JP7616979B2 - Advice Decision System - Google Patents

Description

The present invention relates to an advice decision system that automatically decides advice to be presented to a user.

Conventionally, a system has been proposed that selects recommended information to be presented to a user based on information about the user's daily life activities (see, for example, Patent Document 1).

JP 2017-40981 A

However, in conventional systems, when selecting information to present to a user,
The health checkup data of the user is not taken into consideration at all. However, when selecting advice to be presented to a user based on the user's health checkup data, the contents of the advice presented to the user may contradict each other because the health checkup data contains a large number of data items.

For example, there may be a case where advice such as "Take protein" is selected based on the data item "BMI" of the health check data, while advice such as "Avoid taking too much protein" is selected based on the data item "creatine" of the health check data. In such a case, the user cannot decide which advice to follow, and ends up not trusting the advice (the reliability of the advice decreases).

The present invention has been made in consideration of the above problems, and has an object to provide an advice decision system that can present highly reliable advice to a user.

The advice decision system of the present invention includes a health checkup data acquisition unit that acquires health checkup data of a user, a life log data acquisition unit that acquires life log data of the user, a priority determination unit that calculates a priority score to be presented to the user for each data item of the health checkup data for each of a plurality of advice candidates to be presented to the user, calculates a total score by adding up the priority scores of all data items for each advice candidate, and determines a priority of the advice candidate based on the total score, a presentation feasibility determination unit that determines whether or not each of the plurality of advice candidates should be presented to the user based on the life log data, and an advice decision unit that determines, among the plurality of advice candidates, an advice candidate that has a high priority determined by the priority determination unit and is determined by the presentation feasibility determination unit to be presented to the user, as the advice to be presented to the user.

According to this configuration, for each of a plurality of advice candidates (e.g., "Reduce sugar intake,""Reduce salt intake," etc.), a data item of the health checkup data (e.g., BMI,
A priority score to be presented to the user is calculated for each of the following factors:
For each advice candidate, a total score is calculated by adding up the priority scores of all data items, and the priority of the advice candidate (for example, priority 1 is "Be careful not to eat too much sugar", priority 2 is "Eat vegetables", priority 3 is "Reduce salt" and "Eat protein") is determined based on the total score. In addition, for each of the multiple advice candidates, it is determined whether or not it should be presented to the user based on the user's life log data (for example, "A lot of rice and sugar", "Vegetables are well taken", etc.). (For example, "Be careful not to eat too much sugar" of priority 1 needs to be presented to the user, and "Eat vegetables" of priority 2 does not need to be presented to the user, etc.). Then, among the multiple advice candidates, the advice candidate that is determined to have a high priority and should be presented to the user is determined as the advice to be presented to the user. In this way, advice necessary for the user is presented in consideration of the user's health checkup data and life log data. In this way, advice with high reliability for the user is presented.

The advice decision system of the present invention may include a questionnaire response data acquisition unit that acquires questionnaire response data regarding the user's daily living activities, and the priority determination unit may determine the priority score for each data item of the questionnaire response data for each of the multiple advice candidates, calculate a total score for one advice candidate by adding up the priority scores of all data items, and determine the priority of the advice candidate based on the total score.

According to this configuration, for each of a number of advice candidates (e.g., "Keep an appropriate calorie intake,""Play (high-intensity) sports," etc.), a determination is made as to whether or not the advice should be presented to the user (e.g., "Keep an appropriate calorie intake" needs to be presented to the user, while "Play (high-intensity) sports" does not need to be presented to the user) based on the user's questionnaire response data (e.g., "My knees and hips hurt a lot when I exercise"). In this way, taking into account the user's questionnaire response data,
Necessary advice for the user is presented.

The advice decision system of the present invention may include an attendance data acquisition unit that acquires attendance data of the user, and the priority determination unit may obtain a priority score for each data item of the attendance data for each of the multiple advice candidates, calculate a total score for one advice candidate by adding up the priority scores of all data items, and determine the priority of the advice candidate based on the total score.

