Behavior change intervention system, behavior change intervention method, and program

Abstract

To allow a subject to more effectively carry out habituation of behavior.SOLUTION: A behavior change intervention system is provided which comprises: a determination unit that determines the progress of a behavior change related to a target behavior of a subject to obtain progress data; a decision unit that decides information related to provision of intervention information to the subject on the basis of the progress data; and a providing unit that provides the subject with the intervention information to the subject on the basis of the information related to the provision.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a behavior modification intervention system, a behavior modification intervention method, and a program.

Background Art

[0002] In recent years, in order to encourage specific behaviors (such as healthy behaviors or environmentally conscious behaviors) by a target person, there are many services that provide financial incentives for the implementation of the behaviors. In these services, after a service provider secures the funds for financial incentives, it is necessary to secure an investment effect (such as medical cost reduction or greenhouse gas reduction).

[0003] For example, Patent Document 1 discloses a configuration example of a system that distributes financial incentives to specific members set for each group such as a company, etc., to focus on motivating the promotion of health awareness and behaviors, and promotes their behavior modification. According to this, it is possible to focus on providing financial incentives to a target layer with a high health risk and achieve efficient investment allocation as a group.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, although it is possible to cause temporary behavior induction by the target person by providing a financial incentive, in order for the target person to continue the behavior, it is necessary to continuously provide the financial incentive to the target person while gradually increasing the financial incentive.

[0006] In addition, it is known that when monetary incentives are given to a subject, the subject's intrinsic motivation (motivation generated by internal factors of a person) decreases, and the actions taken by the subject gradually decrease. Furthermore, when the monetary incentives given to the subject are stopped, the actions taken by the subject may also stop. For these reasons, there is a problem that it is difficult to habituate the actions of the subject only by giving monetary incentives.

[0007] Therefore, the main object of the present invention is to solve the above problems and more effectively habituate the actions of the subject.

Means for Solving the Problems

[0008] In order to solve the above problems, according to one aspect of the present invention, there is provided a behavior modification intervention system including a determination unit that determines the progress of behavior modification related to a target behavior of a subject to obtain progress data, a determination unit that determines information related to the provision of intervention information to the subject based on the progress data, and a provision unit that provides the intervention information to the subject based on the information related to the provision.

[0009] The determination unit may acquire the average number of selections at a predetermined time corresponding to each of one or more types to which the intervention information group belongs and the progress data, and determine a selection probability, which is a probability for each type, for selecting the intervention information belonging to the type as the intervention information to the subject based on the average number of selections, and determine for each type whether to make the intervention information a candidate for the intervention information to the subject based on the selection probability as the information related to the provision.

[0010] The intervention information group includes a first type to which intervention information recommending the implementation of the target behavior belongs, and the average number of selections corresponding to the first type may be larger as the progress data is higher in a section where the progress data is lower than a predetermined first progress data, and may be smaller as the progress data is higher in a section where the progress data is higher than the first progress data.

[0011] The intervention information group includes a second type to which the intervention information indicating the internal reward given for the performance of the target behavior belongs, and the average number of selections corresponding to the second type may be larger as the progress data is higher.

[0012] The intervention information group includes a third type to which the intervention information indicating the external reward given for the performance of the target behavior belongs, and the average number of selections corresponding to the third type may be smaller as the progress data is higher.

[0013] The intervention information group includes a fourth type to which the intervention information indicating the effect obtained by the performance of the target behavior belongs, and the average number of selections corresponding to the fourth type may be smaller as the progress data is higher.

[0014] The determination unit may acquire the output probability of the intervention information to the subject corresponding to the time zone to which the current time belongs, and determine the selection probability based on the multiplication of the output probability and the average number of selections.

[0015] When the type candidate data including one or a plurality of types to which the intervention information group belongs includes a predetermined type to which the intervention information regarding the reward given for the performance of the target behavior belongs, the determination unit may determine, as the information regarding the provision, whether to use the intervention information belonging to the predetermined type included in the intervention information group as a candidate for the intervention information to the subject.

[0016] When the predetermined type is included in the type candidate data, the determination unit may determine whether to use the intervention information belonging to the predetermined type included in the intervention information group as a candidate for the intervention information to the subject based on the progress data and the cumulative number of times the target behavior has been performed by the subject after the intervention information belonging to the predetermined type was last provided to the subject.

[0017] When the determination unit determines that the predetermined type is included in the type candidate data, the determination unit may obtain a first value corresponding to the progress data, and determine whether to use the intervention information belonging to the predetermined type included in the intervention information group as a candidate for the intervention information for the target person based on whether the number of times after the random number operation on the cumulative number of actions or the cumulative number of actions is greater than or equal to the first value.

[0018] The first value may be larger as the progress data corresponding to the first value is higher.

[0019] When the determination unit determines that the predetermined type is included in the type candidate data, the determination unit may determine whether to use the intervention information belonging to the predetermined type included in the intervention information group as a candidate for the intervention information for the target person based on the progress data and the elapsed time from when the intervention information belonging to the predetermined type was last provided to the target person until now.

[0020] When the determination unit determines that the predetermined type is included in the type candidate data, the determination unit may obtain a second value corresponding to the progress data, and determine whether to use the intervention information belonging to the predetermined type included in the intervention information group as a candidate for the intervention information for the target person based on whether the time after the random number operation on the elapsed time or the elapsed time is greater than or equal to the second value.

[0021] The second value may be larger as the progress data corresponding to the second value is higher.

[0022] The determination unit obtains the average number of selections at a predetermined time corresponding to each of one or more types to which the intervention information group belongs and the progress data, determines a selection probability, which is a probability for each type for selecting the intervention information belonging to the type as the intervention information for the target person based on the average number of selections, and corrects the selection probability corresponding to the predetermined type to zero when the intervention information belonging to the predetermined type included in the intervention information group is not used as a candidate for the intervention information for the target person.

[0023] The determination unit may identify the group of intervention information corresponding to the situation of the target person, and the provision unit may determine the intervention information for the target person from the group of intervention information.

[0024] The intervention information for the target person may include message data for prompting the target person to perform the target action.

[0025] The determination unit may determine the progress based on at least any one of the access frequency of the service function by the target person, the number of days of performing the target action by the target person, and the acceptance degree of the target person for the intervention information provided to the target person.

[0026] Also, according to another aspect of the present invention for solving the above problems, determining the progress of behavior modification related to the target behavior of the target person to obtain progress data, determining information related to the provision of intervention information to the target person based on the progress data, and providing the intervention information for the target person to the target person based on the information related to the provision, there is provided a behavior modification intervention method executed by a computer.

[0027] Also, according to another aspect of the present invention for solving the above problems, there is provided a program for causing a computer to function as a determination unit that determines the progress of behavior modification related to the target behavior of the target person to obtain progress data, a determination unit that determines information related to the provision of intervention information to the target person based on the progress data, and a provision unit that provides the intervention information for the target person to the target person based on the information related to the provision.

Effect of the Invention

[0028] As described above, according to the present invention, a technology capable of more effectively habituating the behavior of the target person is provided.

