Behavior modification devices

Abstract

The present invention addresses the problem of promoting the user's behavior change in a virtual space. A behavior change apparatus 1 comprises: a contrivance determination unit 11 for determining a contrivance for a user in a virtual space on the basis of a cognitive bias of the user; and a field installation unit 19 for installing, in the virtual space, the contrivance determined by the contrivance determination unit 11. The contrivance determination unit 11 may determine a contrivance by using a prediction model that predicts the degree of the user's behavior change by the contrivance by inputting the degree of the cognitive bias of the user. The contrivance may be a contrivance for guiding the user to predetermined content present in the virtual space. The determination of a contrivance by means of the contrivance determination unit 11 may also be based on the attributes of the user.

Description

[Technical Field]

[0001] One aspect of the present disclosure relates to a behavior modification device that encourages a user to modify their behavior. [Background technology]

[0002] Patent Document 1 below discloses a furniture-type device that can give the user the feeling that they are moving within a virtual space. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7037158 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the furniture-type devices cannot encourage users to change their behavior in a virtual space, for example. Therefore, it is desirable to encourage users to change their behavior in a virtual space. [Means for solving the problem]

[0005] A behavior modification device according to one aspect of the present disclosure includes a determination unit that determines a mechanism for a user in a virtual space based on the user's cognitive bias, and an installation unit that installs the mechanism determined by the determination unit in the virtual space.

[0006] In this aspect, since mechanisms for users are set up in the virtual space, it is possible to encourage users to change their behavior in the virtual space. [Effects of the Invention]

[0007] According to one aspect of the present disclosure, it is possible to encourage users to change their behavior in a virtual space. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 2 is a diagram illustrating an example of the functional configuration of a behavior modification device according to an embodiment. [Figure 2] FIG. 10 is a diagram illustrating an example table of behavior change rates for each nudge for each user. [Figure 3] FIG. 10 illustrates an example table of behavior change rates per nudge for unknown users. [Figure 4] FIG. 10 is a diagram showing an example of a moving mechanism based on a tuning bias. [Figure 5] FIG. 10 is a diagram showing another example of a moving mechanism based on a synchronization bias. [Figure 6] FIG. 10 is a diagram showing an example of a movement mechanism based on decision avoidance. [Figure 7] FIG. 10 is a diagram showing an example of a moving mechanism based on rarity. [Figure 8] FIG. 10 is a diagram showing an example of a moving mechanism based on the mere exposure effect. [Figure 9] FIG. 10 is a diagram showing an example of a moving device based on competitive spirit. [Figure 10] FIG. 10 illustrates an example table of cognitive bias information for a particular user. [Figure 11] FIG. 10 is a diagram illustrating an example of a table of optimal wording information related to a specific user. [Figure 12] FIG. 10 is a diagram showing an example of a table of optimal voice information for a specific user. [Figure 13] FIG. 10 is a diagram illustrating an example of a table of optimal avatar information for a specific user. [Figure 14] FIG. 10 is a diagram illustrating an example of a table of optimal facial expression information for a specific user. [Figure 15] FIG. 2 is a sequence diagram illustrating an example of processing executed by a behavior modification device according to an embodiment. [Figure 16] 10 is a flowchart illustrating an example of processing executed by a behavior modification device according to an embodiment. [Figure 17] FIG. 10 is a diagram illustrating an example of selecting an optimal device based on an individual's cognitive bias. [Figure 18]FIG. 2 is a diagram illustrating an example of the hardware configuration of a computer used in the behavior modification device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the description of the drawings, the same elements are designated by the same reference numerals, and duplicate explanations will be omitted. Furthermore, the embodiments of the present disclosure in the following description are specific examples of the present invention, and the present invention is not limited to these embodiments unless otherwise specified to limit the present invention.

[0010] FIG. 1 is a diagram illustrating an example of the functional configuration of a behavior modification device 1 according to an embodiment.

[0011] The behavior modification device 1 is a computer device that encourages users to change their behavior. More specifically, the behavior modification device 1 can bring benefits to both users and producers by guiding appropriate users to appropriate content in a virtual space (encouraging behavior modification).

