Provision device, provision method, and program

Abstract

The purpose of the present disclosure is to make it easy to obtain assistance design guidelines that improve the motivation of a user and leads to an outcome, with respect to a behavior modification assistant (including a designer of a behavior modification assistance system).　To this end, the present disclosure is for a provision device that provides reference information relating to assistance with behavior modification of a user, the provision device comprising: an acquisition unit that acquires target behavior information indicating a target behavior to be performed by the user and the degree of motivation of the user with respect to the target behavior; a derivation unit that derives an outcome contribution degree corresponding to the target behavior information; and a creation unit that creates a two-dimensional map on the basis of the outcome contribution degree derived by the derivation unit and the target behavior information and the motivation degree acquired by the acquisition unit.

Description

Providing device, providing method, and program

[0001] The present disclosure relates to a technology for supporting behavioral change in users.

[0002] There is a conventional technology that supports users in changing their behavior. This technology uses sensing to obtain lifestyle information about the user's living environment, lifestyle habits, etc., i.e., lifestyle information that can be used to support behavior change (behavior change support), and suggests recommended actions based on the lifestyle information, thereby improving the efficiency of behavior change support (Non-Patent Document 1).

[0003] Naoto Abe, Tae Sato, Reiko Ariga, "Behavioral change support technology that brings about positive psychological change," NTT Technical Journal, May 2021

[0004] However, in conventional technology, the system unilaterally suggests recommended actions, without taking into account the user's own motivation. Therefore, even if the user views the advice presented, they may not be interested and may not carry it out.

[0005] Furthermore, conventional technologies do not take into account the extent to which recommended actions affect outcomes (the results that a certain action brings about), and even if a user puts into action the recommended actions suggested to them, they may not easily lead to the outcomes, which can lead to issues such as the user losing motivation.

[0006] This disclosure has been made in consideration of the above-mentioned circumstances, and aims to make it easier for users themselves or behavioral change supporters (including designers of behavioral change support systems) to obtain design guidelines for support that will lead to improved user motivation and outcomes.

[0007] In order to achieve the above-mentioned object, the invention of claim 1 is a provision device that provides reference information regarding support for user behavior change, and has an acquisition unit that acquires target behavior information indicating a target behavior performed by the user and the user's motivation level for the target behavior, a derivation unit that derives an outcome contribution level corresponding to the target behavior information, and a creation unit that creates a two-dimensional map based on the outcome contribution level derived by the derivation unit, and the target behavior information and motivation level acquired by the acquisition unit.

[0008] As described above, the present disclosure has the effect of enabling behavioral change supporters to easily obtain design guidelines for support that will lead to improved user motivation and outcomes.

[0009] 1 is an overall configuration diagram of a communication system according to an embodiment. FIG. 2 is an electrical hardware configuration diagram of a visualization device and a database server according to an embodiment. FIG. 3 is a functional configuration diagram of a visualization device according to an embodiment. FIG. 4 is a diagram showing a motivation outcome map. FIG. 5 is a flowchart showing processing of a visualization device according to an embodiment. FIG. 6 is a diagram showing an example of a response obtained by an acquisition unit 31 and an outcome contribution rate derived by a derivation unit 32 according to a second embodiment. FIG. 7 is a diagram showing a motivation outcome map in which each target behavior (activity) is plotted according to FIG. 4 according to the second embodiment. FIG. 8 is a diagram showing a motivation outcome map in which each target behavior (activity) is plotted with the horizontal axis representing the motivation level and the vertical axis representing the overall motivation outcome evaluation value according to the second embodiment. FIG. 9 is a diagram showing a map in which the vertical axis represents the overall motivation outcome evaluation value and the target behaviors are arranged in descending order of the overall motivation outcome evaluation value along the horizontal axis according to a third embodiment. FIG. 10 is a diagram showing motivation sums for each combination of a predetermined target behavior (activity) and all other target behaviors (activities). FIG. 11 is a diagram showing motivation sums for each combination of a predetermined target behavior (activity) and all other target behaviors (activities) according to a third embodiment. SUM10 is a diagram showing a map in which the target behaviors b are arranged in descending order of the motivation sum along the horizontal axis. FIG. 11 is a diagram showing the outcome sums for each combination of a predetermined target behavior (activity) and all other target behaviors (activities) according to a fourth embodiment. FIG. 12 is a diagram showing a map in which the outcome sum is shown on the vertical axis and the target behaviors b are arranged in descending order of the outcome sum along the horizontal axis according to a fourth embodiment.

