JP7163130B2 - Information output system, information output method and information output program - Google Patents

Description

The present invention relates to an information output system, an information output method and an information output program.

Conventionally, there is known a system that allows a person receiving counseling to understand the causal relationship between the health condition, mental state, and skin trouble of the person receiving counseling (see, for example, Patent Document 1).

In the technique of Patent Document 1, the person receiving counseling is made aware of the causal relationship in the following manner.

That is, the processing unit of the counseling system, based on the answer (eg, “Yes”) to the question (eg, “13. ) is given 1 point as a score.

Then, the processing unit of the counseling system stores the subjective elements (for example, "coldness") and subjective and non-subjective elements having causality (for example, "poor blood circulation", "skin blood flow, congestion", "dark circles", and "shoulder stiffness").

In addition, the processing unit of the counseling system calculates the correlation coefficient between two elements having the causality stored in the storage unit of the counseling system (for example, the correlation coefficient of "poor blood circulation" to "coldness" is "0.9"). ), a score (eg, 1×0.9=0.9) is given to each recognized element (eg, “poor blood circulation”).

Elements with high evaluation points are highlighted on the image display part along with arrows indicating causal relationships, so that the person receiving counseling is informed of symptoms that the person is unaware of, or that they may develop symptoms in the future based on the results of the answers to the question items. It can call attention to possible skin troubles.

JP 2009-059126 A

However, in the technique of Patent Document 1, based on a causal relationship that has been medically verified in advance or a causal relationship predetermined based on empirical rules, in other words, based on a general causal relationship, the element corresponding to the answer and the related It remains only to find the elements to do.

For this reason, with the technique of Patent Document 1, it was not possible to inform the person receiving the counseling about the causal relationship between the factors in consideration of the constitution of each person receiving the counseling.

In addition, the technique of Patent Document 1 uses causal relationships between factors that have already been medically researched. It was not possible to inform counselees of causal relationships between unfocused factors, such as influences.

In addition, the technique disclosed in Patent Document 1 only displays the causal relationship between elements on the image display unit. For this reason, the technique of Patent Document 1 cannot provide the person receiving counseling with information about what measures are effective for improvement.

Therefore, in view of such problems, the present invention provides an information output system and an information output system that can make a user recognize the relationship between elements in consideration of individual circumstances of the user or the relationship between elements that has not been paid attention to until now. A first object is to provide a method and an information output program.

A second object of the present invention is to provide an information output system, an information output method, and an information output program that enable the user to recognize effective actions.

The information output system of the first aspect of the present invention comprises:
a user information recognition unit that recognizes unique user information of a user;
an output unit that outputs information;
a score information recognition unit that recognizes, based on the user information, score information indicating time-series transitions of scores of a plurality of elements included in the user information ;
a relationship recognition unit that recognizes the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period based on the score information;
a correlation information storage unit that stores information indicating mutually correlated elements and correlation coefficients between the elements based on the correlation;
a current user information recognition unit that recognizes current user information, which is the latest information of the user;
a first element recognition unit that recognizes the first element of the user indicated in the current user information;
a second element extraction unit that extracts a second element of the user that is correlated with the first element based on the information indicating the mutually correlated elements;
a prediction unit that predicts the future score of the second element of the user based on the score of the first element indicated in the correlation coefficient between the elements and the current user information;
and an output control unit that outputs information corresponding to the future score of the user's second element to the output unit .

According to the information output system of the present invention, the score information recognizing unit recognizes the score information indicating the time-series transition of the scores of the plurality of elements included in the user information based on the user's unique user information.

Based on the score information, the relationship recognition unit recognizes the correlation between the time-series transition of the score of one element and the time-series transition of the score of the other element shifted by a predetermined period .

If there is a correlation between the time-series transition of the score of one element and the time-series transition of the score of the other element shifted by a predetermined period, in other words, the change of one element If there is a correlation with a change in the other element after a certain point in time, the change in the one element may cause the change in the other element, or the same cause may cause both changes. there is a possibility.

Further, based on the correlation, the correlation information storage unit stores information indicating mutually correlated elements and correlation coefficients between the elements, and the current user information recognition unit stores the current information, which is the latest information of the user. User information is recognized.

Further, the first element recognizing unit recognizes the first element of the user indicated in the current user information, and the second element extracting unit recognizes the first element based on the information indicating mutually correlated elements. A second factor of the user correlated with is extracted.

Further, the prediction unit predicts the future score of the second element of the user based on the correlation coefficient between the elements and the score of the first element indicated in the current user information. Then, the output control unit outputs information corresponding to the future score of the second element of the user to the output unit.

Accordingly, based on the correlation recognized based on the user's information and the current user information indicating the user's current situation, the user can be made to recognize the user's future state predicted from the user's current situation. gives useful information.

The information output system of the second aspect of the present invention comprises:
a user information recognition unit that recognizes unique user information of a user;
an output unit that outputs information;
a score information recognition unit that recognizes, based on the user information, score information indicating time-series transitions of scores of a plurality of elements included in the user information;
a relationship recognition unit that recognizes, based on the score information, the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period;
a correlation information storage unit that stores information indicating mutually correlated elements and correlation coefficients between the elements based on the correlation;
a current user information recognition unit that recognizes current user information, which is the latest information of the user;
a first element recognition unit that recognizes the first element of the user indicated in the current user information;
a second element extraction unit that extracts a second element of the user that is correlated with the first element based on the information indicating the mutually correlated elements;
a prediction unit that predicts the future score of the second element of the user based on the score of the first element indicated in the correlation coefficient and the current user information;
an action recognition unit that recognizes, when the future score of the second element of the user is within a predetermined range, an action that causes the future score of the second element to fall outside the predetermined range;
an output control unit configured to output information recommending implementation or avoidance of the action to the output unit;
The correlation information storage unit is configured to store correlated behavior information indicating behavior correlated with each element and a correlation coefficient between each element and the behavior,
The action recognition unit has a correlation with the first element or the second element based on the correlated action information when the future score of the second element of the user is included in a predetermined range, and It is characterized in that it is configured to recognize an action that causes the future score of the second element to fall outside the predetermined range.

According to the information output system having this configuration, the score information recognizing unit recognizes the score information indicating the time-series transition of the scores of the plurality of elements included in the user information based on the user's unique user information. Based on the score information, the relationship recognition unit recognizes the correlation between the time-series transition of the score of one element and the time-series transition of the score of the other element shifted by a predetermined period.

If there is a correlation between the time-series transition of the score of one element and the time-series transition of the score of the other element shifted by a predetermined period, in other words, the change of one element If there is a correlation with a change in the other element after a certain point in time, the change in the one element may cause the change in the other element, or the same cause may cause both changes. there is a possibility.

Further, the first element recognizing unit recognizes the first element of the user indicated in the current user information, and the second element extracting unit recognizes the first element based on the information indicating mutually correlated elements. A second factor of the user correlated with is extracted.

Further, the prediction unit predicts the future score of the second element of the user based on the correlation coefficient between the elements and the score of the first element indicated in the current user information. Furthermore, when the future score of the second element of the user is included in the predetermined range, the action recognition unit recognizes an action of making the future score of the second element out of the predetermined range. Then, the output control unit outputs information recommending implementation or avoidance of the action to the output unit.

Thereby, based on the correlation coefficient, if the future score of the second element is predicted to be included in the predetermined range, the future score of the second element is performed outside the predetermined range or Information recommending avoidance is output to the output section. As a result, it is possible to provide the user with an action that is effective from the viewpoint of keeping the future score of the user's second element within the predetermined range or deviating from the predetermined range.

Further, when the behavior recognition unit determines that the future score of the second element of the user is included in a predetermined range, it has a correlation with the first element or the second element based on the correlated behavior information, and , actions that cause the future score of the second factor to fall outside of the predetermined range are recognized.

Here, the correlated behavior information indicates the behavior correlated with each element and the correlation coefficient between each element and the behavior. This allows the user to recognize actions that may have a causal relationship with the first element or the second element.

In addition, even if the relationship between the first element or the second element and the action has not been noticed until now, according to the information output system of the configuration, it is possible to have a causal relationship with the first element or the second element. The user can be made aware of the behavior that may be occurring and that would cause the future score of the second element to fall outside the predetermined range.

In the information output system with this configuration,
Preferably, the first element is an element related to one of skin, body and mind, and the second element is an element related to the other of skin, body and mind.

According to the information output system having this configuration, it is possible to inform the user of the causal relationship between the factors related to two of the skin, body, and mind that the user is likely to be interested in.

In the information output system with this configuration,
Preferably, the first factor is user behavior and the second factor is one of skin, body and mind.

According to the information output system of this configuration, it is possible to inform the user of the relationship between the user's behavior that can be directly controlled by the user and the element related to one of the skin, body, and mind that the user is likely to be interested in. As a result, the user can be given hints to indirectly control the state of one of the skin, body, and mind that the user is likely to be interested in.

In the information output system of the present invention,
The user information recognition unit recognizes at least one of skin cell image data, skin image data, and tongue image data of the user,
The score information recognition unit is configured to recognize information indicating time-series transition of scores of the plurality of elements of the user based on the skin cell image data, skin image data, and tongue image data of the user. is preferred.

A study by the present inventors revealed that the score of each element of the user can be recognized with relatively high accuracy based on at least one of the skin cell image data, the skin image data, and the tongue image data. .

According to the information output system configured in view of this point, the score of each element of the user is recognized based on at least one of the skin cell image data, the skin image data, and the tongue image data. be done. As a result, the score of each element can be recognized with high accuracy while the user is saved from inputting information such as a questionnaire, and the user can be made to recognize the relationship between these elements.

In the information output system of the present invention,
An input unit that receives the user's operation,
The user information recognition unit recognizes information input to the input unit as information of the user,
It is preferable that the score information recognition unit is configured to recognize information indicating time-series transition of scores of the plurality of factors of the user based on the information input to the input unit.

According to the information output system with this configuration, the score of the plurality of elements of the user is recognized based on the information input to the input unit, so that the time series transition of scores for a wide variety of elements and the transition between them The user can be made to recognize the relationship between

The information output program of the third aspect of the present invention is
In a system equipped with an output unit that outputs information,
recognizing the unique user information of the user;
a step of recognizing, based on the user information, score information indicating a time-series transition of scores of a plurality of elements included in the user information;
Based on the score information, recognizing the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period;
Based on the correlation, storing information indicating elements having correlation with each other and correlation coefficients between the elements;
recognizing current user information, which is the latest information of the user;
recognizing a first element of the user indicated in the current user information;
A step of extracting a second element of the user correlated with the first element based on the information indicating the mutually correlated elements;
Predicting the future score of the second element of the user based on the correlation coefficient between the elements and the score of the first element indicated in the current user information;
a step of outputting to the output unit information corresponding to the future score of the second factor of the user.

The information output program of the fourth aspect of the present invention is
In a system equipped with an output unit that outputs information,
recognizing the unique user information of the user;
a step of recognizing, based on the user information, score information indicating a time-series transition of scores of a plurality of elements included in the user information;
Based on the score information, recognizing the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period;
a correlation information storage step of storing information indicating elements having correlation with each other and correlation coefficients between the elements based on the correlation;
a step of recognizing current user information, which is the latest information of the user;
recognizing a first element of the user indicated in the current user information;
A step of extracting a second element of the user correlated with the first element based on the information indicating the mutually correlated elements;
Predicting the future score of the second element of the user based on the score of the first element indicated in the correlation coefficient and the current user information;
an action recognition step of recognizing, when the future score of the second factor of the user is within a predetermined range, an action that causes the future score of the second factor to fall outside the predetermined range;
a step configured to output to the output unit information recommending implementation or avoidance of the action;
The correlation information storing step is configured to store correlated behavior information indicating a behavior correlated with each element and a correlation coefficient between each element and the behavior;
The action recognition step has a correlation with the first element or the second element based on the correlated action information when the future score of the second element of the user is included in a predetermined range, and It is characterized in that it is configured to recognize an action that causes the future score of the second element to fall outside the predetermined range.

In the information output program of the present invention,
Preferably, the first element is an element related to one of skin, body and mind, and the second element is an element related to the other of skin, body and mind.

In the information output program of the present invention,
Preferably, the first factor is user behavior and the second factor is one of skin, body and mind.