[0012] A virtual space is a virtual two-dimensional or three-dimensional space. In this embodiment, the term "space" may be appropriately replaced with "world," or conversely, the term "world" may be appropriately replaced with "space." The virtual space may be, for example, a metaverse, which is a three-dimensional space different from the real world and constructed on a computer or a computer network (such as the Internet).

[0013] The content may be, for example, a store, facility, object, or information for economic activity or entertainment. In this embodiment, the content is assumed to exist or be installed in a virtual space, but is not limited to this. Because a virtual space is not restricted by location or time, a wider variety of content can be installed in the virtual space than in the real world.

[0014] 1, the behavior modification device 1 includes a storage unit 10, a process determination unit 11 (determination unit), and a field installation unit 19 (installation unit). The process determination unit 11 includes a nudge optimization unit 12, a movement process generation unit 13, a wording optimization unit 14, a voice optimization unit 15, an avatar optimization unit 16, a facial expression optimization unit 17, and a navigator generation unit 18.

[0015] Each functional block of the behavior change device 1 is assumed to function within the behavior change device 1, but this is not limited to this. For example, some of the functional blocks of the behavior change device 1 may function within a computer device different from the behavior change device 1, connected to the behavior change device 1 via a network, while appropriately sending and receiving information with the behavior change device 1. Furthermore, some functional blocks of the behavior change device 1 may be omitted, multiple functional blocks may be integrated into one functional block, or one functional block may be separated into multiple functional blocks.

[0016] Below, each function of the behavior modification device 1 shown in FIG. 2 will be explained.

[0017] Storage unit 10 stores any information used in calculations by behavior modification device 1 and the results of calculations by behavior modification device 1. The information stored by storage unit 10 may be referenced by each function of behavior modification device 1 as needed.

[0018] The in-progress determination unit 11 determines (or generates) a device for a user in a virtual space based on the cognitive bias of the user. The in-progress determination unit 11 may determine one or more (including a plurality of) devices for the user in a virtual space based on one or more (including a plurality of) cognitive biases of the user.

[0019] Cognitive bias is a psychological phenomenon in which judgments of things become irrational due to preconceived notions based on intuition or past experiences, or in which people unconsciously make irrational judgments due to their own assumptions or the surrounding environment.

[0020] A device is something devised for a purpose. It is assumed that a device is installed in a virtual space, but this is not the only possibility. A device may also be a device that guides a user to a specific content that exists in the virtual space.

[0021] The mechanism may include a mechanism (movement mechanism) related to the virtual movement of a user in a virtual space. For example, when a user can move his / her own avatar (a user avatar) (in the virtual space) based on the user's instructions in the virtual space, the mechanism is a mechanism that influences or guides the movement.

[0022] The mechanism may include a mechanism (navigator) for guiding the user by a guidance avatar, which is a predetermined avatar in the virtual space. The guidance avatar is an avatar for guiding the user and is assumed to be different from the user avatar, but is not limited to this. At least one of the words (message content, method of communication) spoken by the guidance avatar when guiding, the voice (frequency, strength, volume, intonation) spoken by the guidance avatar when guiding, the appearance of the guidance avatar, or the facial expression of the guidance avatar when guiding may be based on the user's cognitive bias.

[0023] The work-in-progress determination unit 11 may output work-in-progress information regarding the determined work to the field installation unit 19, or may cause the storage unit 10 to store the work-in-progress information.

[0024] The setup determination unit 11 may acquire cognitive bias information regarding the user's cognitive bias, and determine setup information for a setup for the user in the virtual space based on the acquired cognitive bias information. The timing at which the setup determination unit 11 acquires the cognitive bias information may be based on an instruction from an arbitrary person, such as an administrator or user of the behavior modification device 1, or may be periodically (e.g., once an hour). The setup determination unit 11 may acquire the cognitive bias information from the storage unit 10 where the cognitive bias information is stored in advance, or may acquire it from another device via a network. The setup determination unit 11 may refer to information in which the cognitive bias information and the setup information are associated with each other, stored in advance by the storage unit 10, to extract the setup information associated with the acquired cognitive bias information, and determine the extracted setup information as the finally determined setup information (trap).