[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the present invention is not limited to the embodiments shown below, and various modifications are possible within the scope of the technical concept of the present invention. Since the drawings are intended to conceptually explain the present invention, dimensions, ratios, or numbers may be exaggerated or simplified as necessary to facilitate understanding.

[0011] First Embodiment [System Configuration of the Embodiment] First, the overall configuration of a communication system according to the embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing the overall configuration of a communication system according to the embodiment.

[0012] 1, a communication system 10 of this embodiment is constructed by a visualization device 30 and a database server 50. The visualization device 30 and the database server 50 can communicate with each other via a communication network 100 such as a LAN (Local Area Network) or the Internet.

[0013] The communication network 100 may include a dedicated network such as an ISP (Internet Service Provider) network managed and / or operated by a telecommunications carrier. The connection form of the communication network 100 may be either wireless or wired.

[0014] The visualization device 30 is configured by one or more computers. When the visualization device 30 is configured by multiple computers, it may be referred to as a "visualization device" or a "visualization system." The visualization device 30 is a PC (personal computer), a smartphone, or a tablet terminal. The visualization device is an example of a providing device. The visualization system is an example of a providing system.

[0015] The visualization device 30 is a device that visualizes reference information related to support for user behavior change. More specifically, the visualization device 30 is a device that visualizes and provides reference information used by supporters (including designers of behavior change support systems) who provide support for user behavior change when formulating support design guidelines. More specifically, the visualization device 30 plots multiple target behaviors (also referred to as "target activities") on a two-dimensional map in terms of motivation level and outcome contribution level, thereby providing an overview of multiple relationships between motivation and outcomes simultaneously.

[0016] In addition, as a device that visualizes reference information regarding support for user behavior change, the visualization device 30 can provide visualized reference information not only to supporters but also to the users themselves who are making behavioral changes, as a reference for behavioral change.

[0017] The database server 50 is composed of one or more computers. The database server 50 stores a trained machine learning model M or a DB (Data Base) for calculating outcome contribution rates, which will be described later. If the visualization device 30 does not store the trained machine learning model M or the DB (Data Base) for calculating outcome contribution rates, the visualization device 30 accesses the database server 50 and uses the trained machine learning model M or the DB for calculating outcome contribution rates.

[0018] [Hardware Configuration] Next, the electrical hardware configuration of the visualization device 30 will be described with reference to Fig. 2. Fig. 2 is a diagram showing the electrical hardware configuration of the visualization device and database server according to the embodiment.

[0019] As shown in Figure 2, the visualization device 30 has a drive device 1000, an auxiliary storage device 1002, a memory device 1003, a processor 1004, an interface device 1005, a display device 1006, an input device 1007, an output device 1008, etc., which are all interconnected by a bus 1010.

[0020] The program that realizes the processing on the computer is provided by a recording medium 1001, such as a CD-ROM or a memory card. When the recording medium 1001 storing the program is set in the drive device 1000, the program is installed from the recording medium 1001 to the auxiliary storage device 1002 via the drive device 1000. However, the program does not necessarily have to be installed from the recording medium 1001, but may be downloaded from another computer via the communication network 100. The auxiliary storage device 1002 stores the installed program as well as necessary files, data, etc.

[0021] When an instruction to start a program is received, the memory device 1003 reads the program from the auxiliary storage device 1002 and stores it. The processor 1004 realizes functions related to the device in accordance with the program stored in the memory device 1003. The processor 1004 may include not only a CPU (Central Processing Unit) but also a GPU (Graphics Processing Unit).

[0022] The interface device 1005 is used as an interface for connecting to a communication network, etc. The display device 1006 displays a GUI (Graphical User Interface) etc. according to a program. The input device 1007 is composed of a keyboard, mouse, buttons, a touch panel, etc., and is used to input various operation instructions. The output device 1008 outputs the calculation results to the outside.

[0023] The database server 50 has the same hardware configuration as the visualization device 30, and therefore a description thereof will be omitted.

[0024] [Functional Configuration of Visualization Device] Next, the functional configuration of the visualization device 30 will be described with reference to Fig. 3. Fig. 3 is a functional configuration diagram of the visualization device according to the embodiment.

[0025] As shown in Fig. 3, the visualization device 30 has an acquisition unit 31, a derivation unit 32, a creation unit 33, and an output unit 34. Each of these units has a function realized by an instruction from the processor 1004 in Fig. 2 based on a program. The visualization device 30 also has a storage unit 39 constructed by an auxiliary storage device 1002 or a memory device 1003. All information obtained by the acquisition unit 31 and the derivation unit 32 is stored in the storage unit 39.