Brief Description of the Drawings

[0029]

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Mode for Carrying Out the Invention

[0030] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the present specification and the drawings, components having substantially the same functional configuration are denoted by the same reference numerals, and redundant description is omitted.

[0031] <0. Overview> First, an overview of an embodiment of the present invention will be described.

[0032] In an embodiment of the present invention, a technique is mainly proposed for more effectively habituating the behavior of a target person by intervening in the target person according to the progress of the behavior change of the target person. More specifically, in an embodiment of the present invention, the frequency of intervention in the target person is controlled according to the progress of the behavior change of the target person. Thereby, the habituation of the behavior by the target person can be performed more effectively. Further, in an embodiment of the present invention, the timing of intervention in the target person is controlled.

[0033] For example, the frequency of intervention in the target person can be controlled by thinning out the intervention in the target person. Also, the frequency of intervention in the target person can be controlled by selecting the type of intervention based on a selection probability determined for each type of intervention in the target person.

[0034] The timing of intervention in the target person can be controlled by selecting the type of intervention based on an intervention probability determined for each time zone of the intervention in the target person. The intervention probability may correspond to the "message output probability" described later.

[0035] Examples of such control of the frequency or timing of intervention in the target person will be described in detail later.

[0036] Intervention in the target person can be realized by providing the target person with information (hereinafter also referred to as "intervention information") for promoting the performance of the target behavior by the target person. The provision of the intervention information to the target person may be that the terminal of the target person presents the intervention information to the target person, or that the information processing device outputs the intervention information to the terminal of the target person.

[0037] In the following description, it is mainly assumed that the intervention information includes text data (hereinafter also referred to as "message data"). However, the intervention information may include data other than text data. For example, the intervention information may include image data (such as still image data or moving image data).

[0038] The outline of the embodiment of the present invention has been described above.

[0039] <1. Details of Embodiment> Subsequently, details of the embodiments of the present invention will be described.

[0040] [1-1. Explanation of Configuration] A configuration example of a behavior modification intervention system according to an embodiment of the present invention will be described. The user of the behavior modification intervention system according to the embodiment of the present invention (hereinafter, also simply referred to as "user") is a person for whom the target behavior should be habituated. The user performs the target behavior according to the situation. In the embodiment of the present invention, the case where the target behavior is using stairs (hereinafter, also referred to as "stair walking") is mainly assumed. However, as will be described later, the target behavior is not limited to stair walking.

[0041] FIG. 1 is a diagram showing a configuration example of a behavior modification intervention system according to an embodiment of the present invention. As shown in FIG. 1, the behavior modification intervention system 1 according to the embodiment of the present invention includes a server 10 and a user terminal 20. The server 10 and the user terminal 20 are connected to a network, and the server 10 and the user terminal 20 are configured to be able to communicate with each other via the network.

[0042] (Server 10) The server 10 can be realized by a computer. The server 10 includes a control unit (not shown) and a storage unit (not shown). The control unit (not shown) includes an intervention condition determination unit 110, an intervention control unit 120, a thinning determination unit 130, and an intervention unit 140.

[0043] Note that the control unit (not shown) is realized by a program being executed by a processor. Such a program can be recorded on a recording medium and read and executed by the processor from the recording medium. Alternatively, the control unit (not shown) may be configured by dedicated hardware.

[0044] The memory (not shown) can be constituted by a memory. For example, the memory may be a memory such as a RAM (Random Access Memory), a hard disk drive, or a flash memory.

[0045] (Intervention condition determination unit 110) The intervention condition determination unit 110 acquires the terminal response data output from the user terminal 20. Note that the terminal response data may also correspond to behavior data. Then, the intervention condition determination unit 110 outputs type candidate data and progress data to the intervention control unit 120 based on the terminal response data. Details of the type candidate data and the progress data will be described later. Also, the intervention condition determination unit 110 outputs the type candidate data and the progress data to the thinning determination unit 130.

[0046] The intervention condition determination unit 110 outputs message candidate set data to the intervention unit 140 based on the terminal response data. Details of the message candidate set data will be described later.

[0047] (Intervention control unit 120) The intervention control unit 120 acquires the type candidate data and the progress data output from the intervention condition determination unit 110. Further, the intervention control unit 120 acquires the thinning determination data output from the thinning determination unit 130. Details of the thinning determination data will be described later. The intervention control unit 120 outputs type data to the thinning determination unit 130. Details of the type data will be described later. Also, the intervention control unit 120 outputs type data to the intervention unit 140 based on the type candidate data, the progress data, and the thinning determination data.

[0048] (Thinning determination unit 130) The thinning determination unit 130 acquires the type candidate data and the progress data output from the intervention condition determination unit 110. Further, the thinning determination unit 130 acquires the type data output from the intervention control unit 120. The thinning determination unit 130 outputs thinning determination data to the intervention control unit 120 based on the type candidate data, the progress data, and the type data.

[0049] (Intervention unit 140) The intervention unit 140 acquires the type data output from the intervention control unit 120. Further, the intervention unit 140 determines message data based on the type data. Then, the intervention unit 140 outputs the message data to the user terminal 20.

[0050] (User terminal 20) The user terminal 20 can be realized by a computer. For example, the user terminal 20 may be a terminal carried by the user (a mobile terminal) and may be a terminal operated by the user.

[0051] When the user terminal 20 receives the message data output from the intervention unit 140, it presents the received message data to the user. For example, the user terminal 20 may display the message data on the screen by a display, or may output the message data as voice by a speaker. Further, the user terminal 20 outputs terminal response data to the intervention condition determination unit 110 in response to a request from the intervention condition determination unit 110.

[0052] (Service function) The user terminal 20 has a service function for the user. For example, the service function may include at least one of an autonomy function, a sense of competence function, a relationship function, a message browsing function, a message acceptance degree acquisition function, and an action registration function. The service function may be a service function related to stair walking.

[0053] (Autonomy function) The autonomy function may be a function that detects an action selected by the user and presents details of the action selected by the user to the user. For example, the autonomy function may detect the user's daily stair usage situation (for example, the user ascended and descended the stairs for three floors yesterday and the stairs for two floors the day before yesterday), and present the detected stair usage situation to the user.

[0054] (Sense of competence function) The enabling function can be a function that presents excellent results regarding the user's use of the stairs to the user. For example, the excellent results may be information indicating to what extent the user's stair usage situation is better than that of other users (for example, information such as the amount of ascent and descent of the stairs by the user yesterday was one floor more than that of other users yesterday).

[0055] Alternatively, the excellent results may be information indicating to what extent the user's stair usage situation today is better than that of yesterday (for example, information such as the amount of ascent and descent of the stairs by the user today was one floor more than that of the user yesterday). Alternatively, the excellent results may be ranking information obtained by ranking the amounts of ascent and descent of the stairs by a plurality of users including the user and other users in descending order.

[0056] (Relationship function) The relationship function can be a function that shares the user's stair usage situation between the user and other users. For example, the relationship function may be a function that makes the user's stair usage situation viewable not only by the user but also by other users. For example, the stair usage situation may be shared directly via a network by the user terminal 20 used by the user and the terminal used by other users. The stair usage situation may be shared via the server 10 by the user terminal 20 used by the user and the terminal used by other users.

