Advice providing system, advice providing program, and advice providing method

Abstract

To provide an advice providing system, an advice providing program, and an advice providing method for providing advice for reducing body fat or preventing lifestyle-related diseases effectively according to a user.SOLUTION: An advice providing system according to an embodiment of the present invention provides a provision target user with advice for reducing body fat or preventing lifestyle-related diseases, and the advice providing device comprises: an eating habit classification unit that classifies the eating habits of the provision target user to any one of a plurality of eating habit classifications on the basis of eating habit information that is information on the eating habits of the provision target user; an execution period specification unit that specifies an execution period that is a period until a specified date; and an advice generation unit that generates, for every provision target user, according to the execution period, advice for solving the cause of body fat accumulation or the cause of contraction of lifestyle-related diseases according to a result of classification of the eating habits.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an advice providing system, an advice providing program, and an advice providing method for providing advice for reducing body fat or preventing lifestyle-related diseases. [Background technology]

[0002] Technologies have been developed that evaluate a user's diet and activity, and generate advice for reducing body fat or preventing lifestyle-related diseases based on the evaluation results. For example, Patent Document 1 discloses a lifestyle habit improvement suggestion device that generates a message suggesting lifestyle improvements based on lifestyle habit information such as the user's weight gain rate, number of steps, and food intake, as well as environmental information such as weather and temperature. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2020-160569 Summary of the Invention [Problem to be solved by the invention]

[0004] In the invention described in Patent Document 1, when lifestyle habit information and environmental information satisfy predetermined conditions, a predetermined message corresponding to the conditions is presented to the user. However, since the causes of body fat accumulation and lifestyle-related disease differ from user to user, even if advice is presented according to conditions common to all users, it may not be effective advice for reducing body fat or preventing lifestyle-related diseases.

[0005] The present invention relates to an advice providing system, an advice providing program, and an advice providing method for effectively providing advice for reducing body fat or preventing lifestyle-related diseases according to a user. [Means for solving the problem]

[0006] An advice providing device according to one embodiment of the present invention is an advice providing device that provides advice to a target user for reducing body fat or preventing lifestyle-related diseases, an eating habit classification unit that classifies the eating habits of the target user into one of a plurality of eating habit classifications based on eating habit information that is information about the eating habits of the target user; an implementation period specification unit that specifies an implementation period, which is a period up to a specified date; The system further includes an advice generating unit that generates, for each target user, advice for eliminating causes of body fat accumulation or causes of lifestyle-related disease according to the classification results of the eating habits, in accordance with the implementation period.

[0007] An advice providing program according to one aspect of the present invention is an advice providing program that provides advice to a user to whom advice is to be provided for reducing body fat or preventing lifestyle-related diseases, the advice providing program including: A step of classifying the target user's eating habits into one of a plurality of eating habit categories based on eating habit information that is information about the target user's eating habits; Identifying an implementation period, which is a period up to a specified date; and generating, for each target user, advice for eliminating causes of body fat accumulation or causes of lifestyle-related disease according to the classification results of the eating habits, in accordance with the implementation period.

[0008] An advice providing method according to one embodiment of the present invention is a method for providing advice to a target user for reducing body fat or preventing lifestyle-related diseases, the method comprising: classifying the target user's eating habits into one of a plurality of eating habit classifications based on eating habit information that is information about the target user's eating habits; Identify the implementation period, which is the period up to a specified date; Advice is generated for each target user according to the implementation period, for eliminating the causes of body fat accumulation or lifestyle-related disease according to the classification results of the eating habits. [Effects of the Invention]

[0009] According to the present invention, it is possible to provide advice for reducing body fat or preventing lifestyle-related diseases effectively according to the user. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a configuration of an advice providing system according to a first embodiment of the present invention. [Figure 2] 10 is an example of a screen displaying questions presented to a user from an advice providing device included in the advice providing system. [Figure 3] 10 is an example of a presentation screen of an eating habit classification result presented to a user by the advice providing device. [Figure 4] 10 is an example of advice presented to a user from the advice providing device. [Figure 5] These are examples of action items included in the above advice, including action items A to H in a standard period of one month, action items A to H when the implementation period is set to 0.5 months, and the implementation period for action items A to H when the implementation period is set to two months. [Figure 6] 10 is an example of presentation information presented to a user from the advice providing device. [Figure 7] 4 is a flowchart showing the operation of the advice providing device. [Figure 8] FIG. 10 is a block diagram showing a configuration of an advice providing system according to a second embodiment of the present invention. [Figure 9] 10 is a schematic diagram showing a method for classifying eating habits by an advice providing device included in the advice providing system. FIG. [Figure 10] 10 is an example of advice presented to a user from the advice providing device. [Figure 11] 4 is a flowchart showing the operation of the advice providing device. [Figure 12] 10 is an example of a presentation image of a visceral fat index generated by the advice providing device. [Figure 13]10 is an example of a presentation image of a visceral fat index generated by the advice providing device. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, the configurations, numerical values, processing flows, functional elements, etc. described in the following embodiments are merely examples, and are free to be modified or changed. The technical scope of the present invention is not intended to be limited to the following description. In this specification, a "system" includes one or more information processing devices. For example, a system can be configured by a single information processing device, or multiple information processing devices working together to perform functions such as a web server can also constitute a system. Furthermore, as described in the following embodiments, a "system" may include an information processing device functioning as a web server and one or more terminal devices.

[0012] (First embodiment) An advice providing system according to a first embodiment of the present invention will be described. Note that since the advice providing system according to this embodiment also functions as an eating habit classification system, the advice providing system described below can also be read as an eating habit classification system.

[0013] [Configuration of the advice providing system] The configuration of the advice providing system according to this embodiment will be described below. As shown in Fig. 1, the advice providing system 100 according to this embodiment includes an eating habit classification generation device 10 and an advice providing device 20.

[0014] The advice providing system 100 is a system that provides users with advice on reducing body fat or preventing lifestyle-related diseases. "Body fat reduction" includes reducing visceral fat, reducing abdominal circumference, reducing weight, reducing body fat percentage, and eliminating metabolic syndrome. "Preventing lifestyle-related diseases" includes improving high blood pressure, hyperglycemia, and dyslipidemia, and eliminating obesity.

[0015] The advice providing system 100 generates advice using biomarkers, which are indicators that represent the state of a living body, such as waist circumference, VFA (Visceral Fat Area), FBS (Fasting blood sugar), TG (Triglyceride), HDL-C (High Density Lipoprotein Cholesterol), SBP (Systolic Blood Pressure), and DBP (Diastolic Blood Pressure).

[0016] The advice providing system 100 uses specific bioindicators depending on the symptoms of the person receiving advice. Hereinafter, the bioindicators used by the advice providing system 100 will be referred to as "specific bioindicators." Table 1 below shows examples of symptoms and specific bioindicators. Note that the symptoms and specific bioindicators are not limited to those shown here. For example, the specific bioindicator for visceral fat accumulation may be waist circumference or weight.

[0017] [Table 1]

[0018] The eating habit classification generation device 10 is an information processing device that generates eating habit classifications. The eating habit classification generation device 10 analyzes users whose specific biomarkers are equal to or greater than the reference values ​​shown in Table 1 as analysis targets, and generates an eating habit classification. Hereinafter, these users will be referred to as "analysis target users." The number of analysis target users is not particularly limited, but 100 or more is preferable.

[0019] 1, the eating habit classification generation device 10 includes a data acquisition unit 11, an eating habit classification generation unit 12, and a ranking determination unit 13. The data acquisition unit 11 acquires "eating habit information," which is information about the eating habits (including dietary habits, eating behavior, lifestyle behavior, exercise habits, and personality) of each analysis target user, and data on specific biometric indicators for multiple analysis target users.