[0026] The acquisition unit 31 acquires input information from an operator, including target activity information indicating target activities (TA) and the user's motivation level for the target activities, and outputs the target activity information to the derivation unit 32 and also outputs the target activity information and the motivation level to the creation unit 33. The operator is a user to whom a recommended activity is proposed, or a behavior change supporter (including a designer of a behavior change support system).

[0027] "Target behavior" refers to the behavioral change support that will bring about positive psychological changes tailored to the individual characteristics of each user to whom the recommended behavior is proposed. Examples include healthy behaviors such as exercise and dietary restrictions, and learning behaviors such as school homework.

[0028] The input format may be a format in which numerical values ​​are displayed on the display device 1006 of the visualization device 30 and selected, or a format in which the user freely inputs values. Alternatively, a GUI (graphical user interface) such as a slider may be used to intuitively input numerical values.

[0029] Specifically, the acquisition unit 31 displays input fields for the target behavior and the motivation level on the display device 1006 or the like of the visualization device 30, and the user inputs multiple target behaviors and the motivation level for each of these multiple target behaviors via an input unit such as a touch panel. In this case, the acquisition unit 31 displays questions such as "What exercise habits do you think are good to do?" and "What is your favorite activity?" on the display device 1006 or the like, and the user may answer the target behavior in free text or may select from a button or pull-down menu. Alternatively, the target behavior may be set in advance on the visualization device 30 side, such as "going to the gym three times a week."

[0030] The "motivation level" represents the psychological state of the user who is the target of the proposal at a certain point in time, and is, for example, a numerical representation of the user's subjective feelings.

[0031] Examples of motivation levels are as follows: (1) Motivation level is a numerical value that represents the degree of motivation on an 11-point scale from -5 to 5, with 5 being a state in which the user to whom the suggestion is made is highly motivated for the target behavior and -5 being a state in which the user to whom the suggestion is made is completely unmotivated. (2) Motivation level is a numerical value that represents the degree of enjoyment on an 11-point scale from -5 to 5, with 5 being a state in which the user to whom the suggestion is made finds the target behavior very enjoyable and -5 being a state in which the user to whom the suggestion is made finds the target behavior very unenjoyable.

[0032] In this case, the motivation level may be, for example, either (1) or (2) above, or may be the sum of multiple input values ​​(for example, both (1) and (2) above). In either case, the motivation level is expressed as a positive or negative value.

[0033] The motivation level input information received by the acquisition unit 31 does not have to be a numerical value. For example, the input information may be emotion-expressing words such as "exciting" or "gloomy," and the acquisition unit 31 may convert the emotion-expressing words into a motivation level indicated by a numerical value through internal processing.

[0034] <Derivation Unit> The derivation unit 32 infers and derives outcome contributions (METs) in this embodiment by inputting information indicating the target behavior received by the acquisition unit 31 into large language models (LLMs) as the trained machine learning model M. This trained machine learning model M is a model generated by performing a learning process using input data, which is target behavior information, and correct answer data, which is outcome contributions, and the target behavior information and outcome contributions are associated with each other.

[0035] "Outcome contribution" is the degree to which a target behavior contributes to a goal (the result to be achieved). For example, if the outcome is "increasing daily activity," the physical activity intensity (METs) of the target behavior is the outcome contribution. Here, "physical activity intensity (METs)" is a numerical representation of the intensity of various physical activities in daily life.

[0036] In the following, we will explain an example where the outcome is "increasing the amount of activity per day" and the outcome contribution is the intensity of physical activity. Note that the outcome may also be "reducing calorie intake" and the outcome contribution may be calories burned. Alternatively, other indicators may be used, such as "increasing test scores" and the outcome contribution being study time.

[0037] The derivation unit 32 may derive the outcome contribution index value corresponding to the information indicating the target behavior acquired by the acquisition unit 31 by referring to an outcome contribution calculation DB stored in advance in the storage unit 39, without using the trained machine learning model M. The outcome contribution calculation DB stores the target behavior information and the outcome contribution (index value) in association with each other.

[0038] <Creation Unit> The creation unit 33 creates a motivation outcome map as shown in Fig. 4 based on the target behavior information and the corresponding motivation level acquired by the acquisition unit 31, and the outcome contribution level derived by the derivation unit 32. Fig. 4 is an example of a motivation outcome map.