The information output method of the fifth aspect of the present invention includes:
A method performed by a system comprising an output for outputting information, comprising:
recognizing the unique user information of the user;
a step of recognizing, based on the user information, score information indicating a time-series transition of scores of a plurality of elements included in the user information;
Based on the score information, recognizing the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period;
Based on the correlation, storing information indicating elements having correlation with each other and correlation coefficients between the elements;
recognizing current user information, which is the latest information of the user;
recognizing a first element of the user indicated in the current user information;
A step of extracting a second element of the user correlated with the first element based on the information indicating the mutually correlated elements;
Predicting the future score of the second element of the user based on the correlation coefficient between the elements and the score of the first element indicated in the current user information;
a step of outputting to the output unit information corresponding to the future score of the second factor of the user.

The information output method of the sixth aspect of the present invention comprises:
A method performed by a system comprising an output for outputting information, comprising:
recognizing the unique user information of the user;
a step of recognizing, based on the user information, score information indicating a time-series transition of scores of a plurality of elements included in the user information;
Based on the score information, recognizing the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period;
a correlation information storage step of storing information indicating elements having correlation with each other and correlation coefficients between the elements based on the correlation;
a step of recognizing current user information, which is the latest information of the user;
recognizing a first element of the user indicated in the current user information;
A step of extracting a second element of the user correlated with the first element based on the information indicating the mutually correlated elements;
Predicting the future score of the second element of the user based on the score of the first element indicated in the correlation coefficient and the current user information;
an action recognition step of recognizing, when the future score of the second factor of the user is within a predetermined range, an action that causes the future score of the second factor to fall outside the predetermined range;
a step configured to output to the output unit information recommending implementation or avoidance of the action;
The correlation information storing step is configured to store correlated behavior information indicating a behavior correlated with each element and a correlation coefficient between each element and the behavior;
The action recognition step has a correlation with the first element or the second element based on the correlated action information when the future score of the second element of the user is included in a predetermined range, and It is characterized in that it is configured to recognize an action that causes the future score of the second element to fall outside the predetermined range.

In the information output method of the present invention,
The information output method, wherein the first element is an element related to one of skin, body and mind, and the second element is an element related to the other of skin, body and mind.

In the information output method of the present invention,
The information output method, wherein the first element is a user's behavior, and the second element is an element related to one of skin, body and mind.

Explanatory drawing of the utilization form of a skin support server. FIG. 2 is a configuration diagram of a skin support server and user terminals; Explanatory drawing of a series of skin support processing by a skin support server. The first flowchart of processing in the skin support server. A second flowchart of processing in the skin support server. 4 is a first flowchart of processing in a user terminal; The second flowchart of processing in the user terminal. Explanatory drawing of the analysis processing of the initial data of the skin, the mind, and the body based on the subjective data and the objective data. Explanatory drawing of digitization of a body state. Explanatory drawing of digitization of the state of mind. FIG. 11A is a diagram showing the relationship between each measure and its improvement trend, etc., FIG. 11B is a diagram showing the schedule of each process, and FIG. illustration. Explanatory diagram of a progress table based on daily monitoring during the period of image display. Explanatory drawing of the influence on the skin condition by a special case. FIG. 10 is an explanatory diagram of a display screen of a skin condition improvement degree on a user terminal; 15A is an explanatory diagram of an evaluation method of measures, FIG. 15A shows evaluation criteria of measures, and FIG. 15B shows an example of evaluation of measures. It is explanatory drawing of the evaluation method of a living and a special case, FIG. 16A shows the evaluation criteria of a living and a special case, and FIG. 16B shows the evaluation example of a living and a special case. Explanatory drawing of the determination method of a goal achievement level. Explanatory drawing of secondary data. 19A is a flowchart of input reception processing, FIG. 19B is a flowchart of score storage processing, FIG. 19C is a flowchart of correlated action information storage processing, and FIG. 19D is a flowchart of information output processing. The figure which shows an example of a structure of an information input screen. The figure explaining an example of scoring of input information. A diagram showing a skin score graph and an action score graph. FIG. 4 is a diagram for explaining how to obtain a correlation between a skin score and an action score; FIG. 24A is a table storing skin, mind, and body elements, actions, and correlation coefficients, and FIG. 24B is a table storing correlation coefficients between skin elements and body elements. 4 is a flowchart of policy recognition processing; A diagram showing a skin score graph and a body score graph. The figure which shows an example of a measure recommendation screen.

An example of an embodiment of the present invention will be described with reference to FIGS. 1 to 27. FIG.

[A. Usage form of skin support server]
The configuration of the skin support server 10 and its positioning with respect to the system constituting the present invention will be described with reference to FIG. The skin support server 10 is configured by a computer system including a CPU, memory, various interface circuits, and the like. The skin support server 10 executes an image display program held in a memory by a CPU to improve or maintain the skin condition of a plurality of users U (Ua, Ub, and Uc are exemplified in FIG. 1). A function to execute processing (image display processing) is realized.

The skin support server 10 performs data communication with communication terminals (user terminals) 30 (30a, 30b, and 30c are illustrated in FIG. 1) of a plurality of users U via a communication network 1 such as the Internet. , to support the improvement or maintenance of the user U's skin condition. A system configured by the skin support server 10 and the user terminal 30 corresponds to an example of the "information output system" of the present invention.

Communication terminals 50 (50a and 50b are exemplified in FIG. 1) of persons in charge T (Ta and Tb are exemplified in FIG. 1) are also connected to the communication network 1. FIG. The person in charge T sends data including information such as appropriate skin care methods considering the characteristics of the user U's skin, hobbies and tastes, lifestyle, etc. to the skin support server 10 and the user terminal 30 via the communication terminal 50. By transmitting the information, advice on improvement of the user U's skin condition is given. The communication terminal 50 of the person in charge T and the user terminal 30 of the user U do not necessarily have to be one-to-one. It is only necessary to provide the user terminal 30 of the user U with advice on improving U's skin condition. For example, a method of providing advice to one or a plurality of user terminals 30 of a user U by a plurality of persons in charge T via respective communication terminals 50 may be used.

Furthermore, the skin support server 10 has a function of analyzing unique data of each user transmitted from the user terminals 30 of a plurality of users U and generating secondary data that can be used for general purposes. Services that provide data to other systems 60 via the communications network 1 may also be provided.

[B. Configuration of skin support server and user terminal]
The configurations of the skin support server 10 and the user terminal 30 will be described with reference to FIG. The skin support server 10 executes the information output program held in the memory by the CPU, whereby the skin condition recognition unit 11, the measure group selection unit 12, the measure information transmission unit 13, the secondary data generation unit 19, the score information recognition unit It functions as a unit 21 , a relationship recognition unit 22 , a current user information recognition unit 23 , a first element recognition unit 24 , a second element extraction unit 25 , a prediction unit 26 , a target recognition unit 27 and an action recognition unit 28 . Processing by these configurations will be described later. The skin support server 10 also includes a support DB (database) 20 that holds various data used for user U's image display processing. This support DB 20 is developed, for example, on a RAM, and can be rewritten by updating subjective and objective data, which will be described later. The support DB 20 corresponds to an example of the "correlation information storage section" of the present invention.

Next, the user terminal 30 of this embodiment includes a display 31, a touch panel 32, a camera 33, a microphone 34, a CPU 35, a memory 36, and the like. The display 31 corresponds to the "output section" of the present invention. The touch panel 32 corresponds to the "input section" of the present invention. Instead of or in addition to this, by recognizing the designation of the user based on the image of the user U captured by the camera 33 or the voice of the user U input from the microphone 34, the camera 33 or the microphone 34 can be used as the " It may function as an "input unit".

It should be noted that the fact that one device "recognizes" information means that one device receives the information from another device, and that one device recognizes information stored in a storage medium connected to the one device. reading, obtaining information by a device based on a signal output from a sensor connected to the device, or obtaining information received by a device or information stored in a storage medium or from a sensor Derivation of the information by executing predetermined arithmetic processing (calculation processing, search processing, etc.) based on the information obtained, one device passing the information as a result of arithmetic processing by another device to the other device It means that any arithmetic processing for obtaining the information is performed, such as receiving from, or reading the information from an internal storage device or an external storage device according to the received signal.

In the present embodiment, the user terminal 30 is, for example, a smart phone, and is equipped with a camera 33, a microphone 34, and the like. Alternatively, a specialized imaging device may be used. The CPU 35 executes a program of an image display application 35a (hereinafter referred to as a skin support application 35a) held in the memory 36 so that the user U can receive support from the skin support server 10 via the skin support application 35a. make it possible. The CPU 35 executing the application 35a functions as the "user information recognition section" and the "output control section" of the present invention.

In addition, the user U separately has a keratin collecting tape 37 (37a, 37b, and 37c are illustrated in FIG. 1) for obtaining skin cells. The keratin collecting tape 37 may be provided to the user U in advance by the person in charge T, or the user U may purchase a commercially available keratin collecting tape 37 .

In the following description, for convenience of explanation, the user who is the target of the support process for improving or maintaining the skin condition is referred to as user Ua, and users different from "user Ua" are referred to as "other users Ub and Uc". .

[C. Series of processing by skin support server]
Next, a series of processes by the skin support server will be described with reference to FIG. The skin support server 10 performs "1. goal setting", "2. schedule determination", "3. monitoring", "4. schedule correction", and [5. goal determination] and “6. Secondary data generation” are executed according to the flowcharts shown in FIGS. Further, the user terminal 30 executes the processing according to the flowcharts shown in FIGS. 6 and 7 according to the processing by the skin support server 10 in each step. The processing of the skin support server 10 and the CPU 35 of the user terminal 30 in each step will be described below with reference to the flowcharts shown in FIGS.

In STEP 50 of FIG. 6, the user Ua downloads the program of the skin support application 35a to the user terminal 30 and activates the skin support application 35a. Then, the user Ua applies for registration with the skin support application 35a.

When the skin support server 10 receives the registration application from the user Ua in STEP10 of FIG. Then, image display processing after STEP 11 is started.

[D. Goal setting process]
Goal setting” shown in FIG. 3 is executed by the skin condition recognition unit 11 and the measure group selection unit 12 . In STEP 11 of FIG. 4, the measure group selection unit 12 prompts the user terminal 30 to input information for grasping the current skin, body, and mental conditions of the user Ua and the skin condition desired by the user Ua. Send data.

In the user terminal 30 that has received this initial information request data, the skin support application 35a, in STEP 51 of FIG. is displayed on the display 31 to prompt the input of . In response, the user terminal 30 transmits initial user information data including the information input by the user Ua to the skin support server 10 . In addition to or instead of these, the skin support application 35a executes input information reception processing in STEP100 of FIG. Details of the input information acceptance process will be described later.

The skin condition recognition unit 11 receives the initial user information data in STEP 11 of FIG. A target score (goal) for skin condition, mental condition, or body condition is set based on the above.

Here, the initial user information data includes subjective data and objective data regarding skin condition, body condition, and mental condition. Although detailed definitions will be described later, subjective data is an item determined subjectively by the user, and objective data is data based on inspection results, image analysis, and factual information. The subjective data of the skin condition is data indicating the satisfaction level of the user Ua with respect to the current skin condition, for example, in 10 stages. The objective data of the skin condition is skin image data of the user Ua. In addition to the objective skin condition data included in the initial user information data, the objective skin condition data may include analysis data of the stratum corneum cells of the user Ua collected by the horny material collecting tape 37 . Specifically, the analytical data of stratum corneum cells are, for example, data relating to water retention capacity and sebum suitability. In this case, the stratum corneum cells are analyzed by, for example, a measuring device 45 connected to the user terminal 30, or the image data of the stratum corneum sampling tape 37 is transmitted from the user terminal 30 to the skin support server 10, and the skin is analyzed. The image of the keratin collecting tape 37 may be analyzed on the support server 10 side to measure the water retention capacity, the suitability for sebum, and the like.

The degree of satisfaction of the user Ua is acquired by inputting a questionnaire in the skin support application 35a. The skin image data is image data obtained by the user Ua capturing an image of the user Ua's face using the camera 33 of the user terminal 30 or an external camera connected to the connection terminal 40 .

In addition, the skin support application 35a displays the usage of the keratin collecting tape 37 on the display 31 to the user Ua, and prompts the user Ua to use the keratin collecting tape 37 and mail it separately. The skin support application 35 a may prompt the user Ua to use the keratin collecting tape 37 and take a skin image, and the taken image may be transmitted to the skin support server 10 .

