Advice providing device

Abstract

To provide an advice providing device capable of maintaining the effects of body fat reduction or lifestyle-related disease prevention even after the completion of an implementation period of a program for body fat reduction or lifestyle-related disease prevention.SOLUTION: An advice providing device according to one embodiment of the present invention is the advice providing device that provides advice for body fat reduction or lifestyle-related disease prevention to a providing target user, and includes: a first advice generation unit that generates first advice for resolving the body fat accumulation cause or lifestyle-related disease contraction cause; an implementation result acquisition unit that acquires the implementation results in a period regarding the items proposed in the first advice of the providing target user; and a second advice generation unit that, after the end of the period, assigns priority to the items proposed in the first advice according to the implementation results, presents them to the providing target user, proposes implementation of items selected by the providing target user, and generates second advice for resolving the body fat accumulation cause or lifestyle-related disease contraction cause according to the implementation results.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an advice providing device that provides advice for reducing body fat or preventing lifestyle-related diseases. [Background technology]

[0002] A technology has been developed that evaluates the dietary and activity details of a user and generates advice for reducing body fat or preventing lifestyle-related diseases according to the evaluation results. For example, Patent Document 1 discloses a lifestyle habit improvement suggestion device that generates a message proposing lifestyle improvement based on lifestyle habit information such as the user's weight gain rate, number of steps, and dietary intake, and environmental information such as weather and temperature. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-160569 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the invention described in the above Patent Document 1, when the lifestyle habit information and the environmental information satisfy a certain condition, advice for improving the lifestyle habit is given according to the condition. Here, even if the lifestyle habit improvement is carried out for a certain period of time, when the period ends, the user may stop improving the lifestyle habit, and the effect of reducing body fat or preventing lifestyle-related diseases may decrease.

[0005] The present invention relates to an advice providing device that can maintain the effects of a body fat reduction or lifestyle-related disease prevention program even after the implementation period for such a program has ended, and can provide a program for body fat reduction or lifestyle-related disease prevention again if the effect disappears due to rebound or the like after the implementation period has ended. [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 for reducing body fat or preventing lifestyle-related diseases to a target user, a first advice generating unit that generates a first advice for eliminating a cause of body fat accumulation or a cause of lifestyle-related disease during a predetermined period; an implementation result acquisition unit that acquires an implementation result of the target user for the item proposed in the first advice during the period; and a second advice generating unit that generates second advice for eliminating causes of body fat accumulation or causes of lifestyle-related disease after the end of the period, by prioritizing the items suggested in the first advice according to the implementation results and presenting the same to the target user, and suggesting the implementation of the items selected by the target user.

[0007] An advice providing program according to one embodiment of the present invention is 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: generating a first advice for eliminating a cause of body fat accumulation or a cause of lifestyle-related disease in a predetermined period of time; acquiring an implementation result of the target user for the item proposed in the first advice during the period; After the period ends, the device executes a step of generating second advice for eliminating the causes of body fat accumulation or the causes of lifestyle-related disease, which involves prioritizing the items suggested in the first advice according to the implementation results and presenting the advice to the target user, and suggesting the implementation of the items selected by the target user. Effect of the Invention

[0008] According to the present invention, it is possible to maintain the effects of reducing body fat or preventing lifestyle-related diseases even after the implementation period of a program for reducing body fat or preventing lifestyle-related diseases has ended, and it is also possible to provide a program for reducing body fat or preventing lifestyle-related diseases again if the effects disappear due to rebound or the like after the implementation period has ended. [Brief description of the drawings]

[0009] [Figure 1] 1 is a block diagram showing a configuration of an advice providing system according to a first embodiment of the present invention. [Diagram 2] 13 is an example of a screen showing questions presented to a user from an advice providing device included in the advice providing system. [Diagram 3] 13 is an example of a presentation screen of an eating habit classification result presented to a user from the advice providing device. [Figure 4] 4 is an example of advice presented to a user from the advice providing device. [Diagram 5] 10 is an example of presentation information presented to a user from the advice providing device. [Figure 6] 5 is a flowchart showing an operation of the advice providing device. [Figure 7] FIG. 11 is a block diagram showing a configuration of an advice providing system according to a second embodiment of the present invention. [Figure 8] 4 is a schematic diagram showing a method for classifying eating habits by an advice providing device included in the advice providing system. FIG. [Figure 9] 4 is an example of advice presented to a user from the advice providing device. [Figure 10] 5 is a flowchart showing an operation of the advice providing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The present invention is not limited to the embodiment shown below, and various modifications can be made within the scope of the present invention. Below, an embodiment of the present invention in which one information processing device is used will be described, but the present invention also covers a case in which multiple information processing devices work together using distributed processing technology, that is, a series of processes are shared and performed by multiple information processing devices.

[0011] (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.

[0012] [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.

[0013] The advice providing system 100 is a system that provides a user with advice on reducing body fat or preventing lifestyle-related diseases. "Reducing body fat" includes reducing visceral fat, reducing waist circumference, reducing body weight, reducing body fat percentage, and eliminating metabolic syndrome. "Preventing lifestyle-related diseases" includes improving high blood pressure, improving hyperglycemia, improving dyslipidemia, and eliminating obesity.

[0014] The advice providing system 100 generates advice using biomarkers. Biomarkers are indices that indicate 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).

[0015] The advice providing system 100 uses a specific biomarker according to the symptoms of the advice providing target. Hereinafter, the biomarker used by the advice providing system 100 will be referred to as a "specific biomarker." Table 1 below shows examples of symptoms and specific biomarkers. Note that the symptoms and specific biomarkers are not limited to those shown here. For example, the specific biomarker for visceral fat accumulation may be waist circumference or weight.

[0016] [Table 1]

[0017] The eating habit classification generating device 10 is an information processing device that generates an eating habit classification. The eating habit classification generating 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.

[0018] 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 regarding the eating habits (including diet, eating behavior, lifestyle behavior, exercise habits, and personality) of each analysis target user, and data on specific biomarkers for multiple analysis target users.

[0019] 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, if something smells delicious I end up eating it. Q02 When I see someone eating something around me, I eat it too Q03 I eat too much 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 like fish more than meat Q13 I often eat a midnight snack after dinner Q14 I try to limit my intake of animal fats and instead consume vegetable fats and fish fats. Q15 I think I am more prone to gain weight than other people. Q16 I feel like I won't have energy if I don't eat. Q17 Meal times are random Q18 I have a sweet tooth Q19 Eating a midnight 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 if I don't buy more food than I need. Q23 I often have late dinner 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 barely 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

[0020] The user to be analyzed answers each question by selecting one of the following: "Doesn't apply=1", "Doesn't really apply=2", "Can't say either way=3", "Somewhat applies=4", or "Applies=5", and generates eating habit information. Note that the above questions are just examples, 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 biomarker data to the eating habit classification generation unit 12.

