied. The weight of each rule can be indicated as Ww(i). The total number of rules is Nw. As an example, the weekly score can be calculated as Nw*sum(Rw(i)*Ww(i)) / sum(Ww(i)).

[0050] The daily score can be based on at least one daily rule related to a one-day dietary plan; the gene / DNA score can be based on a gene rule related to the dietary requirements according to the user's gene / DNA; the preference score can be based on a preference rule related to the user's dietary preferences. The daily score, the gene / DNA score, and the preference score can be calculated in the same way as for the weekly score.

[0051] Examples of daily rules, genetic rules, and preference rules are shown in Tables 4, 5, and 6, respectively. Table 4. Example of daily rules and weights for the early pregnancy stage. Daily Rule Index Daily Rule Weight 1 Green vegetables >= 1 time 2 2 Soy products >= 1 time 2 3 Iodized salt contains 96 to 144 μg 1 4 Calcium >= 1000 1 Table 5. Example of genetic / DNA rules and weights for the early pregnancy stage. Gene Rule Index Gene Rule Weight 1 High requirement for vitamin D 1 2 High requirement for vitamin B6 2 Table 6. Example of preference rules and weights for the early pregnancy stage.

[0052] The daily score can be calculated in the same way as for the weekly score. Here, the rules mentioned are daily rules. For example, if Rd(i) indicates whether rule i is satisfied, then when Rd(i) = 1, rule i is satisfied; when Rd(i) = 0, rule i is not satisfied. The weight of each rule can be denoted as Wd(i). The total number of rules is Nd. As an example, the daily score can be calculated as Nd * sum(Rd(i) * Wd(i)) / sum(Wd(i)).

[0053] The genetic / DNA score can be calculated in the same way as for the weekly score. Here, the rules mentioned are genetic rules. For example, if Rg(i) indicates whether rule i is satisfied, then when Rg(i) = 1, rule i is satisfied; when Rg(i) = 0, rule i is not satisfied. The weight of each rule can be denoted as Wg(i). The total number of rules is Ng. As an example, the genetic score can be calculated as Ng * sum(Rg(i) * Wg(i)) / sum(Wg(i)). As another example, rs1801133 of the genetic / DNA information from the user can be of one of the types GG, AG, and AA, and the folic acid requirements / absorption capacities of different types are GG > AG > AA, which can be the basis for generating additional genetic rules / scores.

[0054] The preference score can be calculated in the same way as for the weekly score. Here, the rules mentioned are preference rules. For example, if Rp(i) indicates whether rule i is satisfied, then when Rp(i) = 1, rule i is satisfied; when Rp(i) = 0, rule i is not satisfied. The weight of each rule can be denoted as Wp(i). The total number of rules is Np. As an example, the preference score can be calculated as Np * sum(Rp(i) * Wp(i)) / sum(Wp(i)).

[0055] The score of a meal plan can be calculated based on at least two scoring sub-items, and / or the scoring sub-items are ingredient scores, weekly scores, daily scores, gene scores, or preference scores. Weights can be assigned to each scoring sub-item. For example, if the score of scoring sub-item j is s(j) and the weight of scoring sub-item j is w(j), then the meal plan score S can be calculated as S = sum(s(j) * w(j)).

[0056] As disclosed above, when calculating the score of a meal plan, weight values (w(j)) can be used. That is, when calculating the score of a meal plan, weight values can be assigned to each scoring sub-item. The weight value (w(j)) of scoring sub-item (j) can be determined by the following operations: determining the priority of the scoring sub-item; assigning an initial weight value to the scoring sub-item, and assigning an initially higher weight value to the scoring sub-item with a higher priority; selecting a subset of meal plan tests from a predetermined set of meal plans, for example, a part of all meal plans in the predetermined / original set of meal plans; calculating the score of the scoring sub-item based on the initially assigned weight value to the scoring sub-item; if one or more of the calculated scores meet at least one specific condition, determining that the initial weight value is the final weight value, otherwise, iteratively assigning different initial weight values to the scoring sub-item and selecting the test subset and calculating the score. An example of a method for determining weight values for different scoring sub-items is shown in Figure 3 In.