As shown in FIG. 8, the skin condition recognition unit 11 sets the initial value of the skin condition to a subjective score of 3, a skin image score of 3, a water retention ability score of 2, and a sebum suitability score of 1. , and the total value is used as the skin condition score. The score may be represented by a decimal number instead of a positive integer, or may be represented by a negative number.

Recognition of scores obtained by analyzing the state of stratum corneum cells (water retention capacity score, sebum suitability score, stratum corneum barrier, melanin score, etc.) is referred to as a first recognition method. Alternatively or additionally, the first recognition method is at least one of a method of measuring the moisture content of the user's skin with a stratum corneum moisture meter and a method of measuring the sebum content of the user's skin with a sebum meter. including. Further, in order to periodically recognize the user's skin by the first recognition method, the skin condition recognition unit 11 may periodically send a message to the user terminal 30 prompting the user to use the keratin collecting tape 37 or the like. prompts the user to take action. In this case, "regularly" does not have to be strictly at regular time intervals, and may be at regular intervals as long as the skin condition is not affected. For example, when the first recognition method is performed every three months, if the first recognition method is performed on January 1st, not only April 1st, which is exactly three months later, but also March, for example, one week before The message may be sent to the user terminal 30 around the 24th, or may be sent to the user terminal 30 around April 8, one week later, for example. In addition, when providing a long-term service, for example, if you want to check detailed improvements, set the first recognition method every month, and check the effect of seasonal changes on the skin condition every three months. The timing of recognition can be changed, such as by setting the first recognition method.

Recognition of the subjective score based on the subjective data of the skin condition entered in the questionnaire and recognition of the skin image score based on the skin image data of the user Ua are each referred to as a second recognition method. The second recognition method may recognize the skin condition of the user Ua by either recognizing a subjective score based on the subjective data of the skin condition entered in the questionnaire or recognizing the skin image score based on the skin image data of the user Ua. , may include methods different from these. Also, the second recognition method is performed more frequently than the first recognition method. This is because the use and mailing of the keratin collecting tape 37 is time-consuming, complicated, and expensive. In addition, frequent use of the keratin collecting tape 37 may adversely affect the skin of the user Ua.

For example, the first recognition method is performed every three months, and the second recognition method is performed every day. Further, when the timing of performing the first recognition method and the timing of performing the second recognition method overlap, it is preferable that they are performed at the same timing, so that accurate data can be obtained. However, even if the second recognition method is not performed when the first recognition method is performed, the present invention can be implemented and the effect can be obtained.

Note that the subjective score may be a score recognized based on subjective data.

The subjective data and objective data of body condition are separated as shown in FIG. Although detailed definitions will be described later, subjective data is an item determined subjectively by the user Ua, and objective data is data based on test results, image analysis, and factual information, similar to the subjective data and objective data of the skin. The subjective data on the body condition is obtained by the user Ua inputting a questionnaire through the skin support application 35a. The objective data includes image data of the tongue and lips of the user Ua captured by the user Ua using the camera 33 of the user terminal 30, and data of the user Ua measured by the measuring device 45 connected to the connection terminal 40 of the user terminal 30. They are measurement data such as blood pressure, arrhythmia, urinary components, blood components, etc., and data such as the number of headaches, stiff shoulders, and other poor physical conditions obtained by inputting questionnaires in the skin support application 35a, fatigue level determination, and the like. In preparation for future development of services other than skin care, information on the occurrence of various diseases may be included in the initial user information data.

The subjective and objective data of the state of mind are separated as shown in FIG. Although detailed definitions will be described later, subjective data is an item determined subjectively by the user Ua, and objective data is data based on test results, image analysis, and factual information, similar to the subjective data and objective data of the skin. . The subjective data is acquired by inputting a questionnaire in the skin support application 35a. The objective data is obtained by inputting questionnaires (the frequency of laughter per day, etc.) in the skin support application 35a, and by measuring with a measurement device (estimation of psychological state from pulse monitoring, etc.). In preparation for future development of services other than skin care, the initial user information data may include information on the occurrence of various mental disorders.

The skin condition recognition unit 11 stores the recognized subjective data and objective data of the skin, body, and mental conditions of the user Ua in the support DB 20 .

Based on the subjective data and objective data of the user Ua's skin condition, body condition, and mental condition recognized from the initial user information data in this way, the skin condition recognition unit 11 recognizes as indicated by the triangles on the right side of FIG. , the initial values of the three axes are set, which are represented by scores of 1 to 9 for skin conditions, 1 to 6 for body conditions, and 1 to 6 for mental conditions. Similar to the skin condition score, the subjective score and objective score of the physical condition or mental condition are calculated from the subjective data and objective data of the physical condition or mental condition, and based on these, the physical condition score and the mental condition score are calculated. may be treated as

Further, the measure group selection unit 12 acquires the "desired skin score", "categories, desires and frequencies of measures", "allergy information", and "current measures" included in the initial user information data. The "desired skin score", "category, desire and frequency of measures", "allergy information", and "current measures" are input by the user Ua through the skin support application 35a.

Here, in the "desired skin score", "desired skin" means improvement of dryness, improvement of stiffness, improvement of sensitive skin, improvement of acne, improvement of wrinkles, improvement of spots, improvement of wrinkles, improvement of sagging, improvement of dullness, improvement of dark circles, It is a skin condition improvement target selected from complexion improvement and firmness improvement. The “score” is the degree of satisfaction of the user Ua with respect to the skin condition of each target, and is the score selected by the user on a scale of 1 to 10 in the skin support application.

The support DB 20 stores the improvement tendency of the subjective state and the improvement tendency of the objective state of the skin condition associated with each improvement target. For example, as shown in FIG. 11C , “improvement of dryness” includes subjective skin condition “improvement of dryness” and objective criterion of skin condition “minimize difference between water retention capacity and sebum suitability”. ” is associated with.

For example, when the “desired skin” is “improved dryness” and the satisfaction score for the dryness of the current skin condition is 3, it is associated with “improved dryness” according to the selection of the user Ua. A satisfaction level of 5 with respect to the subjective skin condition “improved dryness” of the user Ua is set as a subjective target score.

In addition, the measure group selection unit 12 sets an objective skin condition improvement target associated with "improvement of dryness". For example, the measure group selection unit 12 determines that the user Ua's skin condition is lower than the water retention ability based on the criterion "minimize the difference between the water retention ability and the sebum suitability" associated with the "improvement of dryness". A sebum suitability value of 2 is set as an objective target score.

Similarly, target scores can be set for mental and physical conditions.

In addition, in the "category, preference, frequency of measures", the categories are "dietary habits, behavior at home, exercise, how to use cosmetics, eating and drinking, skin care, behavior", etc., and the preferences are "difficulty, amount restrictions, Time constraints, undesired measures, skin care scenes, preferences regarding categories of measures, etc., and as the frequency, the number of times advice is received per day, etc. are selected.

The "allergy information" includes information on allergens when the user Ua has an allergic disease.

The “measure currently being taken” is a measure that the user Ua has already taken, and is represented by the content and frequency or degree of “soaking in the bathtub” or the like.

The initial user information data also includes information indicating attributes of the user Ua, such as gender, age, area of residence, occupation, personality, and the like.

[E. Schedule decision process]
The process of “2. Determination of schedule” shown in FIG.

The measure group selection unit 12 selects a measure group for achieving the goal (subjective target score and objective target score) in STEP 12 of FIG. This selection is made in consideration of the improvement tendency of the subjective skin condition, the improvement tendency of the objective skin condition, and the suitable surrounding environment, which are associated with each improvement target described above. The measure group selection unit 12 determines the temperature, humidity, etc. of the surrounding environment of the user Ua during the schedule period based on the location information detected by the GPS sensor or the like registered in advance and the date and time acquired from the clock function of the terminal. to predict.

For example, the policy group selection unit 12 selects the desired category as "activity at home, eating habits, exercise", the season when the humidity is expected to average 50%, such as the rainy season, and the subjective goal. If the score is 5 or more in satisfaction for "dryness" and the objective target score is 2 or more in sebum suitability, the subjective condition improvement tendency includes "dryness improvement", and the objective condition improvement tendency is "sebum eligible Including "enhancement of sexuality", the category is any of "action at home, eating habits, exercise", and the suitable surrounding environment is not inconsistent with the humidity of 50%.

Here, the improvement tendency of the subjective state and the improvement tendency of the objective state may be determined in advance by an expert such as the person in charge T, or may be implemented by other users Ub and Uc stored in the support DB 20. It may be an improvement tendency of the subjective state and an improvement tendency of the objective state estimated based on the time-series change of the skin condition of the other users Ub and Uc, or the implemented measure and the skin condition of the user Ua. is stored in the support DB 20, it may be the improvement tendency of the subjective state and the improvement tendency of the objective state estimated based on the information.

For example, the support DB 20 stores measures implemented by each of the other users Ub and Uc, chronological changes in the subjective data of the skin condition of each of the other users Ub and Uc before and after implementation of the measures, and other data. One or both of chronological changes in objective skin condition data for each of users Ub and Uc are stored.

In addition, for example, the support DB 20 stores information on the measure "soaking in the bathtub" implemented by each of the other users Ub and Uc, and subjective skin conditions of each of the other users Ub and Uc before and after implementing the measure. It is assumed that "satisfaction with dry feeling" is stored as a time-series change.

At this time, for each of the other users Ub and Uc, the measure group selection unit 12 selects, for example, the subjective data of the skin condition (dry feeling) on the day before the bath and on the day of the bath for the measure “soak in the bathtub”. Calculate the amount of change in the degree of satisfaction for Then, the measure group selection unit 12 obtains the average value of the amount of change in the degree of satisfaction with regard to dryness for each of the other users Ub and Uc. For example, when the average value of the amount of change in "satisfaction with dryness" is equal to or greater than a predetermined value, the measure group selection unit 12 sets the improvement tendency of the subjective state of the measure "soak in the bathtub" to "improved dryness". Recognize. The improvement tendency of the objective state can also be recognized in the same way as the improvement tendency of the subjective state.

Based on the age and skin condition of the user Ua, the measure group selection unit 12 may narrow down the range of other users Ub and Uc to be processed, such as the same age and the same skin condition.

Further, when the implemented measures and the chronological changes in the skin condition of the user Ua are stored in the support DB, the measure group selection unit 12 estimates based on the information calculated in the same manner as described above. The improvement tendency of the subjective state and the improvement tendency of the objective state may be recognized.

In the first schedule determination process for user Ua, the measure group selection unit 12 recognizes the improvement tendency of the subjective condition and the improvement tendency of the objective condition from the subjective data and objective data of the skin conditions of the other users Ub and Uc. However, in the user Ua's second schedule determination process, the improvement tendency of the subjective condition and the improvement tendency of the objective condition may be recognized from the subjective data and the objective data of the skin condition of the user Ua.

The measure group selection unit 12 recognizes not only the skin condition but also the subjective state improvement tendency and the objective state improvement tendency for the mental state and the physical state. Measures may be selected in consideration of the improvement tendency of the objective state.

The measure group selection unit 12 may exclude measures having the same content and the same frequency or degree as the “current measures” from the measure group. This is because it is considered unnecessary to propose measures that the user has already taken.

In the example of FIG. 11A, the measure group selection unit 12 selects a measure group that is a combination of a plurality of measures so as to include "soak in the bathtub", "vitamin C intake", "vegetable intake", and "walk 10,000 steps or more". .

The policy information transmission unit 13 evaluates the priority of the policy among the policy group according to the user Ua's "policy category, desire, and frequency", and determines the initial policy according to the priority. FIG. 12 shows an example of a schedule in which "soak in a bathtub," "eat vegetables," and "walk 10,000 steps or more" are selected as initial measures. The policy information transmission unit 13 transmits schedule data indicating the details of the schedule thus determined to the user terminal 30 .

The policy information transmission unit 13 also creates a plan for implementing each process for a certain period based on the designation of the user Ua. In FIG. 11B, goal setting and schedule determination are performed at START, daily monitoring is performed, schedule is revised every week, a progress graph is created after one month, and goal determination and new goals are set in F3 months. A series of events of setting up are planned. In addition to or in place of the user Ua's specification, the policy information transmitting unit 13 may use a predetermined plan for performing each process for a certain period of time.

