Program providing device

Abstract

To provide a program providing device that provides a program for reducing body fat or preventing lifestyle-related illnesses in a form allowing a user to easily continue the program.SOLUTION: A program providing device according to one form of the present invention provides a program for reducing body fat or preventing lifestyle-related illnesses to a provision target user, and the program providing device comprises: a recorded information acquisition unit that presents a recorded item group including a plurality of recorded items to the provision target user, and acquires recorded information that is information related to the details of meals and the details of activities recorded by the provision target user in terms of the recorded item group; a continuation intention acquisition unit that acquires a continuation intention of the provision target user for the recorded items in the recorded item group; and a recorded item group generation unit that generates a new recorded item group including recorded items extracted on the basis of the recorded information and the continuation intention.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a program providing device that provides a program 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 predetermined condition, a suggestion for improving the lifestyle is made according to the condition. Here, even if the user improves the lifestyle in response to the suggestion during the period in which the suggestion is made, the user often stops improving the lifestyle when the period in which the suggestion is made ends.

[0005] The present invention relates to a program providing device that provides a program for reducing body fat or preventing lifestyle-related diseases in a form that makes it easy for a user to continuously practice the program. [Means for solving the problem]

[0006] A program providing device according to one embodiment of the present invention is a program providing device that provides a program for reducing body fat or preventing lifestyle-related diseases to a target user, a record information acquisition unit that presents a record item group including a plurality of record items to a target user and acquires record information that is information regarding meal details and activity details recorded by the target user for the record item group; A continuation intention acquisition unit that acquires a continuation intention of a provision target user for each record item of the record item group; and a record item group generation unit that generates a new record item group including the record items extracted based on the record information and the intention to continue.

[0007] A program providing device according to one embodiment of the present invention is a program providing device that provides a program for reducing body fat or preventing lifestyle-related diseases to a target user, the program providing device including: A step of presenting a record item group including a plurality of record items to a target user and acquiring record information which is information regarding meal contents and activity contents recorded by the target user for the record item group; A step of acquiring a continuation intention of a target user for each record item of the record item group; and generating a new group of record items including record items selected based on the record information and the intention to continue.

[0008] A program providing method according to one embodiment of the present invention is a program providing method executed by an information processing device to provide a program for reducing body fat or preventing lifestyle-related diseases to a target user, the program providing method comprising the steps of: Present a record item group including a plurality of record items to a target user, and acquire record information which is information regarding meal contents and activity contents recorded by the target user for the record item group; Obtaining a continuation intention of a target user for each record item of the group of record items; A new group of record items is generated including record items selected based on the record information and the continuation intention. Effect of the Invention

[0009] According to the present invention, it is possible to provide a program for reducing body fat or preventing lifestyle-related diseases in a form that allows the user to easily practice the program on an ongoing basis. [Brief description 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. [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] 11 is an example of a question about a continuation intention presented to a user by the advice providing device. [Figure 7] 13 is an example of continuation intentions by multiple users. [Figure 8] 6 is an example of a group of record items for each user, which are presented to the user by the advice providing device. [Figure 9] 5 is a flowchart showing an operation of the advice providing device. [Figure 10] FIG. 11 is a block diagram showing a configuration of an advice providing system according to a second embodiment of the present invention. [Figure 11] 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 12] 4 is an example of advice presented to a user from the advice providing device. [Figure 13] 5 is a flowchart showing an operation of the advice providing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] 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.

[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 a program providing system, the advice providing system described below can also be read as the program providing 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 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.

[0015] 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).

[0016] 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.

[0017] [Table 1]

[0018] 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.

[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 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.

[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, 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

[0021] 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.

[0022] 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)."

[0023] 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.

[0024] 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.

[0025] [Table 2]

[0026] [Table 3]

[0027] 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.

[0028] 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.

[0029] [Table 4]

[0030] 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."

[0031] 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.

[0032] The advice providing device 20 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." 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, an advice generation unit 26, a presentation information generation unit 27, a continuation intention acquisition unit 30, and a record item group generation unit 31, as shown in FIG.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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 28 and an eating habit category decision unit 29.

[0038] The eating habit classification determination unit 28 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 28 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".

[0039] 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 28 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 28 determines that the target user's eating habit corresponds to "snacks". Similarly, the eating habit classification determination unit 28 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, which is 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 28 determines that the eating habit of the target user falls under "late night meal or late dinner".