[0057] (Message viewing function) The message viewing function can be a function that presents the message data output from the intervention unit 140 to the user.

[0058] (Message acceptance degree acquisition function) The message acceptance degree acquisition function can be a function that acquires the user's acceptance degree (hereinafter also referred to as "message acceptance degree") for the message data presented to the user. The message acceptance degree indicates the response of the user when the user views the message data. Hereinafter, it is mainly assumed that the message acceptance degree is a binary choice and is represented by either "1: acceptable" or "0: unacceptable". However, there may be three or more candidates for the message acceptance degree. For example, the message acceptance degree can be acquired based on the user's operation.

[0059] (Action registration function) The action registration function can be a function that registers the implementation of an action based on the user's operation. For example, the actions whose implementation is registered by the action registration function may mainly be actions that are difficult to detect using sensors (such as eating). For example, the presence or absence of action implementation may be input from the user to a registration page. Alternatively, when the message data includes content asking about the selection of the presence or absence of action implementation, the selected presence or absence of action implementation by the user may be included in the reply to the message data.

[0060] The configuration example of the behavior modification intervention system 1 according to the embodiment of the present invention has been described above.

[0061] [1-2. Description of operations] Subsequently, an operation example of the behavior modification intervention system 1 according to the embodiment of the present invention will be described.

[0062] Figure 2 is a diagram for explaining the operation of the behavior modification intervention system 1 according to the embodiment of the present invention. As shown in Figure 2, the operation of the behavior modification intervention system 1 according to the embodiment of the present invention is divided into the following steps: (S1) determination of intervention conditions and determination of the progress of behavior modification, (S2) determination of reward thinning, (S3) determination of intervention type, (S4) determination and distribution of message data, (S5) determination of whether to end the operation.

[0063] Hereinafter, each of these steps will be described in order.

[0064] (S1) Determination of intervention conditions and determination of progress of behavior modification The intervention condition determination unit 110 determines whether the determination timing has arrived. For example, the determination timing arrives at a predetermined cycle (hereinafter also referred to as the "determination cycle"). Hereinafter, it is mainly assumed that the determination cycle is 10 minutes. However, the determination cycle may be arbitrarily set according to the type of target behavior or the usage form of the behavior modification intervention system 1. For example, the determination cycle may be 1 minute or the like.

[0065] When the determination timing arrives, the terminal response data from 10 minutes before the present to the present is treated as the current terminal response data with respect to the present.

[0066] When the determination timing arrives, the intervention condition determination unit 110 acquires the current terminal response data from the user terminal 20. Then, the intervention condition determination unit 110 analyzes the acquired current terminal response data and the acquired terminal response data to obtain behavior analysis data.

[0067] Here, it is mainly assumed that the behavior analysis data is obtained by analyzing the terminal response data from two weeks before the present to the present with respect to the present. That is, the terminal response data from two weeks before the present to the present is treated as the terminal response data for the current period. However, the period of the terminal response data to be analyzed is not limited to two weeks. For example, the period of the terminal response data to be analyzed may be one week or the like.

[0068] FIG. 3 is a diagram showing an example of terminal response data. As shown in FIG. 3, the terminal response data includes service operation data and behavior execution data.

[0069] (Service operation data) Service operation data is operation data by the user related to the service functions provided by the user terminal 20 to the user. As shown in FIG. 3, the service operation data is composed of the access date and time from the user to the service functions (autonomy function, sense of ability function, relationship function, message browsing function, message acceptance degree acquisition function, and behavior registration function), the message acceptance degree which is the user's acceptance degree for the message data, and the like. The service operation data is acquired by an operation on the user terminal 20.

[0070] (Action execution data) Action execution data is data indicating when the user performed staircase walking and data indicating how much the user performed staircase walking. That is, as shown in FIG. 3, the action execution data may include the execution date and time and amount of staircase walking. The action execution data can be acquired based on a sensor built in the user terminal 20, a sensor provided outside the user terminal 20, or an input operation by the user on the user terminal 20.

[0071] For example, the execution of staircase walking can be detected by the user terminal 20 based on the detection result of air pressure by an air pressure sensor built in the user terminal 20, or the detection result of acceleration by an acceleration sensor built in the user terminal 20.

[0072] Alternatively, the execution of staircase walking may be detected by the user terminal 20 based on the reception of radio waves from a beacon installed on or near the staircase by a receiver built in the user terminal 20. Alternatively, the execution of staircase walking may be detected by the user terminal 20 based on the fact that the position detected by a GNSS (Global Navigation Satellite System) sensor built in the user terminal 20 belongs to a preset staircase area.

[0073] Furthermore, the action execution data includes the user's situation. The user's situation may include the location where the user currently is, the time zone to which the current time belongs, the user's current action, and the like. Note that the user's situation does not necessarily include all of the location where the user currently is, the time zone to which the current time belongs, and the user's current action, and may include any one or two of them.

[0074] For example, the location where the user currently is may be information indicating the area to which the position detected by the GNSS sensor belongs. Also, the time zone to which the current time belongs may be information obtained from the timekeeping function that measures the current time. Furthermore, the user's current action may be detected based on the detection result of the atmospheric pressure by the atmospheric pressure sensor or the detection result of the acceleration by the acceleration sensor, similar to the execution of walking up and down stairs.

[0075] (Action analysis data) FIG. 4 is a diagram showing an example of action analysis data. As shown in FIG. 4, the action analysis data may include service operation frequency data, action execution frequency data, and message acceptance degree data.

[0076] (Service operation frequency data) The service operation frequency data is data indicating the access frequency of each service function by the user calculated by the intervention condition determination unit 110 based on the service operation data among the terminal response data. For example, the access frequency of the user to the autonomous function is the result of counting the number of times the user has accessed the autonomous function during the current period based on the access date and time of the user to the autonomous function.

[0077] (Action execution frequency data) The action execution frequency data is data indicating the frequency of the number of days of walking up and down stairs calculated by the intervention condition determination unit 110 based on the action execution data among the terminal response data. For example, the frequency of the number of days of walking up and down stairs is the result of counting the days when the user has walked up and down stairs more than a predetermined amount during the current period.

[0078] (Message acceptance degree data) Message acceptance data is data indicating the trend of message acceptance, which is calculated by the intervention condition determination unit 110 based on the message acceptance of service operation data among the terminal response data. For example, the trend of message acceptance may be the average value of message acceptance by the user in the current period (i.e., the average acceptance of the message). For example, the average value can be represented by a value between 0 and 1 inclusive.

[0079] (Message database) The intervention condition determination unit 110 pre-holds, as a message database, a database including a message data group including one or more "message data" and "type", "location", "time zone", and "immediately preceding action" respectively associated with each of the one or more "message data". Note that the message data group may correspond to an example of the intervention information group.