When the user terminal 30 receives the schedule data from the skin support server 10 in STEP 52 of FIG. 6, the skin support application 35a displays a schedule execution table as shown in FIG. The plan is made displayable to prompt the user Ua to implement each measure. In the execution table of the schedule in FIG. 12, the contents of each measure (“Soak in a hot tub”, “Eat vegetables”, “Walk 10,000 steps or more”) are displayed.

When the user terminal 30 receives the information indicating the measures recognized in the measure recognition processing in STEP 200 of FIG. 4 from the skin support server 10, the skin support application 35a executes the measure output processing in STEP 110 of FIG. . Details of the policy output process will be described later.

[F. Monitoring process]
The step of “3. Monitoring” shown in FIG. 3 includes the skin condition recognition unit 11, the measure information transmission unit 13, and the score information recognition every period set by the user Ua on the skin support application 35a (every day in FIG. 11B). It is executed by the unit 21 and the relationship recognition unit 22 . The user Ua, who has started using the skin support application 35a, uses the user terminal 30 to check the daily skin, body, and mental conditions, the implementation status of the measures specified by the schedule, and the measures other than the measures specified by the schedule. The implementation status of Ua's voluntary action (action outside the policy) is entered. The implementation status of the measures includes information indicating whether or not each of the measures, "soak in the bathtub,""eatvegetables," and "walk more than 10,000 steps," has been implemented, as well as "30 minutes,""soak in the bathtub,""300 g and "eating vegetables", "15,000 steps", and "walking".

The skin support application 35a advances the processing to STEP100 and STEP60 in FIG. 6 when the user Ua inputs the state of the skin, body, and mind in STEP53 in FIG. Details of the processing of STEP 100 in FIG. 6 will be described later. In STEP 60 of FIG. 6, the skin support application 35a sends current user information data including subjective data and objective data regarding the state of the skin, body, and mind of the user Ua to the skin support server 10 in the same way as the initial user information data described above. and advance the process to STEP54. It should be noted that the skin support application 35a directly advances the processing to STEP54 when the user Ua does not input the state of the skin, body, and mind in STEP53 of FIG. The data transmitted here does not include the data on the stratum corneum cells collected by the keratin collecting tape 37 .

Further, in STEP 54 of FIG. 6, the skin support application 35a advances the process to STEP 61 when the schedule execution status (execution status of each measure) is input by the user Ua, and outputs execution status data indicating the schedule execution status. The data is transmitted to the skin support server 10, and the process proceeds to STEP55 in FIG. When the user Ua does not input the execution status of the schedule (execution status of each measure) in STEP54 of FIG. 6, the skin support application 35a directly proceeds to STEP55.

Furthermore, in STEP55 of FIG. 7, the skin support application 35a advances the process to STEP62 when the user Ua inputs the implementation status of the non-policy action, and performs non-policy action execution data indicating the details of the non-policy action that has been performed. is transmitted to the skin support server 10, and the process proceeds to STEP56. It should be noted that the skin support application 35a advances the processing to STEP56 as it is when the user Ua does not input the implementation status of the action outside the policy in STEP55 of FIG.

When the skin condition recognition unit 11 receives the current user information data from the user terminal 30 in STEP 13, the process proceeds to STEP 200 and STEP 20 in FIG. Details of the processing of STEP 200 in FIG. 4 will be described later. The skin condition recognition unit 11 accumulates the current user information data in the support DB 20 and advances the process to STEP14. When the skin condition recognition unit 11 does not receive the current user information data from the user terminal 30 in STEP 13, the process proceeds to STEP 14 as it is.

Also, when the policy information transmitting unit 13 receives the execution status data from the user terminal 30 in STEP 14, the process proceeds to STEP 300 and STEP 21 in FIG. Details of the processing of STEP 300 in FIG. 4 will be described later. The policy information transmission unit 13 accumulates the execution status data in the support DB 20 and advances the process to STEP15. On the other hand, if it is determined in STEP14 that the execution status data has not been received from the user terminal 30, the process proceeds directly to STEP15.

Further, when the policy information transmitting unit 13 receives the non-policy action execution data from the user terminal 30 in STEP15, the process advances to STEP300 and STEP22 in FIG. Details of the processing of STEP 300 in FIG. 4 will be described later. The policy information transmission unit 13 accumulates the non-policy action execution data in the support DB 20, and advances the process to STEP16 in FIG. On the other hand, when the policy information transmitting unit 13 does not receive the non-policy action execution data from the user terminal 30 in STEP15, the process proceeds to STEP16 in FIG. 5 as it is.

5, in STEP 16, the policy information transmission unit 13, based on the user Ua's initial user information data, current user information data, execution status data, non-policy action execution data, etc., accumulated in the support DB 20, Progress table data indicating the progress of the schedule is created, and this progress table data is transmitted to the user terminal 30 .

Here, for example, as shown in FIG. 12, the progress table of the schedule includes, from top to bottom, a skin condition (here, dry skin) score, a physical condition score, a mental condition score, measures (soaking in a bathtub, vegetables, etc.). Intake, walking 10,000 steps or more), living information (vegetable intake, water intake), and living special cases are displayed on a daily basis.

The schedule progress table also includes the degree of implementation of measures (“30 minutes” soaking in a hot tub, “300g” eating vegetables, “15,000 steps” walking).

In the user terminal 30, the skin support application 35a advances the process to STEP 63 when receiving the progress table data transmitted from the skin support server 10 in STEP 56 of FIG. The skin support application 35 a uses the progress table data stored in the memory 36 to display the progress table shown in FIG. 12 on the display 31 of the user terminal 30 .

In addition to the progress table data, the skin support application 35a also includes the initial user information data, the current user information data, the execution status data, Information indicating transitions and changes in each state of the user Ua's skin, body, and mind is displayed on the display 31 of the user terminal 30 using the non-policy action execution data and the like.

For example, as shown in FIG. 13, the skin support application 35a can be used to determine the behavior of the user Ua other than the measures according to the schedule (change of face washing method, receiving a massage, etc.), and the physical condition of the user (physiology, lack of sleep, etc.). , the relationship of changes in the skin condition of the user Ua is displayed on the display 31 as a daily time-series graph.

In addition, as shown in FIG. 14, the skin support application 35a monitors, on a month-by-month basis, the user Ua's skin condition including keratin condition, psychological condition, skin care satisfaction, and cosmetic usage evaluation values. A radar chart for comparison is displayed on the display 31 .

The user Ua visually recognizes these displays by the skin support application 35a and confirms changes in the conditions of his/her own skin, body, and mind, thereby grasping the degree of improvement in the skin as well as changes in the conditions of the body and mind. Therefore, the target skin condition goal can be achieved while improving the condition of the skin, body, and mind in a well-balanced manner.

[G. schedule modification]
The process of "4. schedule modification" shown in FIG. 3 is executed by the measure information transmitting unit 13 for each period set by the user Ua on the skin support application 35a (every week in FIG. 11B). STEP 17 to STEP 20 and STEP 30 to STEP 33 in FIG.

In STEP 17, the policy information transmission unit 13 incorporates the user Ua's skin condition target achievement level and schedule into the user Ua's initial user information data, current user information data, execution status data, and the like accumulated in the support DB 20. Evaluate the degree of implementation of each measure and the effect of each measure. The effect of each policy is obtained from the change in the score of at least one state (designated state) among the skin condition, physical condition, and mental condition related to the goal of the user Ua before and after execution of each policy. For example, if the specified state score rises from 2 to 3 when a certain policy is executed, the effect can be calculated as +1.

The policy information transmission unit 13 extracts the correlation between the skin condition and the physical or mental condition using the policy evaluation criteria shown in FIG. continuation of the policy, exclusion from the policy group, cancellation of the policy, and change to another policy.

In the evaluation criteria of FIG. 15A, four items are used as judgment items: presence or absence of implementation, effect on skin, effect on body, and effect on mind. If there is, and there is no adverse effect on the body and mind, it will be evaluated as A and will be judged to be continued. Note that, in FIG. 15A, a measure whose implementation rate exceeds a predetermined implementation rate is indicated as "implemented", and a measure whose implementation rate is equal to or lower than the predetermined implementation rate is indicated as "non-implemented". The same applies to FIGS. 12 and 15B.

When the timing of the impact of the policy and the duration of the impact are determined for each policy, the policy information transmission unit 13 calculates the impact of each policy based on the timing of the impact of the policy and the duration of the impact of the policy. You can judge.

For example, if it is determined that the effect of the measure "soaking in the bathtub" on the skin, the body, and the mind will affect the next day and will continue for only one day, the measure information transmission unit 13 will: For example, it is determined that the effect of the measure "soak in the bathtub" on December 15th is reflected in each state on December 16th.

In addition, if the effect of the measure "vegetable intake" on the skin, the body, and the mind is set to occur after one week and continue for only three days, the measure information transmission unit 13 For example, it is determined that the influence of the measure "vegetable intake" on December 15th is reflected in each state from December 22nd to December 24th.

On the other hand, a measure that has a good effect on the skin but has a bad effect on either the body or the mind is given a B rating and is judged to be canceled or changed to another measure. In addition, since it is considered that a measure whose implementation rate is equal to or less than a predetermined implementation rate imposes a heavy burden on the user Ua, it is evaluated as C and canceled or another measure (for example, a measure that reduces the load on the user Ua or another measure is implemented). measures).

The measure information transmission unit 13 may evaluate whether each measure has a good or bad effect on the skin, body, and mind by implementing each measure for only one day, or for a certain period of time such as several days or weeks. Implementation of each measure may be evaluated to determine whether it has a positive or negative effect on the skin, body, or mind. When the evaluation is performed for a certain period of time, the measure information transmission unit 13, for example, gives a B rating when any state is adversely affected at a given rate, a C rating when the implementation rate is below a given rate, and otherwise. In the case of , it is good as A evaluation. Instead of this, the policy information transmitting unit 13 may, for example, give a B evaluation when the average value of satisfaction in any state during a certain period of time is below a predetermined value. In addition, if the satisfaction level of any state in one day is below a predetermined level, and the average value of the satisfaction level of any state over a certain period of time is below a predetermined level, it will be evaluated as C, and any of the evaluations for the day If the level of satisfaction in any state falls below a predetermined level, or if the average value of satisfaction levels in any one state over a certain period of time falls below a predetermined level, it will be evaluated as B, and the level of satisfaction in any state in a day will be a predetermined level. value, and the average value of satisfaction in any state in a certain period of time exceeds a predetermined value, such as A evaluation, such as a one-day evaluation and a certain period of time evaluation may be used in combination. .

In addition, when the degree of improvement of the skin condition score shown in FIG. It may be excluded from the policy group. When the degree of improvement in the score of the physical condition or the mental state is equal to or less than a predetermined degree of improvement, the measure information transmitting unit 13 selects the measures in which the degree of improvement in the score of the physical state or the mental state is equal to or less than the predetermined degree of improvement in the measure group. can be excluded from

FIG. 15B shows an example in which the evaluation criteria shown in FIG. 15A are applied to measures (soaking in a bathtub, eating vegetables, walking 10,000 steps or more) in this embodiment. According to FIG. 15B, the measure information transmission unit 13 gives an A rating for “soak in the bathtub” and determines to continue with the next schedule, and gives a B rating for “vegetable intake” and excludes it from the measure group. Decide to cancel the schedule or change to another measure, and give a C grade for "walking more than 10,000 steps", exclude from the measure group, cancel the next schedule or change to another measure. have decided.

The policy information transmission unit 13 determines the influence of a plurality of policies for each combination of the policies. For example, if the combination of "soaking in a hot tub" and "eating vegetables" is rated B or C, the measure information transmitting unit 13 determines whether only the single measures of "soaking in a hot tub" or "eating vegetables" are considered. , or may be changed to combine any of these measures with other measures.

The measure information transmission unit 13 performs such evaluation processing to determine whether schedule correction (mainly by changing the measure group to another measure) is necessary or not. to correct the schedule.

Then, the user terminal 30 transmits schedule correction data, which is data of the corrected schedule, and the process proceeds to STEP19. On the other hand, when it is determined that the schedule need not be revised, the measure information transmission unit 13 advances the processing from STEP 18 in FIG. 5 to STEP 19 in FIG. 5, and in this case the schedule is not revised.

Next, in STEP 19, the policy information transmission unit 13, based on the initial user information data, the current user information data, the data on the execution of the action outside the policy, etc., each non-policy action performed by the user Ua (included in the schedule In addition to measures, actions performed by users) are evaluated.