[0057] In step 301, the priority of the scoring sub-item can be determined. For example, if the scoring sub-items include ingredient scores, weekly scores, daily scores, gene scores, or preference scores, the priority of the scoring sub-item can be determined as ingredient score >= gene score >= weekly score >= daily score >= preference score, or another priority order can be determined.

[0058] In step 302, an initial weight value can be assigned to the scoring sub-item. For example, a higher initial weight value can be assigned to the scoring sub-item with a higher priority. For example, the initial weight values can be assigned as ingredient score (10) >= gene score (10) >= weekly score (10) >= daily score (10) >= preference score (8), where 10, 10, 10, 10, and 8 are the weight values respectively.

[0059] In step 303, a subset of meal plan tests is formed from a predetermined set of meal plans (i.e., the original set of meal plans or the original meal plan used interchangeably in this document). For example, the total number of meal plans in the test subset can be about 100 to 200 meal plans or even more.

[0060] In step 304, each scoring item in the meal plans in the test subset can be calculated. Then, based on the initial weight values of the scoring items, the score of each meal plan in the test subset can be calculated. During the calculation of the scoring items for each test meal plan, some test / prescribed diet information can be used. The test / prescribed diet information can be randomly generated or selected from an existing diet information database according to some predetermined conditions. Alternatively, the test / prescribed diet information can be the diet information of the current user (i.e., the individual ingredient scores and rule weights are based on the actual diet information of the current user), so that the weight values are personalized.

[0061] The determined weight values can be further personalized so that the meal plans can be scored more accurately. For example, in step 303, the test subset can be selected based on the diet information of the current user, for example, in the same way as when the first meal plan subset was selected in step 102. For example, the test subset can be the same as the first meal plan subset in step 102 or a part of the first meal plan subset in step 102. In this way, the weight values are further personalized according to the diet information of the current user.

[0062] In step 305, if one or more of the calculated scores meet at least one specific condition, the initial weight value is determined to be the final weight value of the scoring item; otherwise, different initial weight values are iteratively assigned to the scoring item, the test subset is selected, and the scores are calculated. For example, the at least one specific condition can be that if the percentage of scores higher than a specific score threshold (e.g., 5, 5.5, 6, 6.5, or other scores) from all test meal plans is higher than a specific percentage threshold, e.g., 50% or other percentage, the initial weight value can be considered good enough and determined to be the final weight value. Another example of a specific condition can be that if the scores of all test meal plans calculated based on the initial weight values have a predetermined error range from a predetermined reference score, e.g., the scores are approximately the same as those predicted by (a)n expert based on the meal plans. "Approximately the same" can mean that the score variation is within a specific range (e.g., 5% or 10%), and / or the distribution of the percentages of scores in different score ranges (e.g., score <2, between 2 and 4, between 4 and 8, >8) matches the prediction of the expert. The expert here can be a person or an artificial intelligence model trained to score meal plans based on diet information.

[0063] In step 305, if a specific condition is not met, at least one of the following is iteratively performed: assign different initial weight values to the scoring sub-items, select a test subset, and calculate the score, that is, calculate the score iteratively based on at least one of the following: the initial weight values newly assigned to the scoring sub-items; and the newly selected test subset. For example, the initial weight values can be adjusted but still satisfy the priority order, and then the adjusted initial weight values can be used to calculate the score. The adjustment of the weight values can be iteratively performed until the at least one specific condition is met (i.e., the adjusted weight values become the final weight values). During each iterative adjustment of the initial weight values, the meal plan test subset can be iteratively reselected. Alternatively, the same meal plan test subset can be used until the final weight values are determined. The reselection of the meal plan test subset can be based on the same method as the selection of the test subset in step 303; in addition, the reselected test subset can only partially overlap with the test subset in the previous iteration. The overlapping part (i.e., the overlapping meal plans) can be constrained to the maximum extent in the new test subset. For example, a specific percentage of the meal plans should not be included in the previous test subset. The percentage can be 20%, 30%, 40%, 50%, 60%, 70%, 80% and other percentages. For example, if the at least one specific condition is not met, the initial weight values can be adjusted and a new test subset can be reselected, and then the score can be calculated based on the adjusted initial weight values and the new test subset; if the at least one specific condition is not met, the initial weight values can be adjusted and a new test subset can be reselected again, and then the score can be calculated again; the iteration can end when the at least one specific condition is met. In addition, the iteration can also end when the total number of iterations is more than a predefined number (e.g., 20 times, 50 times, 100 times or even more times).