According to this configuration, for each of a plurality of advice candidates (e.g., "Go to bed early" or "Take more steps"), the user's attendance data (e.g., "Overtime hours" or "
Based on the user's attendance data (e.g., "Number of remaining paid vacation days,""Work start time,""Work end time,""Late night overtime hours," etc.), it is determined whether or not the advice should be presented to the user (e.g., "Go to bed early" needs to be presented to the user, while "Take more steps" does not need to be presented to the user). This allows the system to present the user with the advice they need, taking into account their attendance data.

The advice decision system of the present invention may include a mental data acquisition unit that acquires mental data of the user, and the priority decision unit may determine the priority score for each data item of the mental data for each of the multiple advice candidates, calculate a total score for one advice candidate by adding up the priority scores of all data items, and decide the priority of the advice candidate based on the total score.

According to this configuration, for each of a number of advice candidates (e.g., "take a rest", "limit your calories", etc.), a determination is made as to whether or not the advice should be presented to the user (e.g., "take a rest" needs to be presented to the user, while "limit your calories" does not need to be presented to the user) based on the user's mental data (e.g., "stress assessment (high/medium/low)", "do you get depressed easily (yes/no)", "do you feel that your workload is heavy (yes/somewhat yes/no)", "do you feel that your work environment has a good atmosphere (yes/no)", etc.). In this way, necessary advice for the user is presented in consideration of the user's mental data.

In addition, in the advice decision system of the present invention, if the advice candidate relates to caution behavior that should be avoided in the user's daily activities, the presentation adequacy determination unit may determine that the advice candidate should be presented to the user if it is determined based on the life log data that the caution behavior presented in the advice candidate has not been avoided in the user's daily activities.

According to this configuration, when an advice candidate is related to a behavior that should be avoided in the user's daily activities (for example, "be careful not to eat too much sugar"), if it is determined from the user's life log data that the behavior has not been avoided, the advice candidate is determined as the advice to be presented to the user. As a result, advice that requires attention for the user is presented.

In addition, in the advice decision system of the present invention, if the advice candidate relates to a recommended action to be performed in the user's daily living activities, the presentation adequacy determination unit may determine that the advice candidate should be presented to the user when it is determined based on the life log data that the recommended action presented in the advice candidate is being performed in the user's daily living activities.

According to this configuration, the advice candidates are related to recommended actions that the user should take in their daily activities (e.g., "It's good to eat vegetables. Let's keep it up.").
For example, if the recommended action is determined to be performed based on the user's life log data, the advice candidate is determined as the advice to be presented to the user, thereby presenting advice that is beneficial to the user.

In addition, in the advice decision system of the present invention, when the priority decision unit determines that one of the advice candidates should not be presented to the user based on the health check data, it may give the advice candidate the lowest priority, or perform taboo processing such that the advice candidate is not prioritized.

According to this configuration, when it is determined that a certain advice candidate should not be presented to the user based on the medical examination data, the advice candidate is subjected to taboo processing (processing in which the priority is set to the lowest or no priority is assigned) and the advice candidate is not presented to the user.
For example, a user with kidney disease can be prevented from being presented with a recommendation such as "eat your vegetables."

The present invention makes it possible to provide highly reliable advice to users.

1 is a block diagram showing a configuration of an advice determination system according to an embodiment of the present invention. FIG. 11 is a diagram showing an example of determining a priority order in the present embodiment. FIG. 4 is a diagram showing an example of life log data in the present embodiment. FIG. 4 is a flow diagram illustrating the operation of the advice determination system in the present embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an advice determination system according to an embodiment of the present invention will be described with reference to the drawings. In this embodiment, an advice determination system used in a health advice system or the like will be described as an example.

The configuration of an advice determination system according to an embodiment of the present invention will be described with reference to the drawings. Fig. 1 is a block diagram showing the configuration of the advice determination system according to the embodiment. As shown in Fig. 1, the advice determination system 1 is connected to a user terminal 3, a health checkup data server 4, an attendance data server 5, and a mental data server 6 via a network 2.