[0020] The dietary information consisted of responses to 35 questions about dietary habits, including but not limited to the following: Q01 Even if I'm not hungry, I end up eating when something smells delicious. Q02 If someone around me is eating something, I eat it too Q03 I overeat when I'm feeling depressed Q04 I consciously limit my food intake to avoid gaining weight Q05 I feel hungry all day Q06 I feel guilty about eating too much, so I eat less. Q07 I try to buy low-calorie foods Q08 I think I eat faster than other people Q09 I actively eat green and yellow vegetables Q10 I like hamburgers, donuts, and potato chips Q11 I actively choose foods that are high in dietary fiber. Q12 I prefer fish to meat Q13 I often eat a late-night snack after dinner Q14 I try to limit my intake of animal fats and instead eat vegetable fats and fish fats. Q15 I think I am more prone to gaining weight than other people. Q16 I feel like I won't feel energized if I don't eat. Q17 Meal times are random Q18 I have a sweet tooth Q19 Eating a late-night snack Q20 When I see fruit or sweets, I can't help but reach for them. Q21 When I receive food, I eat it because I feel it would be a waste Q22 I feel uneasy unless I buy more food than I need. Q23 I often have late dinners because of work. Q24 If there is an elevator or escalator, I will use it. Q25 I finish dinner more than two hours before going to bed Q26 I don't like walking or cycling. Q27 I don't exercise much Q28 I hardly ever chew Q29 I think it's more that I'm not exercising enough than that I'm eating too much. Q30 I'm a night owl and have trouble in the mornings Q31 I'm lazy Q32 Insightful Q33 Cooperative Q34 Proactive Q35 I get nervous easily

[0021] The user to be analyzed answers each question by selecting one of the following: "Does not apply = 1," "Does not really apply = 2," "Can't say either way = 3," "Somewhat applies = 4," or "Applies = 5," and eating habit information is generated. Note that the above questions are just an example, and other questions may be used. The number of questions is not limited to 35. The data acquisition unit 11 supplies the acquired eating habit information and biometric indicator data to the eating habit classification generation unit 12.

[0022] The eating habit classification generation unit 12 performs factor analysis on the data supplied from the data acquisition unit 11 and generates multiple eating habit classifications according to factors for the biomarkers of the eating habit information. The eating habit classifications are classifications of the eating habits of the user to be analyzed, such as "low health consciousness," "snacking," "late-night snacking," "fast eating," "lack of exercise," "late-night eating," "low basal metabolic rate," and "other (does not fall into any category)."

[0023] "Low health consciousness" is a classification of eating habits in which people are indifferent to diet and exercise and eat without thinking about their health, and has the following characteristics, for example: [Diet] Not buying low-calorie foods Not actively eating green and yellow vegetables - Not actively choosing foods high in dietary fiber - Not limiting animal fats and consuming vegetable fats and fish fats I like hamburgers, donuts, and potato chips. I don't like fish more than meat - Not consciously limiting the amount of food you eat to avoid gaining weight Feeling guilty about overeating and not reducing the amount you eat Meal times are random Eat a late-night snack [motion] Use elevators and escalators if available Low step count -Doesn't exercise much I think it's more a lack of exercise than eating too much. [Personality] ·Being lazy Lack of insight Not proactive

[0024] "Snacking" is a category of eating habits in which people have a sweet tooth and can't help but eat sweets, and has the following characteristics: [Diet] I like hamburgers, donuts, and potato chips. - Has a sweet tooth When fruits and sweets are available, I can't help but reach for them. If someone around me is eating something, I eat it too Even if I'm not hungry, I can't help but eat something that smells delicious. Overeating when feeling depressed · If I receive food, I eat it because I don't want to waste it. [Meal Contents] High intake of fat, vegetable fat, saturated fatty acids, monounsaturated fatty acids, and sucrose [motion] Low muscle mass [Personality] I think I'm more prone to gaining weight than other people I feel like I won't have energy if I don't eat I feel uncomfortable unless I buy more groceries than I need. Short -Low basal metabolic rate

[0025] "Late-night snacking" is a classification of eating habits in which a person is a night owl and eats a late-night snack after dinner, and has the following characteristics, for example: [Diet] Eat a late-night snack after dinner Eat a late-night snack Dinner is often late due to work Meal times are random If someone around me is eating something, I eat it too Even if I'm not hungry, I can't help but eat something that smells delicious. - Not consciously limiting the amount of food you eat to avoid gaining weight Overeating when feeling depressed Feeling hungry all day - Has a sweet tooth When fruits and sweets are available, I can't help but reach for them. · If I receive food, I eat it because I don't want to waste it. [Meal Contents] High energy, carbohydrate, and sucrose intake Low intake of animal protein, vitamin D, niacin, vitamin B6, vitamin B12, and alcohol [motion] -Doesn't exercise much [Personality] I think I'm more prone to gaining weight than other people I feel uncomfortable unless I buy more groceries than I need. I'm a night owl and have a weak morning temperament. · Lazy Easily nervous Younger ·Emotionally unstable

[0026] "Speed ​​eaters" is a classification of eating habits of people with a large build who eat food without chewing it much, and has the following characteristics: [Diet] I think I eat faster than other people Hardly chews · If I receive food, I eat it because I don't want to waste it. - Not having dinner more than two hours before going to bed [Personality] Heavy weight High muscle mass High basal metabolic rate

[0027] "Lack of exercise" is a classification of eating habits such as disliking exercise and not eating enough meat or vegetables, and has the following characteristics: [Diet] Not actively eating green and yellow vegetables - Not actively choosing foods high in dietary fiber I don't like fish more than meat - Not limiting animal fats and consuming vegetable fats and fish fats Feeling guilty about overeating and not reducing the amount you eat [Meal Contents] Low intake of beta-carotene [motion] I don't like walking or cycling Low step count -Doesn't exercise much I think it's more a lack of exercise than eating too much. [Personality] · Lazy High body fat percentage

[0028] "Late-night eating" is a category of eating habits such as eating dinner while drinking alcohol late at night, and has the following characteristics, for example: [Diet] Dinner is often late due to work - Not having dinner more than two hours before going to bed Meal times are random - Has a sweet tooth [Meal Contents] Low intake of lipids, K, Ca, Fe, Zn, Cu, vitamin B1, vitamin C, saturated fatty acids, soluble dietary fiber, insoluble dietary fiber, total dietary fiber, and sucrose High alcohol intake

[0029] "Decreased basal metabolic rate" is a classification of eating habits that result in weight gain even if you eat the same amount of food as before, and has the following characteristics: [Meal content, meal amount, meal time, exercise] No problem [Personality] Older

[0030] The dietary habit classification unit 12 performs factor analysis on the dietary habit information and biometric data of multiple users to be analyzed. While not limited to specific methods, a preferred method is data obtained by statistically investigating the dietary habit information and biometric data. For example, various models can be employed, including multiple regression analysis using statistical data, a logistic regression model, a multilayer perceptron, neural networks such as a convolutional neural network (CNN) or a recurrent neural network (RNN), a support vector machine using an arbitrary kernel function such as a Gaussian kernel, a random forest modeled as a regression tree, a model using a hidden Markov model, a statistical model, a probability model, a correspondence table using factor analysis and tables, etc. A model that combines various models to perform a comprehensive assessment can also be employed. Among these, factor analysis using a principal factor method with varimax rotation is particularly preferred.

[0031] Tables 2 and 3 below show the "factor loadings" of each question item, "eigenvalues," "contribution rates," and "cumulative contribution rates" for each factor calculated using the "principal factor method with varimax rotation" for VFA, a biomarker of visceral fat accumulation.

[0032] [Table 2]

[0033] [Table 3]

[0034] The eating habit classification generation unit 12 sorts each question item into factors according to the factor loading, as shown in Tables 2 and 3. For example, Q04, Q07, Q06, Q09, and Q11 are sorted into "Factor 1" because their factor loadings (inside the black squares) for "Factor 1" exceed a reference value. The reference value is, for example, 0.4. Similarly, Q02, Q01, and Q20 are sorted into "Factor 2," Q19 and Q13 into "Factor 3," and Q08 and Q28 into "Factor 4." Furthermore, Q27 and Q29 are sorted into "Factor 5," and Q25 and Q23 into "Factor 6." The eating habit classification generation unit 12 generates an eating habit classification, such as "low health consciousness," for each factor according to the content of the question items sorted into each factor.

[0035] Similarly, for biometric indicators other than VFA, the eating habit classification generation unit 12 sorts each question item by factor according to the factor loading. Table 4 below shows the eating habit classification by factor for each symptom. As shown in Table 4, the eating habit classification by factor for each symptom and the corresponding question items differ. The eating habit classification generation unit 12 supplies the generated eating habit classification and "contribution rate" to the ranking determination unit 13.