[0064] Return to Figure 1 , in step 104, the scores of the meal plans in the second subset can be calculated according to the method disclosed above.

[0065] In step 105, the meal plans in the second meal plan subset are ranked. In addition, multiple top-ranked meal plans can be displayed / suggested to the user, and then user input can be received to select one of the top-ranked meal plans. Alternatively, the meal plan with the highest score from the second subset can be suggested to the user and / or the meal plan can be output to the user. The user input can be food preferences, recent updates of the health condition, types of available ingredients, etc.

[0066] After step 105, the user may be able to select and change one or more of the proposed meal plans in step 105. Then, the selected one or more meal plans can be replaced with other meal plans. That is, an input can be received from the user to change the first meal plan within the top-ranked meal plans and then a second meal plan can be proposed based on the first meal plan. Additionally or alternatively, the user may be able to select and change one or more elements of the proposed meal plans in step 105. Then, the selected one or more elements can be replaced with elements. That is, an input can be received from the user to change a staple food element (e.g., bread) to another staple food element (e.g., bread). By replacing the selected element with a different but similar element, a second meal plan can be formed.

[0067] The second meal plan can be determined based on at least one of the following: one or more properties of the first meal plan (e.g., the similarity between the two meal plans), dietary information (e.g., dietary preferences, user pattern data, etc.), some general rules obtained from a group of users (e.g., milk in breakfast can be replaced with fried eggs, bread can be used to replace potatoes, etc.), and meal plans preferred by other similar users (e.g., if two users are determined to be similar, the replacement meal plan may be from the preferred meal plan of another user). If the user only selects to replace one or more elements in the first meal plan, the formation of the second meal plan can be further based at least on the similarity between the at least one element to be replaced and the at least one different element to replace it, as well as the dietary information. For example, when forming the second meal plan, similar staple food elements can replace each other, or similar dairy products can replace each other.

[0068] For example, the user pattern data in the dietary information may include data on at least one of the user behavior information regarding the meal plan, which can be used to select the second meal plan as an alternative to the first meal plan. As another example, the dietary preferences in the dietary information can be used to select the second meal plan.

[0069] The similarity between meal plans can be determined based on at least one of the following: the total weight of the meal, the included ingredients, the proportion of ingredients, the food category composition, the macronutrient composition, the micronutrient composition, the total calories, etc.

[0070] At Figure 1In the method, steps 102 (forming a first subset of meal plans) and / or step 103 (generating a second subset of meal plans) can be iteratively executed until a part or all of the meal plans in the second subset of meal plans have a score higher than or equal to a second predetermined threshold (i.e., calculated by step 104). For example, the first subset of meal plans and / or the second subset of meal plans can be regenerated until at least some of the meal plans in the second subset are good enough for the user. This ensures that the recommended meal plans are higher than or equal to at least the threshold score.

[0071] If steps 102 (forming a first subset of meal plans) and step 103 (generating a second subset of meal plans) are executed more than a predetermined number of iterations, step 105 (selecting a meal plan from the second subset of meal plans) can be executed even if none of the meal plans in the second subset of meal plans have a score higher than or equal to the second predetermined threshold. This can avoid endless iterations.

[0072] Figure 2 An example of an evolutionary algorithm when generating the second subset in step 103 is shown.

[0073] In step 201, an evolutionary set of meal plans can be formed by selecting a number N of first initial meal plans. The N first initial meal plans can be randomly selected from the first subset of meal plans or selected based on certain conditions, such as the most frequently recommended meal plans, the least frequently recommended meal plans, the most popular meal plans based on a survey of the user, etc.