The user terminal 3 is a terminal device operated by a user, and is, for example, a smartphone, etc. The user terminal 3 has a function of recording life log data of the user.
The user terminal 3 also has a function of recording questionnaire response data regarding the user's daily activities.

The health checkup server is a server device operated by a health checkup business operator or the like, and stores the health checkup data of users. The attendance server is a server device operated by an attendance management business operator or the like, and stores the attendance data of users. Mental data server 6
is a server device operated by a mental health care business operator or the like, and records mental data of users.

As shown in Figure 1, the advice decision system 1 includes a health checkup data acquisition unit 10, a life log data acquisition unit 11, a questionnaire response acquisition unit 12, an attendance data acquisition unit 13, a mental data acquisition unit 14, a priority decision unit 15, a presentation feasibility determination unit 16, an advice decision unit 17, an advice sending unit 18, and a memory unit 19.

The health check data acquisition unit 10 has a function of acquiring the health check data of the user from the health check data server 4. The health check data includes, for example, "BMI", "waist circumference", "blood glucose level", etc.
etc. (see FIG. 2 ). If the user terminal 3 has a function for recording medical checkup data, the medical checkup data acquisition unit 10 may acquire the user's medical checkup data from the user terminal 3.

The life log data acquisition unit 11 has a function of acquiring the user's life log data from the user terminal 3. The life log data includes, for example, "meals,""number of steps,""sleep," and "exercise."
This includes log data such as "body" (see Figure 3).

The questionnaire response data acquisition unit has a function of acquiring questionnaire response data on the user's daily activities from the user terminal 3. The questionnaire response data may include, for example, "My knees and lower back hurt a lot when I exercise."

The attendance data acquisition unit 13 has a function of acquiring the attendance data of the user from the attendance data server 5. The attendance data includes, for example, "overtime hours." If the user terminal 3 has a function of recording attendance data, the attendance data acquisition unit 13 may acquire the attendance data of the user from the user terminal 3.

The mental data acquisition unit 14 has a function of acquiring the user's mental data from the mental data server 6. The mental data includes, for example, "stress judgment (high/medium/low)
In addition, in a case where the user terminal 3 has a function for recording mental data, the mental data acquisition section 14 may acquire the mental data of the user from the user terminal 3.

A plurality of advice candidates to be presented to the user are recorded in the storage unit 19. The advice candidates suggest various tasks to the user, and include, for example, "avoid sugar", "reduce salt", "keep an appropriate calorie intake", "play (high-intensity) sports", "go to bed early", "increase the number of steps", "take rest", "limit calories", "eat vegetables", "take protein", and the like.

In this case, possible advice would be, for example, "Be careful not to eat too much sugar."
The advice candidates include advice regarding behaviors that should be avoided in the user's daily activities. In addition, the advice candidates include advice regarding recommended behaviors that should be performed in the user's daily activities, such as "It's good to eat plenty of vegetables. Let's keep doing that."

Furthermore, the memory unit 19 also records life log data, questionnaire response data, and the like acquired from the user terminal 3 .

The priority order determination unit 15 determines a priority score to be presented to the user for each data item of the health checkup data for each of the advice candidates stored in the storage unit 19. For example, in the example of FIG. 2, for the advice candidate "Task A", the priority order score for "BMI" in the health checkup data is "0", the priority order score for "waist circumference" is "+10",
The priority score for "Blood Glucose Level" is determined to be "0".

Here, the priority score of the health checkup data can be calculated based on a predetermined criterion. For example, the priority score of "BMI" can be calculated based on the following criteria: if the "BMI" value is less than 23, the priority score is "0", if the "BMI" value is 23 or more and less than 25, the priority score is "+5", if the "BMI" value is 25 or more, the priority score is "+10". Similarly, the "waist circumference" and "blood glucose level" can be calculated based on a predetermined criterion.