The policy information transmitting unit 13 uses the evaluation criteria for the behavior outside the policy shown in FIG. 16A to determine whether the behavior outside the policy is recommended or not. In the evaluation criteria of FIG. 16A, influences on each state of skin, body, and mind are used as judgment items.

If "there is a positive effect on the skin, body, or mind, and there is no negative effect on any of them," the evaluation is D, and implementation is recommended. On the other hand, if it "has a negative effect on the skin, body or mind," an E rating is given and a ban is proposed. In addition, if "there is no effect on the skin, body, or mind," the evaluation is F, and implementation is not recommended.

FIG. 16B shows an example in which the evaluation criteria shown in FIG. 16A are applied to behaviors outside the policy (receiving a massage, ingesting a large amount of chocolate) in this embodiment. According to FIG. 16B, the measure information transmitting unit 13 determines that “get a massage” is evaluated as D and is recommended to be implemented in the next schedule. In addition, regarding "a large amount of chocolate intake", it is evaluated as E, and it is decided to propose a measure to stop it in the next schedule (for example, an alternative to ingesting Japanese sweets).

When the measure information transmitting unit 13 decides to recommend or prohibit the execution of the measures incorporated in the schedule, or to propose to cancel or change the measures outside the measures, the process advances from STEP 20 to STEP 31 . Then, when the recommendation is determined, the process proceeds from STEP31 to STEP33, the non-policy action recommendation data recommending the execution of the non-policy action is transmitted to the user terminal 30, and the process proceeds to STEP13 in FIG.

On the other hand, when the policy information transmitting unit 13 decides to propose the prohibition, the process advances from STEP 31 to STEP 32, and transmits to the user terminal 30 non-policy action prohibition data proposing prohibition of the non-policy action. The process proceeds to STEP13.

If there is no non-policy action to be recommended or prohibited, the policy information transmitting unit 13 advances the process from STEP 20 to STEP 13 in FIG. If the non-policy action proposed to be prohibited includes an alternative action, non-policy action prohibition data proposing a change to the alternative action may be transmitted to the user terminal 30 instead of the prohibition proposal.

In the user terminal 30, when the schedule correction data is received from the skin support server 10 in STEP57 of FIG. 7, the skin support application 35a proceeds to STEP65 and corrects the schedule based on the schedule correction data. The skin support application 35a then updates the schedule data held in the memory 36 and advances the process to STEP58.

Further, when the skin support application 35a receives the non-policy action recommendation data or the non-policy action prohibition data from the skin support server 10 in STEP 58 of FIG. is displayed on the display device 31 of the user terminal 30 to inform the user of the recommendation or prohibition proposal. Note that this notification may be performed by displaying a notification message on the progress table shown in FIG. 12, for example. In addition, the recommended non-policy behavior may be used as a new policy to modify the schedule.

In addition, the relationship recognition unit 22, the current user information recognition unit 23, the first element recognition unit 24, the second element extraction unit 25, the prediction unit 26, the goal recognition unit 27, and the action recognition unit 28, in STEP400 of FIG. A recognition process may be performed to modify the schedule. Details of the policy recognition process will be described later.

When skin support application 35a receives information indicating a measure recognized in the measure recognition process from skin support server 10, skin support application 35a executes measure output processing shown in STEP 110 of FIG. Details of the policy output process will be described later.

[H. Goal Judgment Process]
The process of "5. Goal determination" shown in FIG. It is executed by the selection unit 12 and the policy information transmission unit 13 .

The skin support application 35a displays on the display 31 of the user terminal 30 a message prompting the user to use the keratin collecting tape 37 and to send the keratin collecting tape 37 by mail.

The skin condition recognition unit 11 recognizes objective values of the skin condition (water retention capacity, sebum suitability, etc.) based on the information obtained from the mailed keratin collecting tape 37 . The objective value may be a value that indicates the physical state of the skin, such as a moisture content of 0-100 μS, or normalized by assigning each physical state to one of 1 to 10 according to the magnitude. can be a value.

In addition, the skin support application 35a displays on the display 31 of the user terminal 30 a message prompting the user to answer the questionnaire and use imaging of the skin, etc., and transmits the obtained information.

The skin condition recognition unit 11 recognizes the subjective value of skin dryness (subjective score) and the objective value of skin condition (skin image score) based on the information obtained from the skin support application 35a.

The measure group selection unit 12 and the measure information transmission unit 13 compare the evaluation values at the start of skin condition support and after 3 months based on the subjective value of skin dryness and the objective value of skin condition, Determine goal achievement.

In the example of FIG. 17, the subjective score of dryness increased from 3 to 5 in 3 months, so it was determined to be achieved (○). , the water retention capacity was maintained at 2, and the sebum suitability increased from 1 to 2, so it was determined to be achieved (○). Since both the subjective value and the objective value have achieved the goal, it is determined that the goal has been achieved as a whole.

In order to set a new goal when the goal is achieved, the skin condition recognition unit 11 and the measure group selection unit 12 repeat the process of "1. Goal setting" shown in FIG. The skin condition recognizing unit 11 and the measure group selecting unit 12 may perform the step of "2. Determine schedule" when the goal is not achieved, or perform the step of "1. Goal setting" according to the user's desire or progress. The process may be performed again.

[I. Secondary data generation]
The step of “6. Secondary data generation” shown in FIG. 3 is executed by the secondary data generator 19 . The secondary data generation unit 19 generates various data transmitted from a plurality of users U, such as initial user information data, stratum corneum analysis data, current user information data, execution status data, non-measure action execution data (hereinafter referred to as user-specific data). data) are sequentially stored in the support DB 20 for each user.

Then, the secondary data generation unit 19 analyzes the individual user data accumulated in the support DB 20, and, for example, as shown in FIG. ), support improvement targets (dryness, stiffness, sensitivity, acne, wrinkled feeling, etc.), measures proposed by the schedule (soaking in a bathtub, eating vegetables, walking more than 10,000 steps, etc.), and skin by implementing measures, Secondary data is generated that associates changes in each state of the body and mind.

18, the secondary data generation unit 19 also generates secondary data that associates each user's characteristics with the effects of each user's skin, body, and mental state due to the implementation of the non-policy action, as in FIG. Generate.

The secondary data generated in this manner is effectively used when selecting effective measures for improving the skin condition while having a positive effect on the body and mind of users with similar characteristics. be able to. For example, in the process of "2. schedule determination" in FIG. 3 described above, the measure group selection unit 12 can refer to the secondary data and select a measure suitable for the characteristics of the user Ua.

In addition, in the process of "4. Schedule correction" in FIG. can be referred to and the schedule can be changed to include more effective measures or actions outside the measures.

In addition, the secondary data can be effectively used to support skin conditions, and can be used for marketing purposes targeting users with certain characteristics (for example, outdoor-oriented women in their 30s living in Tokyo). can also be used. As such, the secondary data may be provided to other systems such as 60 shown in FIG.

[J. Input reception processing]
Next, with reference to FIG. 19A, the details of the input reception processing in STEP 100 of FIG. 6 will be described.

In STEP 102 of FIG. 19A, skin support application 35a displays information input screen P1 shown in FIG.

The information input screen P1 includes an input display P11 regarding skin care, an input display P12 regarding body condition, an input display P13 regarding mood and behavior, a disease state P14, and an enter button P15.

In the input display P11 regarding skin care, input forms regarding each element regarding skin are nested, such as an input form P111 for acne and an input form P112 regarding the state of milky lotion during skin care.

The input display P12 regarding the physical condition includes, for example, a constipation input form P121, a physical condition input form P122, a tongue color input form P123, a tongue spot input form P124, a cracked tongue input form P125, and a tongue coating input form P126. , an input form for each element related to the body is nested. Here, the term "physical condition" is used as a term that means a condition of the body other than the skin and includes diseases and the like, but does not include a mental condition like a mental condition.

In the input display P13 regarding mood/behavior, there is an input form P131 for today's mood, an input form P132 for feelings of fatigue, an input form P133 for whether or not you were able to interact comfortably, and an input form P134 for yourself today. Or the input form for each element related to action is nested.

In the input display P14 regarding the state of the disease, input forms regarding each element regarding the disease such as a today's state input form P141 and a blood value input form P142 are nested.

Each input form may be an input form that accepts input of numerical values such as 5, an input form in which numerical values can be specified using a slide bar, or a pull-down menu that indicates each state. It may be a selectable selection box, a text box in which the user Ua can arbitrarily enter text, or another input form.

In STEP 104 of FIG. 19A , the skin support application 35a recognizes the input information when detecting the operation of the user Ua via the touch panel 32 .

In STEP 106 of FIG. 19A, the skin support application 35a refers to the clock function (not shown) to recognize the input date and time. Additionally or alternatively, the skin support application 35a may recognize the date and time specified by the user via the touch panel 32. FIG.

Upon detecting the press operation of the enter button P15 via the touch panel 32, the skin support application 35a transmits the input information and the input date and time to the skin support server 10 in STEP108 of FIG. 19A.

[K. Input reception processing]
Next, with reference to FIG. 19B, the score storage processing executed in STEP200 of FIG. 4 will be described.

In STEP 202 of FIG. 19B, the score information recognition unit 21 scores the state of each element included in the information received from the user terminal 30 according to specified criteria. For example, the score information recognizing unit 21 uses a table or a function (not shown) to score the received information such as "five" pimples into a score of pimples 50, as shown in FIG. When the state of each element is represented by a numerical value, the score information recognizing unit 21 may recognize the numerical value as the score as it is, or may calculate the score by performing an operation or the like using a predetermined formula. The score information recognition unit 21 may score text information freely input by the user, for example, using a filter generated by machine learning. The score information recognition unit 21 may score the text information freely input by the user by counting a predetermined number of words. The score information recognition unit 21 may recognize one score from a plurality of pieces of input information, or may recognize a plurality of scores from one piece of input information.

In STEP 204 of FIG. 19B, the score information recognition unit 21 stores each score and the date of input in the support DB 20 as part of the current user information data. Instead of using the information received from the user terminal 30, or in addition to using the information received from the user terminal 30, the score information recognition unit 21 refers to the clock function of the skin support server 10 for the input date, thereby recognizing the date when STEP 204 is executed. You may

[L. Correlated behavior information memory processing]
Next, with reference to FIG. 19C, the correlated behavior information storage processing executed in STEP300 of FIG. 4 will be described.

In STEP302 of FIG. 19C, the score information recognition unit 21 scores the actions of the user Ua indicated in the received information. The received information may be, for example, the policy implementation data received in STEP 14 of FIG. 5, the non-policy action implementation data received in STEP 15 of FIG. It may be information input on the information input screen P1 of FIG. 20 or a screen similar thereto, or may be information received at any timing from the wearable device used by the user Ua of the user terminal 30. , information received from an external server such as a SNS (Social Networking System) used by the user Ua.

In STEP304 of FIG. 19C, the score information recognition unit 21 stores the score and date of the behavior of the user Ua in the support DB20. The date is the same as in STEP204.

In STEP306 of FIG. 19C, the relationship recognition unit 22 analyzes the score of the skin, mind, or body element of the user Ua in each period, and extracts the period when the score satisfies a predetermined condition. For example, the relationship recognition unit 22 extracts a period during which the score of the skin, mind, or body element of the user Ua is included in a predetermined first range. In addition, the relationship recognition unit 22 recognizes the differential value or the amount of change of the score of the skin, mind, or body element of the user Ua. In addition, the relationship recognition unit 22 extracts a period in which the differential value or the amount of change of the score of the skin, mind, or body element of the user Ua is included in a predetermined second range.

More specifically, for example, the relationship recognition unit 22 refers to the table on the right side of FIG. 21 stored in the support DB 20 as shown in FIG. (t is a variable indicating a date) recognizes the periods t _i1 to t _i2 and t _j1 to t _j2 in which it is equal to or greater than a predetermined threshold value C1.

Then, in STEP 308 of FIG. 19C, the relationship recognition unit 22 refers to the table on the right side of FIG. Recognize behavioral scores prior to a predetermined first time period.