[0074] In step 202, the score of each first initial meal plan can be calculated, where the score can be represented as Xi for the i-th first initial meal plan. The score calculation method can be the same calculation method as disclosed in step 104 for Figure 1 . Additionally, if one, some, or all of the scores of Xi meet the condition of being equal to or higher than the first predetermined threshold, the first initial meal plan can be directly output as the second subset of meal plans and the later steps in Figure 2 can be not executed.

[0075] In step 203, one or more elements can be exchanged between two or more first initial meal plans to form a second initial meal plan. One or more elements in each meal plan can include at least one of the following items: breakfast menu, one or more lunch main courses, one or more dinner main courses, extra meal menu, one or more lunch dishes, and one or more dinner dishes. The exchange of these elements can be performed randomly. For example, the number of meal plans can be randomly determined, and then a random number of elements in these meal plans can be exchanged. For example, exchange the lunch main course in the first meal plan with the lunch main course in the second meal plan, and exchange the breakfast menu in the third meal plan with the breakfast menu in the first meal plan. In this step, a new (i.e., second initial) meal plan is generated based on the previous (i.e., first initial) meal plan.

[0076] In step 204, the score of each second initial meal plan is calculated, where the new score can be expressed as Yi for the i-th second initial meal plan. The score calculation method can be the same as the calculation methods disclosed for step 104 and step 202 in Figure 1 In addition, if one, some, or all of the scores of Yi meet the condition of being equal to or higher than the first predetermined threshold, the second initial meal plan can be directly output as the second subset of the meal plan and the later steps in Figure 2 can be skipped. Otherwise, if one, some, or all of the scores of Yi are lower than the first predetermined threshold, the later steps can be executed.

[0077] In step 205, the evolved meal plan set is updated by replacing the i-th first initial meal plan in the evolved meal plan set with the i-th second initial meal plan when Yi >= Xi. In this way, meal plans with higher scores can be included in the evolved meal plan set.

[0078] In step 206, steps 202 to 205 are iteratively executed. The iteration can end if the iteration has been performed more than a predetermined number of times and / or until some or all of the meal plans in the evolved meal plan set have scores equal to or higher than the first predetermined threshold. For example, if one of the scores of the meal plans is equal to or higher than the first predetermined threshold, the iteration can end; or the iteration can end only when all the scores of the meal plans are equal to or higher than the first predetermined threshold.

[0079] In step 207, the evolved meal plan set is output as the second subset of the meal plan.

[0080] Regarding Figure 2In the method, high - scoring meal plans (e.g., based on the user's specific dietary information) are included in the second subset, and at the same time, the meal plans in the second subset have been randomly generated (e.g., through step 203) such that the same meal plan will always be recommended to the same user.

[0081] Figure 4 Fig. 4 shows a device 400 for implementing the present invention, such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, a smart watch, a television, etc.

[0082] The device 400 may include a processor 401, a display 402, a communication unit 403, a memory 405, a camera 406, and other input / output units 407.

[0083] The processor 401 is configured to execute programs / instructions stored in the memory 405 by, for example, controlling other components such as the display 402, the communication unit 403, the memory 405, the camera 406, and other input / output units 407.

[0084] The display 402 may be controlled by the processor 401 to perform all display functions (and input functions if the display is a touch screen) in the present invention, such as in step 101.

[0085] The communication unit 403 may be controlled by the processor 401 to perform all communication functions in the present invention. For example, if an external device 410 (e.g., a server) is used to perform Figure 1 and / or Figure 2 some of the functions in the steps (e.g., step 101 may be performed on the user device 400 and the final meal plan recommendation may also be displayed on the user device 400, but Figure 1 and Figure 2 other steps in may be performed on the external device 410 (e.g., a server); or all steps may be performed on the user device 400; or a part of the steps may be performed on the user device 400 and the rest of the steps may be performed on at least one or more external devices 410), then messages may be transmitted via the communication unit 403. Optionally, the databases used in the present invention may be stored in the external device 410 or the user device 400, such as a lookup table for ingredient scoring, an original set of meal plans, weekly rules, daily rules, genetic rules, preference rules, the user's dietary information, etc.