[0040] Here, if the answer scores meet the conditions in multiple eating habit classifications, the eating habit classification determination unit 28 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 28 determines that the target user's eating habits do not fall into any of the eating habit classifications.

[0041] As another method, the eating habit classification determination unit 28 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 28 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 28 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.

[0042] 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 28 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 28 determines that the target user's eating habits do not fall into any eating habit classification.

[0043] The eating habit classification determination unit 29 determines the eating habit classification of the target user upon receiving the determination result by the eating habit classification determination unit 28. When the eating habit classification determination unit 28 determines that the target user's eating habit falls into one eating habit classification, the eating habit classification determination unit 29 determines that the target user's eating habit falls into that eating habit classification.

[0044] Furthermore, when the eating habit classification determination unit 28 determines that the eating habit of the target user falls under a plurality of eating habit classifications, the eating habit classification determination unit 29 determines that the eating habit of the target user falls under 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 28 to fall under "snacks" and "lack of exercise or exercise", the eating habit classification determination unit 29 determines that the eating habit of the target user falls under the eating habit classification of "snacks" with the higher priority.

[0045] Furthermore, when the eating habit classification determination unit 28 determines that the target user's eating habits do not fall into any of the eating habit classifications, the eating habit classification determination unit 29 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 29 classifies the target user's eating habits into "low 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 low basal metabolism.

[0046] 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.

[0047] 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.

[0048] [Table 5]

[0049] [Table 6]

[0050] 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.

[0051] 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.

[0052] [Table 7]

[0053] 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."

[0054] The record information acquisition unit 24 presents the generated group of record items to the target user. The target user inputs the record contents, and the record information acquisition unit 24 acquires the record information. The target user inputs the record contents on a daily or weekly basis. Hereinafter, the period during which the target user inputs the record contents will be referred to as the "program implementation period." The record information acquisition unit 24 supplies the acquired record information to the evaluation unit 25.

[0055] 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.

[0056] [Table 8]

[0057] [Table 9]

[0058] The evaluation unit 25 can tally up the record information for each target user for a certain evaluation period during the program implementation period, for example, for 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" for "low health consciousness or health consciousness," the evaluation unit 25 determines the score for that record item by the average number of "o"s for 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 for one week.

[0059] The evaluation unit 25 can extract the record items with the highest scores (hereinafter, higher record items) and the record items with the lowest scores (hereinafter, 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.

[0060] 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".

[0061] 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 advice generation unit 26.

[0062] The advice generating unit 26 generates 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 advice generating unit 26 can generate advice according to the evaluation result of the causes of body fat accumulation or the causes of lifestyle-related disease by the evaluation unit 25, i.e., the score of each record item.

[0063] Specifically, the advice generating unit 26 can generate advice that praises higher-ranked recorded items and suggests improvements to lower-ranked recorded items. The advice generating unit 26 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 advice generating unit 26 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 advice generating unit 26 can generate advice that encourages the eating habits of the target user from the next time onwards.

[0064] For example, as shown in FIG. 4, the advice generating unit 26 can generate advice including advice S1 praising higher-level recorded items in an evaluation period, advice S2 proposing improvements to lower-level recorded items, and advice S3 offering encouragement for the next evaluation period.

[0065] The advice generating unit 26 can also generate advice using a standard phrase prepared for each record item. Tables 10 and 11 below are examples of standard phrases for each record item. The advice generating unit 26 can generate advice including 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 the menu is preferably something that can be eaten without much effort. The standard phrases for 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 advice generating unit 26 can generate advice according to the season at the time of advice generation by using standard phrases prepared for each season.

[0066] [Table 10]

[0067] [Table 11]

[0068] The advice generating unit 26 can generate advice according to the evaluation result regarding the nutrient intake of the target user. The advice generating unit 26 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 acid" and "dietary fiber" estimated by the evaluation unit 25. Furthermore, the 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".

[0069] Furthermore, the advice generating unit 26 can generate 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 advice generating unit 26 may generate advice suggesting the intake of functional foods, foods for specified health uses, and foods containing functional ingredients. Note that the advice generating unit 26 does not necessarily have to generate advice regarding the amount of nutrient intake.

[0070] The method of generating advice by the advice generating unit 26 is not particularly limited, but in addition to using the above-mentioned fixed phrases, the advice generating unit 26 can accumulate obtained data, perform machine learning, and generate advice using the learning results. In addition, the advice generating unit 26 can generate advice based on nutritional guidance by a specialist such as a registered dietitian.