[0036] [Table 4]

[0037] The ranking determination unit 13 determines the priority order among the eating habit categories in descending order of contribution rate. In the examples of Tables 2 and 3, the contribution rate (0.12) of "low health consciousness" is the highest, followed by the contribution rate (0.11) of "snacking." The contribution rates decrease in the following order: "late-night snack," "quick eating," "lack of exercise," and "late-night eating." For this reason, the ranking determination unit 13 gives the highest priority to "low health consciousness," and then prioritizes "snacking," "late-night snack," "quick eating," "lack of exercise," and "late-night eating" in that order.

[0038] Table 4 shows the priority order of eating habit classifications based on the contribution rate for each symptom. As shown in Table 4, the ranking unit 13 assigns the highest priority to "late-night snacking" for "dyslipidemia," followed by "overeating," "lack of exercise," and "low health consciousness" in that order. For "high blood pressure," the ranking unit 13 assigns the highest priority to "late-night snacking," followed by "snacking," "lack of exercise," "low health consciousness," and "fast eating" in that order. For "hyperglycemia," the ranking unit 13 assigns the highest priority to "snacking," followed by "late-night snacking," "lack of exercise," "irregular meal times," and "food portion control" in that order. The ranking unit 13 provides the eating habit classifications and priorities to the advice providing device 20. Note that the values ​​obtained for the eating habit classification names, contribution rates, and priorities vary depending on the data used. The results disclosed herein are merely examples. The priorities may be changed depending on the user's desired improvement goals.

[0039] The advice providing device 20 is an information processing device that provides users with advice for reducing body fat or preventing lifestyle-related diseases. Hereinafter, a user to whom advice is to be provided is referred to as a "target user." As shown in FIG. 1 , the advice providing device 20 includes a target user determination unit 21, an eating habit information acquisition unit 22, an eating habit classification unit 23, a record information acquisition unit 24, an evaluation unit 25, an implementation period identification unit 26, an advice generation unit 27, and a presentation information generation unit 28.

[0040] The provision target user determination unit 21 determines a provision target user. The provision target user determination unit 21 can determine a user whose specific biological index exceeds a specified value as a provision target user. For example, in the case of VFA, the specified value is 10 cm for men. 2 More than 50cm, preferably 2 More than 80cm, preferably 2 More preferably, 100 cm 2 Above, women 10cm 2 More than 30cm, preferably 2 More than 50cm, preferably 2 More than 80cm, preferably 2 For example, waist circumference is 85cm or more for men and 90cm or more for women.

[0041] The provision target user determination unit 21 can determine as a provision target user a user whose own specific biometric index input by the user exceeds a specified value. In addition, the provision target user determination unit 21 can estimate the specific biometric index of the user from the information input by the user, and determine as a treatment target user a user whose estimated value exceeds the above-mentioned specified value.

[0042] For example, the provision target user determination unit 21 can estimate the amount of visceral fat accumulation such as VFA from physical information input by the user, such as gender, age, height, weight, abdominal circumference, and image data from which this information can be estimated, and determine a user whose estimated value exceeds a specified value as a provision target user. Note that the provision target user determination unit 21 may determine provision target users based on information other than the specific biometric indicators, or may determine all users as provision target users.

[0043] The eating habit information acquisition unit 22 acquires the eating habit information of the target user. The eating habit information is the answers to the 35 questions about eating habits described above. The eating habit information acquisition unit 22 can generate a screen for presenting the questions as shown in FIG. 2, and by displaying one question per screen, it is possible to make it easier for the user to answer. The eating habit information acquisition unit 22 may also acquire personal data that affect the target user's eating habits, such as the gender, age, place of residence, place of origin, occupation, and income.

[0044] The eating habit classification unit 23 classifies the eating habits of the target user based on the eating habit information into one of the eating habit categories generated by the eating habit category generation unit 12. As shown in FIG. 1 , the eating habit classification unit 23 includes an eating habit category determination unit 29 and an eating habit category determination unit 30.

[0045] The eating habit classification determination unit 29 determines whether or not the eating habits of each target user correspond to each eating habit classification based on the eating habit information of each target user. The eating habit classification determination unit 29 can determine whether or not the eating habits of the target user correspond to each eating habit classification according to the answer scores of the questions selected for each eating habit classification. The rotation scores are the above-mentioned scores of "does not apply = 1", "does not really apply = 2", "cannot say either way = 3", "somewhat applies = 4", and "applies = 5".

[0046] For example, with regard to visceral fat accumulation, if the answer scores for Q04, Q07, Q06, Q09, and Q11, which are questions selected as "low health consciousness" (see Table 4), are all "not applicable" (1) or "somewhat not applicable" (2), the eating habit classification determination unit 29 determines that the target user's eating habits are "low health consciousness." Furthermore, if the answer scores for Q02, Q01, and Q20, which are questions selected as "snacking," are all "applicable" (5) or "somewhat applicable" (4), the eating habit classification determination unit 29 determines that the target user's eating habits are "snacking." Similarly, the eating habit classification determination unit 29 determines whether the target user's eating habits are "late-night snacking," "quick eating," and "lack of exercise" based on the answer scores for the questions selected as "snacking." Furthermore, if the answer score for Q25, which is a question item selected as "eating late at night", is "not applicable = 1" or "somewhat not applicable = 2", and the answer score for Q23 is "applicable = 5" or "somewhat applicable = 4", the eating habit classification determination unit 29 determines that the target user's eating habit falls under "eating late at night".

[0047] Here, if the answer scores meet the conditions in multiple eating habit categories, the eating habit classification determination unit 29 determines that the target user's eating habits fall into multiple eating habit categories. Also, if the answer scores do not meet any of the conditions in any of the eating habit categories, the eating habit classification determination unit 29 determines that the target user's eating habits do not fall into any of the eating habit categories.

[0048] As another method, the eating habit classification determination unit 29 determines that the target user's eating habits correspond to "low health consciousness" if the sum of the answer scores for Q04, Q07, Q06, Q09, and Q11, which are question items selected as "low health consciousness" (see Table 4), is equal to or greater than a threshold. Furthermore, the eating habit classification determination unit 29 determines that the target user's eating habits correspond to "snacking" if the sum of the answer scores for Q02, Q01, and Q20, which are question items selected as "snacking," is equal to or greater than a threshold. Similarly, the eating habit classification determination unit 29 determines whether the target user's eating habits correspond to "late-night snacking," "fast eating," "lack of exercise," and "late-night eating" based on the answer scores for the question items classified as each of these eating habits.

[0049] In this case, the eating habit classification determination unit 29 determines that the target user's eating habits fall into multiple eating habit classifications if the answer scores in multiple eating habit classifications are equal to or greater than the thresholds. Also, the eating habit classification determination unit 29 determines that the target user's eating habits do not fall into any eating habit classification if the answer scores in any eating habit classification are less than the thresholds.

[0050] The eating habit classification determination unit 30 determines the eating habit classification of the target user in response to the determination result by the eating habit classification determination unit 29. When the eating habit classification determination unit 29 determines that the target user's eating habits fall into one eating habit classification, the eating habit classification determination unit 30 determines that the target user's eating habits fall into that eating habit classification.

[0051] Furthermore, when the eating habit classification determination unit 29 determines that the eating habits of the target user fall into multiple eating habit classifications, the eating habit classification determination unit 30 determines that the eating habits of the target user should be classified into the eating habit classification with the highest priority (see Table 4) determined by the ranking determination unit 13. For example, when the eating habit classification determination unit 29 determines that the eating habits of a specific target user with regard to visceral fat accumulation fall into "snacking" and "lack of exercise", the eating habit classification determination unit 30 determines that the eating habits of the target user should be classified into the eating habit classification of "snacking", which has the higher priority.