For example, as shown in FIG. 22, the relationship recognizing unit 22 detects a period ti1, which is a predetermined first period Δt1 before the periods t _i1 to t _i2 and t _j1 to t _j2 recognized in STEP 306 of FIG. 19C. Recognize the score D(t) of the behavior of −Δt1˜t _i3 −Δt1, t _j1 −Δt1˜t _i3 −Δt1. Here, the first period Δt1 may be a predetermined period, such as one day, three days, one week, or one month. It may be a period determined for each element of the skin, mind, or body, such as a period corresponding to dryness and vegetable intake, and each element of the skin, mind, or body and behavior. It may be a period specified in Also, the first period Δt1 may be a period determined in advance by a developer or the like, or may be a period determined based on past data of the user Ua.

Also, the length of the period recognized in STEP 306 and the length of the period recognized in STEP 308 do not have to be the same. may be longer. Also, the length of the period recognized in STEP 308 may be calculated by multiplying the length of the period recognized in STEP 306 by an appropriate factor.

In STEP 310 of FIG. 19C , the relationship recognition unit 22 obtains the score C(t) of the element of the skin, mind, or body during the period when the score of the element of the skin, mind, or body satisfies a predetermined condition, and a predetermined number from that period. Recognize the correlation coefficient r(C, D) with the behavior score D(t) one period before.

For example, the relationship recognition unit 22 uses Equation 1 below to determine the score C(t) of the element of the skin, mind, or body during the period when the score of the element of the skin, mind, or body satisfies a predetermined condition, and a predetermined period from the period. A correlation coefficient r(C, D) with the behavior score D(t) before the first period is obtained.

Here, S _CD is the score C(t) of the skin, mind, or body element during the period when the score of the skin, mind, or body element satisfies a predetermined condition, and the behavior during a predetermined first period before that period. with the score D(t).

Sc is the score C(t) of the skin, mind, or body element in the period under consideration that is longer than the period and the first period, including the period in which the score of the skin, mind, or body element satisfies a predetermined condition. is the standard deviation of

_SD is the standard deviation of the behavioral component scores D(t) for the period under consideration, including the skin and the first period.

The first period is, for example, one week, and the period under consideration is, for example, three months.

The relationship between the covariance S _CD and the standard deviations S _C and S _D is shown in FIG.

If the number of scores C(t) for the elements of the skin, mind, or body during the period satisfying a predetermined condition is different from the number of scores D(t) for the behavior during a predetermined first period prior to the period, relationship recognition The unit 22 may calculate the covariance by adjusting as follows, for example.

(1) Complementary scores are created based on a small number of scores.

For example, there are two scores C(t _i1 ) and C(t _i2 ) for the elements of the skin, mind, or body during the period that satisfies the predetermined condition, and the action score before the first predetermined period is the score for the period. If there are three of D(t _i1 −Δt ₁ ), D(t _i2 −Δt ₁ ), and D(t _i3 −Δt ₁ ), the relationship recognition unit 22 determines C(t _i1 ) or C(t ₂ ) is increased by one, or the average of C(t _i1 ) and C(ti ₂ ) (C(t _i1 )+C(t _i2 ))/2 between C(t ₁ ) and C(t ₂ ) By adding, the number of scores for skin, mind, or body elements in a period that satisfies a predetermined condition used for covariance and the number of scores for actions in a predetermined first period before that period are made the same. do.

(2) ignore some of the large number of scores;

For example, there are two scores C(t _i1 ) and C(t _i2 ) for the elements of the skin, mind, or body during the period that satisfies the predetermined condition, and the action score before the first predetermined period is the score for the period. When there are three of D(t _i1 −Δt ₁ ), D(t _i2 −Δt ₁ ), and D(t _i3 −Δt ₁ ), the relationship recognition unit 22 recognizes D(t _i1 −Δt ₁ ), D( By ignoring either t _i2 −Δt ₁ ) or D(t _i3 −Δt ₁ ), the number of scores of the skin, mind or body element in the period satisfying the predetermined condition used for covariance and the period The number of behavior scores before the predetermined first period is made the same.

Note that the relationship recognition unit 22 calculates the covariance while maintaining the time-series order of the scores.

In STEP 312 of FIG. 19C , the relationship recognition unit 22 recognizes the elements and behaviors related to the skin, mind, or body having a correlation coefficient in a predetermined third range (for example, a range of 0.5 or more or less than −0.5) and and the correlation coefficient are stored in the support DB 20 .

For example, as shown in FIG. 24A, the relationship recognizing unit 22 recognizes the elements "acne", "dryness", "headache" and the behaviors "eating vegetables", "drinking water", drink" and these correlation coefficients "-0.8", "-0.6" and "0.8" are associated and stored in the support DB20.

[M. Policy Recognition Processing]
Next, with reference to FIG. 25, the policy recognition processing executed in STEP400 of FIG. 5 will be described.

In STEP402 of FIG. 25, the relationship recognition unit 22 analyzes the score of each element of the skin in each period and extracts the period in which the score satisfies a predetermined condition. For example, as shown in FIG. 26, the relationship recognition unit 22 extracts periods _tk1 to t _k3 and t _l1 to t l3 during which the acne score E(t) is equal to or greater than E1. The processing of STEP 402 in FIG. 25 is the same as the processing of STEP 306 in FIG. 19, so the description is omitted.

Hereinafter, the element to be processed will be described as the first element. The first element may be an element selected according to some criteria, or may be an element designated by the user Ua or the like. Alternatively, the processing from STEP 404 in FIG. 25 may be performed with each element of the skin as the first element.

In STEP 404 of FIG. 25, the relationship recognition unit 22 recognizes the score of each element of the body after a predetermined period of time from the period in which the score of the first element satisfies the predetermined condition.

For example, as shown in FIG. 26, the relationship recognition unit 22 performs periods t _k1 +Δt2 to t _k2 +Δt2, t _l1 +Δt2 to t _l2 after a predetermined period Δt2 of the periods t k1 to t _k3 and t _l1 to t _l3 . By _extracting + _Δt2 and _referring to the table shown on the right side of FIG. ), F(t _l2 +Δt2). STEP404 in FIG.
The details of the process are the same as those of STEP 308 in FIG. 19C, so description thereof is omitted.

In STEP406 of FIG. 25, the relationship recognition unit 22 recognizes the correlation coefficient between the skin element and the body element. The processing of STE406 in FIG. 25 is the same processing as STEP310 in FIG. 19C. As shown in FIG. 24B , the relationship recognition unit 22 associates the skin element, the body element, and their correlation coefficients and stores them in the support DB 20 .

In STEP 410 of FIG. 25 , the current user information recognition unit 23 recognizes the latest current user information data stored in the support DB 20 .

In STEP410 of FIG. 25, the first element recognition unit 24 recognizes the latest score of the first element of the skin of the user Ua based on the latest current user information data.

In STEP412 of FIG. 25, the second element extraction unit 25 refers to the support DB 20, and the correlation coefficient with the first element is included in a predetermined four ranges (for example, 0.7 or more, or less than -0.7). Extract the second element of the body. For example, from the table shown in FIG. 24B, the second element extraction unit 25 determines that the correlation coefficient with the first element “acne” is included in four predetermined ranges (for example, 0.7 or more, or less than −0.7). Extract the second element of the body, "headache".

In STEP414 of FIG. 25, the prediction unit 26 predicts future scores of the second element based on past data and correlation coefficients. For example, if the correlation coefficient is a positive first predetermined value (for example, 0.7) or more, the prediction unit 26 increases the current score by the average value of increments in the past data, and predicts the future value of the second element. Predict score. For example, if the correlation coefficient is a negative predetermined value (for example, -0.7) or less, the prediction unit 26 determines that the current score is decreased by the average value of the decrement in the past data, and the future value of the second element is Predict score.

In STEP416 of FIG. 25, the target recognition unit 27 recognizes the target score for the second element of the user Ua's body. This target score may be, for example, the target score set in STEP 11 of FIG. 4 and stored in the support DB 20 .

In STEP418 of FIG. 25, the action recognition unit 28 determines whether or not the future score of the second element is within a predetermined fifth range.

For example, a fifth range may be the range in which the future score for the second factor is further from the target score for the second factor than the current score for the second factor. For example, if the target score is 100 and the current score is 50, the fifth range is the range below 50. Also, the fifth range may be a predetermined range in which a symptom of a disease such as headache appears. The fifth range may be a range designated by the user Ua or the like.

Additionally or alternatively, the action recognizer 28 may determine whether the future score of the second factor is less than the target score and a predetermined value (eg, the current score of the second factor). Additionally or alternatively, the action recognizer 28 may determine whether the future score of the second factor is further from the target score than the current score of the second factor.

If the determination result is affirmative (YES in STEP418 of FIG. 25), in STEP420 of FIG. 25, the action recognition unit 28 recognizes measures for avoiding the score of the second element from the predetermined fifth range. The action recognizing unit 28 may recognize a measure in which the future score of the second element is out of the predetermined fifth range by referring to a table or the like in which the second element and countermeasures are stored in advance. . Further, based on the correlation coefficient between the first element or the second element and the behavior shown in FIG. may be recognized as a measure that causes the future score of the second element to fall outside the predetermined fifth range.

If the determination result is negative (NO in STEP418 of FIG. 25), in STEP420 of FIG. 25, the action recognition unit 28 recognizes that the current action is maintained as a measure.

In STEP422 of FIG. 25, the action recognition unit 28 transmits information including the recognized measure, the state corresponding to the current score of the first element, and the state corresponding to the future score of the second element to the user terminal 30. .

[N. Policy Recognition Processing]
Next, with reference to FIG. 19D, the policy output processing executed in STEP 110 of FIG. 7 will be described.

In STEP 112 of FIG. 19D, the skin support application 35a creates a measure recommendation screen P2 based on the received information.

In STEP 112 of FIG. 19D, the skin support application 35a outputs the measure recommendation screen P2 to the display 31. In FIG.

As shown in FIG. 27, the policy recommendation screen P2 includes a display P21 of prediction information and a display P22 of policy information.

The display P21 of the prediction information shows the state ("increasing" in FIG. 27) according to the current score of the first element ("acne" in FIG. 27) and the state of the second element ("headache" in FIG. 27). FIG. 27 is a display of information including a state ("worse" in FIG. 27) according to the future score.

The policy information display P22 is a display of information indicating the policy recognized in STEP418 or STEP420 of FIG.

(Action and effect of the present embodiment)
According to the information output system of the present embodiment, the score information recognizing unit 21 recognizes the time-series transition of scores of a plurality of elements of the user based on the user's unique user information, as shown on the right side of FIG. The indicated information is recognized (STEP202 to STEP204 in FIG. 19B).

Based on the information, the relationship recognition unit 22 recognizes the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period. (STEP402 to STEP404 in FIG. 25).

If there is a correlation between the time-series transition of the score of one element and the time-series transition of the score of the other element shifted by a predetermined period, in other words, the change of one element If there is a correlation with changes in the other element after a point in time, changes in the one element may cause changes in the other element. That is, one element may have some causal relationship with another element.

Then, based on the correlation, the skin support application 35a outputs a display P21 of prediction information indicating one or more sets of elements to the display 31 (STEP 114 in FIG. 19D).

This allows the user Ua to recognize a set of elements that may have a causal relationship.

Also, the above correlation is a correlation obtained from the time-series transition of scores of each of a plurality of unique elements of the user Ua. Therefore, such a correlation can be recognized without prior research or the like.

Therefore, even if two elements have not received attention until now, the information output system having this configuration can make the user Ua recognize the relationship between these elements.

Further, according to the information output system having this configuration, the first element recognition unit 24 recognizes the first element of the user indicated in the current user information (STEP 410 in FIG. 25).

Then, the second element extracting unit 25 extracts the second element of the user correlated with the first element based on the information indicating the mutually correlated elements (STEP 412 in FIG. 25).

Then, the prediction unit 26 predicts the future score of the second element of the user based on the information indicating the mutually correlated elements and the score of the first element indicated in the current user information (STEP 414 in FIG. 25). .

Then, the skin support application 35a outputs a display P21 of prediction information corresponding to the future score of the second factor of the user to the display 31 (STEP 114 in FIG. 19D).

This allows the user Ua to recognize the future state of the user Ua predicted from the current state of the user Ua based on the correlation recognized based on the information of the user Ua and the current user information indicating the current state of the user Ua. can provide more useful information to the user Ua.

Moreover, according to the information output system of the said structure, the user Ua can be informed of the causal relationship between the elements regarding the skin and the body which the user Ua tends to be interested in.