[0021] The eating habit classification generating unit 12 performs factor analysis on the data supplied from the data acquiring unit 11, and generates multiple eating habit classifications according to factors for the biomarkers of the eating habit information. The eating habit classification is a classification of the eating habits of the user to be analyzed, such as "not very health-conscious or health-conscious," "snacks," "late-night snacks," "fast eating," "lack of exercise or exercise," "late-night meals or late dinner," and "other (not applicable to any category)."

[0022] The eating habit classification generating unit 12 performs factor analysis on the eating habit information and biomarker data of multiple analysis target users. Although the method of factor analysis is not limited, data obtained by statistically investigating data on eating habit information and biomarkers is shown as being preferable. For example, various models can be adopted, such as multiple regression analysis using statistical data, logistic regression model, multilayer perceptron, neural networks such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network), support vector machine using any kernel function such as Gaussian kernel, random forest modeled as a regression tree, model using hidden Markov model, statistical model, probability model, factor analysis, and correspondence table using table. Also, a model that performs comprehensive judgment by combining various models can be adopted. Among them, it is particularly preferable to use factor analysis using a principal factor method with varimax rotation.

[0023] Tables 2 and 3 below show the "factor loadings" of each question item, the "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.

[0024] [Table 2]

[0025] [Table 3]

[0026] The eating habit classification generating unit 12 classifies each question item into each factor according to the factor loading as shown in Tables 2 and 3. For example, Q04, Q07, Q06, Q09, and Q11 are selected into "factor 1" because their factor loadings (inside black squares) for "factor 1" exceed a reference value. The reference value is, for example, 0.4. Similarly, Q02, Q01, and Q20 are selected into "factor 2," Q19 and Q13 into "factor 3," and Q08 and Q28 into "factor 4." Furthermore, Q27 and Q29 are selected into "factor 5," and Q25 and Q23 into "factor 6." The eating habit classification generating unit 12 generates an eating habit classification such as "low health consciousness or health consciousness" for each factor according to the content of the question items selected into each factor.

[0027] Similarly, for biomarkers other than VFA, the eating habit classification generating unit 12 selects each question item for each factor according to the factor loading. Table 4 below shows the eating habit classification for each symptom for each factor. As shown in Table 4, the eating habit classification for each symptom for each factor and the corresponding question items differ. The eating habit classification generating unit 12 supplies the generated eating habit classification and the "contribution rate" to the ranking determining unit 13.

[0028] [Table 4]

[0029] The ranking determination unit 13 determines the priority order between the eating habit categories in descending order of contribution rate. In the examples of Tables 2 and 3, "low health consciousness or health consciousness" has the highest contribution rate (0.12), followed by "snacks" with the next highest contribution rate (0.11). The contribution rates decrease in the following order: "late-night snacks," "quick eating," "lack of exercise or exercise," and "late-night meals or late dinner." For this reason, the ranking determination unit 13 gives the highest priority to "low health consciousness or health consciousness," followed by "snacks," "late-night snacks," "quick eating," "lack of exercise or exercise," and "late-night meals or late dinner."

[0030] Table 4 shows the priority order of the eating habit classification according to the contribution rate for each symptom. As shown in Table 4, the ranking determination unit 13 gives the highest priority to "night snack" for "dyslipidemia", followed by "overeating", "lack of exercise or exercise", and "low health consciousness or health consciousness" in that order. Also, for "high blood pressure", the ranking unit 13 gives the highest priority to "night snack", followed by "snack", "lack of exercise or exercise", "low health consciousness or health consciousness", and "fast eating". For "hyperglycemia", the ranking unit 13 gives the highest priority to "snack", followed by "night snack", "lack of exercise or exercise", "irregular meal times", and "food quantity restriction" in that order. The ranking determination unit 13 supplies the eating habit classification and the priority order to the advice providing device 20. Note that the values ​​obtained for the eating habit classification name, contribution rate, and priority order differ depending on the data used. The results disclosed in this specification are merely examples. The priority order may be changed according to the improvement purpose desired by the user.

[0031] 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 "recipient user." As shown in FIG. 1, the advice providing device 20 includes a recipient 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, a first advice generation unit 26, an implementation result acquisition unit 27, a second advice generation unit 28, and a presentation information generation unit 29.

[0032] 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 biometric index exceeds a prescribed value as a provision target user. For example, in the case of VFA, the prescribed value is 10 cm for a male. 2 More than 50cm, preferably 50cm 2 More than 80cm, preferably 80cm 2 More preferably, 100 cm 2 Above, women 10cm 2 More than 30cm, preferably 30cm 2 More than 50cm, preferably 50cm 2 More than 80cm, preferably 80cm 2For example, waist circumference is 85cm or more for men and 90cm or more for women.

[0033] The provision target user determination unit 21 can determine a user whose own specific biomarker input by the user exceeds a specified value as a provision target user. In addition, the provision target user determination unit 21 can estimate the specific biomarker of the user from the information input by the user, and determine a user whose estimated value exceeds the above-mentioned specified value as a treatment target user.

[0034] 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 sex, age, height, weight, waist circumference, and image data from which these 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 the provision target user based on information other than the specific biomarker, or may determine all users as provision target users.

[0035] 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 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 further acquire personal data that affects the target user's eating habits, such as the gender, age, place of residence, place of origin, occupation, and income.

[0036] 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 30 and an eating habit category decision unit 31.

[0037] The eating habit classification determination unit 30 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 30 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".

[0038] For example, regarding visceral fat accumulation, if the answer scores of Q04, Q07, Q06, Q09, and Q11, which are questions selected as "low health consciousness or health consciousness" (see Table 4), are all "not applicable = 1" or "slightly not applicable = 2", the eating habit classification determination unit 30 determines that the target user's eating habit corresponds to "low health consciousness or health consciousness". Also, if the answer scores of Q02, Q01, and Q20, which are questions selected as "snacks", are all "applicable = 5" or "slightly applicable = 4", the eating habit classification determination unit 30 determines that the target user's eating habit corresponds to "snacks". Similarly, the eating habit classification determination unit 30 determines whether the target user's eating habit corresponds to "late-night snacking", "fast eating", and "lack of exercise or exercise" according to the answer scores of the questions classified as each of these eating habits. Furthermore, if the answer score for Q25, a question item selected as “late night meal or late dinner”, is “not applicable = 1” or “slightly not applicable = 2”, and the answer score for Q23 is “applicable = 5” or “slightly applicable = 4”, the eating habit classification determination unit 30 determines that the eating habit of the target user falls into the category of “late night meal or late dinner”.