[0052] Furthermore, if the eating habit classification determination unit 29 determines that the target user's eating habits do not fall into any of the eating habit classifications, the eating habit classification determination unit 30 can classify the target user's eating habits into a classification different from the eating habit classifications generated for each factor. For example, if the target user's eating habits are determined to fall into any of the eating habit classifications for visceral fat accumulation, the eating habit classification determination unit 30 classifies the target user's eating habits into "decreased basal metabolic rate." This is because, even though there is no problem with the target user's eating habits, the amount of visceral fat accumulation exceeds a specified value, and therefore it can be determined that this is due to a decreased basal metabolic rate.

[0053] The eating habit classification unit 23 classifies the target user's eating habits into one of the above-mentioned ways. The eating habit classification unit 23 can generate a presentation screen of eating habit classifications as shown in FIG. 3 and visually notify the target user of the classification results. The radar chart shows the answer scores for each eating habit classification. The eating habit classification unit 23 may accept the target user's selection of an eating habit classification, and the eating habit classification selected by the target user may be the user's eating habit classification. This is effective in cases where the target user is aware of his or her own eating habit classification that was previously notified. The eating habit classification unit 23 supplies the target user's eating habit classification to the record information acquisition unit 24.

[0054] The record information acquisition unit 24 acquires record information, which is information about the meal contents and activity contents recorded by the target users. For each target user, the record information acquisition unit 24 generates a record item group including record items according to the target user's eating habit classification. Tables 5 and 6 below are examples of record item groups generated by the record information acquisition unit.

[0055] [Table 5]

[0056] [Table 6]

[0057] As an option, the items recorded may also include the intake of functional ingredients such as chlorogenic acid and ALA-DAG (alpha-linolenic acid diacylglycerol), which contribute to suppressing the accumulation of body fat and preventing lifestyle-related diseases.

[0058] As shown in Tables 5 and 6, the record item group generated by the record information acquisition unit includes "eating habit category items." "Eating habit category items" are record items for each eating habit category classified by the eating habit classification unit 23, and Tables 5 and 6 show examples of record items for "low health consciousness." Table 7 below shows record items for other eating habit categories. As shown in these tables, the record items for each eating habit category have different content for each eating habit category. In addition, the record items for each eating habit category may also include calories burned other than from walking.

[0059] [Table 7]

[0060] Furthermore, the group of recording items may include "nutrient intake," as shown in Table 5. "Nutrient intake" is a recording item for the intake of one or more nutrients selected from protein, omega-3 fatty acids, and dietary fiber. Furthermore, the group of recording items may include recording items such as "lifestyle points," "daily calorie intake," "number of steps," and "body measurements," as shown in Table 6. Items in the group of recording items other than "items by dietary habit classification" are common to all target users. Hereinafter, "items by dietary habit classification," "protein," "dietary fiber," "omega-3 fatty acids," and "lifestyle points" will each be referred to as a "major item."

[0061] The record information acquisition unit 24 presents the generated group of record items to the user to whom the information is to be provided. The user to whom the information is to be provided inputs the record content, and the record information acquisition unit 24 acquires the record information. The user to whom the information is to be provided inputs the record content on a daily or weekly basis. The record information acquisition unit 24 supplies the acquired record information to the evaluation unit 25.

[0062] The evaluation unit 25 evaluates the cause of body fat accumulation or the cause of lifestyle-related disease for each user to be provided based on the recorded information. Tables 8 and 9 below show the evaluation method used by the evaluation unit 25.

[0063] [Table 8]

[0064] [Table 9]

[0065] The evaluation unit 25 can tally up the record information for each target user for a certain evaluation period, for example, one week, and score each record item by calculating the average number of affirmative responses for that record item. For example, for the record item "I ate meals with consideration for nutritional balance" under "low health consciousness," the evaluation unit 25 calculates the score for that record item by calculating the average number of "o"s over one week. Similarly, for other record items such as "I did not eat until I was full at any meal," the score for that record item is calculated by calculating the average number of "o"s over one week.

[0066] The evaluation unit 25 can extract the record items with the highest scores (hereinafter referred to as the record items) and the record items with the lowest scores (hereinafter referred to as the lower record items) from among the record items. The higher record items are, for example, the record items with the highest scores from 1st to 3rd, and the lower record items are, for example, the record items with the lowest scores from 1st to 3rd. The evaluation unit 25 can evaluate that the lower record items are items that require improvement in order to eliminate the cause of body fat accumulation or the cause of lifestyle-related disease.

[0067] Furthermore, the evaluation unit 25 can evaluate the intake amount of nutrients for each target user based on the recorded information. The evaluation unit 25 can estimate the intake ratio and intake status of each nutrient from the scores for each recorded item of "protein," "omega-3 fatty acids," and "dietary fiber." The evaluation unit 25 can also estimate the "protein / fat ratio," "omega-3 fatty acids / fat ratio," and "dietary fiber / sugar or carbohydrate ratio" from the scores for each recorded item of "protein," "omega-3 fatty acids," and "dietary fiber."

[0068] In addition, the evaluation unit 25 can evaluate the "dietary behavior" or "personal information" for each target user based on the recorded information. "Dietary behavior" refers to one or more of the target user's meal quality, quantity, time, eating style, and activity level, and "personal information" refers to at least one or more of weight, waist circumference, etc. The priority of the evaluation by the evaluation unit 25 is not limited, but as an example, it is preferable to give the highest priority to "meal amount," followed by "activity level," "meal time," "meal quality," "eating style," and "weight," in that order. The evaluation unit 25 supplies the evaluation results to the advice generation unit 27.

[0069] The implementation period specifying unit 26 specifies the implementation period of the body fat reduction or lifestyle-related disease prevention program to be provided to the target user by the advice providing system 100. The implementation period specifying unit 26 sets the period up to a specified date (hereinafter referred to as the specified date) as the implementation period. The specified date is a date specified by the target user, such as an anniversary such as a wedding or the date of a health check. The implementation period specifying unit 26 can acquire the specified date through operational input by the target user, etc.

[0070] The implementation period determination unit 26 determines the period between the scheduled start date of the program and the specified date as the implementation period. The scheduled start date of the program may be specified by the target user, or may be the day on which the target user inputs the indicated date or the day after that. The implementation period is not particularly limited, but is preferably one week, more preferably two weeks, more preferably three weeks, and even more preferably four weeks. The implementation period determination unit 26 supplies the determined implementation period to the advice generation unit 27.

[0071] The advice generating unit 27 generates advice for eliminating causes of body fat accumulation or causes of lifestyle-related disease according to the results of the dietary habit classification. The advice generating unit 27 can generate advice according to the evaluation results of the causes of body fat accumulation or causes of lifestyle-related disease by the evaluation unit 25, i.e., the scores of each record item.

[0072] Specifically, the advice generation unit 27 can generate advice that praises higher-ranking record items and suggests improvements to lower-ranking record items. The advice generation unit 27 can also select record items with lower scores that are likely to be improved from among the record items with lower scores, and generate advice that suggests improvements to the record items with improved scores. The advice generation unit 27 can select record items with improved scores from among the lower-ranking record items by excluding items with low scores in each record. Furthermore, the advice generation unit 27 can generate advice that encourages the target user's eating habits from the next time onwards.

[0073] For example, as shown in FIG. 4, the advice generation unit 27 can generate advice including advice S1 praising higher-level recorded items during the evaluation period, advice S2 suggesting improvements to lower-level recorded items, and advice S3 encouraging the next evaluation period.

[0074] The advice generation unit 27 can also generate advice using standard phrases prepared for each record item. Tables 10 and 11 below are examples of standard phrases for each record item. The advice generation unit 27 can generate advice that includes at least one of the standard phrases for higher-level record items and the standard phrases for lower-level record items. The standard phrases may include specific menu suggestions, and it is preferable that the menus are easy to eat. The standard phrases for each record item may have different content depending on the season, and for example, they may suggest seasonal ingredients and cooking methods that are easy to find and purchase depending on the season. The advice generation unit 27 can generate advice according to the season at the time of advice generation by using standard phrases prepared for each season.

[0075] [Table 10]

[0076] [Table 11]

[0077] Furthermore, the advice generation unit 27 can generate advice according to the evaluation result regarding the nutrient intake of the target user. The advice generation unit 27 can generate advice praising the intake of nutrients with a high intake amount and encouraging the intake of nutrients with a low intake amount regarding the intake amounts of "protein," "omega-3 fatty acids," and "dietary fiber" estimated by the evaluation unit 25. Furthermore, the advice generation unit 27 can generate advice praising points with good evaluations and encouraging points with poor evaluations according to the evaluation result of the target user's "dietary behavior" or "personal information."