[0086] The memory 405 may be configured to store instructions for performing the methods of the present invention. For example, a lookup table for ingredient scoring, an original meal plan set, weekly rules, daily rules, genetic rules, preference rules, the user's dietary information, or other necessary information may also be stored in the memory 405. The device may provide at least one entry for the user to review / overview this data.

[0087] The camera 406 is configured to capture images, and the camera is optional in the present invention.

[0088] The other input / output unit 407 may be configured to perform other input / output functions of the present invention, for example, to receive user input of dietary information and output the proposed meal plan to the user.

[0089] In the present invention, at least a part of the device (e.g., Figure 4 ) or the method (e.g., Figure 1 , Figure 2 and / or Figure 3 , as a computer-implemented algorithm) may be implemented as instructions stored, for example, in the form of program modules, software, mobile applications, and / or other forms in a non-transitory computer-readable storage medium. When executed by a processor (e.g., the processor 401), the instructions may enable the processor to perform the corresponding functions according to the present invention. The non-transitory computer-readable storage medium may be the memory 405.

[0090] The present invention includes a method performed by an electronic device for suggesting a meal plan, the method including: obtaining the user's dietary information; forming a first subset of meal plans from a predetermined set of meal plans according to the dietary information; generating a second subset of meal plans from the first subset of meal plans via an evolutionary algorithm; calculating the score of each meal plan in the second subset of meal plans; ranking the meal plans in the second subset of meal plans, wherein the score of each meal plan is calculated based on the dietary information and one or more elements included in each meal plan.

[0091] The above method may further include: displaying a certain number of top-ranked meal plans and / or receiving input from the user to select a meal plan from the displayed meal plans; or suggesting the meal plan with the highest score from the second subset of meal plans.

[0092] The above method may further include: receiving input from the user to change the first meal plan within the top-ranked meal plans, and suggesting a second meal plan based on the first meal plan; or receiving input from the user to change at least one element in the first meal plan, and replacing the at least one element in the first meal plan with at least one different element to form a second meal plan.

[0093] In the above method, the recommendation of the second dietary plan can be further based at least on the similarity between the first dietary plan and the second dietary plan and the dietary information, and / or the formation of the second dietary plan can be further based at least on the similarity between the at least one element to be replaced and the at least one different element and the dietary information.

[0094] The above method can further include: determining at least one intake target for each food category, each macronutrient, and / or each micronutrient according to the dietary information, where each food category, each macronutrient, and each micronutrient are predefined.

[0095] In the above method, one or more elements in each dietary plan can include at least one of the following items: breakfast menu, one or more lunch main courses, one or more dinner main courses, extra meal menu, one or more lunch dishes, and one or more dinner dishes.

[0096] In the above method, the dietary information can include at least one of the following items: allergens, restricted ingredients, gene / DNA information, number of meals per day, height, weight, gender, activity level, location information, dietary preferences, dietary history data, user pattern data, family history information, and the stage the user is in, such as the stage being pre-pregnancy, pregnancy, puerperium, or lactation.

[0097] In the above method, the gene / DNA information of rs1801133 can include types GG, AG, and AA, and the folic acid requirements for different types are GG > AG > AA.

[0098] In the above method, the formation of the first subset of dietary plans can include removing zero, one, or more dietary plans including allergens and / or restricted ingredients from the predefined set of dietary plans, and / or removing dietary plans that can have ingredients other than the existing dietary ingredients from the predefined set of dietary plans.