Furthermore, the priority determination unit 15 obtains a priority score for each data item of the questionnaire response data for each of the advice candidates stored in the storage unit 19. For example, in the example of FIG. 2, for an advice candidate called "Task A", the priority score of "Questionnaire A" in the questionnaire response data is "+3", and the priority score of "Questionnaire B" is "0".
It is required that:

Here, the priority score of the questionnaire response data can be calculated based on a predetermined criterion. For example, the priority score of the questionnaire response data is "+3" if there is a relationship between the contents of the questionnaire and the contents of the task, and is "0" if there is no relationship.
It can be calculated based on criteria such as:

Furthermore, the priority order determination unit 15 obtains a priority score for each data item of the attendance data for each of the advice candidates recorded in the storage unit 19. For example, in the example of Fig. 2, for an advice candidate called "Task C", the priority score of "overtime hours" in the attendance data is obtained as "+3".

Here, the priority score of the attendance data can be obtained based on a predetermined criterion.
For example, the priority score of "overtime hours" is "0" if the value of "overtime hours" is less than 5 hours per week, and "+3" if the value is 5 hours or more per week.
It can be calculated based on criteria such as:

In addition, the priority order determination unit 15 determines a priority score for each data item of the mental data for each of the advice candidates stored in the storage unit 19. For example,
In the example, for the advice candidate "task D", the priority score of the "stress assessment" of the mental data is determined to be "+5".

Here, the priority score of the mental data can be calculated based on a predetermined criterion. For example, the priority score of the "stress assessment" can be calculated based on the following criteria: if the result of the "stress assessment" is "low", the priority score is "0", if it is "medium", the priority score is "+3", if it is "high", the priority score is "+5", etc.

Furthermore, when it is determined that a certain advice candidate should not be presented to the user based on the medical checkup data, the priority order determination unit 15 sets the priority order of the advice candidate to the lowest, or performs taboo processing to not assign a priority order to the advice candidate. For example, advice such as "eat vegetables" should not be presented to a user with kidney disease.

Here, taboo processing can be performed in various ways. For example, in the example of FIG. 2, the total value of the priority scores is multiplied by a "coefficient" to give the advice candidate the lowest priority. Specifically, the "coefficient" of the advice candidate that is the subject of taboo processing is set to "0" to give the advice candidate the lowest priority. Examples of taboo processing are not limited to this, and for example, the "coefficient" may be a negative value. Also, no priority is assigned to the advice candidate.

Then, the priority order determination unit 15 calculates a total score for one advice candidate by adding up the priority scores of all data items, and determines the priority order of the advice candidate based on the total score.

For example, in the example of FIG. 2, the total score of task A is "+13", the total score of task B is "+18", the total score of task C is "+11", and the total score of task D is "0". In this case, the priority of task A is "2", the priority of task B is "
The priority of task A is determined to be "1", the priority of task B is determined to be "3", and the priority of task D is determined to be "- (no priority)".

The presentation possibility determination unit 16 has a function of determining, for each of a plurality of advice candidates recorded in the storage unit 19, whether or not the advice candidate should be presented to the user based on the life log data.

For example, if an advice candidate is related to caution behavior that should be avoided in the user's daily activities (e.g., "Be careful not to consume too much carbohydrate"), the presentation adequacy determination unit 16 determines, based on the life log data (carbohydrate intake per day: 350g or more), that the caution behavior presented in the advice candidate has not been avoided in the user's daily activities (high carbohydrate intake), and determines that the advice candidate should be presented to the user.

In addition, when the advice candidate is related to a recommended behavior to be performed in the user's daily activities (for example, "It's good to eat plenty of vegetables. Let's keep it up"), the presentation adequacy determination unit 16 determines, based on the life log data (daily vegetable intake: 350g or more), that the recommended behavior presented in the advice candidate is being performed in the user's daily activities (vegetables are being consumed in good amounts), and determines that the advice candidate should be presented to the user.

The advice determination unit 17 selects, from among a plurality of advice candidates stored in the storage unit 19,
The advice candidate that is determined to have a high priority by the priority order determination unit 15 and that is determined to be presented to the user by the presentation possibility determination unit 16 is determined as the advice to be presented to the user. Therefore, even if the advice candidate is determined to have a high priority by the priority order determination unit 15, the advice candidate that is determined not to be presented to the user by the presentation possibility determination unit 16 is determined as the advice to be presented to the user.
It is never presented to the user.