[0078] Furthermore, the advice generating unit 27 can generate advice that praises the intake of nutrients with a high "protein / fat ratio," "omega-3 fatty acid / fat ratio," and "dietary fiber / sugar or carbohydrate ratio" estimated by the evaluating unit 25, and encourages the intake of nutrients with a low ratio. In this case, the advice generating unit 27 may generate advice that suggests the intake of functional foods, foods for specified health uses, and foods containing functional ingredients. Note that the advice generating unit 27 does not necessarily have to generate advice regarding nutrient intake.

[0079] The method of generating advice by the advice generation unit 27 is not particularly limited, but in addition to using the fixed phrases described above, the advice generation unit 27 can accumulate obtained data, perform machine learning, and generate advice using the learning results. Furthermore, the advice generation unit 27 can generate advice based on nutritional guidance by an expert such as a registered dietitian.

[0080] Besides this, the advice generation unit 27 may generate advice according to the eating habit classification for each target user. The advice generation unit 27 is not limited to generating advice according to the evaluation result of the recorded information by the evaluation unit 25, and may generate advice according to the eating habit classification once the eating habit classification of the target user is determined by the eating habit classification unit 23.

[0081] The advice generating unit 27 can also generate advice according to the implementation period identified by the implementation period identifying unit 26. Specifically, the advice generating unit 27 identifies items that the target user should implement in a standard period (hereinafter, "necessary implementation items"). The standard period is not particularly limited, but is, for example, one month. The necessary implementation items are, for example, the amount and intensity of exercise that the target user should implement and the amount of nutrients that should be ingested. FIG. 5 shows, as an example, necessary implementation items A to H for a standard period of one month.

[0082] The advice generation unit 27 can identify the items to be implemented by one or more of the above-mentioned methods, and can also identify the items to be implemented based on the evaluation results by the evaluation unit 25. The advice generation unit 27 can determine the items that the target user should implement for each of the above-mentioned evaluation periods. Figure 5 shows an example in which items A and B are implemented in the first evaluation period (7 days), items C and D are implemented in the next evaluation period, and each item is implemented for each subsequent evaluation period.

[0083] The advice generating unit 27 generates advice so that the to-be-implemented items are implemented according to the implementation period identified by the implementation period identifying unit 26. When the implementation period is 0.5 months as shown in Fig. 5, the advice generating unit 27 allocates the to-be-implemented items (A to H) to each evaluation period so that they are implemented in 0.5 months, and generates advice so that they are implemented. The "0.5 month" example in Fig. 5 shows an example in which items A to D are implemented in the first evaluation period (7 days), and items E to H are implemented in the next evaluation period (7 days).

[0084] Furthermore, if the implementation period is two months, the advice generation unit 27 allocates the necessary action items (A to H) so that they can be carried out within two months, and generates advice to carry them out. In the example of "two months" in Fig. 5, item A is carried out in the first evaluation period (seven days), item B is carried out in the next evaluation period (seven days), and so on, with each item being carried out for each evaluation period thereafter. The advice generation unit 27 supplies the generated advice to the presentation information generation unit 28.

[0085] The presentation information generation unit 28 generates presentation information including the advice supplied from the advice generation unit 27. The presentation information generation unit 28 can generate a presentation image including the generated presentation information and present the presentation information to the user. As shown in FIG. 6, the presentation information includes "advice." The presentation information can also include scores for each of the major categories of "diet quality," "eating habits rhythm," and "low health consciousness" visually displayed with a number of stars, or progress in the scores for each major category visually displayed in a radar chart. The presentation information is not limited to what is shown here, and may include, for example, the "desired state" of the target user. The "desired state" of the target user is a target state of the target user that is input by the target user himself or proposed by the advice providing device 20.

[0086] The advice providing system 100 has the above-described configuration. Each component of the eating habit classification generation device 10 and the advice providing device 20 is a functional configuration realized by cooperation between hardware and software of an information processing device configured with a CPU (Central Processing Unit), RAM (Random Access Memory), etc. The software is a program executable by the information processing device, and may be a program recorded on a recording medium readable by the information processing device. Furthermore, the eating habit classification generation device 10 and the advice providing device 20 may be separate information processing devices or may be a single information processing device.

[0087] [Operation of the advice providing system] The operation of the advice providing system 100 will now be described. As described above, the eating habit classification generation device 10 generates eating habit classifications based on the eating habit information and biometric indicator data of multiple users to be analyzed. Generation of eating habit classifications may be performed once when the advice providing system 100 is constructed, but may also be performed again after construction.

[0088] The advice providing device 20 provides a body fat reduction or lifestyle-related disease prevention program to a target user. The advice providing device 20 operates "before the program is implemented" and "during the program is implemented" as shown in Figure 7. Table 12 below shows the contents of "input," "determination," and "output" before and during the program.

[0089] [Table 12]

[0090] Before the program is implemented, the provision target user determination unit 21 determines the provision target users (St11), as shown in Fig. 7. The provision target user determination unit 21 can accept input of physical information from the user and calculate specific biometric indicators, as shown in Table 12. Furthermore, the provision target user determination unit 21 can determine whether or not each user corresponds to a provision target user who is a target for the program implementation based on the specific biometric indicators, and output to each user whether or not the user is a target for the program implementation.

[0091] Next, the eating habit information acquisition unit 22 acquires the eating habit information of the target users (St12), and the eating habit classification unit 23 determines the eating habit classification of the target users (St13). The eating habit information acquisition unit 22 accepts input of eating habit information from the target users as shown in Table 12, and the eating habit classification unit 23 determines the eating habit classification of the target users. Furthermore, the eating habit classification unit 23 can output the eating habit classification (see FIG. 3) corresponding to each target user.

[0092] At this time, the advice generation unit 27 may generate advice for eliminating causes of body fat accumulation or causes of lifestyle-related disease according to the eating habit classification, and output the generated advice to each target user. Also, the presentation information generation unit 28 may predict physical information of the target user after implementing the program from physical information input by the target user, and output the predicted physical information to the target user.

[0093] Furthermore, the presentation information generation unit 28 may present each target user with the visceral fat index of the target user before the program implementation. Fig. 12 is a schematic diagram showing a presentation image of the visceral fat index generated by the presentation information generation unit 28. As shown in the figure, the presentation information generation unit 28 can present, as the visceral fat index, the visceral fat level or the ranking of the visceral fat level in a specific group. The specific group may be, for example, a group of people of the same sex and generation, an administrative unit, or a group with the same eating habit classification.

[0094] The acquired information on visceral fat level is typically calculated from the sex, age, height, weight, and abdominal circumference (or abdominal thickness or width) acquired from each target user, and converted into a visceral fat level based on a certain standard. For example, if the visceral fat area is 10 cm 2 is level 1, and visceral fat area is 1cm 2 Conversion can be performed based on criteria such as increasing the level by 0.1 for each increase.

[0095] Next, the implementation period determination unit 26 determines the implementation period, which is the period up to the specified date specified by the target user (St14). The implementation period determination unit 26 acquires the specified date by operation input by the target user, etc., and can determine the period between the specified date and the scheduled start date of the program and the specified date as the implementation period.

[0096] While the program is being executed, the record information acquisition unit 24 acquires record information (see Table 5) (St15), and the evaluation unit 25 evaluates the record information (St16). The record information acquisition unit 24 accepts input of record information from the target user as shown in Table 12. The evaluation unit 25 calculates the score for each record item and each major item, and extracts the record items and sub-record items. The evaluation unit 25 may also evaluate the amount of nutrient intake.

[0097] Next, the advice generating unit 27 generates advice for eliminating the causes of body fat accumulation or the causes of lifestyle-related disease according to the eating habit classification (St17). At this time, the advice generating unit 27 can generate advice according to the implementation period specified by the implementation period specifying unit 26, and can generate advice so that the implementation of the required implementation items will be completed within the implementation period (see FIG. 5).