[0039] Here, if the answer scores meet the conditions in multiple eating habit classifications, the eating habit classification determination unit 30 determines that the target user's eating habits fall into multiple eating habit classifications. Also, if the answer scores do not meet any of the conditions in any of the eating habit classifications, the eating habit classification determination unit 30 determines that the target user's eating habits do not fall into any of the eating habit classifications.

[0040] As another method, the eating habit classification determination unit 30 determines that the target user's eating habits correspond to "low health consciousness or health consciousness" if the total of the answer scores of Q04, Q07, Q06, Q09, and Q11, which are questions selected as "low health consciousness or health consciousness" (see Table 4), is equal to or greater than a threshold value. Also, the eating habit classification determination unit 30 determines that the target user's eating habits correspond to "snacks" if the total of the answer scores of Q02, Q01, and Q20, which are questions selected as "snacks", is equal to or greater than a threshold value. Similarly, the eating habit classification determination unit 30 determines whether the target user's eating habits correspond to "late-night snacks," "fast eating," "lack of exercise or exercise," and "late-night meal or late dinner" according to the answer scores of the questions classified into each of these eating habits.

[0041] In this case, if the answer scores in multiple eating habit classifications are equal to or greater than the threshold, the eating habit classification determination unit 30 determines that the target user's eating habits fall into multiple eating habit classifications. Also, if the answer scores in all eating habit classifications are less than the threshold, the eating habit classification determination unit 30 determines that the target user's eating habits do not fall into any eating habit classification.

[0042] The eating habit classification determination unit 31 determines the eating habit classification of the target user in response to the determination result by the eating habit classification determination unit 30. When the eating habit classification determination unit 30 determines that the target user's eating habit falls into one eating habit classification, the eating habit classification determination unit 31 determines that the target user's eating habit falls into that eating habit classification.

[0043] Furthermore, when the eating habit classification determination unit 30 determines that the eating habit of the target user falls into a plurality of eating habit classifications, the eating habit classification determination unit 31 determines that the eating habit of the target user falls 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 of a specific target user with regard to visceral fat accumulation is determined by the eating habit classification determination unit 30 to fall into "snacks" and "lack of exercise or exercise", the eating habit classification determination unit 31 determines that the eating habit of the target user falls into the eating habit classification of "snacks" with the higher priority.

[0044] Furthermore, when the eating habit classification determination unit 30 determines that the target user's eating habits do not fall into any of the eating habit classifications, the eating habit classification determination unit 31 can classify the target user's eating habits into a classification different from the eating habit classifications generated for each factor. For example, when 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 31 classifies the target user's eating habits into "decreased basal metabolism." This is because, although 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 metabolism.

[0045] The eating habit classification unit 23 classifies the eating habits of the target user into one of the above-mentioned ways. The eating habit classification unit 23 can generate a presentation screen of the eating habit classification as shown in FIG. 3, and visually notify the target user of the classification result. The radar chart shows the answer scores for each eating habit classification. The eating habit classification unit 23 may accept the selection of the eating habit classification from the target user, and the eating habit classification selected by the target user may be the eating habit classification of the user. This is effective in cases where the target user is aware of his / her own eating habit classification that was notified previously. The eating habit classification unit 23 supplies the eating habit classification of the target user to the record information acquisition unit 24.

[0046] The record information acquisition unit 24 acquires record information, which is information on the meal contents and activity contents recorded by the target user. For each target user, the record information acquisition unit 24 generates a record item group including record items according to the eating habit classification of the target user. The following Tables 5 and 6 are examples of the record item group generated by the record information acquisition unit.

[0047] [Table 5]

[0048] [Table 6]

[0049] In addition, as an option, the items recorded may also include the intake of functional ingredients such as tea catechins, chlorogenic acid, and ALA-DAG (alpha-linolenic acid diacylglycerol), which are effective in preventing the accumulation of body fat and the onset of lifestyle-related diseases.

[0050] As shown in Tables 5 and 6, the record item group generated by the record information acquisition unit includes "eating habit classification items." "Eating habit classification items" are record items for eating habit classifications classified by the eating habit classification unit 23, and Tables 5 and 6 show record items for "low health consciousness or health consciousness" as examples. Table 7 below shows record items for other eating habit classifications. As shown in these tables, the record items for eating habit classifications have different contents for each eating habit classification. Furthermore, calories burned other than from walking may also be included in the record items for eating habit classifications.

[0051] [Table 7]

[0052] 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 each target user. Hereinafter, "items by dietary habit classification," "protein," "dietary fiber," "omega-3 fatty acids," and "lifestyle points" are each referred to as a "major item."

[0053] The record information acquisition unit 24 presents the generated group of record items to the provided user. The provided user inputs the record contents, and the record information acquisition unit 24 acquires the record information. The provided user inputs the record contents on a daily or weekly basis. The record information acquisition unit 24 supplies the acquired record information to the evaluation unit 25.

[0054] 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.

[0055] [Table 8]

[0056] [Table 9]

[0057] 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 the average number of affirmative responses to that record item. For example, for the record item "I ate meals with consideration for nutritional balance" under "low health consciousness or health consciousness," the evaluation unit 25 determines the score for that record item by the average number of "o"s in 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 determined by the average number of "o"s in one week.

[0058] The evaluation unit 25 can extract the record items with the highest scores (hereinafter, the record items) and the record items with the lowest scores (hereinafter, the lower record items) from among the record items. The higher record items are, for example, the record items with the top 1st to 3rd highest scores, and the lower record items are, for example, the record items with the bottom 1st to 3rd lowest scores. The evaluation unit 25 can evaluate that the lower record items are items that require improvement to eliminate the cause of body fat accumulation or the cause of lifestyle-related disease.

[0059] 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 of each recorded item of "protein", "omega-3 fatty acid", and "dietary fiber". The evaluation unit 25 can also estimate the "protein / fat ratio", "omega-3 fatty acid / fat ratio", and "dietary fiber / sugar or carbohydrate ratio" from the scores of each recorded item of "protein", "omega-3 fatty acid", and "dietary fiber".