[0071] Besides, the advice generating unit 26 may generate advice according to the eating habit classification for each target user. The advice generating unit 26 is not limited to generating advice according to the evaluation result of the recorded information by the evaluation unit 25, and can generate 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 advice generating unit 26 supplies the generated advice to the presentation information generating unit 27.

[0072] The presentation information generating unit 27 generates presentation information including the advice supplied from the advice generating unit 26. The presentation information generating unit 27 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". The presentation information can also include the scores of each of the major categories of "quality of food," "eating rhythm," and "low health consciousness or health consciousness" visually displayed with the number of stars, or the progress of the scores of each major category 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.

[0073] The continuation intention acquisition unit 30 acquires the continuation intention of each provision target user for each record item of the record item group. The continuation intention acquisition unit 30 acquires the continuation intention of the provision target user for each record item shown in Table 5 and Table 6. The continuation intention acquisition unit 30 asks the provision target user whether or not there is a continuation intention for each record item as shown in FIG. 6, and can acquire the continuation intention of each provision target user based on the answer.

[0074] The continuation intention acquisition unit 30 acquires the continuation intention of the target user after the program implementation period ends. FIG. 7 is an example of the continuation intention of a plurality of target users for each record item, and is an example of the continuation intention of 56 target users after the program implementation period of four weeks. As shown in the figure, the continuation intention of the target users differs depending on the record item. The continuation intention acquisition unit 30 supplies the acquired continuation intention of each target user to the record item group generation unit 31.

[0075] The record item group generating unit 31 extracts record items based on the record information and the intention to continue, and generates a new record item group including the record items. Specifically, the record item group generating unit 31 specifies the "degree of implementation," which is the implementation ratio of each record item during the program implementation period. For example, if the program implementation period is four weeks (28 days) and the record content is input once a day, the degree of implementation of one record item (see Table 6), such as "take catechin-containing foods," is the ratio of the number of "o"s recorded to the number of "o"s (28) for four weeks. If the number of "o"s is 21, the degree of implementation is 75%, and if the number of "o"s is 14, the degree of implementation is 50%. Note that if the record item is a numerical value, such as "measure weight," if a numerical value is recorded, it is treated as "o," and the ratio of the number is the degree of implementation.

[0076] In addition, the degree of practice of a record item that includes multiple subitems, such as "Pay attention to meal times," can be calculated by the percentage of the total number of "O"s. For example, "Pay attention to meal times" includes four subitems, such as "Eat breakfast within an hour of waking up," so the number of "O"s for four weeks is 112 (4 items x 28 days). If the total number of "O"s for these four record items during the program implementation period is 84, the degree of practice of "Pay attention to meal times" is 75%.

[0077] The record item group generating unit 31 extracts record items whose degree of practice is equal to or greater than a predetermined value and whose target user has an intention to continue. In other words, the record item group generating unit 31 does not extract record items whose degree of practice is equal to or greater than a predetermined value but whose target user does not have an intention to continue, and does not extract record items whose degree of practice is less than a predetermined value even if the target user has an intention to continue. The predetermined value is, for example, 80%. Hereinafter, record items whose degree of practice is equal to or greater than a predetermined value and whose target user has an intention to continue extracted by the record item group generating unit 31 are referred to as "continuation record items".

[0078] FIG. 8 shows an example of continuation record items for each target user extracted by the record item group generation unit 31, with continuation record items shown in black text and other record items shown in white text. As shown in the figure, different continuation record items are extracted for each target user. In addition, the number of continuation record items extracted for each target user also differs. The record item group generation unit 31 generates a record item group including the extracted continuation record items (hereinafter, continuation record item group) and presents it to the target user.

[0079] 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.

[0080] [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.

[0081] 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," "during the program is implemented," and "at the end of the program" as shown in Fig. 9. Table 12 below shows the contents of "input," "determination," and "output" before the program is implemented, during the program, and at the end of the program.

[0082] [Table 12]

[0083] Before the program is implemented, as shown in Fig. 9, the target user determination unit 21 determines the target users (St11). The target user determination unit 21 can receive input of physical information from the user and calculate specific biometric indicators as shown in Table 12. Furthermore, the target user determination unit 21 can determine whether or not each user corresponds to a target user for which the program is to be implemented based on the specific biometric indicators, and output to each user whether or not the user is a target for the program to be implemented.

[0084] 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 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.

[0085] At this time, the 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 27 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.