The advice sending unit 18 has a function of sending the advice determined by the advice determining unit 17 as the advice to be presented to the user to the user terminal 3 .

The operation of the advice decision system 1 configured as above will be described with reference to the flow chart of FIG.

As shown in Fig. 4, the advice determination system 1 of this embodiment first acquires the user's health checkup data from the health checkup data server 4 (S1), and acquires the user's life log data from the user terminal 3 (S2). The life log data previously acquired from the user terminal 3 can be acquired from the storage unit 19. The advice determination system 1 also acquires questionnaire response data on the user's daily activities from the user terminal 3 (S3), and acquires the user's attendance data from the attendance data server 5 (S4). Furthermore, the advice determination system 1 acquires the user's mental data from the mental data server 6 (S5).

Next, as shown in Fig. 2, the advice decision system 1 obtains a priority score for each of the advice candidates to be presented to the user, calculates a total score for each advice candidate by adding up the priority scores of all data items, and decides the priority of the advice candidate based on the total score (S6). Also, for each of the advice candidates, it is determined whether or not to present the advice candidate to the user based on the life log data (S7).

Then, the advice decision system 1 decides that the advice candidate among the multiple advice candidates has a high priority as determined by the priority decision unit 15 and is determined by the presentation eligibility determination unit 16 to be presented to the user as the advice to be presented to the user (S8), and transmits the advice to the user terminal 3 (S9).

According to the advice decision system 1 of this embodiment, for each of a plurality of advice candidates (e.g., "limit carbohydrate intake", "reduce salt intake", etc.), a priority score to be presented to the user is calculated for each data item of the health checkup data (e.g., BMI, waist circumference, blood sugar level, etc.). Then, for one advice candidate, a total score is calculated by adding up the priority scores of all the data items, and the priority order of the advice candidate (e.g., priority 1 is "be careful not to eat too much carbohydrate"
The first priority is determined as "eat vegetables," the second priority is "eat less salt," and the third priority is "eat more protein."

In addition, for each of the multiple advice candidates, the user's life log data (e.g.,
Based on the priority level (e.g., "high carbohydrate intake (daily carbohydrate intake: 350g or more)" or "good vegetable intake (daily vegetable intake: 350g or more)"), it is determined whether or not a message should be presented to the user (for example, priority 1 "be careful not to eat too much carbohydrate" needs to be presented to the user, while priority 2 "eat your vegetables" does not need to be presented to the user).

Then, among the plurality of advice candidates, the advice candidate that has been determined to have a high priority and should be presented to the user is determined as the advice to be presented to the user.
Necessary advice for the user is presented in consideration of the user's health checkup data and life log data, thus providing highly reliable advice to the user.

Furthermore, in this embodiment, for each of a number of advice candidates (e.g., "Keep an appropriate calorie intake,""Play (high-intensity) sports," etc.), a determination is made as to whether or not the advice should be presented to the user (e.g., "Keep an appropriate calorie intake" needs to be presented to the user, while "Play (high-intensity) sports" does not need to be presented to the user) based on the user's questionnaire response data (e.g., "My knees and hips hurt a lot when I exercise"). In this way, necessary advice for the user is presented in consideration of the user's questionnaire response data.

In addition, in this embodiment, multiple advice candidates (for example, "Go to bed early"
For each of the advice items (e.g., "increase the number of steps you take"), a decision is made based on the user's attendance data (e.g., "overtime hours") as to whether or not it should be presented to the user (e.g., "go to bed early" needs to be presented to the user, while "increase the number of steps you take" does not need to be presented to the user). This allows the system to present the necessary advice to the user, taking into account the user's attendance data.

In addition, in this embodiment, multiple advice candidates (for example, "Take a rest"
For each of these, the user's mental data (e.g.,
Based on the user's mental data, the system determines whether or not to present the advice to the user (for example, "Take a rest" needs to be presented to the user, while "Limit your calories" does not need to be presented to the user). This allows the system to present the user with the advice they need, taking into account their mental data.