[0060] In addition, the evaluation unit 25 can evaluate the "dietary behavior" or "personal information" for each target user based on the recorded information. The "dietary behavior" is one or more of the target user's meal quality, amount, time, eating style, and activity level, and the "personal information" is at least one or more of weight, waist circumference, etc. The priority order 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 result to the first advice generation unit 26.

[0061] The first advice generating unit 26 generates advice for eliminating the causes of body fat accumulation or lifestyle-related disease according to the results of the classification of eating habits for a predetermined period. Hereinafter, this period will be referred to as the "implementation period", and the advice generated by the first advice generating unit 26 will be referred to as the first advice. The implementation period includes one or more of the above-mentioned evaluation periods, and is, for example, one month. The first advice generating unit 26 can generate the first advice according to the evaluation results of the causes of body fat accumulation or lifestyle-related disease by the evaluation unit 25, i.e., the scores of each record item.

[0062] Specifically, the first advice generating unit 26 can generate first advice that praises higher-level recorded items and suggests improvements to lower-level recorded items. The first advice generating unit 26 can also select record items that can be improved from the lower-level recorded items and generate first advice that suggests improvements to the record items that can be improved. The first advice generating unit 26 can select record items that can be improved from the lower-level recorded items by excluding items with low scores in each recording. Furthermore, the first advice generating unit 26 can generate first advice that encourages the eating habits of the target user from the next time onwards.

[0063] For example, as shown in FIG. 4, the first advice generation unit 26 can generate first 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 providing encouragement for the next evaluation period.

[0064] The first advice generating unit 26 can also generate the first advice using a fixed phrase prepared for each record item. Tables 10 and 11 below are examples of fixed phrases for each record item. The first advice generating unit 26 can generate the first advice including at least one of the fixed phrases of the upper record items and the fixed phrases of the lower record items. The fixed phrases may include specific menu suggestions, and the menus are preferably ones that can be eaten without much effort. The fixed phrases of each record item may have different content for each season, and may suggest, for example, seasonal ingredients and cooking methods that are easy to obtain information about and purchase for each season. The first advice generating unit 26 can generate the first advice according to the season at the time of advice generation by using fixed phrases prepared for each season.

[0065] [Table 10]

[0066] [Table 11]

[0067] Furthermore, the first advice generating unit 26 can generate the first advice according to the evaluation result regarding the nutrient intake of the target user. The first advice generating unit 26 can generate the first 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 acid" and "dietary fiber" estimated by the evaluation unit 25. Furthermore, the first advice generating unit 26 can generate advice praising the points with a good evaluation and encouraging the points with a bad evaluation according to the evaluation result of the target user's "dietary behavior" or "personal information".

[0068] Furthermore, the first advice generating unit 26 can generate first advice praising the intake of nutrients with high "protein / fat ratio," "omega-3 fatty acid / fat ratio," and "dietary fiber / sugar or carbohydrate ratio" estimated by the evaluation unit 25, and encouraging the intake of nutrients with low "protein / fat ratio," "omega-3 fatty acid / fat ratio," and "dietary fiber / sugar or carbohydrate ratio" estimated by the evaluation unit 25. In this case, the first advice generating unit 26 may generate first advice suggesting the intake of functional foods, foods for specified health uses, and foods containing functional ingredients. Note that the first advice generating unit 26 does not necessarily have to generate first advice regarding the amount of nutrient intake.

[0069] The method of generating the first advice by the first advice generating unit 26 is not particularly limited, but in addition to using the above-mentioned fixed phrases, the first advice generating unit 26 can accumulate obtained data, perform machine learning, and generate the first advice using the learning results. Furthermore, the first advice generating unit 26 can generate the first advice based on nutritional guidance by an expert such as a registered dietitian.

[0070] In addition, the first advice generating unit 26 may generate the first advice according to the eating habit classification for each target user. The first advice generating unit 26 is not limited to generating the first advice according to the evaluation result of the recorded information by the evaluation unit 25, and may generate the first advice according to the eating habit classification when the eating habit classification of the target user is determined by the eating habit classification unit 23. The first advice generating unit 26 supplies the generated first advice to the presentation information generating unit 29.

[0071] The implementation result acquisition unit 27 acquires the implementation results of the target user for the record items proposed in the first advice during the implementation period described above. Hereinafter, this implementation result will be referred to as the "implementation result during the period." The record items proposed in the first advice are, for example, lower-level record items proposed for improvement (see advice S2 in FIG. 4). The implementation result acquisition unit 27 can tally up the record information for the implementation period, and record items for which the number of positive answers for each record item is equal to or greater than a preset value can be regarded as record items that the target user was able to implement, and record items for which the number of positive answers is less than the preset value can be regarded as record items that the target user was not able to implement. The implementation result acquisition unit 27 supplies the implementation results during this period to the second advice generation unit 28.

[0072] The implementation result acquisition unit 27 may further acquire an index indicating the accumulation of body fat of the target user or an index causing the onset of lifestyle-related diseases, measured after the implementation period. Hereinafter, this index is referred to as a "post-period index". The post-period index is the above-mentioned bioindicator, and is one or more of weight, body fat percentage, waist circumference, VFA, FBS, TG, HDL-C, SBP, DBP, etc. The implementation result acquisition unit 27 can acquire these bioindicators through input by the target user or communication with a bioindicator measuring device such as a weighing scale. The implementation result acquisition unit 27 supplies the acquired post-period index to the second advice generation unit 28.

[0073] After the implementation period ends, the second advice generating unit 28 generates second advice that presents the record items proposed in the first advice to the target user with a priority according to the implementation result during the period and suggests implementing the items selected by the target user. The second advice is also advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease. Specifically, the second advice generating unit 28 can determine a priority for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease for the record items that the target user was able to implement in the implementation result during the period, and generate the second advice according to this priority.

[0074] Table 12 below shows factors that contribute highly to body fat reduction by eating habit classification after the implementation period, and was identified by factor analysis of the questionnaire results and weight change after the implementation period. In each eating habit classification, factor 1 has the highest contribution rate, and the contribution rate gradually decreases toward factor 5. The second advice generation unit 28 can set record items that correspond to factors with high contribution rates as record items with high priority for eliminating the causes of body fat accumulation or lifestyle-related disease.