[0086] 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 12. The evaluation unit 25 calculates the score of each record item and each major item, and extracts the upper record items and lower record items. The evaluation unit 25 may also evaluate the amount of nutrients taken.

[0087] Next, the advice generating unit 26 generates 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 27 generates presentation information including the advice (St17). As shown in Fig. 5, the presentation information generating unit 27 generates presentation information including advice, major item scores and time trends, upper record items and lower record items, etc., and outputs it to each target user.

[0088] At the end of the program, the continuation intention acquisition unit 30 acquires the target user's intention to continue for each record item (St18), and the record item group generation unit 31 extracts continuation record items based on the record information for the program implementation period and the intention to continue (St19). The record item group generation unit 31 can extract record items whose practice level is equal to or greater than a predetermined value and whose target user has an intention to continue as continuation record items. The record item group generation unit 31 provides the target user with a continuation record item group including the continuation record items.

[0089] 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, four weeks, and the advice providing device 20 executes the operation "during program implementation" once or multiple times within the program implementation period.

[0090] 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-mentioned operation of the advice providing system 100 is an example, and the advice providing system 100 may provide the target user with a group of continuing record items at the end of the program. 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., in addition to providing advice.

[0091] [Effects of the advice provision system] As described above, in the advice providing system 100, the advice generation unit 26 generates 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 effective advice for eliminating the causes of body fat accumulation or the causes of lifestyle-related disease for each target user.

[0092] 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 advice generation unit 26 generates advice according to the evaluation result, so that advice in accordance with the dietary contents and activity contents of the target user can be provided. In addition, 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, so that it is possible to acquire record information valid for each dietary habit classification and use it for advice generation.

[0093] Furthermore, the advice generating unit 26 generates advice using standard phrases prepared for each record item, thereby making it possible to automatically generate advice according to the evaluation results of the record items. By preparing standard phrases for each season, it becomes possible to provide advice appropriate for each season, such as advice about seasonal ingredients.

[0094] In addition, the evaluation unit 25 evaluates the intake of nutrients from the recorded information, and the advice generation unit 26 generates advice according to the evaluation result, so that advice can be provided 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 effect on body fat accumulation and the incidence of lifestyle-related diseases, so advice regarding these nutrients is particularly effective.

[0095] 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.

[0096] 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.

[0097] Additionally, in the advice providing system 100, the record item group generating unit 31 extracts continuation record items based on the record information during the program implementation period and the intention to continue. The record item group generating unit 31 extracts continuation record items based on the record information that is the implementation result by the target user and the intention to continue of the target user, so that the continuation record items are record items that the target user can easily continue to implement even after the program has ended. Therefore, the target user can continue to practice the record items even after the program has ended, and achieve body fat reduction and prevention of lifestyle-related diseases.

[0098] If the record item group generating unit 31 extracts the continuation record items based only on the intention to continue, it does not reflect whether the target user actually performed the record items, and it is unclear whether the target user will continue to perform the record items in the future. Also, even if the record item group generating unit 31 extracts the continuation record items based only on the recording information, there is a possibility that the target user performed the record items forcibly only during the program implementation period. Therefore, in this case, it is unclear whether the target user will continue to perform the record items.

[0099] In contrast, in the advice providing system 100, the record item group generating unit 31 extracts continuation record items based on the record information and the intention to continue, so that the results of implementing the actual record items and the intention of the target user are reflected in the extraction of continuation record items. Therefore, the continuation record items can be record items that the target user can easily implement continuously, and it is possible to maintain the effect of the program for the target user over the long term.

[0100] 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 a program providing system, the advice providing system described below can also be read as the program providing system.

[0101] [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.

[0102] 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.

[0103] 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. 10 , 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 advice generating unit 55, a presentation information generating unit 56, a continuation intention acquiring unit 57, and a record item group generating unit 58.

[0104] 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. 11. 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.

[0105] 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. 11, 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. 11 shows the questions and eating habit classifications regarding visceral fat accumulation, and different questions and eating habit classifications can be used for other symptoms.

[0106] 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.

[0107] 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 13 is an example of a record item group generated by the record information acquisition unit 53.

[0108] [Table 13]

[0109] As shown in Table 13, 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 13 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.

[0110] Furthermore, the group of recording items may include "nutrient intake", as shown in Table 13. "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 13. 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".

[0111] The record information acquisition unit 53 presents the generated group of record items to the target user. The target user inputs the record contents, and the record information acquisition unit 53 acquires the record information. The target user inputs the record contents on a daily or weekly basis. Hereinafter, the period during which the target user inputs the record contents is referred to as the "program implementation period." The record information acquisition unit 53 supplies the acquired record information to the evaluation unit 54.