[0099] In the above method, the evolutionary algorithm can include: a. Forming an evolutionary set of dietary plans by selecting a number N of first initial dietary plans; b. Calculating the score of each first initial dietary plan, where the score of the i-th first initial dietary plan is Xi; c. Exchanging one or more elements between two or more first initial dietary plans to form a second initial dietary plan; d. Calculating the score of each second initial dietary plan, where the score of the i-th second initial dietary plan is Yi; e. Update the set of evolved dietary plans by replacing the i-th first initial dietary plan in the set of evolved dietary plans with the i-th second initial dietary plan when Yi >= Xi; f. Perform steps b to e for a predetermined number of iterations and / or until some or all of the dietary plans in the set of evolved dietary plans have a score equal to or higher than a first predetermined threshold; g. Output the set of evolved dietary plans as a second subset of dietary plans.

[0100] In the above method, if the score Xi of one, some, or all of the first initial dietary plans is equal to or higher than the first predetermined threshold, the N first initial dietary plans can be output as a second subset of dietary plans, and steps c to g are not performed.

[0101] In the above method, if the score Yi of one, some, or all of the second initial dietary plans is equal to or higher than the first predetermined threshold, the N second initial dietary plans can be output as a second subset of dietary plans, and steps f and g are not performed.

[0102] In the above method, steps e to g can be performed only when the score Yi of one, some, or all of the second initial dietary plans is lower than the first predetermined threshold.

[0103] In the above method, the formation of the first subset of dietary plans and the generation of the second subset of dietary plans can be iteratively performed until some or all of the dietary plans in the second subset of dietary plans have a score higher than or equal to a second predetermined threshold.

[0104] In the above method, if the formation of the first subset of dietary plans and the generation of the second subset of dietary plans can be performed more than a predetermined number of iterations, the selection of dietary plans from the second subset of dietary plans can be performed even if none of the dietary plans in the second subset of dietary plans has a score higher than or equal to the second predetermined threshold.

[0105] In the above method, the score of a dietary plan can be calculated based on at least two score components, and / or the score components are ingredient scores, weekly scores, daily scores, gene scores, or preference scores.

[0106] In the above method, the ingredient score can be calculated based on at least one of the total food category score, the total macronutrient score, and the total micronutrient score, and / or the total food category score can be the sum of the individual food category scores, and the individual food category scores can be calculated according to the quality of the food categories included in the meal plan and the intake targets of the food categories. For example, the food categories are at least one of grains, cereal foods, dairy products, fruits, vegetables, soy products, nuts, sweets, water, meat, fish, and substitutes. The total macronutrient score can be the sum of the individual macronutrient scores, and the individual macronutrient scores can be obtained according to the quality of the macronutrients included in the meal plan and the intake targets of the macronutrients, and / or the total micronutrient score can be the sum of the individual micronutrient scores, and the individual micronutrient scores can be obtained according to the quality of the micronutrients included in the meal plan and the intake targets of the micronutrients.

[0107] In the above method, the weekly score can be based on weekly rules related to the meal plan for a week.

[0108] In the above method, the daily score can be based on daily rules related to the meal plan for a day.

[0109] In the above method, the genetic score can be based on genetic rules related to the dietary requirements according to the user's genes.

[0110] In the above method, the preference score can be based on preference rules related to the user's dietary preferences.

[0111] In the above method, when calculating the score of the meal plan, a weight value can be assigned to each scoring item.

[0112] In the above method, the weight value of the scoring item can be determined by the following operations: Determine the priority of the scoring item; Assign an initial weight value to the scoring item, and assign an initially higher weight value to the scoring item with a higher priority; Select a test subset of meal plans from the predetermined set of meal plans, for example, a part of all the meal plans in the predetermined set of meal plans; Calculate the score of the scoring item based on the initial weight value assigned to the scoring item; If one or more of the calculated scores meet at least one specific condition, determine that the initial weight value is the final weight value, otherwise, iteratively calculate the score based on at least one of the following items: assign different initial weight values to the scoring item; and select the test subset.

[0113] In the above method, at least one specific condition may include at least one of the following items: a percentage of the calculated score that is higher than a specific score threshold is higher than a specific percentage threshold; and the calculated score calculated based on the initial weight value has a predetermined error range with respect to a predetermined reference score.

[0114] In the above method, the determination of the ingestion target may be further based on standard nutrient recommendations according to the user's dietary information.