Further, according to the information output system of this configuration, it is possible to inform the user of the relationship between the behavior of the user Ua that can be directly controlled by the user Ua and the element related to one of the skin, body, and mind that the user Ua tends to be interested in. can be done. As a result, the user Ua can be given a hint to indirectly control the state of one of the skin, body, and mind that the user Ua is likely to be interested in.

Further, according to the information output system having this configuration, the current user information recognition unit 23 recognizes the current user information data, which is the latest information of the user Ua (STEP 408 in FIG. 25).

Then, the first element recognition unit 24 recognizes the first element, which is an element related to at least one of the skin, body, and mind of the user Ua indicated in the current user information data (STEP 410 in FIG. 25).

Then, the second element extraction unit 25 extracts an element that is correlated with the first element and related to at least one of the skin, body, and mind of the user Ua based on the information indicating the mutually correlated elements. is extracted (STEP 412 in FIG. 25).

The prediction unit 26 predicts the future score of the second element of the user based on the correlation coefficient and the score of the first element indicated in the current user information data (STEP 414 in FIG. 25).

When the future score of the second element of the user is included in the predetermined range by the action recognition unit (YES in STEP418 in FIG. 25), the action of setting the future score of the second element outside the predetermined range is recognized (STEP 420 in FIG. 25).

The output control unit outputs the information recommending the execution of the action to the output unit (SETP114 in FIG. 19D).

Thereby, based on the correlation coefficient, if the future score of the second element is predicted to be included in the predetermined range, the future score of the second element is performed outside the predetermined range or Information recommending avoidance is output to the output section. As a result, it is possible to provide the user Ua with an effective action from the viewpoint of keeping the future score of the second element of the user Ua within the predetermined range or deviating from the predetermined range.

Further, according to the information output system having this configuration, when the future score of the second element of the user Ua is included in the predetermined range, the action recognition unit 28 detects the first element based on the correlated action information (FIG. 24A). Alternatively, an action that has a correlation with the second element and causes the future score of the second element to fall outside the predetermined range is recognized (FIG. 5/STEP 420).

Here, the correlated behavior information indicates the behavior correlated with each element and the correlation coefficient between each element and the behavior.

This allows the user Ua to recognize actions that may have a causal relationship with the first element or the second element.

In addition, even if the relationship between the first element or the second element and the action has not been noticed until now, according to the information output system of the configuration, it is possible to have a causal relationship with the first element or the second element. It is possible to make the user Ua aware of the behavior that may cause the future score of the second element to fall outside the predetermined range.

According to the information output system with this configuration, when it is predicted that the future score of the second element is included in the range (fifth range) that deviates from the target score (FIG. 25/YES in STEP 418), that is, the second When the future score of the two elements is predicted to be far from the target score, the action recognition unit 28 recognizes an action that makes the future score of the second element out of the predetermined range (FIG. 25/STEP 420 ).

Then, the skin support application 35a outputs to the display device 31 information recommending the implementation of the action.

Thereby, when there is a possibility that the future score of the second element deviates from the target, it is possible to recommend the user Ua to take action to avoid the situation.

(Modification mode)
Based on the information of the plurality of users Ua, Ub, and Uc, the above information output system allows the support DB 20 to display information between each element for each attribute such as age, gender, and address of each of the plurality of users Ua, Ub, and Uc. may be recognized.

In this case, based on the correlation corresponding to the target attribute, the skin support application 35a outputs to the display device 31 information indicating each element that corresponds to the target attribute and is correlated with each other. may be

With this configuration, even at a stage where sufficient information on one user Ua is not collected, such as at the start of service provision, information on other users Ub and Uc having the same attributes as the user Ua can be collected. If so, the correlation between the elements is recognized for each attribute based on a plurality of pieces of information. Therefore, the user Ua is informed of each element that corresponds to the target attribute (attribute of the user Ua) and has a correlation with each other. can be tightened.

In the above embodiment, the skin support application 35a includes the score information recognition unit 21, the relationship recognition unit 22, the current user information recognition unit 23, the first element recognition unit 24, the second element extraction unit 25, the prediction unit 26, the target It may function as part or all of the recognition unit 27 and the action recognition unit 28 . In this case, the skin support application 35a may communicate with the skin support server 10 in advance or during execution of the process to download data necessary for the process, or cause the skin support server 10 to execute an appropriate function such as searching. , may receive its results.

In the above embodiment, when the future score of the second element is within the predetermined range (FIG. 25/STEP 418 . YES), the action recognition unit 28 determines that the future score of the second element is within the predetermined range. I recognized a measure that would be outside (FIG. 25/STEP 420). This assumes that the predetermined range is not preferable for the user Ua.

Alternatively, if the future score of the second factor is within a preferable range, such as approaching or achieving the target score of the user Ua, the action recognition unit 28 determines that the future score of the second factor is Behavior that falls outside of a predetermined range may be recognized. The skin support application 35a provides an action avoidance such as "It seems that the target score of the second element can be achieved if things continue as they are. Let's avoid actions like XX that hinder the achievement of this target score." may be displayed on the display device 31.

The relationship recognition unit 22 determines the ratio of the scores of other elements exceeding or falling below a predetermined second threshold before and after a predetermined period of time during which the score of one element exceeds or falls below a predetermined first threshold, or other elements The correlation coefficient may be calculated by calculating the ratio of the score of , including the inflection point.

The relationship recognition unit 22 determines the ratio of the scores of other elements exceeding or falling below a predetermined second threshold for a predetermined period of time before and after the inflection point of the score of one element, or the score of the other elements being inflection. You may calculate a correlation coefficient by calculating the ratio containing a point.

In the above embodiment, the correlation is recognized by the score of each element, but instead of or in addition to this, each score may be a binary value indicating whether or not a specific state has occurred. good.

Note that in the above embodiment, the score information recognition unit 21 recognizes the scores of the first element, the second element, etc. based on the information input in the input reception process of FIG. 19A, but the present invention is not limited to this.

For example, based on the information included in the current user information data received from the user terminal 30, the score information recognition unit 21 determines the skin condition, body condition, mental condition, and user Ua's condition according to specified criteria. Each component associated with each behavior may be scored.

For example, the score information recognition unit 21, based on the data received from the user terminal 30, the stratum corneum moisture content, the sebum content, the amount of water evaporation, the viscoelasticity, the stratum corneum state, the electrical conductivity, the electrical signal, the pulse rate, and the like. Waves, sweat, blood flow, nail color, photographic data, body odor, body color, subjectivity, tongue state, lifestyle, and palmistry scores may be calculated.

For example, the score information recognition unit 21 receives data (skin potential, sweat, heart rate, blood flow state) from a wearable device attached to the skin of the user Ua every day and user Ua's subjectivity received from the user terminal 30. Then, each element related to the user Ua's skin condition, body condition, mental condition, and behavior of the user Ua is scored. Each element related to the skin condition of the user Ua may be scored based on the analysis result of the keratin collecting tape.

For example, the score information recognition unit 21 recognizes the condition of the skin of the user Ua based on the image data of the skin of the user Ua received from the user terminal 30 and the subjective opinion of the user Ua received from the user terminal 30 every day. Each element is scored, and in addition, the score information recognition unit 21 scores each element related to the skin condition of the user Ua based on the analysis results of the keratin collecting tape periodically mailed by the user Ua. may

For example, the score information recognition unit 21 receives data (skin potential, sweat, heart rate, blood flow state) from a wearable device attached to the skin of the user Ua every day and user Ua's subjectivity received from the user terminal 30. Then, each element related to the user Ua's skin condition, body condition, mental condition, and behavior of the user Ua is scored. Each element related to the skin condition of the user Ua may be scored based on the received image data of the skin of the user Ua.

In addition, for example, the score information recognition unit 21, based on the user Ua's subjective opinion received from the user terminal 30 every day, generates a In addition, the score information recognizing unit 21 scores each element related to the skin condition of the user Ua based on the analysis results of the keratin collecting tape periodically mailed by the user Ua. may be changed.

For example, the score information recognition unit 21 receives data (skin potential, sweat, heart rate, blood flow state) received from the wearable device attached to the skin of the user Ua every day or at regular intervals, and data received from the user terminal 30 Each element related to each of the user Ua's skin condition, body condition, mental condition, and behavior of the user Ua may be scored based only on the subjectivity of the user Ua.

Further, for example, the score information recognition unit 21, based only on the subjectivity of the user Ua received from the user terminal 30 every day or at regular intervals, recognizes the user Ua's skin condition, body condition, mental condition, user Each factor associated with each of Ua's actions may be scored.

Also, for example, the score information recognition unit 21 scores each element related to the skin condition of the user Ua based only on image data of the skin of the user Ua received daily or periodically from the user terminal 30. may

The score information recognition unit 21 scores each element related to the state of the body of the user Ua based on the body odor of the user Ua detected by the odor sensor attached to the user Ua every day or periodically. good too.

The prediction unit 26 refers to, for example, a correspondence table (not shown) based on one element such as the behavior of the user Ua during a certain period of time (eg, one week) in the past. You may predict the score of another element, or its change amount.

Also, the prediction unit 26 may recognize the periodicity of the presence or absence or degree of one element of the user Ua, and predict the score of another element after a predetermined period from the current time or the amount of change thereof based on the periodicity. good.
For example, the prediction unit 26 decomposes the time-series data of the presence or absence or degree of one element of the user Ua into frequency components by discrete Fourier transform, and obtains the ratio of the peak at each frequency exceeding the threshold value. Recognize the periodicity of the presence or degree of the elements of Then, the prediction unit 26 assumes that the cycle of one element continues, and estimates the cumulative value of the number of times or degree of one element in a predetermined period. Then, the prediction unit 26 predicts the score of another element after a predetermined period from the current time or the amount of change thereof using the cumulative value of the number of times or the degree of one element during the predetermined period from the current time and a correspondence table (not shown). You may

The prediction unit 26 considers not only the data of the user Ua, but also the scores of other elements with respect to one element in the past indicated by the other users Ub and Uc or the amount of change thereof, and You may predict the score of one element of or its change amount.

For example, the prediction unit 26 extracts users who share at least one of the same constitution, attributes, and goals as the user Ua, and extracts these users' past actions after a predetermined period of time from the time of processing each of these users' past actions. Recognize the amount of change in the user's score for one factor. The prediction unit 26 calculates the amount of change in the score of the other element after a predetermined period from the current time with respect to the score of the past one element of these users, and the score of the one element of the user Ua from the current time. You may predict the score of other elements of Ua, or its change amount.

The prediction unit 26 may, for example, refer to a correspondence table to predict other factors of the user based on one factor of the user for each part of the user's face or body.

The output control unit selects, for example, an image obtained by correcting the face photograph or body photograph of the user Ua, an avatar image imitating the user Ua, an animation character, etc. , or a figure such as a circle may be output to the display 31 .

In this case, for example, the output control unit may change the displayed image according to the operation on the input unit such as the touch panel 32 .

For example, the output control unit switches the reference time point for prediction in response to a first operation (for example, a horizontal sliding operation) on the input unit, and indicates the prediction of each element of the user Ua at the time point after switching. Images may be output to the display 31 . For example, in response to the first operation on the input unit, the output control unit outputs to the display 31 an image showing predictions of the scores of the user's elements one day, two days, three days, . . . from the current time. may The image showing the prediction of each factor of the user may be an image showing the prediction of the score (for example, skin condition) of one factor of the user Ua after a predetermined period of time from the current time. The image showing the prediction of the score (for example, skin condition) of one element of the user Ua after a predetermined period of time from the present time, for example, shows the condition of the skin of each part of the face or body of the user Ua three days later in words or colors. image, or an image showing the condition of the stratum corneum of the skin at each part of the face of the user Ua.

In addition, for example, the output control unit, in response to a second operation (for example, a slide operation in the vertical direction) on the input unit, determines the score of another element (for example, body condition) different from the one element for the user Ua. An image showing the prediction may be displayed.

The current user information recognition unit 23 may collect data indicating the physical constitution of the user Ua from a biological information collection device such as a weight scale, blood pressure monitor, heart rate monitor, or the like.

In addition, the current user information recognition unit 23 may collect data indicating the behavior of the user Ua from a living information collection device such as a pedometer, sleep depth sensor, wake-up time sensor, or the like.