[0080] FIG. 5 is a diagram showing an example of the message database. As shown in FIG. 5, the message database has registered one or more pieces of data in which "type", "message data", "location", "time zone", and "immediately preceding action" are associated. In the example shown in FIG. 5, the number of "message data" associated with each "type" is one. However, a plurality of "message data" may be associated with each "type".

[0081] "Type" indicates the type of message data. In the following description, mainly the case where four types, namely type "trigger", type "intrinsic reward", type "extrinsic reward", and type "literacy", are used as examples of "type" is assumed. However, "type" is not limited to such examples. In the following description, "intrinsic reward" and "extrinsic reward" may also be collectively referred to as "reward" without particularly distinguishing between them.

[0082] The category "Trigger" is the category (the first category) to which the message data that recommends the user to perform staircase walking belongs. Providing the user with the message data belonging to the category "Trigger" can be a trigger for the user to start staircase walking.

[0083] The category "Internal Reward" is the category (the second category) to which the message data indicating the internal reward among the rewards given for the performance of staircase walking belongs. For example, the category "Internal Reward" may be the category to which the message data that responds to the user's internal desires (such as self-discipline, sense of competence, relationship, etc.) among the message data provided to the user immediately after the user performs staircase walking belongs.

[0084] The category "External Reward" is the category (the third category) to which the message data indicating the external reward among the rewards given for the performance of staircase walking belongs. For example, the category "External Reward" may be the category to which the message data that responds to the external desires given by others such as monetary incentives or items among the message data provided to the user immediately after the user performs staircase walking belongs.

[0085] The category "Literacy" is the category (the fourth category) to which the message data indicating the effects obtained by the performance of staircase walking belongs. The effects obtained by the performance of staircase walking may be physical effects. For example, the category "Literacy" may be the category to which the message data corresponding to the knowledge that enhances the user's ability to obtain, understand, evaluate, and utilize information related to staircase walking belongs.

[0086] Each of "Location", "Time Zone", and "Immediate Previous Action" may correspond to an example of the user's situation. "Location" and "Time Zone" may correspond to the conditions related to the user's environment. In particular, "Location" may correspond to the conditions related to the location where the user is present. Also, "Time Zone" may correspond to the conditions related to the time zone at the location where the user is present. "Immediate Previous Action" may correspond to the conditions related to the user's actions from a predetermined time before the present (for example, 2 hours before the present, etc.) to the present (that is, immediately before).

[0087] In other words, it can be said that "location", "time zone", and "immediately preceding action" are conditions indicating when, where, and what the user was doing. Note that in the example shown in FIG. 5, "×" indicates that the condition does not exist.

[0088] (Determination of intervention conditions) Based on the user's situation included in the current terminal response data output from the user terminal 20, the intervention condition determination unit 110 identifies a message data group corresponding to the user's situation. At this time, not only the current terminal response data but also the immediately preceding terminal response data may be considered for identifying the message data group.

[0089] More specifically, the intervention condition determination unit 110 identifies "message data" corresponding to "location", "time zone", and "immediately preceding action" that match the user's situation, that is, the location where the user is present, the time zone, and the user's action, from the message database.

[0090] Based on the identified "message data", the intervention condition determination unit 110 generates message candidate set data. Then, the intervention condition determination unit 110 outputs the generated message candidate set data to the intervention unit 140.

[0091] FIG. 6 is a diagram showing an example of message candidate set data. As shown in FIG. 6, the message candidate set data is configured by associating "type", "message data", and "candidate". A value indicating a provision candidate to the user is set for the "candidate" corresponding to the message data identified by the intervention condition determination unit 110. In the example shown in FIG. 6, the value indicating the provision candidate to the user is "1". However, the value indicating the provision candidate to the user is not limited to "1".

[0092] For example, assume that at the current time of 10 o'clock, the user is in the elevator hall and the user's sitting time was 2 hours or more immediately before the current time. In such a case, from the message database (Figure 5), "message data" is specified where the "location" is the elevator hall, 10 o'clock belongs to the "time zone", and the "immediate previous action" is "sitting time of 2 hours or more".

[0093] In the example shown in Figure 5, the message data "Let's use the stairs" and the message data "Using the stairs has the effect of lowering blood sugar levels" are specified. Then, among the message candidate set data (Figure 6), "1 (offer candidate)" is set for the "candidate" corresponding to the "message data" specified in this way.

[0094] Furthermore, the intervention condition determination unit 110 acquires the "type" corresponding to the "message data" for which "1 (offer candidate)" is set for the "candidate" in the message candidate set data, and generates type candidate data including the acquired "type". The type candidate data may include one type, may include multiple types, or may not include any type.

[0095] In the example shown in Figure 6, "1 (offer candidate)" is set for the "candidate" corresponding to each of the message data "Let's use the stairs" and the message data "Using the stairs has the effect of lowering blood sugar levels". Therefore, the intervention condition determination unit 110 generates type candidate data including the type "trigger" and the type "literacy" corresponding to these message data.

[0096] Then, the intervention condition determination unit 110 outputs the generated type candidate data to the intervention control unit 120 and the thinning determination unit 130 respectively.

[0097] (Determination of the progress of behavior modification) The intervention condition determination unit 110 functions as an example of a determination unit, and determines the progress of the behavioral variation related to the user's stair walking based on the terminal response data in the current period to obtain progress data. For example, the intervention condition determination unit 110 may determine the progress based on the service operation frequency data, the action execution frequency data, and the message acceptance degree data based on the terminal response data in the current period. For example, the progress data may be expressed by a numerical value in 10 levels from 1 to 10, but the number of levels of the numerical value expressing the progress data may be other than 10 levels.

[0098] For example, the intervention condition determination unit 110 may calculate the progress data by weighted addition of the service operation frequency data, the action execution frequency data, and the message acceptance degree data respectively. For example, the closer the time when the data is obtained is to the present, the greater the weight corresponding to the data may be. However, the calculation method of the progress data may not be limited. As an example, in the calculation of the progress data, any one or two of the service operation frequency data, the action execution frequency data, and the message acceptance degree data may be used. Subsequently, the operation proceeds to S2.

[0099] (S2) Determination of reward thinning The thinning determination unit 130 acquires the type candidate data and the progress data output from the intervention condition determination unit 110. Further, the thinning determination unit 130 acquires, as type data, data indicating the type to which the message data output from the intervention unit 140 to the user terminal 20 belongs. The thinning determination unit 130 functions as an example of a determination unit, and determines information regarding the provision of message data to the user based on the progress data.

[0100] For example, when the type candidate data includes the type "intrinsic reward", the thinning determination unit 130 determines whether to use, as candidate message data for the user, the message data belonging to the type "intrinsic reward" as information regarding the provision. In the following description, not using the message data belonging to the type included in the type candidate data as candidate message data for the user is also referred to as "thinning" the message data or "thinning" of the message data.

[0101] Hereinafter, the thinning out of message data belonging to the type "intrinsic reward" will be mainly described. However, the thinning out of message data belonging to the type "extrinsic reward" may be executed in the same manner. Further, when the type "intrinsic reward" and the type "extrinsic reward" are not particularly distinguished, etc., the thinning out of message data belonging to the type "reward" may be executed in the same manner. The type "reward" may correspond to an example of a predetermined type.