[0025] The setup determination unit 11 may refer to information stored in advance in the storage unit 10 that corresponds cognitive bias information with wording information regarding words uttered by the guidance avatar when guiding, extract wording information that corresponds to the acquired cognitive bias information, and determine the extracted wording information as the finally determined wording information (wording to be uttered by the guidance avatar when guiding).

[0026] The setup determination unit 11 may refer to information previously stored in the storage unit 10 that corresponds cognitive bias information with audio information regarding the audio uttered by the guiding avatar during guidance, extract audio information that corresponds to the acquired cognitive bias information, and determine the extracted audio information as the finally determined audio information (the audio uttered by the guiding avatar during guidance).

[0027] The setup determination unit 11 may refer to information previously stored in the storage unit 10 that corresponds cognitive bias information with appearance information regarding the appearance of the guidance avatar, extract appearance information that corresponds to the acquired cognitive bias information, and determine the extracted appearance information as the finally determined appearance information (appearance of the guidance avatar).

[0028] The setup determination unit 11 may refer to information previously stored in the storage unit 10 that corresponds cognitive bias information with facial expression information regarding the facial expression of the guidance avatar during guidance, extract facial expression information that corresponds to the acquired cognitive bias information, and determine the extracted facial expression information as the finally determined facial expression information (the facial expression of the guidance avatar during guidance).

[0029] The setup determination unit 11 may determine a nudge for the user based on the cognitive bias of the user, and may determine a setup for the user in the virtual space based on the determined nudge.

[0030] A nudge is a device or environmental change that encourages users to voluntarily choose a desired behavior without being forced to do so, or that subtly makes users aware of the behavior and guides them in the appropriate direction unconsciously or reflexively.

[0031] More specifically, the setup determination unit 11 may acquire cognitive bias information regarding the cognitive bias of the user, determine nudge information regarding a nudge for the user based on the acquired cognitive bias information, and determine setup information regarding a setup for the user in the virtual space based on the determined nudge information. The setup determination unit 11 may refer to information in which cognitive bias information and nudge information are associated with each other and stored in advance by the storage unit 10, extract nudge information associated with the acquired cognitive bias information, refer to information in which nudge information and setup information are associated with each other and stored in advance by the storage unit 10, extract setup information associated with the extracted nudge information, and determine the extracted setup information as the finally determined setup information (trap).

[0032] The setup determination unit 11 may determine a setup using a prediction model that predicts the degree of user behavior change due to a setup by inputting the degree of the user's cognitive bias. The degree of cognitive bias may be, for example, a real number from "0" to "1," where the closer to "0" the number is, the lower the degree (tendency) and the closer to "1" the number is, the higher the degree (tendency). Similarly, the degree of behavior change may be, for example, a real number from "0" to "1," where the closer to "0" the number is, the lower the degree (tendency) and the closer to "1" the number is, the higher the degree (tendency). The prediction model may be, for example, a trained model generated by machine learning or a mathematical model. The setup determination unit 11 may, for example, input the degree of the user's cognitive bias into the prediction model and determine the setup with the highest degree of user behavior change due to the setup predicted, or may determine the setup with the top N degrees (N is an integer equal to or greater than 1).

[0033] The setup determination unit 11 may determine a setup further based on the attributes of the user. Examples of the attributes include gender and age. That is, the setup determination unit 11 may determine a setup for the user in the virtual space based on the cognitive bias of the user and the attributes of the user. The setup determination unit 11 may determine one or more (including multiple) setups for the user in the virtual space based on one or more (including multiple) cognitive biases of the user and one or more (including multiple) attributes of the user.