[0098] Next, the presentation information generation unit 28 generates presentation information including advice (St18). As shown in Fig. 6, the presentation information generation unit 28 generates presentation information including advice, scores and time trends of major items, higher-level record items and lower-level record items, etc., and outputs the information to each target user.

[0099] Furthermore, the presentation information generation unit 28 may present the visceral fat index of each target user during the implementation of the program to each target user. Fig. 13 is a schematic diagram showing a presentation image of the visceral fat index generated by the presentation information generation unit 28. As shown in the figure, the presentation information generation unit 28 can present the visceral fat level or the ranking of visceral fat levels in a specific group as the visceral fat index. In this case, the presentation information generation unit 28 can also visually display the difference in ranking from before the implementation of the program.

[0100] The advice providing device 20 executes the operations "before program implementation" and then executes the operations "during program implementation" for each evaluation period. The evaluation period is, for example, one week. The program implementation period is, for example, one month, and the advice providing device 20 executes the operations "during program implementation" one or more times during the program implementation period.

[0101] In this way, the advice providing system 100 provides advice to each target user for eliminating the causes of body fat accumulation or lifestyle-related disease. The above-described operation of the advice providing system 100 is an example, and the advice providing system 100 may provide the target user with advice for eliminating the causes of body fat accumulation or lifestyle-related disease according to the eating habit classification at least either before or during the program. The eating habit classification of the target user can be used for product recommendations, service provision and collaboration, future predictions (body shape, lifestyle-related diseases, etc.), introductions to friends (community formation), etc., in addition to providing advice.

[0102] [Effects of the advice provision system] As described above, in the advice providing system 100, the advice generation unit 27 generates advice according to the eating habit classification of the target user by the eating habit classification unit 23, so it is possible to provide effective advice for eliminating the causes of body fat accumulation or the causes of lifestyle-related diseases for each target user.

[0103] In this case, the evaluation unit 25 evaluates the causes of body fat accumulation or lifestyle-related disease on the basis of record information relating to dietary content and activity content input by the target user, and the advice generation unit 27 generates advice according to the evaluation result, thereby making it possible to provide advice that is in line with the dietary content and activity content of the target user. Also, the record information acquisition unit 24 presents the target user with a group of record items including record items according to the dietary habit classification result, making it possible to acquire valid record information for each dietary habit classification and use it for generating advice.

[0104] Furthermore, the advice generation unit 27 generates advice using standard phrases prepared for each record item, thereby enabling advice to be automatically generated according to the evaluation results of the record item, and by preparing standard phrases for each season, it becomes possible to provide advice appropriate for each season, such as advice about seasonal ingredients.

[0105] Furthermore, the evaluation unit 25 evaluates the intake of nutrients from the recorded information, and the advice generation unit 27 generates advice based on the evaluation results, making it possible to provide advice according to the excess or deficiency of nutrients taken by the user. As nutrients, protein, omega-3 fatty acids, and dietary fiber have a large impact on body fat accumulation and the risk of lifestyle-related diseases, so advice regarding these nutrients is particularly effective.

[0106] In providing advice, the advice providing system 100 may refer to the content of the previous advice and the above-mentioned recorded information, determine whether the target user was able to perform the items pointed out by the evaluation unit 25 in the previous advice, and generate advice including an evaluation of the pointed out items according to the determination result by the advice generation unit 27. For example, if the target user was able to perform the pointed out items, the advice generation unit 27 may generate a standard phrase praising the item, and if the target user was not able to perform the item, it may generate a standard phrase encouraging the item.

[0107] Furthermore, the target user determination unit 21 estimates the user's amount of visceral fat accumulation and determines the target user based on the estimated amount of visceral fat accumulation, making it possible to provide advice to users who are in high need of advice regarding body fat accumulation and lifestyle-related diseases.

[0108] Regarding the generation of eating habit classifications, the eating habit classification generation unit 12 can generate multiple eating habit classifications for each factor by performing factor analysis on data related to eating habit information and biomarkers, and can appropriately classify the eating habits of the target user by prioritizing them according to the contribution rates obtained from the factor analysis. In this case, the eating habit classification generation unit 12 calculates the factor loadings of the questions to be presented to the target user, and selects each question item for each factor according to the factor loadings, thereby generating eating habit classifications based on the contents of the selected questions.

[0109] It is also possible to provide a separate advice selection unit that allows the target user to select whether or not to adopt the advice generated by the advice generation unit 27. In this way, it is possible to provide advice that is easier for the target user to continue.

[0110] The advice providing system 100 may also be combined with the advice providing system 100 to propose optimal insurance products. Furthermore, as part of the services provided by health guidance institutions and the like, the advice providing system 100 may be provided in combination with health consultations, health checkups, health education, health coaching, health programs for smoking and lack of exercise, etc. Furthermore, as part of the services provided by screening institutions and the like, the advice providing system 100 may be provided in combination with health checkups, cancer screenings, lifestyle-related disease screenings, prenatal checkups, and checkups conducted by companies for employee health management, etc.

[0111] In addition, the advice providing system 100 can generate advice according to an implementation period, which is the period up to a specified date specified by the target user, and present it to the target user. By setting a date on which the target user particularly wants to achieve results, such as a health checkup date or an anniversary, as the specified date, the target user can receive advice generated according to the implementation period, making it possible to aim for the best results within a limited period.

[0112] (Second embodiment) An advice providing system according to a second embodiment of the present invention will be described. Note that since the advice providing system according to this embodiment also functions as an eating habit classification system, the advice providing system described below can also be read as an eating habit classification system.

[0113] [Configuration of the advice providing system] The configuration of the advice providing system according to this embodiment will be described below. As shown in FIG.

[0114] The advice providing system 200 is a system that provides users with advice on reducing body fat or preventing lifestyle-related diseases. "Body fat reduction" includes reducing visceral fat, reducing abdominal circumference, and reducing body weight. This includes reducing blood pressure, reducing body fat percentage, and eliminating metabolic syndrome. "Prevention of lifestyle-related diseases" also includes improving high blood pressure, hyperglycemia, and dyslipidemia, and eliminating obesity.

[0115] The advice providing device 50 is an information processing device that provides users with advice for reducing body fat or preventing lifestyle-related diseases. Hereinafter, a user to whom advice is to be provided is referred to as a "recipient user." As shown in FIG. 8 , the advice providing device 50 includes an eating habit information acquiring unit 51, an eating habit classifying unit 52, a record information acquiring unit 53, an evaluation unit 54, an implementation period identifying unit 55, an advice generating unit 56, and a presentation information generating unit 57.

[0116] The eating habit information acquisition unit 51 acquires the eating habit information of the target user. The eating habit information is responses to questions about eating habits such as those shown in Fig. 9. The eating habit information acquisition unit 51 may also acquire personal data that affect the target user's eating habits, such as the gender, age, place of residence, place of origin, occupation, and income.

[0117] The eating habit classification unit 52 classifies the target user's eating habits into one of the eating habit classifications based on the eating habit information. As shown in Fig. 9, the eating habit classification unit 52 can classify the target user's eating habits into one of "low health consciousness," "snacking," "late-night snacking," "fast eating," "lack of exercise," "late-night eating," and "low basal metabolism" based on the answers to seven questions. Note that Fig. 9 shows the questions and eating habit classifications regarding visceral fat accumulation, and different questions and eating habit classifications can be used for other symptoms.

[0118] After classifying the target user's eating habits, the eating habit classification unit 52 can generate an eating habit classification presentation screen as shown in Fig. 3 and visually display the classification results. The eating habit classification unit 52 supplies the target user's eating habit classification to the record information acquisition unit 53.

[0119] The record information acquisition unit 53 acquires record information, which is information about the meal contents and activity contents recorded by the target users. The record information acquisition unit 53 first generates, for each target user, a record item group including record items according to the target user's eating habit classification. Table 13 below is an example of a record item group generated by the record information acquisition unit 53.

[0120] [Table 13]

[0121] As shown in Table 13, the record item group generated by the record information acquisition unit 53 includes "eating habit category items." "Eating habit category items" are record items for each eating habit category classified by the eating habit classification unit 52, and Table 13 shows an example of a record item for "low health consciousness." The record items for each eating habit category have different content for each eating habit category.