[0102] More specifically, when the type candidate data includes the type "intrinsic reward", the thinning determination unit 130 determines whether to thin out the message data belonging to the type "intrinsic reward" based on the progress data and the type data.

[0103] Here, when the type data acquired from the intervention unit 140 includes the type "intrinsic reward", the thinning determination unit 130 holds the time when the type data was acquired as the final reward time when the message data belonging to the type "intrinsic reward" was provided to the user terminal 20. Then, based on the action execution data, the thinning determination unit 130 counts the number of times of staircase walking performed by the user after the final reward time as the cumulative action count.

[0104] Then, when the type candidate data includes the type "intrinsic reward", the thinning determination unit 130 determines whether to thin out the message data belonging to the type "intrinsic reward" based on the progress data and the cumulative action count. At this time, it is desirable that the thinning of the message data belonging to the type "intrinsic reward" is based on the concepts shown in the following (a1) and (a2).

[0105] The concept is that (a1) the lower the progress data of the action variation of staircase walking, the higher the frequency of rewards given to the user for performing staircase walking, so that the action variation progresses more easily, and (a2) the higher the progress data of the action variation of staircase walking, the lower the frequency of rewards given to the user for performing staircase walking, so that the action variation progresses more easily.

[0106] Therefore, when the type "intrinsic reward" is included in the candidate type data, the thinning determination unit 130 acquires a first value corresponding to the progress data (hereinafter, also referred to as "cumulative action count parameter N"). Then, the thinning determination unit 130 applies a random number to the cumulative action count counted as described above.

[0107] Here, it is mainly assumed that the action of the random number on the cumulative action count is the addition of the random number to the cumulative action count. At this time, the random number may be, for example, a random integer value belonging to the range from -n to +n (where n is a positive integer). However, the action of the random number on the cumulative action count may be the multiplication of the random number to the cumulative action count, or other operations of the random number.

[0108] The thinning determination unit 130 determines whether to thin out the message data belonging to the type "intrinsic reward" based on whether the count after the action of the random number is equal to or greater than the value of the cumulative action count parameter N. At this time, as shown in the above concepts (a1) and (a2), it is desirable that the cumulative action count parameter N increases as the progress data corresponding to the cumulative action count parameter N increases.

[0109] FIG. 7 is a diagram showing an example of the correspondence relationship between the progress data and the cumulative action count parameter N. As shown in FIG. 7, the correspondence relationship between the progress data and the cumulative action count parameter N is held in advance by the thinning determination unit 130. Also in the example shown in FIG. 7, it can be understood that the cumulative action count parameter N increases as the progress data corresponding to the cumulative action count parameter N increases.

[0110] The thinning determination unit 130 outputs a value indicating whether to thin out the message data belonging to the type "intrinsic reward" to the intervention control unit 120 as thinning determination data.

[0111] For example, when the thinning determination unit 130 determines that the type candidate data includes the type "intrinsic reward" and thins out the message data belonging to the type "intrinsic reward", the thinning determination unit 130 may output, as the thinning determination data, a value "1: (to be thinned out)" indicating that the message data belonging to the type "intrinsic reward" is to be thinned out, to the intervention control unit 120. Note that the value indicating that the message data belonging to the type "intrinsic reward" is to be thinned out does not have to be "1".

[0112] Alternatively, when the thinning determination unit 130 determines that the type candidate data includes the type "intrinsic reward" and thins out the message data belonging to the type "intrinsic reward", the thinning determination unit 130 may output, as the thinning determination data, a value "0: (not to be thinned out)" indicating that the message data belonging to the type "intrinsic reward" is not to be thinned out, to the intervention control unit 120. Note that the value indicating that the message data belonging to the type "intrinsic reward" is not to be thinned out does not have to be "0".

[0113] On the other hand, when the type candidate data does not include the type "intrinsic reward", the thinning determination unit 130 may output, to the intervention control unit 120, a value "2: (no type)" indicating that the type candidate data does not include the type "intrinsic reward". Note that the value indicating that the type candidate data does not include the type "intrinsic reward" does not have to be "2". Subsequently, the operation proceeds to S3.

[0114] (S3) Determination of intervention type The intervention control unit 120 acquires the type candidate data and the progress data output from the intervention condition determination unit 110. The intervention control unit 120 also acquires the thinning determination data output from the thinning determination unit 130. Based on the type candidate data and the progress data, the intervention control unit 120 determines a probability for each type (hereinafter also referred to as "selection probability") for selecting the message data belonging to the type included in the type candidate data as the message data to the user, and corrects the selection probability based on the thinning determination data.

[0115] In the following description, when the intervention control unit 120 determines the selection probability for each type, the case where all of the average number of selections per day, the message output probability, and the thinning determination data, which will be described later, are considered will be mainly described. However, when the intervention control unit 120 determines the selection probability for each type, it may consider any one or two of the average number of selections per day, the message output probability, and the thinning determination data.

[0116] (Use of the average number of selections per day) The intervention control unit 120 acquires the average number of selections per day (hereinafter also referred to as the "average number of selections per day") corresponding to the type and progress data included in the type candidate data. Then, based on the acquired average number of selections per day, the intervention control unit 120 determines the probability for each type (hereinafter also referred to as the "selection probability") for selecting the message data belonging to the type included in the type candidate data as the message data for the user.

[0117] Note that the calculation period (predetermined period) of the average number of selections does not have to be limited to one day. Also, it can be assumed that the appropriate average number of selections per day according to the progress data may vary depending on the type to which the message data belongs. For example, it is desirable that the average number of selections per day set according to the progress data and the type to which the message data belongs is based on the following concepts (b1) to (b4).

[0118] The concept is that (b1) when the trigger for an action is provided after the consciousness has changed to a certain extent and is gradually weakened as the fixation of the action progresses, the action transformation is likely to progress; (b2) when the internal reward is provided immediately after the action is performed regardless of the progress of the action transformation, the habituation is likely to progress; (b3) the external reward can induce an action when the progress of the action transformation is small, but if it is not gradually weakened as the fixation of the action progresses, the external reward becomes the purpose and the action is less likely to be habituated; and (b4) literacy is effective in causing a change in consciousness at a stage where the progress of the action transformation is low.

[0119] With reference to FIG. 8, a setting example of the average number of selections per day corresponding to the type to which the progress data and the message data belong will be described. FIG. 8 is a diagram showing an example of the average number of selections per day corresponding to the type to which the progress data and the message data belong. As shown in FIG. 8, the correspondence relationship between the type to which the progress data and the message data belong and the average number of selections per day is held in advance by the intervention control unit 120.

[0120] Referring to FIG. 8, the average number of selections per day corresponding to the type "trigger" is set to be larger as the progress data is higher in the section where the progress data is lower than the section where the progress data is lower than "7". Also, the average number of selections per day corresponding to the type "trigger" is set to be smaller as the progress data is higher in the section where the progress data is higher than "7". Note that the progress data "7" is pre-determined progress data (first progress data).