In addition, in this embodiment, if the advice candidate is related to a caution behavior that should be avoided in the user's daily activities (for example, "Be careful not to consume too much sugar"),
When it is determined from the user's life log data (daily sugar intake: 350 g or more) that the caution behavior is not avoided (high sugar intake), the advice candidate is determined as the advice to be presented to the user. In this way, advice that requires attention from the user is presented.

In addition, in this embodiment, when the advice candidate is related to a recommended behavior to be performed in the user's daily activities (for example, "It's good to eat vegetables well. Let's keep it up"), if it is determined from the user's life log data (daily vegetable intake: 350g or more) that the recommended behavior is being performed (vegetables are being consumed well),
The advice candidate is determined as the advice to be presented to the user, thereby providing the user with advice that is beneficial to the user.

In addition, in this embodiment, if it is determined that a certain advice candidate should not be presented to the user based on the medical examination data, the advice candidate is subjected to taboo processing (lowest priority or no priority is assigned) and is not presented to the user. This makes it possible to avoid presenting advice that should not be presented to the user (for example, "eat vegetables" to a user with kidney disease).

The above describes the embodiments of the present invention by way of example, but the scope of the present invention is not limited to these, and can be modified and changed according to the purpose within the scope of the claims.

INDUSTRIAL APPLICABILITY As described above, the advice decision system according to the present invention has the effect of being able to present highly reliable advice to a user, and is useful when applied to a health advice system or the like.

REFERENCE SIGNS LIST 1 Advice decision system 2 Network 3 User terminal 4 Health checkup data server 5 Attendance data server 6 Mental data server 10 Health checkup data acquisition unit 11 Life log data acquisition unit 12 Questionnaire response acquisition unit 13 Attendance data acquisition unit 14 Mental data acquisition unit 15 Priority order decision unit 16 Presentation possibility determination unit 17 Advice decision unit 18 Advice transmission unit 19 Storage unit

Claims (5)

an examination data acquisition unit that acquires examination data of a user;
A data acquisition unit that acquires data of the user;
a priority order determination unit that obtains a priority score to be presented to the user for each data item of the test data for each of a plurality of advice candidates to be presented to the user, calculates a total score by adding up the priority scores of all data items for one advice candidate, and determines a priority order for the advice candidate based on the total score;
a presentation possibility determination unit that determines whether each of the plurality of advice candidates should be presented to the user based on data of the user;
an advice determination unit that determines, among the plurality of advice candidates, an advice candidate that has a high priority as determined by the priority determination unit and that is determined by the presentation advisability determination unit to be presented to the user , as advice to be presented to the user;
An advice decision system comprising: a questionnaire response data acquisition unit for acquiring questionnaire response data regarding the user's daily life behavior,
2. The advice decision system according to claim 1, wherein the priority determination unit determines the priority score for each data item of the questionnaire response data for each of the plurality of advice candidates, calculates a total score by adding up the priority scores of all data items for one advice candidate, and determines the priority of the advice candidate based on the total score. An attendance data acquisition unit for acquiring attendance data of the user,
3. The advice decision system according to claim 1, wherein the priority determination unit obtains the priority score for each data item of the attendance data for each of the plurality of advice candidates, calculates a total score by adding up the priority scores of all data items for one advice candidate, and determines the priority of the advice candidate based on the total score. A mental data acquisition unit for acquiring mental data of the user,
The advice decision system according to any one of claims 1 to 3, wherein the priority determination unit determines the priority score for each data item of the mental data for each of the plurality of advice candidates, calculates a total score by adding up the priority scores of all data items for one advice candidate, and determines the priority of the advice candidate based on the total score. The priority order determination unit
An advice decision system as described in any one of claims 1 to 4, wherein, when it is determined that one of the advice candidates should not be presented to the user based on the test data, the advice candidate is given the lowest priority, or a taboo processing is performed in which the advice candidate is not given a priority.