[0075] [Table 12]

[0076] The second advice generating unit 28 can assign a high priority to the record items proposed in the first advice that the target user was able to perform in the implementation results during the period. For example, for a specific target user classified as "low health consciousness or health consciousness," if the record items corresponding to factors 2, 4, and 5 are the record items that the target user was able to perform, the second advice generating unit 28 assigns priorities to factors 2, 4, and 5 in descending order of contribution rate. The second advice generating unit 28 also assigns priorities to the record items that the target user was not able to perform in the implementation results during the period in descending order of contribution rate, but assigns a lower priority than the record items that the target user was able to perform.

[0077] Furthermore, the second advice generating unit 28 can also assign a high priority to record items proposed in the first advice that the target user was unable to perform in the implementation results during the period. For example, for a specific target user classified as "low health consciousness or health consciousness," if the record items corresponding to factors 1 and 3 are record items that the target user was able to perform, the second advice generating unit 28 assigns priorities to factors 1 and 3 in descending order of contribution rate. The second advice generating unit 28 also assigns priorities to record items that the target user was able to perform in the implementation results during the period in descending order of contribution rate, but assigns a lower priority than record items that the target user was unable to perform.

[0078] Furthermore, the second advice generating unit 28 can also determine the priority of the items proposed in the first advice by the sum of the ranking of the items proposed in the first advice in order of the implementation rate of the target user and the ranking of the items proposed in the first advice in order of the contribution rate to body fat reduction or lifestyle-related disease prevention. The second advice generating unit 28 ranks the items proposed in the first advice in order of the implementation rate of the target user in the implementation results during the above-mentioned period. Also, the second advice generating unit 28 ranks the items proposed in the first advice in order of the contribution rate to body fat reduction or lifestyle-related disease prevention. The second advice generating unit 28 prioritizes each item proposed in the first advice in order of the smallest sum of these two rankings.

[0079] The second advice generating unit 28 assigns priorities to the record items proposed in the first advice and presents them to the target user. The user selects record items that the user intends to continue to implement from among the record items proposed in the first advice. At this time, the user can select record items that the user intends to continue to implement by referring to the priorities assigned according to the implementation results during the period. The second advice generating unit 28 can generate second advice that suggests implementing the record items selected by the target user. The second advice generating unit 28 supplies the generated second advice to the presentation information generating unit 29.

[0080] The second advice generating unit 28 can further generate second advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease based on the post-period index. For example, if the post-period index is weight, the second advice generating unit 28 can generate second advice according to the amount of weight change after the implementation period, and can generate advice to praise if weight is reduced and to encourage if weight is increased. In addition, the second advice generating unit 28 can generate second advice to praise if the post-period index has improved and to encourage if the post-period index has not improved.

[0081] The presentation information generating unit 29 generates presentation information including the first advice supplied from the first advice generating unit 26. The presentation information generating unit 29 can generate a presentation image including the generated presentation information and present the presentation information to the user. As shown in FIG. 5, the presentation information includes "advice" which is the first advice. The presentation information can also include the scores of each of the major categories of "quality of meals," "eating rhythm," and "low health consciousness or health consciousness" visually displayed with the number of stars, or the progress of the scores of each of the major categories visually displayed with a radar chart. The presentation information is not limited to those 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 input by the target user himself or proposed by the advice providing device 20.

[0082] Furthermore, the presentation information generating unit 29 generates presentation information including the second advice supplied from the second advice generating unit 28. The presentation information generating unit 29 can generate a presentation image including the generated presentation information and present the presentation information to the user. The presentation information generating unit 29 may also generate a presentation image including a transition of the after-period index represented by a graph or the like.

[0083] The advice providing system 100 has the above-mentioned 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 composed of a CPU (Central Processing Unit), a 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.

[0084] [Operation of the advice providing system] The operation of the advice providing system 100 will be described. As described above, the eating habit classification generation device 10 generates an eating habit classification based on the eating habit information and biomarker data of multiple users to be analyzed. The generation of the eating habit classification may be performed once when the advice providing system 100 is constructed, but may also be performed again after the construction.

[0085] The advice providing device 20 provides a body fat reduction or lifestyle-related disease prevention program to a target user. As shown in FIG. 6, the advice providing device 20 operates "before the program is implemented," "during the program is implemented," and "after the program is implemented." Note that the above-mentioned "implementation period" refers to the period "during the program is implemented." Table 13 below shows the contents of "input," "judgment," and "output" before, during, and after the program is implemented.

[0086] [Table 13]

[0087] Before the program is implemented, the provided-user determination unit 21 determines the provided-user (St11), as shown in Fig. 6. The provided-user determination unit 21 can receive input of physical information from the user and calculate specific biometric indicators, as shown in Table 13. Furthermore, the provided-user determination unit 21 can determine whether or not each user corresponds to a provided-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.

[0088] 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 the input of eating habit information from the target users as shown in Table 13, 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.

[0089] At this time, the first advice generating unit 26 may generate advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease according to the eating habit classification, and output the generated advice to each target user. Also, the presentation information generating unit 29 may predict the target user's physical information after the program is implemented from the physical information input by the target user, and output the predicted physical information to the target user.

[0090] During the execution of the program, the record information acquisition unit 24 acquires record information (see Table 5) (St14), and the evaluation unit 25 evaluates the record information (St15). The record information acquisition unit 24 accepts input of record information from the intended user as shown in Table 13. The evaluation unit 25 calculates the score of 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 nutrients taken.

[0091] Next, the first advice generating unit 26 generates first advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease according to the eating habit classification (St16), and the presentation information generating unit 29 generates presentation information including the first advice (St17). As shown in Fig. 5, the presentation information generating unit 29 generates presentation information including the first advice, the scores and time trends of the major items, the higher record items and the lower record items, etc., and outputs it to each target user.

[0092] The advice providing device 20 executes the operation "before program implementation" and then executes the operation "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 operation "during program implementation" once or multiple times during the program implementation period.

[0093] After the program is implemented, the implementation result acquisition unit 27 acquires the implementation result during the period (St18). Next, the second advice generation unit 28 prioritizes the record items proposed in the first advice based on the implementation result during the period and presents them to the target user, generating second advice proposing implementation of the record items selected by the target user (St19). Furthermore, the implementation result acquisition unit 27 acquires a post-period index (St18), and the second advice generation unit 28 may generate second advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease according to the post-period index (St19). The presentation information generation unit 29 generates presentation information including the second advice and the transition of the post-period index, and outputs it to each target user.