[0121] Also, referring to FIG. 8, the average number of selections per day corresponding to the type "intrinsic reward" is set to be larger as the progress data is higher. On the other hand, the average number of selections per day corresponding to the type "extrinsic reward" is set to be smaller as the progress data is higher. Also, referring to FIG. 8, the average number of selections per day corresponding to the type "literacy" is set to be smaller as the progress data is higher.

[0122] (Use of Message Output Probability) The message output probability is the output probability of message data to the user for each time period, and is determined according to the user's lifestyle pattern or the type of target behavior, and is held in advance by the intervention control unit 120. The length of each time period may be 10 minutes or the like. However, the length of each time period does not have to be limited to 10 minutes. For example, the total value of the message output probabilities for 24 hours may be set in advance to be "1.0".

[0123] As an example, assume that the user is a worker who works in an office building from 9:00 to 17:00 on weekdays, and the target behavior is walking up and down the stairs in the office building. In such a case, it is desirable to set the message output probability from 9:00 to 17:00 on weekdays to be higher than the message output probability in other time periods.

[0124] The intervention control unit 120 may obtain the message output probability corresponding to the time period to which the current time belongs, and determine the selection probability for each type based on the obtained message output probability and the average number of selections per day. For example, the intervention control unit 120 may determine the selection probability for each type based on the multiplication of the obtained message output probability and the average number of selections per day.

[0125] In the example shown in FIG. 8, assume that the progress data is "8" and the message output probability in the current time period is "0.1". In such a case, the intervention control unit 120 may calculate the selection probability corresponding to the type "trigger" as 1.0 (average number of selections per day) × 0.1 (message output probability) = 0.1 (selection probability).

[0126] In addition, the intervention control unit 120 may calculate the selection probability corresponding to the type "intrinsic reward" as 2.0 (average number of selections per day) × 0.1 (message output probability) = 0.2 (selection probability). Furthermore, the intervention control unit 120 may calculate the selection probability corresponding to the type "extrinsic reward" as 0.2 (average number of selections per day) × 0.1 (message output probability) = 0.02 (selection probability). Also, the intervention control unit 120 may calculate the selection probability corresponding to the type "literacy" as 0 (average number of selections per day) × 0.1 (message output probability) = 0 (selection probability).

[0127] (Use of thinning judgment data) The intervention control unit 120 corrects the selection probability based on the thinning judgment data. For example, when there is a type for which a value "1: (thinning)" indicating that the message data is thinned is output as the thinning judgment data, the intervention control unit 120 may correct the selection probability corresponding to that type to zero.

[0128] On the one hand, when there is a type for which a value "0: (not thinned out)" indicating not to thin out the message data is output as the thinning-out determination data, the intervention control unit 120 may not correct the selection probability corresponding to that type. Further, when there is a type for which a value "2: (no type)" indicating that the type candidate data does not include a type is output as the thinning-out determination data, the intervention control unit 120 may not correct the selection probability corresponding to that type.

[0129] (Selection of type) The intervention control unit 120 functions as an example of a determination unit, and based on the selection probability for each type, determines for each type whether to use the message data as a candidate for the message data to be provided to the user as information regarding the provision. At this time, the intervention control unit 120 selects at most one type from the one or more types included in the type candidate data based on the selection probability for each type. Then, the intervention control unit 120 outputs the type data including the selected type to the intervention unit 140.

[0130] Note that when none of the types are selected, the intervention control unit 120 may output type data that does not include a type (that is, empty type data) to the intervention unit 140. Subsequently, the operation proceeds to S4.

[0131] (S4) Determination and distribution of message data The intervention unit 140 functions as an example of a provision unit, acquires the type data output from the intervention control unit 120, and acquires the message candidate set data output from the intervention condition determination unit 110. Then, the intervention unit 140 determines the message data to be provided to the user based on the type data and the message candidate set data.

[0132] More specifically, the intervention unit 140 extracts from the message candidate set data (FIG. 6) the message data that belongs to the type indicated by the type data and for which "candidate" is "1 (provision candidate)".

[0133] Then, when there are a plurality of extracted message data, the intervention unit 140 may randomly determine one message data from the plurality of extracted message data as the message data to the user. Alternatively, when there is one extracted message data, the intervention unit 140 may determine the extracted message data as the message data to the user.

[0134] As an example, assume a case where the type data acquired by the intervention unit 140 indicates the type "trigger". In such a case, the intervention unit 140 extracts from the message candidate set data (Fig. 6) the message data "Let's use the stairs", which belongs to the type "trigger" and for which "candidate" is "1 (offer candidate)", and may determine the extracted message data as the message data to the user.

[0135] The intervention unit 140 outputs the determined message data to the user terminal 20. Note that a case where the type data acquired by the intervention unit 140 does not include a type (that is, the type data is empty type data) may also be assumed. In such a case, the intervention unit 140 does not have to output the message data to the user terminal 20. Subsequently, the operation proceeds to S5.

[0136] (S5) Determination of whether to end the operation When the user inputs an operation to end the use to the user terminal 20, the operation of the behavior modification intervention system 1 ends. Otherwise, the operation proceeds to S1.

[0137] The operation example of the behavior modification intervention system 1 according to the embodiment of the present invention has been described above.

[0138] [1-3. Explanation of effects] As described above, in the behavior modification intervention system 1 according to the embodiment of the present invention, the intervention condition determination unit 110 acquires terminal response data indicating the user's behavior from the user terminal 20 and outputs type candidate data, progress data, and message candidate set data. Further, the thinning determination unit 130 determines whether to thin out the intervention by the message data belonging to the type "reward" based on the type candidate data, the progress data, and the type data at the time of past intervention, and outputs thinning determination data.

[0139] Further, the intervention control unit 120 outputs one type of data based on the type candidate data, the progress data, and the thinning determination data. Further, the intervention unit 140 selects one message data based on the type data and the message candidate set data and outputs it to the user terminal 20. As a result, the user can view and act on the message data via the user terminal 20.

[0140] The type data output from the intervention control unit 120 is the type of message data suitable for the progress of the user's behavior modification, determined by the intervention control unit 120 based on the type candidate data and the progress data output by the intervention condition determination unit 110. Further, the message data belonging to the type "reward" can be effectively thinned out by the thinning determination unit 130 based on the progress of the user's behavior modification.

[0141] In this way, the behavior modification intervention system 1 according to the embodiment of the present invention can effectively control the frequency or timing of each type of message data provided to the user according to the progress of the user's behavior modification.

[0142] From the above, the behavior modification intervention system 1 according to the embodiment of the present invention solves the problem that it is difficult to habituate behavior only with monetary incentives, effectively intervenes in the user according to the progress of the user's behavior modification, and can lead to the habituation of specific behaviors.

[0143] The details of the embodiment of the present invention have been described above.

[0144] <2. Hardware Configuration Example> Next, the hardware configuration of the information processing apparatus 900 as an example of the hardware configuration of the server 10 according to the embodiment of the present invention will be described. FIG. 9 is a diagram showing the hardware configuration of the information processing apparatus 900 as an example of the hardware configuration of the server 10 according to the embodiment of the present invention. Note that the hardware configuration of the user terminal 20 may also be realized in the same manner as the hardware configuration of the information processing apparatus 900 shown in FIG. 9.