[0122] Furthermore, the group of recording items may include "nutrient intake," as shown in Table 13. "Nutrient intake" is a recording item for the intake of one or more nutrients selected from protein, omega-3 fatty acids, and dietary fiber. Furthermore, the group of recording items may include recording items such as "amount of food," "weight," etc., as shown in Table 13. Items in the group of recording items other than "items by dietary habit classification" are common to all target users. Hereinafter, "quality of life," "quality of food," and "amount of food" will each be referred to as a "major item."

[0123] The record information acquisition unit 53 presents the generated group of record items to the user to whom the information is to be provided. The user to whom the information is to be provided inputs the record content, and the record information acquisition unit 53 acquires the record information. The user to whom the information is to be provided inputs the record content on a daily or weekly basis. The record information acquisition unit 53 supplies the acquired record information to the evaluation unit 54.

[0124] The evaluation unit 54 evaluates the causes of body fat accumulation or lifestyle-related disease for each target user based on the recorded information. The evaluation unit 54 aggregates the recorded information for each target user for a certain evaluation period, for example, one week, and can score each record item by averaging the number of affirmative responses to that record item. For example, the evaluation unit 54 scores the record item by averaging the number of "o"s for that record item over one week.

[0125] The evaluation unit 54 can extract the record items with the highest scores (hereinafter referred to as the record items) and the record items with the lowest scores (hereinafter referred to as the lower record items) from among the record items. The higher record items are, for example, the record items with the top 1 to 3 scores, and the lower record items are, for example, the record items with the bottom 1 to 3 scores. The evaluation unit 54 can evaluate the lower record items as items that require improvement in order to eliminate the causes of body fat accumulation or the causes of lifestyle-related disease.

[0126] Furthermore, the evaluation unit 54 can evaluate the intake amount of nutrients for each target user based on the recorded information. The evaluation unit 54 can estimate the intake ratio and intake status of each nutrient from the score of each recorded item of "diet quality." The evaluation unit 54 can also estimate the "protein / fat ratio," "omega-3 fatty acid / fat ratio," and "dietary fiber / sugar or carbohydrate ratio" from the score of each recorded item of "diet quality." The evaluation unit 54 supplies the evaluation results to the advice generation unit 56.

[0127] The implementation period determination unit 55 determines the implementation period of the body fat reduction or lifestyle-related disease prevention program provided by the advice providing system 200 to the target user. The implementation period determination unit 55 determines the period up to a specified date (hereinafter referred to as the specified date) as the implementation period. The specified date is a date specified by the target user, such as an anniversary such as a wedding or the date of a health check. The implementation period determination unit 55 can obtain the specified date through operational input by the target user, etc.

[0128] The implementation period determination unit 55 determines the period between the scheduled start date of the program and the specified date as the implementation period. The scheduled start date of the program may be specified by the target user, or may be the day on which the target user inputs the indicated date or the day after that. The implementation period is not particularly limited, but is preferably one week, more preferably two weeks, more preferably three weeks, and even more preferably four weeks. The implementation period determination unit 55 supplies the determined implementation period to the advice generation unit 56.

[0129] The advice generating unit 56 generates advice for eliminating the causes of body fat accumulation or the causes of lifestyle-related disease according to the results of the dietary habit classification. The advice generating unit 56 can generate advice according to the evaluation results of the causes of body fat accumulation or the causes of lifestyle-related disease by the evaluation unit 54, i.e., the scores of each record item.

[0130] Specifically, the advice generation unit 56 can generate advice that praises higher-ranking record items and suggests improvements to lower-ranking record items. The advice generation unit 56 can also select record items with lower scores that are likely to be improved from among the record items with lower scores, and generate advice that suggests improvements to the record items with improved scores. The advice generation unit 56 can select record items with improved scores from among the lower-ranking record items by excluding items with low scores in each record. Furthermore, the advice generation unit 56 can generate advice that encourages the target user's eating habits from the next time onwards.

[0131] Furthermore, the advice generation unit 56 can generate solution advice for each target user using standard phrases prepared for each eating habit classification. Tables 14 and 15 below show standard phrases for each eating habit classification. The advice generation unit 56 can generate solution advice including the standard phrases in Tables 14 and 15 according to the eating habit classification of the target user.

[0132] [Table 14]

[0133] [Table 15]

[0134] Furthermore, the advice generating unit 56 can generate advice using a template prepared for each record item as shown in the following Table 16. The advice generating unit 56 supplies the generated advice to the presentation information generating unit 57.

[0135] [Table 16]

[0136] Furthermore, the advice generating unit 56 can generate advice according to the evaluation result regarding the nutrient intake of the target user. With regard to the intake of "protein," "omega-3 fatty acids," and "dietary fiber" estimated by the evaluation unit 54, the advice generating unit 56 can generate advice that praises the intake of nutrients with a high intake amount and encourages the intake of nutrients with a low intake amount.

[0137] Furthermore, the advice generating unit 56 can generate advice that praises the intake of nutrients with a high "protein / fat ratio," "omega-3 fatty acid / fat ratio," and "dietary fiber / sugar or carbohydrate ratio" estimated by the evaluating unit 54, and encourages the intake of nutrients with a low ratio. In this case, the advice generating unit 56 may generate advice that suggests the intake of functional foods, foods for specified health uses, and foods containing functional ingredients. Note that the advice generating unit 56 does not necessarily have to generate advice regarding nutrient intake.

[0138] The method of generating advice by the advice generation unit 56 is not particularly limited, but in addition to using the fixed phrases described above, the advice generation unit 56 can accumulate obtained data, perform machine learning, and generate advice using the learning results. Furthermore, the advice generation unit 56 can generate advice based on nutritional guidance by an expert such as a registered dietitian.

[0139] Alternatively, the advice generating unit 56 may generate advice according to the eating habit classification for each target user. The advice generating unit 56 is not limited to generating advice according to the evaluation result of the recorded information by the evaluation unit 54, and may generate advice according to the eating habit classification once the eating habit classification of the target user is determined by the eating habit classification unit 52.

[0140] The advice generating unit 56 can also generate advice according to the implementation period identified by the implementation period identifying unit 55. Specifically, the advice generating unit 56 identifies items that the target user should implement during the standard period (hereinafter, "need-to-implement items"), similar to the first embodiment. The advice generating unit 56 can identify the need-to-implement items by one or more of the above-described methods, and can also identify the need-to-implement items according to the evaluation result by the evaluating unit 54.

[0141] The advice generating unit 56 generates advice so that the necessary action items are carried out in accordance with the implementation period identified by the implementation period identifying unit 55 (see FIG. 5). The advice generating unit 56 supplies the generated advice to the presentation information generating unit 57.

[0142] The presentation information generation unit 57 generates presentation information including the advice supplied from the advice generation unit 56. The presentation information generation unit 57 can generate a presentation image including the generated presentation information and present the presentation information to the user. As shown in FIG. 10, the presentation information can include advice for each major category. In the presentation information, the content of the "Quality of Life" column differs for each eating habit classification shown in Tables 14 and 15, while the content of the other columns is the common content shown in Table 16. In addition to advice, the presentation information may also include scores for each major category and changes over time. The presentation information is not limited to what is shown here and may include, for example, the "desired state" of the target user. The "desired state" of the target user is a goal of the target user that is input by the target user himself or proposed by the advice providing device 50.

[0143] The advice providing system 200 has the above-described configuration. Each component of the advice providing device 50 is a functional configuration realized by cooperation between hardware and software of an information processing device configured with a CPU (Central Processing Unit), RAM (Random Access Memory), etc. The software is, for example, a program executable by the information processing device, and may be a program recorded on a recording medium readable by the information processing device.

[0144] [Operation of the advice providing system] The operation of the advice providing system 200 will be described. The advice providing device 50 provides a body fat reduction or lifestyle-related disease prevention program to a target user. The advice providing device 20 operates "before the program is implemented" and "during the program is implemented" as shown in FIG. 11. Table 17 below shows the contents of "input," "determination," and "output" before and during the program.