[0094] In this way, the advice providing system 100 provides advice for eliminating the causes of body fat accumulation or lifestyle-related disease for each target user. The above-mentioned operation of the advice providing system 100 is an example, and the advice providing system 100 may provide the target user with a first advice during the program and a second advice after the program. The classification of the target user's eating habits can be used for product recommendations, service provision and collaboration, future predictions (body type, lifestyle-related diseases, etc.), introductions to friends (community formation), and the like, in addition to providing advice.

[0095] [Effects of the advice provision system] As described above, in the advice providing system 100, the first advice generation unit 26 generates the first advice in accordance with the eating habit classification of the target user by the eating habit classification unit 23, so it is possible to provide each target user with first advice for effectively eliminating the causes of body fat accumulation or the causes of lifestyle-related disease.

[0096] At this time, the evaluation unit 25 evaluates the cause of body fat accumulation or the cause of lifestyle-related disease based on the record information on the dietary contents and activity contents input by the target user, and the first advice generation unit 26 generates the first advice according to the evaluation result, so that it is possible to provide the first advice in accordance with the dietary contents and activity contents of the target user. In addition, since the record information acquisition unit 24 presents the target user with a group of record items including record items according to the eating habit classification result, it is possible to acquire record information valid for each eating habit classification and use it for generating the first advice.

[0097] Furthermore, the first advice generation unit 26 generates first advice using standard phrases prepared for each recorded item, thereby making it possible to automatically generate first advice in accordance with the evaluation results of the recorded items. By preparing standard phrases for each season, it becomes possible to provide first advice appropriate for each season, such as seasonal ingredients.

[0098] Moreover, the evaluation unit 25 evaluates the intake amount of nutrients from the recorded information, and the first advice generation unit 26 generates the first advice according to the evaluation result, so that the first advice can be provided according to the excess or deficiency of nutrients taken by the user. As nutrients, protein, omega-3 fatty acid, and dietary fiber have a large effect on body fat accumulation and the incidence of lifestyle-related diseases, so advice regarding these nutrients is particularly effective.

[0099] 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 the first advice to users who are in high need of advice regarding body fat accumulation and lifestyle-related disease.

[0100] Regarding generation of eating habit classification, the eating habit classification generating unit 12 can generate multiple eating habit classifications by factor by performing factor analysis on the eating habit information and data related to biomarkers, and it becomes possible to appropriately classify the eating habits of the target user by prioritizing according to the contribution rate obtained by the factor analysis. At this time, the eating habit classification generating unit 12 calculates the factor loading of the question items presented to the target user, and selects each question item for each factor according to the factor loading, thereby generating an eating habit classification based on the contents of the selected question items.

[0101] In addition, the second advice generating unit 28 prioritizes the record items according to the implementation results during the period, i.e., the implementation results of each target user for the record items proposed in the first advice, and presents them to the target user, proposing implementation of the items selected by the target user. This allows the target user to select items that are likely to be implemented even after the program ends while referring to the priorities of the implementation results during the period, and to receive the second advice for the selected items, making it easier to maintain the effects of suppressing body fat accumulation and lifestyle-related disease after the implementation period.

[0102] 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.

[0103] [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.

[0104] The advice providing system 200 is a system that provides a user with advice on reducing body fat or preventing lifestyle-related diseases. "Prevention of lifestyle-related diseases" includes reducing hypertension, hyperglycemia, and dyslipidemia, and eliminating obesity.

[0105] The advice providing device 50 is an information processing device that provides a user 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. 7 , 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, a first advice generating unit 55, an implementation result acquiring unit 56, a second advice generating unit 57, and a presentation information generating unit 58.

[0106] The eating habit information acquiring unit 51 acquires the eating habit information of the target user. The eating habit information is answers to questions about eating habits as shown in Fig. 8. The eating habit information acquiring unit 51 may further acquire personal data that affects the eating habits of the target user, such as the gender, age, place of residence, place of origin, occupation, and income.

[0107] The eating habit classification unit 52 classifies the eating habits of the target user into one of the eating habit classifications based on the eating habit information. As shown in Fig. 8, the eating habit classification unit 52 can classify the eating habits of the target user into one of "low health consciousness or health consciousness", "between meals", "late-night snacks", "quick eating", "lack of exercise or exercise", "late-night meals or late dinner", and "low basal metabolism" based on the answers to seven questions. Note that Fig. 8 shows the questions and eating habit classifications regarding visceral fat accumulation, and different questions and eating habit classifications can be used for other symptoms.

[0108] After classifying the target user's eating habits, the eating habit classification unit 52 can generate a presentation screen for the eating habit classification as shown in Fig. 3, and visually display the classification result. The eating habit classification unit 52 supplies the target user's eating habit classification to the record information acquisition unit 53.

[0109] The record information acquisition unit 53 acquires record information, which is information on the meal contents and activity contents recorded by the target user. The record information acquisition unit 53 first generates a record item group for each target user, including record items according to the eating habit classification of the target user. The following Table 14 is an example of a record item group generated by the record information acquisition unit 53.

[0110] [Table 14]

[0111] As shown in Table 14, the record item group generated by the record information acquisition unit 53 includes "eating habit classification items." The "eating habit classification items" are record items for each eating habit classification classified by the eating habit classification unit 52, and Table 14 shows an example of a record item for "low health consciousness or health consciousness." The record items for each eating habit classification have different contents for each eating habit classification.

[0112] Furthermore, the group of recording items may include "nutrient intake", as shown in Table 14. "Nutrient intake" is a recording item regarding 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 "catechin-containing food intake", "meal amount", and "body weight", as shown in Table 14. Items in the group of recording items other than "items by dietary habit classification" are common to each target user. Hereinafter, "quality of life", "catechin-containing food intake", "meal quality", and "meal amount" are each referred to as a "major item".

[0113] The record information acquisition unit 53 presents the generated group of record items to the provided user. The provided user inputs the record contents, and the record information acquisition unit 53 acquires the record information. The provided user inputs the record contents on a daily or weekly basis. The record information acquisition unit 53 supplies the acquired record information to the evaluation unit 54.

[0114] The evaluation unit 54 evaluates the cause of body fat accumulation or the cause of lifestyle-related disease for each target user based on the record information. The evaluation unit 54 can compile record information for each target user for a certain evaluation period, for example, one week, and score the record item by taking the average number of positive answers for each record item. For example, the evaluation unit 54 takes the average number of "◯"s for the record item over one week as the score for the record item.

[0115] The evaluation unit 54 can extract the record items with the highest scores (hereinafter, the record items) and the record items with the lowest scores (hereinafter, the lower record items) from among the record items. The higher record items are, for example, the record items with the top 1st to 3rd highest scores, and the lower record items are, for example, the record items with the bottom 1st to 3rd lowest scores. The evaluation unit 54 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.