[0145] As shown in FIG. 9, the information processing apparatus 900 includes a CPU (Central Processing Unit) 901, a ROM (Read Only Memory) 902, a RAM (Random Access Memory) 903, a host bus 904, a bridge 905, an external bus 906, an interface 907, an input device 908, an output device 909, a storage device 910, and a communication device 911.

[0146] The CPU 901 functions as an arithmetic processing unit and a control unit, and controls the overall operation within the information processing apparatus 900 according to various programs. The CPU 901 may be a microprocessor. The ROM 902 stores programs and arithmetic parameters used by the CPU 901. The RAM 903 temporarily stores programs used in the execution of the CPU 901 and parameters that appropriately change during the execution. These are interconnected by a host bus 904 composed of a CPU bus or the like.

[0147] The host bus 904 is connected to an external bus 906 such as a PCI (Peripheral Component Interconnect / Interface) bus via the bridge 905. Note that it is not always necessary to separately configure the host bus 904, the bridge 905, and the external bus 906, and these functions may be implemented on one bus.

[0148] The input device 908 is composed of input means such as a mouse, keyboard, touch panel, buttons, microphone, switches, and levers for the user to input information, and an input control circuit that generates an input signal based on the input by the user and outputs it to the CPU 901. The user who operates the information processing device 900 can input various data to the information processing device 900 or instruct processing operations by operating this input device 908.

[0149] The output device 909 includes, for example, display devices such as CRT (Cathode Ray Tube) display devices, liquid crystal displays (LCDs), OLED (Organic Light Emitting Diode) devices, lamps, and audio output devices such as speakers.

[0150] The storage device 910 is a device for storing data. The storage device 910 may include a storage medium, a recording device for recording data on the storage medium, a reading device for reading data from the storage medium, and a deleting device for deleting data recorded on the storage medium. The storage device 910 is composed of, for example, an HDD (Hard Disk Drive). This storage device 910 drives a hard disk and stores programs and various data executed by the CPU 901.

[0151] The communication device 911 is a communication interface composed of, for example, a communication device for connecting to a network. Also, the communication device 911 may support either wireless communication or wired communication.

[0152] Above, the hardware configuration example of the information processing device 900 as an example of the server 10 according to the embodiment of the present invention has been described.

[0153] <3. Variation example> The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to such examples. It is obvious that those having ordinary knowledge in the technical field to which the present invention pertains can conceive of various modification examples or correction examples within the scope of the technical idea described in the claims, and it is naturally understood that these also belong to the technical scope of the present invention.

[0154] [3-1. Variation Example Regarding Thinning-Out Determination Unit] In the above, an example has been described in which the thinning-out determination unit 130 determines whether to thin out message data belonging to the type "intrinsic reward" based on whether the number of times after applying a random number to the cumulative action count is equal to or greater than the value of the cumulative action count parameter N. However, the thinning-out determination unit 130 does not necessarily have to apply a random number to the cumulative action count.

[0155] That is, the thinning-out determination unit 130 may determine whether to thin out message data belonging to the type "intrinsic reward" based on whether the cumulative action count is equal to or greater than the value of the cumulative action count parameter N.

[0156] Also, in the above, the case where the thinning-out determination unit 130 mainly uses the cumulative action count, which is the number of times of staircase walking performed by the user after the final reward time, to determine whether to thin out message data belonging to the type "intrinsic reward" has been mainly described. However, the thinning-out determination unit 130 may use the elapsed time from the final reward time to the present to determine whether to thin out message data belonging to the type "intrinsic reward".

[0157] That is, the thinning-out determination unit 130 may calculate the elapsed time from the final reward time when the message data belonging to the type "intrinsic reward" was provided to the user terminal 20 to the present. And when the type "intrinsic reward" is included in the type candidate data, the thinning-out determination unit 130 may determine whether to thin out the message data belonging to the type "intrinsic reward" based on the progress data and the elapsed time.

[0158] More specifically, when the type candidate data includes the type "intrinsic reward", the decimation determination unit 130 may obtain a second value corresponding to the progress data (hereinafter, also referred to as "elapsed time parameter T"). Then, the decimation determination unit 130 may apply a random number to the elapsed time calculated as described above.

[0159] Here, it is mainly assumed that the action of the random number on the elapsed time is the addition of the random number to the elapsed time. At this time, the random number may be, for example, a random integer value belonging to the range from -m to +m (where m is a positive integer). However, the action of the random number on the elapsed time may be the multiplication of the random number to the elapsed time, or other operations of the random number.

[0160] Then, the decimation determination unit 130 may determine whether to decimate the message data belonging to the type "intrinsic reward" based on whether the time after the action of the random number is equal to or greater than the value of the elapsed time parameter T. At this time, as also shown in the above concepts (a1) and (a2), it is desirable that the elapsed time parameter T increases as the progress data corresponding to the elapsed time parameter T increases.

[0161] FIG. 10 is a diagram showing an example of the correspondence relationship between the progress data and the elapsed time parameter T. As shown in FIG. 10, the correspondence relationship between the progress data and the elapsed time parameter T is held in advance by the decimation determination unit 130. Also in the example shown in FIG. 10, it can be understood that the elapsed time parameter T increases as the progress data corresponding to the elapsed time parameter T increases.

[0162] Note that, similar to the case of using the cumulative action count, the decimation determination unit 130 may not apply a random number to the elapsed time from the final reward time to the present. That is, the decimation determination unit 130 may determine whether to decimate the message data belonging to the type "intrinsic reward" based on whether the elapsed time from the final reward time to the present is equal to or greater than the value of the elapsed time parameter T.

[0163] [Modification Example Regarding Correction of Selection Probability] In the above description, the case where the intervention control unit 120 corrects the selection probability corresponding to a type to zero when there is a type for which a value "1: (thinning out)" indicating thinning out of message data is output as the thinning out determination data has been described. At this time, the selection probability corresponding to a type other than the type for which "1: (thinning out)" is output (hereinafter also referred to as "other type") may or may not be corrected.

[0164]

[0165] As an example, the intervention control unit 120 may correct the selection probability corresponding to the other type by distributing the selection probability before correction corresponding to the type for which "1: (thinning out)" is output to the selection probability corresponding to the other type. [Modification Example Regarding Average Number of Daily Selections] In the above description, the case where the correspondence relationship (FIG. 8) between the progress data and the average number of daily selections is held in advance for each type to which the message data belongs has been mainly described. However, the correspondence relationship between the progress data and the average number of daily selections may be held for each combination of a plurality of thinning out determination data.

[0166] That is, the correspondence relationship between the progress data and the average number of daily selections may be held for each combination (2×2 = 4 types) of the presence or absence of thinning out of the message data belonging to the type "intrinsic reward" (2 types) and the presence or absence of thinning out of the message data belonging to the type "extrinsic reward" (2 types).