[0039] As shown in Figure 4, the creation unit 33 plots, for example, target behaviors TA1 to TA4 on a motivation outcome map, with the vertical axis representing motivation and the horizontal axis representing outcome contribution. In the representation format shown in Figure 4, the axes are set so that the origin is the midpoint between the maximum possible value of motivation (Motivation(max)) and the minimum possible value of motivation (Motivatino(min)), i.e., {Motivation(max) + Motivation(min)} / 2, and the origin is the midpoint between the maximum possible value of outcome contribution (Outcome(max)) and Outcome(min), i.e., {Outcome(max) + Outcome(min)} / 2. The representation format, such as the way the origin is set, is not limited to this.

[0040] In the motivation outcome map shown in Figure 4, the four quadrants have the following meanings. The first quadrant, where TA1 is plotted, indicates "the desire to perform the target behavior and the ability to obtain the outcome." The second quadrant, where TA2 is plotted, indicates "the desire to perform the target behavior but not obtaining the outcome or obtaining a negative outcome." The third quadrant, where TA3 is plotted, indicates "the desire not to perform the target behavior and not obtaining the outcome or obtaining a negative outcome." The fourth quadrant, where TA4 is plotted, indicates "the desire not to perform the target behavior but obtaining the outcome."

[0041] Here, the specific situation of each quadrant shown in FIG. 4 will be described.

[0042] An example of the first quadrant is the situation where you like jogging and it is good for your health. Behaviors in this area are things that people naturally do and have a positive effect on them.

[0043] An example of a situation in the second quadrant would be someone who likes reading and reads whenever they have time, but whose physical health outcomes are low in terms of activity level. Also, if we consider outcomes in terms other than physical activity intensity (METs), a person who has the habit of eating chocolate for a late-night snack would also be plotted in the second quadrant because liking chocolate and eating it for a late-night snack would lead to negative outcomes. When building a system aimed at increasing the outcomes of highly motivated activities, it is appropriate to focus on this area and suggest activities with higher outcomes by partially replacing the target behavior.

[0044] An example of the third quadrant is not liking to sleep until noon on a day off from work. Behaviors in this area are those that people naturally avoid, and avoiding the behavior has a positive effect on the person (leading to the achievement of an outcome). When considering outcomes other than physical activity intensity (METs), examples include not wanting to smoke because one dislikes cigarettes, or not consuming alcohol because one dislikes drinking.

[0045] Finally, an example of the fourth quadrant is a situation where another persona (external aspect) does not like exercise, and although it would be better for them to take action to increase their daily activity for health reasons, they are reluctant to do so. For target behaviors in this area, it is considered appropriate to refer to the target behaviors in the first quadrant and suggest similar behaviors that will motivate the user.

[0046] <Output Unit> The output unit 34 outputs the motivation outcome map created by the creation unit 33. Examples of output include displaying the map on the display device 1006 shown in FIG. 2 or on a display external to the output device 1008, or transmitting the map to an external device (printer, another PC, etc.) via the interface device 1005.

[0047] [Processing According to the Embodiment] Next, processing according to the embodiment will be described with reference to Fig. 5. Fig. 5 is a flowchart showing processing according to the embodiment of the visualization device.

[0048] S11: The acquisition unit 31 acquires, from the operator, input information including target behavior information indicating a target behavior to be performed by the user and the user's motivation level for the target behavior.

[0049] S12: The derivation unit 32 derives the outcome contribution corresponding to the target behavior information based on the target behavior information acquired by the acquisition unit 31 using the trained machine learning model M or a DB for calculating outcome contribution.

[0050] S13: The creation unit 33 creates a motivation outcome map as shown in Figure 4 based on the outcome contribution rate derived by the derivation unit 32, as well as the target behavior information and corresponding motivation rate acquired by the acquisition unit 31.

[0051] S14: The output unit 34 outputs the motivation outcome map created by the creation unit 33.

[0052] [Major Effects of the First Embodiment] As described above, according to the present embodiment, by plotting multiple target behaviors on a two-dimensional map in terms of motivation level and outcome contribution, it is possible to simultaneously obtain an overview of the relationship between multiple motivations and outcomes. That is, the supporter can easily understand the order of the target behaviors, such as from highest to lowest motivation level or highest to highest outcome contribution level, and compare the relative motivation levels and outcome contribution levels of each target behavior. This allows the supporter to intuitively grasp the motivation trends of the user to whom the recommended behavior is to be proposed, and also intuitively grasp the relationship between the target behavior and the outcome. This allows the supporter to consider the content of the recommendation while predicting what behaviors the user is likely to prefer to perform. That is, it is possible to obtain design guidelines for the content of the recommendation, such as how to suggest behaviors that the user should perform in order to improve the user's motivation.