[0112] 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.

[0113] The evaluation unit 54 can extract the record items with the highest scores (hereinafter, higher record items) and the record items with the lowest scores (hereinafter, 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.

[0114] 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 advice generation unit 55.

[0115] The advice generating unit 55 generates 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 advice generating unit 55 can generate 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.

[0116] Specifically, the advice generating unit 55 can generate advice that praises higher-ranked record items and suggests improvements to lower-ranked record items. The advice generating unit 55 can also select record items that can be improved from the lower-ranked record items with high scores, and generate advice that suggests improvements to the record items that can be improved. The advice generating unit 55 can select record items that can be improved from the lower-ranked record items by excluding items with low scores in each record. Furthermore, the advice generating unit 55 can generate advice that encourages the eating habits of the target user from the next time onwards.

[0117] Furthermore, the advice generating unit 55 can generate solution advice for each target user using standard phrases prepared for each eating habit classification. The following Tables 14 and 15 are tables showing standard phrases for each eating habit classification. The advice generating unit 55 can generate solution advice including the standard phrases in Tables 14 and 15 according to the eating habit classification of the target user.

[0118] [Table 14]

[0119] [Table 15]

[0120] Furthermore, the advice generating unit 55 can generate advice using a template prepared for each record item as shown in the following Table 16. The advice generating unit 55 supplies the generated advice to the presentation information generating unit 56.

[0121] [Table 16]

[0122] In addition, the "catechin-containing food" in Table 16 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.

[0123] Furthermore, the advice generating unit 55 can generate 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 advice generating unit 55 can generate advice praising the intake of nutrients with a high intake and encouraging the intake of nutrients with a low intake.

[0124] Furthermore, the advice generating unit 55 can generate 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 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 advice generating unit 55 does not necessarily have to generate advice regarding the amount of nutrient intake.

[0125] The method of generating advice by the advice generating unit 55 is not particularly limited, but in addition to using the above-mentioned fixed phrases, the advice generating unit 55 can accumulate obtained data, perform machine learning, and generate advice using the learning results. In addition, the advice generating unit 55 can generate advice based on nutritional guidance by a specialist such as a registered dietitian.

[0126] Besides, the advice generating unit 55 may generate advice according to the eating habit classification for each target user. The advice generating unit 55 is not limited to generating advice according to the evaluation result of the recorded information by the evaluation unit 54, and can generate 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 advice generating unit 55 supplies the generated advice to the presentation information generating unit 56.

[0127] The presentation information generating unit 56 generates presentation information including the advice supplied from the advice generating unit 26. The presentation information generating unit 56 can generate a presentation image including the generated presentation information and present the presentation information to the user. As shown in FIG. 12, 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 14 and 15, and the content of the other columns is common content shown in Table 16. In addition to the 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.

[0128] The continuation intention acquisition unit 57 acquires the continuation intention of each provided target user for each record item of the record item group. The continuation intention acquisition unit 57 acquires the continuation intention of the provided target user for each record item shown in Table 13. The continuation intention acquisition unit 57 asks the provided target user whether or not he / she has an intention to continue for each record item (see FIG. 6), and can acquire the continuation intention of each provided target user based on the answer. The continuation intention acquisition unit 57 acquires the continuation intention of the provided target user after the program implementation period ends. The continuation intention acquisition unit 57 supplies the acquired continuation intention of each provided target user to the record item group generation unit 58.

[0129] The record item group generating unit 58 extracts record items based on the record information during the program implementation period and the intention to continue, and generates a new record item group including the record items. Specifically, similar to the first embodiment, the record item group generating unit 58 specifies the "degree of implementation," which is the implementation rate of each record item during the program implementation period.

[0130] The record item group generating unit 58 extracts record items whose practice level is equal to or greater than a predetermined value and whose target user has an intention to continue. In other words, the record item group generating unit 58 does not extract record items whose practice level is equal to or greater than a predetermined value but whose target user does not have an intention to continue, and does not extract record items whose practice level is less than a predetermined value even if the target user has an intention to continue. The predetermined value is, for example, 80%. Hereinafter, the record items whose practice level is equal to or greater than a predetermined value and whose target user has an intention to continue extracted by the record item group generating unit 58 are referred to as "continuation record items" (see FIG. 8). The record item group generating unit 31 generates a record item group including the extracted continuation record items (hereinafter, continuation record item group) and presents it to the target user.