[0115] In the above method, the determination of the ingestion target may be further based on the user's biometric information, microbiome information, and / or blood glucose information.

[0116] The present invention may include an apparatus including at least one processor, wherein the at least one processor is configured to execute the above method.

[0117] The present invention may include a storage medium storing computer instructions, wherein the instructions are configured to control at least one processor to execute the above method.

Claims

1. A method performed by an electronic device for suggesting a meal plan, the method comprising, obtaining dietary information of a user; Form a first subset of meal plans from a set of predetermined meal plans according to the dietary information; Generate a second subset of meal plans from the first subset of meal plans via an evolutionary algorithm; Calculate the score of each meal plan in the second subset of meal plans; Rank the meal plans in the second subset of meal plans, wherein the score of each meal plan is calculated based on the dietary information and one or more elements included in each meal plan.

2. The method according to claim 1, further comprising displaying a certain number of top-ranked meal plans and / or receiving input from the user to select a meal plan from the displayed meal plans; or suggesting the meal plan with the highest score from the second subset of meal plans.

3. The method according to any one of the preceding claims, further comprising receiving input from the user to change a first meal plan within the top-ranked meal plans and suggesting a second meal plan based on the first meal plan; or, receiving input from the user to change at least one element in the first meal plan and replacing the at least one element in the first meal plan with at least one different element to form a second meal plan.

4. The method according to claim 3, wherein, The recommendation of the second meal plan is further at least based on the similarity between the first meal plan and the second meal plan and the dietary information, or The formation of the second meal plan is further at least based on the similarity between the at least one element to be replaced and the at least one different element and the dietary information.

5. The method according to any one of the preceding claims, further comprising determining at least one intake target for each food category, each macronutrient, and / or each micronutrient according to the dietary information, wherein, Each food category, each macronutrient, and each micronutrient is predetermined.

6. The method according to any one of the preceding claims, wherein, The one or more elements in each meal plan include at least one of the following: breakfast menu, one or more lunch main courses, one or more dinner main courses, extra meal menu, one or more lunch dishes, and one or more dinner dishes.

7. The method according to any one of the preceding claims, wherein, The dietary information includes at least one of the following: allergens, restricted ingredients, gene / DNA information, number of meals per day, height, weight, gender, activity level, location information, dietary preferences, dietary history data, user pattern data, family history information, and the stage the user is in, such as the stage being pre-pregnancy, pregnancy, postpartum, or lactation.

8. The method according to claim 7, wherein, The gene / DNA information of rs1801133 includes types GG, AG, and AA, and the folic acid requirements for different types are GG > AG > AA.

9. The method according to any one of claims 7 and 8, wherein, The formation of the first subset of meal plans includes removing zero, one, or more meal plans that include these allergens and / or these restricted ingredients from the set of predetermined meal plans, and / or removing meal plans that have ingredients other than the existing meal ingredients from the set of predetermined meal plans.

10. The method according to any one of the preceding claims, wherein, The evolutionary algorithm includes, a. Form an evolutionary set of meal plans by selecting a number N of first initial meal plans; b. Calculate the score of each first initial meal plan, and the score of the i-th first initial meal plan is Xi; c. Exchange one or more elements between two or more first initial meal plans to form a second initial meal plan; d. Calculate the score of each second initial meal plan, and the score of the i-th second initial meal plan is Yi; e. Update the evolutionary set of meal plans by replacing the i-th first initial meal plan in the evolutionary set of meal plans with the i-th second initial meal plan when Yi >= Xi; f. Perform steps b to e for a predetermined number of iterations and / or until a part or all of the meal plans in the evolutionary set of meal plans have scores equal to or higher than a first predetermined threshold; g. Output the evolutionary set of meal plans as the second subset of meal plans.

11. The method according to claim 10, wherein, If the scores Xi of one, some, or all of the first initial meal plans are equal to or higher than the first predetermined threshold, output these N first initial meal plans as the second subset of meal plans, and do not perform steps c to g.