[0145] [Table 17]

[0146] Before the program is implemented, as shown in FIG. 11, the eating habit information acquisition unit 51 acquires the eating habit information of the target users (St21), and the eating habit classification unit 52 determines the eating habit classification of the target users (St22). The eating habit information acquisition unit 51 accepts input of eating habit information from the target users as shown in Table 17, and the eating habit classification unit 52 determines the eating habit classification of the target users. Furthermore, the eating habit classification unit 52 can output the eating habit classification (see FIG. 3) corresponding to each target user. At this time, the advice generation unit 56 may generate advice for eliminating the causes of body fat accumulation or lifestyle-related disease according to the eating habit classification, and output the generated advice to each target user.

[0147] When generating advice for eliminating causes of body fat accumulation or lifestyle-related disease according to the eating habit classification, the advice generation unit 56 can prioritize guidance on factors that contribute highly to visceral fat reduction for each target user's eating habit classification. Factors that contribute highly to visceral fat reduction can be identified by factor analysis. Table 18 below shows factors that contribute highly to visceral fat reduction for each eating habit classification. For example, if the eating habit classification is "low health consciousness," guidance will be prioritized in the following order: "consciously reduced the amount of food eaten to avoid gaining weight," "did not eat until full at any meal," "did not eat processed meat such as bacon, sausage, or salami," "exercise intensity: 12-3 p.m.," and "ate soy products such as tofu, natto, and soybeans."

[0148] [Table 18]

[0149] Next, the implementation period determination unit 55 determines the implementation period, which is the period up to the specified date specified by the target user (St23). The implementation period determination unit 55 acquires the specified date by operation input by the target user, etc., and can determine the period between the specified date and the scheduled start date of the program as the implementation period.

[0150] While the program is being executed, the record information acquisition unit 53 acquires record information (see Table 13) (St24), and the evaluation unit 54 evaluates the record information (St25). The record information acquisition unit 53 accepts input of record information from the target users as shown in Table 17. When generating a record item group as shown in Table 13, the record information acquisition unit 53 can generate a record item group that prioritizes input of factors (see Table 18) that have a high contribution rate to visceral fat reduction for each target user's eating habit classification. The record information acquisition unit 53 can, for example, generate a record item group that includes record items that correspond to factors whose contribution rate is equal to or greater than a certain value. The evaluation unit 54 can calculate the score of each record item and each major item, and extract the record items and sub-record items. The evaluation unit 54 may also evaluate nutrient intake.

[0151] Next, the advice generating unit 56 generates advice for eliminating the causes of body fat accumulation or the causes of lifestyle-related diseases according to the eating habit classification (St26). At this time, the advice generating unit 56 can generate advice according to the implementation period specified by the implementation period specifying unit 55, and can generate advice so that the implementation of the required implementation items will be completed within the implementation period (see FIG. 5).

[0152] Next, the presentation information generating unit 57 generates presentation information including the advice (St27). As shown in Fig. 10, the presentation information generating unit 57 generates presentation information including the advice and outputs it to each target user.

[0153] The advice providing device 50 executes the operations "before program implementation" and then executes the operations "during program implementation" for each evaluation period. The evaluation period is, for example, one week. The program implementation period is, for example, one month, and the advice providing device 50 executes the operations "during program implementation" one or more times during the program implementation period.

[0154] In this way, the advice providing system 200 provides advice to each target user for eliminating the causes of body fat accumulation or the causes of lifestyle-related disease. The above-described operation of the advice providing system 200 is an example, and the advice providing system 200 may provide the target user with advice for eliminating the causes of body fat accumulation or the causes of lifestyle-related disease according to the eating habit classification at least either before or during the program implementation.

[0155] The dietary habit classification of the target user can be used for providing advice, product recommendations, service provision and collaboration, future predictions (body shape, lifestyle-related diseases, etc.), introductions to friends (community formation), etc. The effects of the advice providing system 200 are similar to those of the advice providing system 100 according to the first embodiment.

[0156] In providing advice, the advice providing system 200 may refer to the content of the previous advice and the above-mentioned recorded information, determine whether the target user was able to perform the items pointed out by the evaluation unit 25 in the previous advice, and the advice generation unit 56 may generate advice including an evaluation of the pointed out items according to the determination result. For example, if the target user was able to perform the pointed out items, the advice generation unit 56 may generate a standard phrase praising the item, and if the target user was not able to perform the item, it may generate a standard phrase encouraging the item.

[0157] Furthermore, an advice selection unit may be provided separately to allow the target user to select whether or not to adopt the advice generated by the advice generation unit 56. In this way, it becomes possible to provide advice that is easier for the target user to continue.

[0158] Furthermore, the advice providing system 200 may be combined with the advice providing system 200 to propose optimal insurance products. As part of the services provided by health guidance institutions and the like, the advice providing system 200 may be provided in combination with health consultations, health checkups, health education, health coaching, health programs for smoking and lack of exercise, etc. As part of the services provided by screening institutions and the like, the advice providing system 200 may be provided in combination with health checkups, cancer screenings, lifestyle-related disease screenings, prenatal checkups, and checkups conducted by companies for employee health management, etc.

[0159] While the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the spirit of the present invention. Suitable examples of the advice providing program and advice providing method are similar to those described above with respect to the advice providing system proposal and selection device. [Explanation of symbols]

[0160] 20, 50...Advice Providing Device 23, 52...Eating habits classification department 26, 55...Implementation period specific section 27, 56...Advice generation section

Claims

1. An advice providing system that provides advice to a target user for reducing body fat or preventing lifestyle-related diseases, an eating habit classification unit that classifies the eating habits of the target user into one of a plurality of eating habit classifications based on eating habit information that is information about the eating habits of the target user; an implementation period specification unit that specifies an implementation period, which is a period up to a specified date; an advice generating unit that generates advice for eliminating causes of body fat accumulation or causes of lifestyle-related disease according to the classification result of the eating habits for each target user in accordance with the implementation period; An advice providing system comprising:

2. The advice generation unit generates necessary action items that are items that a target user should perform within a predetermined standard period, and generates advice according to the implementation period so that the necessary action items are achieved within the implementation period. The advice providing system according to claim 1 .

3. an evaluation unit that evaluates the cause of body fat accumulation or the cause of lifestyle-related disease for each target user based on recorded information that is information about dietary details and activity details recorded by the target user; The advice generation unit generates advice according to the evaluation result of the cause of fat accumulation or the cause of lifestyle-related disease by the evaluation unit and the implementation period. The advice providing system according to claim 1 .

4. The eating habit classification unit classifies the eating habits of the target user into the plurality of eating habit classifications according to body fat accumulation factors or lifestyle-related disease susceptibility factors. The advice providing system according to claim 1 .

5. The evaluation unit further evaluates at least one of nutrient intake, dietary behavior, and personal information for each target user based on the recorded information; The advice generation unit generates advice according to the evaluation result by the evaluation unit. The advice providing system according to claim 2 .

6. The nutrients are one or more selected from protein, omega-3 fatty acids, and dietary fiber. The advice providing system according to claim 5 .

7. a provision target user determination unit that determines a user whose visceral fat accumulation amount exceeds a specified value as a provision target user; The advice providing system according to claim 1 , further comprising:

8. The provision target user determination unit estimates the amount of accumulated visceral fat of the user based on the user's physical information, and sets the estimated amount of accumulated visceral fat as the amount of accumulated visceral fat of the user. The advice providing system according to claim 7 .

9. An advice providing program for providing advice to a target user for reducing body fat or preventing lifestyle-related diseases, the advice providing program including: A step of classifying the target user's eating habits into one of a plurality of eating habit categories based on eating habit information that is information about the target user's eating habits; Identifying an implementation period, which is a period up to a specified date; generating advice for each target user for eliminating causes of body fat accumulation or lifestyle-related disease according to the classification result of the eating habits in accordance with the implementation period; An advice providing program that executes the above.

10. An advice providing method for providing advice to a target user for reducing body fat or preventing lifestyle-related diseases, comprising: classifying the target user's eating habits into one of a plurality of eating habit classifications based on eating habit information that is information about the target user's eating habits; Identify the implementation period, which is the period up to a specified date; For each target user, advice is generated for eliminating causes of body fat accumulation or lifestyle-related disease according to the classification results of the eating habits, depending on the implementation period. How advice is provided.

Images