[0131] 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.

[0132] [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 "at the end of the program" as shown in FIG. 13. Table 17 below shows the contents of "input," "determination," and "output" before the program is implemented, during the program, and at the end of the program.

[0133] [Table 17]

[0134] Before the program is implemented, as shown in FIG. 13, 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 17, 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 advice generation unit 55 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.

[0135] During the program execution, the record information acquisition unit 53 acquires record information (see Table 13) (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 17. The evaluation unit 25 can calculate the score of each record item and each major item, and extract the upper record items and lower record items. The evaluation unit 25 may also evaluate the amount of nutrients taken.

[0136] Next, the 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 56 generates presentation information including the advice (St26). As shown in Fig. 12, the presentation information generating unit 56 generates the presentation information including the advice and outputs it to each target user.

[0137] At the end of the program, the continuation intention acquisition unit 57 acquires the target user's intention to continue for each record item (St27), and the record item group generation unit 58 extracts continuation record items based on the record information and the continuation intention (St28). The record item group generation unit 58 can extract record items whose practice level is equal to or greater than a predetermined value and whose target user has an intention to continue as continuation record items. The record item group generation unit 58 presents the continuation record item group including the continuation record items to the target user.

[0138] The advice providing device 50 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, four weeks, and the advice providing device 50 executes the operation "during program implementation" once or multiple times within the program implementation period.

[0139] 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 for each target user. The above-described operation of the advice providing system 200 is an example, and the advice providing system 200 may be any system that provides a group of continued record items to the target user at the end of the program.

[0140] 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.

[0141] 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]

[0142] 20, 50...Advice Providing Device 24, 53…Record information acquisition section 30, 57…Continuation Intention Acquisition Department 31, 58...Record item group generation unit

Claims

1. A program provider that provides programs to target users for reducing body fat or preventing lifestyle-related diseases, A record information acquisition unit presents a set of record items containing multiple record items to the target user and acquires record information which is information about the meal content and activity content recorded by the target user for the said set of record items. A continuation intention acquisition unit that acquires the continuation intention of the target user for each record item in the aforementioned group of record items, A record item group generation unit generates a new set of record items that includes the record information and the record items extracted based on the intention to continue. A program-providing device equipped with the following features.

2. The record item group generation unit extracts record items from the record item group whose practice level, which indicates the degree to which they were practiced within a certain period, is equal to or greater than a predetermined value, and for which the target user intends to continue using the service. The program providing device according to claim 1.

3. The record item group generation unit extracts from each record item in the record item group the record items for which the implementation rate is 80% or higher and which the target users intend to continue using. The program providing device according to claim 2.

4. The system further includes a dietary habit classification unit that classifies the dietary habits of target users into one of several dietary habit classifications based on factors for body fat accumulation or factors for lifestyle-related disease incidence, based on dietary habit information, which is information about the dietary habits of target users. The record information acquisition unit presents the target user with a set of record items, including record items corresponding to the classification result of the eating habits. A program providing device according to any one of claims 1 to 3.

5. The system further includes an evaluation unit that evaluates the causes of body fat accumulation or lifestyle-related disease for each target user based on the aforementioned recorded information. A program providing device according to any one of claims 1 to 3.

6. The system further includes an advice generation unit that generates advice for each target user to eliminate the causes of body fat accumulation or lifestyle-related diseases, based on the evaluation results from the aforementioned evaluation unit. The program providing device according to claim 5.

7. A program provider program that provides target users with a program for reducing body fat or preventing lifestyle-related diseases, and the information processing device is: The steps include: presenting a set of record items containing multiple record items to the target user, and obtaining record information which is information about the meals and activities recorded by the target user for the set of record items; A step of obtaining the user's intention to continue receiving the service for each record item in the aforementioned group of record items, A step of generating a new set of record items that includes the record information and the record items selected based on the intention to continue. A program that provides a program to execute other programs.

8. A program provision method executed by an information processing device that provides a program for reducing body fat or preventing lifestyle-related diseases to target users, A set of record items containing multiple record items is presented to the target user, and record information, which is information about the meals and activities recorded by the target user for the said set of record items, is obtained. The intention of the target user to continue receiving the service for each record item in the aforementioned group of record items is obtained. A new set of record items is generated, which includes the record items selected based on the aforementioned record information and the intention to continue. Program delivery method.

Images