[0053] In this embodiment, an approach in which an operator inputs data via the visualization device 30 is described, but it is also possible to automatically calculate the motivation level and outcome contribution rate for target behavior and create a motivation outcome map by analyzing dialogue data with chatbots, smart speakers, etc., actual dialogue data from health guidance interviews, etc., sensor data obtained from wearable sensors, smart homes, IoT devices, etc., and various other life log data.

[0054] ●Second embodiment The second embodiment is similar to the first embodiment in terms of system configuration (Figure 1), hardware configuration (Figure 2), functional configuration (Figure 3), and general processing (see Figure 5), so only the differences will be explained below.

[0055] In the first embodiment, the supporter (including the system designer) manually considers the recommended content proposal based on the visualized outcome motivation map. In contrast, in the second embodiment, a new evaluation value, the motivation-outcome comprehensive evaluation value V MO and the motivation outcome overall evaluation value V MO The aim is to make it easier to understand the relative relationships of target behaviors by visualizing them based on the above. This has the effect of enabling supporters to consider the balance between motivation and outcome when making recommendations.

[0056] In the second embodiment, a support system for establishing exercise habits aimed at improving numerical values ​​related to dyslipidemia and the like is considered, and the outcome is the aforementioned physical activity intensity (METs). In the second embodiment, automatic generation of recommended content proposals for a user support system in the healthcare field is described, but the present invention may also be applied to other fields, and may be used not for the operation of a user support system but as a presentation of a list of recommended content proposals for expert support.

[0057] Various cognitive behavioral therapy methods have been proposed for depression, but behavioral activation has been shown to be as effective as full-package cognitive therapy. Behavioral activation is based on the idea that depression can be overcome by increasing opportunities to receive positive reinforcement (pleasure, fun, etc.), and incorporates into the treatment program the gradual introduction of activities that the individual finds enjoyable, based on the Pleasant Events Schedule, which lists activities in daily life that cause pleasant feelings.

[0058] According to this knowledge, in order to improve outcomes, it is better to recommend activities with a higher outcome contribution rate, but in order to increase the rate of implementation, it is thought that it is better to gradually present activities starting with those with a higher motivation level. However, if the activities with a high motivation level are all activities with a low physical activity intensity, it is inefficient to present them preferentially only considering the motivation level. For this reason, in the second embodiment, the derivation unit 32 multiplies the motivation level and the outcome contribution rate to calculate the motivation-outcome comprehensive evaluation value V MO is derived.

[0059] Motivation / Outcome Overall Evaluation Value V MO is defined as follows: V MO =α*Motivation×β*Outcome Here, Motivation is the level of motivation, and Outcome is the degree of contribution to the outcome. α and β are coefficients, and may be constants specified on the derivation unit 32 side, or may be learned for each user to whom the proposal is made. Here, for example, α=1, β=1.

[0060] The visualization procedure in the second embodiment will be described below.

[0061] The acquisition unit 31 requests input regarding multiple (N) target behaviors. N may be a fixed value on the acquisition unit 31 side, or N may be the number input by the user in the form of "Please answer about m or more activities."

[0062] The acquisition unit 31 presents, for example, a question such as "Please tell us about a favorite activity that you can practice in your current life" for the purpose of acquiring activities that are uniquely motivating to the user, and a question such as "Please tell us about an exercise habit that you think is good to do" for the purpose of acquiring activities that the user feels are necessary.

[0063] Furthermore, the acquisition unit 31 presents questions such as "What do you think about jogging for 30 minutes three times a week?", "What do you think about walking for 30 minutes three times a week?", and "What do you think about doing stretching, muscle training, etc. for 30 minutes three times a week?" with the aim of acquiring the user's motivation for activities known to contribute to outcomes. Here, the motivation level is input on a 7-point scale, with 3 representing a state of being highly motivated for the target behavior and -3 representing a state of being completely unmotivated.

[0064] FIG. 6 is a diagram showing an example of the response obtained by the acquisition unit 31 and the outcome contribution rate derived by the derivation unit 32 according to the second embodiment.

[0065] The derivation unit 32 multiplies the motivation level and the outcome contribution level to obtain a motivation-outcome overall evaluation value V MO are derived and sorted in descending order.

[0066] The creation unit 33 generates the rearranged motivation outcome comprehensive evaluation value V MO A two-dimensional map is created by plotting the target behaviors and displaying the corresponding target behavior labels near the plotted points.

[0067] FIG. 7 is a diagram showing a motivation outcome map in which each target behavior (activity) is plotted in accordance with FIG. 4 according to the second embodiment.