[0034] The setup determination unit 11 may acquire cognitive bias information related to a user's cognitive bias and attribute information related to the user's attributes, and determine setup information related to a setup for the user in the virtual space based on the acquired cognitive bias information and attribute information. The setup determination unit 11 may acquire the cognitive bias information and attribute information from the storage unit 10 where they are stored in advance, or may acquire them from another device via a network. The setup determination unit 11 may refer to information in which the cognitive bias information, attribute information, and setup information are associated with each other, which is stored in advance by the storage unit 10, to extract setup information associated with the acquired cognitive bias information and attribute information, and determine the extracted setup information as the finally determined setup information (trap).

[0035] The process determination unit 11 includes a nudge optimization unit 12, a movement process generation unit 13, a wording optimization unit 14, a voice optimization unit 15, an avatar optimization unit 16, a facial expression optimization unit 17, and a navigator generation unit 18.

[0036] The nudge optimization unit 12 determines an optimal nudge for a user based on the user's cognitive biases. An optimal nudge is a nudge that has the greatest effect on the user or whose effect on the user meets a predetermined criterion. The nudge optimization unit 12 may determine one or more nudges suitable for the user based on one or more cognitive biases of the user. A suitable nudge is a nudge whose effect on the user meets a predetermined criterion. The nudge optimization unit 12 may determine one or more nudges suitable for the user based on one or more cognitive biases of the user and one or more attributes of the user. The nudge optimization unit 12 may output nudge information regarding the determined nudge to the movement setup generation unit 13 and the wording optimization unit 14, or may store it in the storage unit 10.

[0037] The nudge optimization unit 12 may acquire and use the behavior change rate (probability of behavior change) of each nudge for each user, which is stored in advance by the storage unit 10.

[0038] FIG. 2 is a diagram showing an example table of behavioral change rates for each nudge for each user. The example table in FIG. 2 corresponds to a user ID identifying a user, the degree of conformity bias, which is the user's cognitive bias, the degree of time preference, which is the user's cognitive bias, the degree of risk preference, which is the user's cognitive bias, the degree of scarcity, which is the user's cognitive bias, the degree of the bandwagon effect, which is the user's cognitive bias, the degree of any other cognitive bias of the user, the behavioral change rate of the user due to a predetermined nudge, nudge (1), the behavioral change rate of the user due to a predetermined nudge, nudge (2), the behavioral change rate of the user due to a predetermined nudge, nudge (3), and the behavioral change rate of the user due to any other predetermined nudge. The behavioral change rate may be a real number between "0" and "1," for example. The closer to "0," the lower the probability (tendency) of behavioral change, and the closer to "1," the higher the probability (tendency) of behavioral change.

[0039] In Figure 2, the cognitive bias is used as an explanatory variable, and the behavior change rate due to each nudge is used as a target variable, and the behavior change device 1 may learn to estimate the behavior change rate due to each nudge from the cognitive bias. Random nudges may be used for learning. As a result of learning, a predictive model may be generated. As a result of learning by the behavior change device 1, the nudge optimization unit 12 may predict the behavior change rate due to each nudge for an unknown user using the cognitive bias as an input. The nudge optimization unit 12 may determine (select) the nudge that is expected to be most effective (having the largest predicted value).

[0040] Figure 3 is a diagram showing an example of a table of the behavioral change rate for each nudge for an unknown user. In the example table of Figure 3, a user ID for identifying an (unknown) user, the degree of conformity bias of the user, the degree of time preference of the user, the degree of risk preference of the user, the degree of rarity of the user, the degree of bandwagon effect of the user, the degree of any other cognitive bias of the user, the behavioral change rate of the user due to nudge (1), the behavioral change rate of the user due to nudge (2), the behavioral change rate of the user due to nudge (3), and the behavioral change rate of the user due to any other specified nudge are associated with each other. For example, the nudge optimization unit 12 may input the degree of conformity bias of the user, the degree of time preference of the user, the degree of risk preference of the user, the degree of rarity of the user, the degree of bandwagon effect of the user, and the degree of any other cognitive bias of the user into a prediction model to obtain the behavioral change rate of the user due to nudge (1), the behavioral change rate of the user due to nudge (2), the behavioral change rate of the user due to nudge (3), and the behavioral change rate of the user due to any other specified nudge, which are output, and determine the optimal nudge for the user based on the obtained behavioral change rates.