[0116] 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 result to the first advice generation unit 55.

[0117] The first advice generating unit 55 generates first advice for eliminating the causes of body fat accumulation or the causes of lifestyle-related disease according to the result of the classification of eating habits. The first advice generating unit 55 can generate the first advice according to the evaluation result of the causes of body fat accumulation or the causes of lifestyle-related disease by the evaluation unit 54, i.e., the score of each record item.

[0118] Specifically, the first advice generating unit 55 can generate advice that praises higher-ranked recorded items and suggests improvements to lower-ranked recorded items. The first advice generating unit 55 can also select record items that can be improved from the lower-ranked recorded items with high scores, and generate advice that suggests improvements to the record items that can be improved. The first advice generating unit 55 can select record items that can be improved from the lower-ranked recorded items by excluding items with low scores in each recording. Furthermore, the first advice generating unit 55 can generate first advice that encourages the eating habits of the target user from the next time onwards.

[0119] Furthermore, the first advice generating unit 55 can generate first advice for each target user using standard phrases prepared for each eating habit classification. The following Tables 15 and 16 are tables showing standard phrases for each eating habit classification. The first advice generating unit 55 can generate first advice including the standard phrases in Tables 15 and 16 according to the eating habit classification of the target user.

[0120] [Table 15]

[0121] [Table 16]

[0122] Furthermore, the first advice generating unit 55 can generate first advice by using a template prepared for each record item as shown in the following Table 17. The first advice generating unit 55 supplies the generated first advice to the presentation information generating unit 58.

[0123] [Table 17]

[0124] In addition, the "catechin-containing food" in Table 17 is preferably a food containing catechin as an active ingredient, and the catechin content in the catechin-containing food is preferably 0.03% by mass or more, more preferably 0.07% by mass or more, even more preferably 0.10% by mass or more, and even more preferably 0.13% by mass or more. These may be replaced with the total intake per day. As the catechin-containing food, for example, a packaged beverage disclosed in JP-A-2006-77026 can be used. These may be replaced with the intake per day.

[0125] Furthermore, the first advice generating unit 55 can generate the first advice according to the evaluation result regarding the nutrient intake of the target user. Regarding the intake of "protein", "omega-3 fatty acid" and "dietary fiber" estimated by the evaluation unit 54, the first advice generating unit 55 can generate the first advice praising the intake of nutrients with a high intake and encouraging the intake of nutrients with a low intake.

[0126] Furthermore, the first advice generating unit 55 can generate first advice praising the intake of nutrients with high "protein / fat ratio," "omega-3 fatty acid / fat ratio," and "dietary fiber / sugar or carbohydrate ratio" estimated by the evaluating unit 54, and encouraging the intake of nutrients with low "protein / fat ratio," "omega-3 fatty acid / fat ratio," and "dietary fiber / sugar or carbohydrate ratio" estimated by the evaluating unit 54. In this case, the first advice generating unit 55 may generate advice suggesting the intake of functional foods, foods for specified health uses, and foods containing functional ingredients. Note that the first advice generating unit 55 does not necessarily have to generate advice regarding the amount of nutrient intake.

[0127] Although the method of generating the first advice by the first advice generating unit 55 is not particularly limited, the first advice generating unit 55 can use the above-mentioned template, accumulate obtained data, perform machine learning, and generate the first advice using the learning result. Furthermore, the first advice generating unit 55 can generate the first advice based on nutritional guidance by an expert such as a registered dietitian.

[0128] In addition, the first advice generating unit 55 may generate first advice according to the eating habit classification for each target user. The first advice generating unit 55 is not limited to generating first advice according to the evaluation result of the recorded information by the evaluation unit 54, and may generate first advice according to the eating habit classification when the eating habit classification of the target user is determined by the eating habit classification unit 52. The first advice generating unit 55 supplies the generated first advice to the presentation information generating unit 58.

[0129] The implementation result acquisition unit 56 acquires the implementation results of the target user for the record items proposed in the first advice during the implementation period described above. Hereinafter, this implementation result will be referred to as the "implementation result during the period." The record items proposed in the first advice are, for example, lower-level record items proposed for improvement. The implementation result acquisition unit 56 can tally up the record information for the implementation period, and record items for which the number of positive answers for each record item is equal to or greater than a preset value can be regarded as record items that the target user was able to implement, and record items for which the number of positive answers is less than the preset value can be regarded as record items that the target user was not able to implement. The implementation result acquisition unit 56 supplies the implementation results during this period to the second advice generation unit 57.

[0130] The implementation result acquisition unit 56 may further acquire an index indicating the accumulation of body fat of the target user or an index causing the onset of lifestyle-related diseases, measured after the implementation period. Hereinafter, this index is referred to as a "post-period index". The post-period index is the above-mentioned bioindicator, and is one or more of weight, body fat percentage, waist circumference, VFA, FBS, TG, HDL-C, SBP, DBP, etc. The implementation result acquisition unit 56 can acquire these bioindicators through input by the target user or communication with a bioindicator measuring device such as a weighing scale. The implementation result acquisition unit 56 supplies the acquired post-period index to the second advice generation unit 57.

[0131] As in the first embodiment, after the implementation period ends, the second advice generating unit 57 generates second advice that prioritizes the record items proposed in the first advice according to the implementation results during the period and presents them to the target user, and suggests implementing the items selected by the target user. The second advice is also advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease.

[0132] Specifically, the second advice generating unit 57 assigns a priority to the record items according to the contribution rate to the body fat reduction, and at this time, it can assign a high priority to the record items that the target user was able to perform in the implementation result during the period, and assign a lower priority to the record items that the target user was not able to perform than the record items that the target user was able to perform. Also, the second advice generating unit 57 can assign a high priority to the record items that the target user was not able to perform in the implementation result during the period, and assign a lower priority to the record items that the target user was able to perform than the record items that the target user was not able to perform.

[0133] The second advice generating unit 57 assigns priorities to the record items proposed in the first advice and presents them to the target user. The user selects record items that the user intends to continue to implement from among the record items proposed in the first advice. At this time, the user can select record items that the user intends to continue to implement by referring to the priorities assigned according to the implementation results during the period. The second advice generating unit 57 can generate second advice that suggests implementing the record items selected by the target user. The second advice generating unit 57 supplies the generated second advice to the presentation information generating unit 58.