12. The method according to any one of claims 10 and 11, wherein, If the scores Yi of one, some, or all of the second initial dietary plans are equal to or higher than the first predetermined threshold, then output these N second initial dietary plans as the subset of the second dietary plan, and do not perform steps f and g.

13. The method according to any one of claims 10 to 12, wherein, Steps e to g are only performed when the scores Yi of one, some, or all of the second initial dietary plans are lower than the first predetermined threshold.

14. The method according to any one of the preceding claims, wherein, Iteratively perform the formation of the first subset of dietary plans and the generation of the second subset of dietary plans until some or all of the dietary plans in the second subset of dietary plans have scores higher than or equal to the second predetermined threshold.

15. The method according to claim 14, wherein, If the formation of the first subset of dietary plans and the generation of the second subset of dietary plans are performed more than a predetermined number of iterations, then select the dietary plan from the second subset of dietary plans even if none of the dietary plans in the second subset of dietary plans have scores higher than or equal to the second predetermined threshold.

16. The method according to any one of the preceding claims, wherein, Calculate the score of a dietary plan based on at least two scoring items, and / or the scoring items are component scores, weekly scores, daily scores, gene scores, or preference scores.

17. The method according to any one of claims 2 to 16, wherein, Calculate the component score based on at least one of the total food category score, the total macronutrient score, and the total micronutrient score, and / or wherein, the total food category score is the sum of the individual food category scores, and the individual food category scores are calculated according to the quality of the food categories included in the dietary plan and the intake target of the food category. For example, the food category is at least one of grains, cereal foods, dairy products, fruits, vegetables, soy products, nuts, sweets, water, meat, fish, and substitutes. wherein, the total macronutrient score is the sum of the individual macronutrient scores, and the individual macronutrient scores are obtained according to the quality of the macronutrients included in the dietary plan and the intake target of the macronutrient, and / or wherein, the total micronutrient score is the sum of the individual micronutrient scores, and the individual micronutrient scores are obtained according to the quality of the micronutrients included in the dietary plan and the intake target of the micronutrient.

18. The method according to claim 17, wherein, The weekly score is based on weekly rules related to the dietary plan in a week.

19. The method according to any one of claims 17 and 18, wherein, The daily score is based on daily rules related to the daily dietary plan.

20. The method according to any one of claims 17 to 19, wherein, The gene score is based on gene rules related to the dietary requirements according to the user's genes.

21. The method according to any one of claims 17 to 20, wherein, The preference score is based on preference rules related to the user's dietary preferences.

22. The method according to any one of claims 17 to 21, wherein, When calculating the score of the dietary plan, weight values are assigned to each scoring item.

23. The method according to claim 22, wherein, The weight values of these scoring items are determined by the following operations: Determine the priorities of these scoring items; Assign initial weight values to these scoring items, and assign relatively higher initial weight values to the scoring items with higher priorities; Select a subset of dietary plan tests from the predetermined set of dietary plans. For example, a part of all the dietary plans in the predetermined set of dietary plans. Calculate the scores of the scoring items based on the initial weight values assigned to these scoring items. If one or more of the calculated scores meet at least one specific condition, determine that the initial weight value is the final weight value; otherwise, iteratively calculate the scores based on at least one of the following: assign different initial weight values to score sub-items; and select a test subset.

24. The method according to claim 23, wherein, The at least one specific condition includes at least one of the following: The percentage of the calculated scores that are higher than a specific score threshold is higher than a specific percentage threshold, and The calculated scores calculated based on these initial weight values have a predetermined error range from a predetermined reference score.

25. The method according to any one of claims 5 to 24, wherein The determination of the intake target is further based on standard nutrient recommendations according to the user's dietary information.

26. The method according to any one of claims 5 to 25, wherein The determination of the intake target is further based on the user's biometric information, microbiome information, and / or blood glucose information.

27. An apparatus comprising at least one processor, wherein The at least one processor is configured to execute any one of claims 1 to 26.

28. A storage medium storing computer instructions, wherein The instructions are configured to control at least one processor to execute any one of claims 1 to 26.

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