[0068] Figure 8 is a diagram showing a motivation outcome map according to the second embodiment, in which each target behavior (activity) is plotted with the horizontal axis representing motivation and the vertical axis representing the overall motivation / outcome evaluation value. In Figure 8, activities with high outcome contribution and high motivation are plotted toward the top right, and activities with high outcome contribution but low motivation are plotted toward the bottom left. Activities near the origin have medium levels of motivation and outcome contribution. Compared to Figure 7, Figure 8 allows for a more intuitive understanding of the relationship between motivation and outcome contribution.

[0069] While departing from the example where the outcome is the amount of activity, the behaviors plotted in the second quadrant in FIG. 8 are those for which the motivation level is a negative value and the outcome contribution level is also a negative value. That is, the behaviors are those that the user finds unpleasant and that have a negative impact on the user. For example, the smoking habit of a person who dislikes cigarettes would be plotted in the second quadrant. The behaviors plotted in the fourth quadrant in FIG. 8 are those for which the motivation level is a positive value and the outcome contribution level is a negative value. That is, the behaviors are those that the user finds enjoyable and that have a negative impact on the user. For example, the habit of a person who finds pleasure in eating chocolate late at night would be plotted in the fourth quadrant in FIG. 8.

[0070] 9 is a diagram showing a map according to the second embodiment, in which the vertical axis represents the overall motivation / outcome evaluation value and the horizontal axis represents the target behaviors arranged in descending order of overall motivation / outcome evaluation value. In FIG. 9, activities with high outcome contribution and high motivation are plotted toward the upper left, and activities with high outcome contribution but low motivation are plotted toward the lower right. MO Activities near 0 (horizontal axis) are activities with medium levels of motivation and outcome contribution. Figure 9 is a simplified representation of Figures 7 and 8, making it easy for supporters to interpret.

[0071] The plotting methods shown in FIGS. 7 to 9 are merely examples, and other combinations of horizontal and vertical axes may be used.

[0072] [Major Effects of the Second Embodiment] The visualization of the second embodiment, which is easy to interpret as shown in Figures 8 and 9, allows the supporter to simultaneously consider the motivation and outcome contribution of the user to whom the recommendation is made, which can be useful in considering the content of the recommendation or can be automated. For example, among the actions plotted in Figure 9, the motivation / outcome overall evaluation value V MO For example, the recommendation order is from highest to lowest.

[0073] ●Third embodiment The third embodiment is similar to the first embodiment in terms of system configuration (Figure 1), hardware configuration (Figure 2), functional configuration (Figure 3), and general processing (see Figure 5), so only the differences will be explained below.

[0074] The third embodiment relates to visualization aimed at obtaining design guidelines for proposing new activities that increase motivation by combining a low-motivation activity with other activities.

[0075] According to the knowledge of the behavioral activation method described in the second embodiment, it is possible to activate the entire daily life by gradually incorporating activities that the user finds enjoyable. In contrast, based on this knowledge, the third embodiment aims to support the suggestion of new activities that can provide high motivation by combining low-motivation activities with other high-motivation activities.

[0076] To achieve this, in the third embodiment, the motivation sum M SUM By introducing an evaluation value (a, b), we can obtain design guidelines for which behavior (activity) a should be combined with in order to increase motivation for that behavior (activity).

[0077] Motivation and M SUM (a, b) are defined as follows:

[0078] MSUM (a, b) = γ * Motivation(a) + δ * Motivation(b) Here, Motivation(a) is the motivation level for target behavior (activity) a, and Motivation(b) is the motivation level for target behavior (activity) b. γ and δ are coefficients, and constants may be specified on the visualization device 30 side, or they may be learned for each user. In this embodiment, γ = 1, δ = 1.

[0079] Motivation sum is a way of expressing the evaluation value of the motivation for a new activity that is created when elements of multiple activities are combined. The above explanation was given for two actions, target action a and target action b, but it is also possible to add up the motivation for three or more actions.

[0080] The visualization procedure in the third embodiment will be described below.

[0081] The input information acquired by the acquisition unit 31 is the same as that in the second embodiment. An example of the user's answer obtained by the acquisition unit 31 and the outcome contribution rate derived (calculated) by the derivation unit 32 is shown in FIG. 6, as in the second embodiment.