[0134] The second advice generating unit 57 can further generate second advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease based on the post-period index. For example, if the post-period index is weight, the second advice generating unit 57 can generate second advice according to the amount of weight change after the implementation period, and can generate advice to praise if weight is reduced and to encourage if weight is increased. In addition, the second advice generating unit 57 can generate second advice to praise if the post-period index has improved and to encourage if the post-period index has not improved.

[0135] The presentation information generating unit 58 generates presentation information including the first advice supplied from the first advice generating unit 55. The presentation information generating unit 58 can generate a presentation image including the generated presentation information and present the presentation information to the user. As shown in FIG. 9, the presentation information can include advice for each major category. In the presentation information, the content of the "quality of life" column is different for each eating habit classification shown in Tables 15 and 16, and the content of the other columns is common content shown in Table 17. In addition to advice, the presentation information may include scores for each major category and changes over time. The presentation information is not limited to those 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 input by the target user himself or proposed by the advice providing device 50.

[0136] Furthermore, the presentation information generating unit 58 generates presentation information including the second advice supplied from the second advice generating unit 57. The presentation information generating unit 58 generates a presentation image including the generated presentation information, and can present the presentation information to the user. The presentation information generating unit 58 may also generate a presentation image including a transition of the after-period index represented by a graph or the like.

[0137] The advice providing system 200 has the above-mentioned 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 composed of a CPU (Central Processing Unit), a 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.

[0138] [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", "during the program is implemented", and "after the program is implemented" as shown in FIG. 10. Note that the above "implementation period" refers to the period "during the program is implemented". Table 18 below shows the contents of "input", "judgment", and "output" before and during the program.

[0139] [Table 18]

[0140] Before the program is implemented, as shown in FIG. 10, the eating habit information acquisition unit 51 acquires the eating habit information of the target user (St21), and the eating habit classification unit 52 determines the eating habit classification of the target user (St22). The eating habit information acquisition unit 51 accepts the input of eating habit information from the target user as shown in Table 18, and the eating habit classification unit 52 determines the eating habit classification of the target user. 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 first advice generation unit 55 may generate first advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease according to the eating habit classification, and output the generated first advice to each target user.

[0141] During the execution of the program, the record information acquisition unit 53 acquires record information (see Table 14) (St23), and the evaluation unit 54 evaluates the record information (St24). The record information acquisition unit 53 accepts input of record information from the intended user as shown in Table 18. The evaluation unit 25 can calculate the score of each record item and each major item, and extract the record items and sub-record items. The evaluation unit 25 may also evaluate the amount of nutrient intake.

[0142] Next, the first advice generating unit 55 generates advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease according to the eating habit classification (St25), and the presentation information generating unit 58 generates presentation information including the advice (St26). As shown in Fig. 9, the presentation information generating unit 58 generates the presentation information including the advice and outputs it to each target user.

[0143] The advice providing device 50 executes the operation "before the program is implemented" and then executes the operation "during the program is implemented" 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 operation "during the program is implemented" once or multiple times during the program implementation period.

[0144] After the program is implemented, the implementation result acquisition unit 56 acquires the implementation result during the period (St27). Next, the second advice generation unit 57 prioritizes the items proposed in the first advice based on the implementation result during the period and presents them to the target user, generating second advice proposing implementation of the items selected by the target user (St28). Furthermore, the implementation result acquisition unit 56 acquires a post-period index (St27), and the second advice generation unit 57 may generate second advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease according to the post-period index (St28). The presentation information generation unit 58 generates presentation information including the second advice and the transition of the post-period index, and outputs it to each target user.

[0145] In this way, the advice providing system 200 provides advice for eliminating the cause of body fat accumulation or the cause of lifestyle-related disease to each target user. 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 the first advice during the implementation of the program and the second advice after the implementation of the program.

[0146] The dietary habit classification of the target user can be used for product recommendations, service provision and collaboration, future predictions (body type, 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.

[0147] Although the embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and it goes without saying that various modifications can be made without departing from the spirit of the present invention. [Explanation of symbols]

[0148] 20, 50...Advice Providing Device 26, 55…First advice generating unit 27, 56…Implementation result acquisition section 28, 57…Second advice generation unit

Claims

1. An advice providing device that provides advice to a target user for reducing body fat or preventing lifestyle-related diseases, a first advice generating unit that generates a first advice for eliminating a cause of body fat accumulation or a cause of lifestyle-related disease during a predetermined period; an implementation result acquisition unit that acquires an implementation result of the target user for the item proposed in the first advice during the period; a second advice generating unit that generates second advice for eliminating a cause of body fat accumulation or a cause of lifestyle-related disease, after the end of the period, by prioritizing the items proposed in the first advice according to the implementation result and presenting the same to the target user and proposing implementation of the items selected by the target user; An advice providing device comprising:

2. The second advice generating unit determines the priority of the items proposed in the first advice by summing the ranking of the items proposed in the first advice in descending order of implementation rate of the provision target user and the ranking of the items proposed in the first advice in descending order of contribution rate to body fat reduction or lifestyle-related disease prevention. The advice providing device according to claim 1 .

3. The implementation results are items that the target user was able to implement during the period among the items proposed in the first advice. The advice providing device according to claim 1 .

4. The implementation result is an item that the target user was unable to implement during the period among the items proposed in the first advice. The advice providing device according to claim 1 .

5. The second advice generating unit assigns a high priority to an item that the provision target user was able to implement, and assigns a low priority to an item that the provision target user was unable to implement. The advice providing device according to claim 3 .

6. The second advice generating unit assigns a high priority to an item that the target user was unable to perform, and assigns a low priority to an item that the target user was able to perform. The advice providing device according to claim 4.

7. The second advice generating unit assigns the priority to the items that the provision target user has been able to implement in order of the contribution rate to a body fat reduction. The advice providing device according to claim 5 .

8. The second advice generating unit assigns the priorities to the items that the provision target user was unable to carry out in order of the contribution rate to a body fat reduction. The advice providing device according to claim 6.

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: generating a first advice for eliminating a cause of body fat accumulation or a cause of lifestyle-related disease in a predetermined period of time; an implementation result acquisition unit that acquires an implementation result of the target user for the item proposed in the first advice during the period; a step of generating second advice for eliminating a cause of body fat accumulation or a cause of lifestyle-related disease, after the end of the period, prioritizing the items proposed in the first advice according to the implementation result and presenting the advice to the target user, and proposing implementation of the items selected by the target user; An advice providing program that causes the

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