The biological information collection device and the life information collection device may be, for example, a device carried by the user Ua like a smart phone, or a device worn by the user Ua like a smart watch. Each data may be transmitted from these devices to the skin support server 10 along with the identification information of the user Ua via a network such as the Internet, or data may be sent from these devices to the user terminal 30a of the user Ua via a USB connection or the like. Each data may be transmitted to the skin support server 10 together with the identification information of the user Ua at the user terminal 30a. These data may be collected by installing an application for measurement in the user terminal 30a.

Further, according to the study of the present inventors, from skin cell image data that is a captured image of skin cells, skin image data that is a captured image of skin, and tongue image data that is a captured image of the tongue, user behavior, It was found that the values of items related to at least one of physical condition, mental condition, attributes such as user's gender and age, user's constitution, and user's lifestyle (habitual behavior such as drinking alcohol) can be estimated with high accuracy. .

Utilizing this, the current user information recognition unit 23 recognizes at least one of the user Ua's skin cell image data, skin cell data, and tongue image data, and the score information recognition unit 21 recognizes the information entered in the input form. In addition to or instead of each information, based on at least one of user Ua's skin cell image data, skin cell data, and tongue image data, user Ua's behavior, attributes, constitution, physical condition, mental condition, and life Scores for elements such as style may be recognized.

More specifically, the score information recognition unit 21 recognizes at least one of pre-collected skin cell image data, skin cell data, and tongue image data of a plurality of people, and behavior, physical condition, and mental state of each of the plurality of people. A model such as a linear estimation formula or a convolutional neural network is constructed by machine learning using characteristics such as state, attributes, constitution and lifestyle as teacher data. Here, the skin cell image data may be, for example, image data obtained by enlarging and imaging cells collected with a keratin collection tape, or cells after treatment with a chemical such as a keratin stain or a melanin stain. Image data captured may be used.

For example, an imaging unit provided in the skin support server 10 may image skin cells, or the skin support server 10 may receive image data of skin cells captured by a camera of a smartphone or the like from a smartphone or the like.

Also, the skin image data may be an image of the skin of a person's face or arm captured by a smartphone or the like. For example, the imaging unit provided in the skin support server 10 may image the skin, or the skin support server 10 may receive image data of the skin captured by a camera such as a smartphone from the smartphone or the like.

Also, the tongue image data may be an image of a person's tongue captured by a smartphone or the like.
For example, an imaging unit provided in the skin support server 10 may image the tongue, or the skin support server 10 may receive image data of the tongue captured by a camera such as a smartphone from a smartphone or the like.

Characteristics such as behavior, attributes, constitution, physical condition, state of mind, and lifestyle of each of the plurality of persons may be obtained by hearing from the plurality of persons, for example. Data indicating the constitution of each of a plurality of persons may be collected from a biological information collection device such as a weight scale, blood pressure monitor, heart rate monitor, or the like. Data indicating the lifestyles of each of a plurality of people may be collected from a lifestyle information collection device such as a pedometer, a sleep depth sensor, or a wake-up time sensor.

In addition, the current user information recognition unit 23 recognizes skin cell image data, which is a captured image of cells collected by the keratin collecting tape 37a of the user Ua, skin image data, which is a captured image of the skin of the user Ua, and the tongue of the user Ua. Tongue image data, which is a captured image, is recognized.

The current user information recognition unit 23 may acquire image data of skin cells of the user Ua collected by the keratin collection tape 37a of the user Ua, which is imaged by the imaging unit provided in the skin support server 10 .
In addition to or instead of this, the current user information recognition unit 23 transmits image data of user Ua's skin cells collected by the keratin collection tape 37a of the user Ua captured by the camera 33a of the user terminal 30a from the user terminal 30a. may receive.

In addition, the current user information recognition unit 23 may acquire image data of the skin of the user Ua captured by the imaging unit provided in the skin support server 10 . Instead of or in addition to this, the current user information recognition unit 23 may receive skin image data captured by the camera 33a of the user terminal 30a from the user terminal 30a.

1... communication network, 10... skin support server, 11... skin condition recognition unit, 12... measure group selection unit, 13... measure information transmission unit, 19... secondary data generation unit, 20... support DB (database), 21... Score information recognition unit 22 Relationship recognition unit 23 Current user information recognition unit 24 First element recognition unit 25 Second element extraction unit 26 Prediction unit 27 Goal recognition unit 28 Action recognition Part 30 (30a, 30b, 30c) User terminal (smartphone) 31 Display (output unit) 32 Touch panel (input unit) 33 Camera 34 Microphone 35 CPU 35a Skin support Application (user information recognition unit, output control unit), 36 memory, 40 connection terminal, 45 measuring device.

Claims (15)

a user information recognition unit that recognizes unique user information of a user;
an output unit that outputs information;
a score information recognition unit that recognizes, based on the user information, score information indicating time-series transitions of scores of a plurality of elements included in the user information ;
a relationship recognition unit that recognizes the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period based on the score information;
a correlation information storage unit that stores information indicating mutually correlated elements and correlation coefficients between the elements based on the correlation;
a current user information recognition unit that recognizes current user information, which is the latest information of the user;
a first element recognition unit that recognizes the first element of the user indicated in the current user information;
a second element extraction unit that extracts a second element of the user that is correlated with the first element based on the information indicating the mutually correlated elements;
a prediction unit that predicts the future score of the second element of the user based on the score of the first element indicated in the correlation coefficient between the elements and the current user information;
An information output system , comprising: an output control unit that outputs information corresponding to the future score of the second element of the user to the output unit . a user information recognition unit that recognizes unique user information of a user;
an output unit that outputs information;
a score information recognition unit that recognizes, based on the user information, score information indicating time-series transitions of scores of a plurality of elements included in the user information;
a relationship recognition unit that recognizes, based on the score information, the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period;
a correlation information storage unit that stores information indicating mutually correlated elements and correlation coefficients between the elements based on the correlation;
a current user information recognition unit that recognizes current user information, which is the latest information of the user;
a first element recognition unit that recognizes the first element of the user indicated in the current user information;
a second element extraction unit that extracts a second element of the user that is correlated with the first element based on the information indicating the mutually correlated elements;
a prediction unit that predicts the future score of the second element of the user based on the score of the first element indicated in the correlation coefficient and the current user information;
an action recognition unit that recognizes, when the future score of the second element of the user is within a predetermined range, an action that causes the future score of the second element to fall outside the predetermined range;
an output control unit configured to output information recommending implementation or avoidance of the action to the output unit;
The correlation information storage unit is configured to store correlated behavior information indicating behavior correlated with each element and a correlation coefficient between each element and the behavior,
The action recognition unit has a correlation with the first element or the second element based on the correlated action information when the future score of the second element of the user is included in a predetermined range, and An information output system configured to recognize an action that causes a future score of the second element to fall outside the predetermined range . In the information output system according to claim 1 or 2,
The information output system , wherein the first element is an element related to one of skin, body and mind, and the second element is an element related to the other of skin, body and mind . In the information output system according to claim 1 or 2 ,
The information output system , wherein the first element is a user's behavior, and the second element is an element related to one of skin, body and mind . In the information output system according to claim 2 ,
a target recognition unit that recognizes a target related to the second element of the user;
The action recognition unit, when the future score of the second element of the user is predicted to move away from the target, recognizes an action that brings the future score of the second element closer to the target,
The information output system , wherein the output control unit is configured to output information recommending implementation of the action to the output unit . In the information output system according to any one of claims 1 to 5 ,
The user information recognition unit recognizes at least one of skin cell image data, skin image data, and tongue image data of the user,
The score information recognition unit is configured to recognize information indicating time-series transition of scores of the plurality of elements of the user based on the skin cell image data, skin image data, and tongue image data of the user. An information output system characterized by: In the information output system according to any one of claims 1 to 6 ,
An input unit that receives the user's operation,
The user information recognition unit recognizes information input to the input unit as information of the user,
The information output, wherein the score information recognition unit is configured to recognize information indicating a time-series transition of scores of the plurality of factors of the user based on information input to the input unit. system. In a system equipped with an output unit that outputs information,
recognizing the unique user information of the user;
a step of recognizing, based on the user information, score information indicating a time-series transition of scores of a plurality of elements included in the user information;
Based on the score information, recognizing the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period;
Based on the correlation, storing information indicating elements having correlation with each other and correlation coefficients between the elements;
recognizing current user information, which is the latest information of the user;
recognizing a first element of the user indicated in the current user information;
A step of extracting a second element of the user correlated with the first element based on the information indicating the mutually correlated elements;
Predicting the future score of the second element of the user based on the correlation coefficient between the elements and the score of the first element indicated in the current user information;
and a step of outputting information corresponding to the future score of the second element of the user to the output unit. In a system equipped with an output unit that outputs information,
recognizing the unique user information of the user;
a step of recognizing, based on the user information, score information indicating a time-series transition of scores of a plurality of elements included in the user information;
Based on the score information, recognizing the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period;
a correlation information storage step of storing information indicating elements having correlation with each other and correlation coefficients between the elements based on the correlation;
a step of recognizing current user information, which is the latest information of the user;
recognizing a first element of the user indicated in the current user information;
A step of extracting a second element of the user correlated with the first element based on the information indicating the mutually correlated elements;
Predicting the future score of the second element of the user based on the score of the first element indicated in the correlation coefficient and the current user information;
an action recognition step of recognizing, when the future score of the second factor of the user is within a predetermined range, an action that causes the future score of the second factor to fall outside the predetermined range;
a step configured to output to the output unit information recommending implementation or avoidance of the action;
The correlation information storing step is configured to store correlated behavior information indicating a behavior correlated with each element and a correlation coefficient between each element and the behavior;
The action recognition step has a correlation with the first element or the second element based on the correlated action information when the future score of the second element of the user is included in a predetermined range, and An information output program configured to recognize an action that causes a future score of the second element to fall outside the predetermined range. In the information output program according to claim 8 or 9,
The information output program, wherein the first element is an element related to one of skin, body and mind, and the second element is an element related to the other of skin, body and mind. In the information output program according to claim 8 or 9,
The information output program, wherein the first element is a user's behavior, and the second element is an element relating to one of skin, body and mind. A method performed by a system comprising an output for outputting information, comprising:
recognizing the unique user information of the user;
a step of recognizing, based on the user information, score information indicating a time-series transition of scores of a plurality of elements included in the user information;
Based on the score information, recognizing the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period;
Based on the correlation, storing information indicating elements having correlation with each other and correlation coefficients between the elements;
recognizing current user information, which is the latest information of the user;
recognizing a first element of the user indicated in the current user information;
A step of extracting a second element of the user correlated with the first element based on the information indicating the mutually correlated elements;
Predicting the future score of the second element of the user based on the correlation coefficient between the elements and the score of the first element indicated in the current user information;
and a step of outputting information corresponding to the future score of the second element of the user to the output unit. A method performed by a system comprising an output for outputting information, comprising:
recognizing the unique user information of the user;
a step of recognizing, based on the user information, score information indicating a time-series transition of scores of a plurality of elements included in the user information;
Based on the score information, recognizing the correlation between the time-series transition of the score of one element and the time-series transition obtained by shifting the time-series transition of the score of another element by a predetermined period;
a correlation information storage step of storing information indicating elements having correlation with each other and correlation coefficients between the elements based on the correlation;
a step of recognizing current user information, which is the latest information of the user;
recognizing a first element of the user indicated in the current user information;
A step of extracting a second element of the user correlated with the first element based on the information indicating the mutually correlated elements;
Predicting the future score of the second element of the user based on the score of the first element indicated in the correlation coefficient and the current user information;
an action recognition step of recognizing, when the future score of the second factor of the user is within a predetermined range, an action that causes the future score of the second factor to fall outside the predetermined range;
a step configured to output to the output unit information recommending implementation or avoidance of the action;
The correlation information storing step is configured to store correlated behavior information indicating a behavior correlated with each element and a correlation coefficient between each element and the behavior;
The action recognition step has a correlation with the first element or the second element based on the correlated action information when the future score of the second element of the user is included in a predetermined range, and An information output method, wherein the information output method is configured to recognize an action that causes the future score of the second element to fall outside the predetermined range. In the information output method according to claim 12 or 13,
The information output method, wherein the first element is an element related to one of skin, body and mind, and the second element is an element related to the other of skin, body and mind. In the information output method according to claim 12 or 13,
The information output method, wherein the first element is a user's behavior, and the second element is an element related to one of skin, body and mind.

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