[0082] After calculating the outcome contribution rate, the derivation unit 32 calculates the motivation sum M SUM (a, b) is derived by exhaustively testing all combinations of target actions. In the following, in FIG. 6, the activity "30 minutes of jogging," which has the lowest motivation and the highest outcome contribution, is designated as target action a. Target action a and multiple other activities obtained by the acquisition unit 31 are designated as target actions b, and the motivation sum is obtained by exhaustive testing. FIG. 10 is a diagram showing the motivation sums for each combination of a specific target action (activity) and all other target actions (activities) according to the third embodiment.

[0083] The creation unit 33 generates the rearranged motivation outcome comprehensive evaluation value V MO A two-dimensional map is created by plotting the target behaviors and displaying the corresponding target behavior labels near the plotted points.

[0084] FIG. 11 relates to the third embodiment, and the vertical axis represents the sum of motivation M SUM 11 is a diagram showing a map in which target behaviors b are arranged in descending order of motivation sum along the positive horizontal axis. Combinations of activities with higher motivation sums are plotted toward the upper left, and combinations of activities with lower motivation sums are plotted toward the lower right in FIG. 11. By visualizing the original motivation "original_motivation" of "30 minutes jogging," which is the target behavior a in this embodiment, superimposing it on FIG. 11, the supporter can intuitively predict whether the motivation for the original activity will increase or decrease.

[0085] The plotting method in Figure 11 is an example, and other combinations of horizontal and vertical axes may be set. Also, in Figure 11, for example, "30 minutes of jogging x going to the nearby beach" is displayed, but since "30 minutes of jogging" is common, it is possible to simply display "going to the nearby beach" without displaying "30 minutes of jogging x". Similarly, it is not necessary to display "30 minutes of jogging x" in other plotted areas.

[0086] [Major Effects of the Third Embodiment] As shown in FIG. 11 obtained in the third embodiment, visualization that is easy to interpret is possible, which helps a supporter to consider recommendations that combine multiple activities based on the motivation specific to the user to whom the recommendations are made. For example, the supporter can recommend combinations of activities plotted in FIG. 11 in descending order of motivation sum. Furthermore, the derivation unit 32 may automatically generate new activities that combine the target behavior a with each target behavior b using a generation AI (artificial intelligence) and present the new activities in descending order of motivation sum.

[0087] ●Fourth embodiment The fourth embodiment is similar to the first embodiment in terms of system configuration (Figure 1), hardware configuration (Figure 2), functional configuration (Figure 3), and general processing (see Figure 5), so only the differences will be explained below.

[0088] The fourth embodiment relates to visualization aimed at obtaining design guidelines for proposing new activities that improve outcomes by combining activities that have a low degree of contribution to outcomes with other activities.

[0089] According to the knowledge of the behavioral activation method explained in the second embodiment, it is possible to activate the entire daily life by gradually incorporating activities that the user finds enjoyable. In contrast, based on this knowledge, the fourth embodiment aims to support the suggestion of new activities that will produce better outcomes than the original activities by combining activities that are highly motivating but have a low outcome contribution with other activities that have a high outcome contribution.

[0090] To achieve this, in the fourth embodiment, the outcome sum O SUM By introducing evaluation values ​​(a, b), we can obtain design guidelines for which actions (activities) a should be combined with in order to increase the outcome contribution of that action (activity).

[0091] Outcome and O SUM (a, b) are defined as follows:

[0092] O SUM (a, b) = ε * Outcome(a) + ζ * Outcome(b) Here, Outcome(a) is the outcome contribution of target behavior (activity) a, and Outcome(b) is the outcome contribution of target behavior (activity) b. ε and ζ are coefficients, and constants may be specified on the visualization device 30 side, or they may be learned for each user to whom the proposal is made. In this embodiment, ε = 1, ζ = 1.

[0093] The sum of outcomes is a way of expressing the evaluation value of the outcome of a new activity that results from combining elements of multiple activities. The above describes two actions, target action a and target action b, but it is also possible to add up the outcome contributions of three or more actions.

[0094] The visualization procedure in the fourth embodiment will be described below.

[0095] The input information acquired by the acquisition unit 31 is the same as that in the second and third embodiments. An example of the user's response obtained by the acquisition unit 31 and the outcome contribution rate derived (calculated) by the derivation unit 32 is shown in FIG. 6, as in the second and third embodiments.

[0096] After calculating the outcome contribution, the derivation unit 32 calculates the outcome sum O SUM (a, b) is derived by exhaustively testing all combinations of target behaviors. In the following, in FIG. 6, the activity "reading," which has the lowest outcome contribution rate among the motivated (arbitrary) activities, is designated as target behavior a. Target behavior a and multiple other activities obtained by the acquisition unit 31 are designated as target behavior b, and the outcome sum is obtained by exhaustive testing. FIG. 12 is a diagram showing the outcome sum for each combination of a specific target behavior (activity) and all other target behaviors (activities) according to the fourth embodiment.