[0167] [Modification Example Regarding Target Behavior] In the above, the case where the user habituates staircase walking as an example of the target behavior has been described, but the target behavior is not limited to staircase walking.

[0168] For example, the target behavior may be walking, eating, exercising, communicating, learning, or environmentally considerate behavior. At this time, by defining the type of terminal response data according to the type of target behavior, an effect similar to the effect enjoyed when the target behavior is staircase walking can be enjoyed. For example, when the target behavior is environmentally considerate behavior, the greenhouse gas amount of the purchased product may be defined as the terminal response data.

[0169] [3-5. Modification Examples Regarding System Configuration] In the above, the intervention condition determination unit 110, the intervention control unit 120, the thinning determination unit 130, and the intervention unit 140 are provided in the server 10, and the case where the user terminal 20 is a mobile terminal has been mainly described. However, the configuration of the behavior modification intervention system 1 is not limited to such an example.

[0170] For example, all or part of the intervention condition determination unit 110, the intervention control unit 120, the thinning determination unit 130, and the intervention unit 140 may be provided in the user terminal 20, and the user terminal 20 may be a signage terminal, a smart speaker, or a communication robot, etc. Also, it is not necessary for all of the terminal response data input to the intervention condition determination unit 110 to be acquired from the user terminal 20. For example, a camera system installed in a building may photograph the user, and the behavior data of the user recognized from the image photographed by the camera system may be output to the intervention condition determination unit 110.

Description of Reference Numerals

[0171] 1 Behavior Modification Intervention System 10 Server 110 Intervention Condition Determination Unit 120 Intervention Control Unit 130 Thinning Determination Unit 140 Intervention Unit 20 User Terminal

Claims

1. A determination unit that determines a progress of a behavioral change regarding a target behavior of a subject and obtains progress data; A decision unit that decides information regarding the provision of intervention information to the subject based on the progress data; A providing unit that provides intervention information for the subject to the subject based on information regarding the provision; A behavior change intervention system that:

2. The determination unit acquires an average selection frequency in a predetermined time period corresponding to one or more types to which the intervention information group belongs and the progress data, and determines a selection probability for each type, which is a probability for selecting the intervention information belonging to the type as the intervention information for the subject, based on the average selection frequency, and determines, for each type, whether or not to select the intervention information as a candidate for the intervention information for the subject based on the selection probability, as information regarding the provision. The behavior change intervention system according to claim 1 .

3. The intervention information group includes a first type to which intervention information recommending the performance of the target behavior belongs, an average number of selections corresponding to the first type is larger as the progress data is higher in a section in which the progress data is lower than a predetermined first progress data, and is smaller as the progress data is higher in a section in which the progress data is higher than the first progress data; The behavior change intervention system according to claim 2 .

4. The intervention information group includes a second type to which intervention information indicating an intrinsic reward to be given for performing the target behavior belongs, the average number of selections corresponding to the second type is larger as the progress data is higher; The behavior change intervention system according to claim 2 .

5. The intervention information group includes a third type to which intervention information indicating an external reward to be given for performing the target behavior belongs, the average number of selections corresponding to the third type is smaller as the progress data is higher; The behavior change intervention system according to claim 2 .

6. The intervention information group includes a fourth type to which intervention information indicating an effect obtained by performing the target behavior belongs, the average number of selections corresponding to the fourth type is smaller as the progress data is higher; The behavior change intervention system according to claim 2 .

7. The determination unit obtains a probability of outputting intervention information to the subject corresponding to a time period to which the current time belongs, and determines the selection probability based on a multiplication of the output probability and the average number of selections. The behavior change intervention system according to claim 2 .

8. When the type candidate data including one or more types to which the intervention information group belongs includes a predetermined type to which the intervention information related to the reward to be given for the performance of the target behavior belongs, the determination unit determines whether or not to set the intervention information belonging to the predetermined type included in the intervention information group as a candidate for the intervention information for the subject as the information regarding the provision. The behavior change intervention system according to claim 1 .

9. When the type candidate data includes the predetermined type, the determination unit determines whether or not to set the intervention information belonging to the predetermined type included in the intervention information group as a candidate for intervention information for the subject based on the progress data and a cumulative number of actions, which is the number of times the subject has performed the target behavior since intervention information belonging to the predetermined type was last provided to the subject. The behavior change intervention system according to claim 8.

10. When the type candidate data includes the predetermined type, the determination unit obtains a first value corresponding to the progress data, and determines whether or not intervention information belonging to the predetermined type included in the intervention information group is to be a candidate for intervention information for the subject, based on whether or not the number of times after a random number is applied to the cumulative number of actions or the cumulative number of actions is equal to or greater than the first value. The behavior change intervention system according to claim 9.

11. the first value is larger as the progress data corresponding to the first value is higher; The behavior change intervention system according to claim 10.

12. When the type candidate data includes the predetermined type, the determination unit determines whether or not to set the intervention information belonging to the predetermined type included in the intervention information group as a candidate for intervention information for the subject based on the progress data and the elapsed time from when intervention information belonging to the predetermined type was last provided to the subject to the present. The behavior change intervention system according to claim 8.

13. When the type candidate data includes the predetermined type, the determination unit obtains a second value corresponding to the progress data, and determines whether or not to set the intervention information belonging to the predetermined type included in the intervention information group as a candidate for intervention information for the subject based on whether or not the time after a random number is applied to the elapsed time or the elapsed time is equal to or greater than the second value. The behavior change intervention system according to claim 12.

14. the second value is larger as the progress data corresponding to the second value is higher; The behavior change intervention system according to claim 13.

15. The determination unit is Obtain an average number of selections in a predetermined time period corresponding to each of one or more types to which the intervention information group belongs and the progress data, and determine a selection probability for each type, which is a probability for selecting intervention information belonging to the type as intervention information for the subject, based on the average number of selections; When the intervention information belonging to the predetermined type included in the intervention information group is not to be a candidate for the intervention information for the subject, a selection probability corresponding to the predetermined type is corrected to zero. The behavior change intervention system according to claim 8.

16. The determination unit identifies the intervention information group corresponding to the subject's condition, The providing unit determines intervention information for the subject from the intervention information group. The behavior change intervention system according to any one of claims 2 to 15.

17. The intervention information for the subject includes message data for encouraging the subject to perform the target behavior. The behavior change intervention system according to any one of claims 1 to 15.

18. The determination unit determines the progress based on at least one of a frequency of access to a service function by the subject, a number of days the subject has performed the target behavior, and a degree of acceptance of the subject with respect to intervention information provided to the subject. The behavior change intervention system according to any one of claims 1 to 15.

19. determining a progress of a behavioral change regarding a target behavior of a subject to obtain progress data; determining information regarding the provision of intervention information to the subject based on the progress data; Providing intervention information for the subject based on the information regarding the provision to the subject; A computer-implemented behavior change intervention method comprising:

20. Computer, A determination unit that determines a progress of a behavioral change regarding a target behavior of a subject and obtains progress data; A decision unit that decides information regarding the provision of intervention information to the subject based on the progress data; A providing unit that provides intervention information for the subject to the subject based on information regarding the provision; A program that functions as a

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