[0097] The creation unit 33 generates the motivation outcome comprehensive evaluation value V MO A two-dimensional map is created by plotting O and displaying the label of the corresponding target behavior near the plot.

[0098] Figure 13 is a diagram showing a map relating to the fourth embodiment, in which the vertical axis represents the outcome sum and the horizontal axis represents the target behaviors b arranged in descending order of outcome sum. In Figure 13, combinations that result in higher outcome sums when combined with target behavior a are plotted toward the upper left, and combinations of activities that result in lower outcome sums are plotted toward the lower right. By visualizing the original motivation (original_motivation) for "reading," which is target behavior a in this embodiment, by superimposing it on Figure 13, the supporter can intuitively grasp how much the outcome of the original activity will increase or decrease when combined with target behavior b.

[0099] The plotting method in Figure 13 is an example, and other combinations of horizontal and vertical axes may be set. Also, in Figure 13, for example, "Reading x Swimming" is displayed, but since "Reading" is a common activity, it is possible to simply display "Swimming" without displaying "Reading x". Similarly, it is not necessary to display "Reading x" in other plotted areas.

[0100] [Major Effects of the Fourth Embodiment] The fourth embodiment provides visualization that is easy to interpret, as shown in Figure 13, which allows supporters to consider recommendations that combine multiple activities based on the specific motivations of the user to whom the recommendations are made. For example, the supporter can recommend combinations of activities plotted in Figure 13 in descending order of the sum of outcomes. The derivation unit 32 may also automatically generate new activities that combine target behavior a and target behavior b using a generation AI and present them in descending order of the sum of outcomes.

[0101] [Supplementary Note] The present invention is not limited to the above-described embodiment, and may have the following configurations or processes (operations), for example.

[0102] (1) The visualization device 30 can be realized by a computer and a program, but this program can also be recorded on a (non-temporary) recording medium or provided via a communication network 100 such as the Internet.

[0103] (2) The processor 1004, which is hardware, may be a single processor or multiple processors.

[0104] (3) The maps shown in FIGS. 4, 7, 8, 9, 11, and 13 are examples of two-dimensional maps.

[0105] 10 Communication system 30 Visualization device (an example of a providing device) 31 Acquisition unit 32 Derivation unit (also referred to as "calculation unit") 33 Creation unit 34 Output unit 39 Storage unit

Claims

1. A provision device that provides reference information regarding support for a user's behavioral change, comprising: an acquisition unit that acquires target behavior information indicating a target behavior performed by the user and the user's motivation level for the target behavior; a derivation unit that derives an outcome contribution level corresponding to the target behavior information; and a creation unit that creates a two-dimensional map based on the outcome contribution level derived by the derivation unit, and the target behavior information and motivation level acquired by the acquisition unit.

2. The provision device according to claim 1, wherein the creation unit creates the two-dimensional map in which the target behavior is plotted with the motivation level on the vertical axis and the outcome contribution level on the horizontal axis.

3. The provision device of claim 1, wherein the derivation unit derives a motivation / outcome overall evaluation value by multiplying the motivation level and the outcome contribution level, and the creation unit creates the two-dimensional map in which the motivation / outcome overall evaluation value is on the vertical axis and each target behavior is arranged in descending order of the motivation / outcome overall evaluation value on the horizontal axis.

4. The provision device of claim 1, wherein the derivation unit derives a motivation sum, which is the sum of the motivation level of a first target behavior and the motivation level of a second target behavior, and the creation unit creates the two-dimensional map in which the motivation sum is on the vertical axis and the target behaviors are arranged in descending order of motivation sum on the horizontal axis.

5. The provision device of claim 1, wherein the derivation unit derives an outcome sum, which is the sum of the outcome contribution of a first target behavior and the outcome contribution of a second target behavior, and the creation unit creates the two-dimensional map in which the outcome sum is on the vertical axis and the target behaviors are arranged in descending order of the outcome sum on the horizontal axis.

6. A provision method executed by a provision device that provides reference information regarding support for user behavior change, the provision method executing: an acquisition process that acquires target behavior information indicating a target behavior performed by the user and the user's motivation level for the target behavior; a derivation process that derives an outcome contribution level corresponding to the target behavior information; and a creation process that creates a two-dimensional map based on the outcome contribution level derived by the derivation process, and the target behavior information and motivation level acquired by the acquisition process.

7. A program for causing a computer to execute the method according to claim 6.

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