[0041] A learning example in which the nudge optimization unit 12 determines (estimates) the optimal nudge for a user will be described. To collect data, the behavior modification device 1 randomly intervenes with an unspecified number of users using "nudges linked to conformity bias," "nudges linked to scarcity," and "nudges linked to the mere exposure effect." As a result, data is collected showing that, for example, nudges linked to conformity bias are effective for people in their 20s, nudges linked to scarcity for people in their 30s and 40s, and nudges linked to the mere exposure effect are effective for people in their 50s and older (there are tendencies for effective nudges depending on attributes and cognitive biases). By training this data using a machine learning model, a model can be created in which the estimated value of nudges linked to conformity bias is high for people in their 20s and the estimated value of nudges linked to the mere exposure effect is high for people in their 50s and older. In other words, it becomes possible to determine (select) the optimal (appropriate) nudge for unknown users. In this embodiment, a general machine learning method is used for learning.

[0042] The nudge optimization unit 12 may input at least one of the attribute information stored in advance by the storage unit 10 and the scores of each cognitive bias stored in advance by the storage unit 10. The nudge optimization unit 12 may estimate the effectiveness (0% to 100%) of each nudge from nudge presets (nudge (1), nudge (2), nudge (3), ...) based on the input data and select which one is most effective. For example, if a user estimates that nudge (1) is 70%, nudge (2) is 50%, and nudge (3) is 40%, the user selects nudge (1) because it can be said that nudge (1) is the most effective. Note that the presets are, for example, nudges linked to conformity bias (see Figures 4 to 9 described below). The nudge optimization unit 12 may output nudge information regarding the most effective nudge selected from the nudge presets to the movement setup generation unit 13 and the wording optimization unit 14, or the nudge information may be stored by the storage unit 10.

[0043] The moving device generation unit 13 generates a moving device based on the nudge determined by the nudge optimization unit 12. More specifically, the moving device generation unit 13 references information previously stored by the storage unit 10, in which nudge information is associated with moving device information related to the moving device, extracts the moving device information associated with the nudge information input from the nudge optimization unit 12, and generates (in the virtual space) a moving device indicated by the extracted moving device information. The moving device generation unit 13 may output information to the effect that a moving device has been generated, or information related to the generated moving device, to the field installation unit 19, or may have the storage unit 10 store it.

[0044] Specific examples of moving devices generated by the moving device generation unit 13 will be described with reference to FIGS.

[0045] FIG. 4 is a diagram showing an example of a movement mechanism based on a synchronization bias. FIG. 4 (also shown in FIGS. 5 to 9) is a diagram showing a virtual space. In the virtual space, there is a path along which a user can move, and content icons corresponding to various pieces of content are placed along or at the ends of the path. A user avatar is represented by an icon in the shape of a full human body. A user can consume the content corresponding to the content icon by moving the user avatar to the position of the content icon. Note that FIGS. 4 to 9 are merely examples and are not limiting. For example, the placement of a content icon is not essential, and if the type of content can be identified through other display formats, the content icon need not be placed.

[0046] In FIG. 4, it is assumed that a user who has a tendency toward conformity bias as a cognitive bias is guided to T-shirt content, which is content represented in the shape of a T-shirt. In this case, the movement mechanism generation unit 13 (and the field installation unit 19 described below) moves the user to the T-shirt content by generating and placing a group of NPCs (Non-Player Characters) around the icon of the T-shirt content to which the user is to be guided. Furthermore, a guide avatar generated by the navigator generation unit 18 described below may guide the user to the T-shirt icon by saying or displaying a message such as "There's a crowd of people over there."

[0047] FIG. 5 is a diagram showing another example of a movement mechanism based on conformity bias. In FIG. 5, it is assumed that a user who does not have a tendency toward conformity bias in T-shirt content is guided. In this case, the movement mechanism generation unit 13 (and the field installation unit 19 described below) generates and places a group of NPCs on a route other than the desired route, thereby encouraging the user to avoid congestion. Furthermore, a guide avatar generated by the navigator generation unit 18 described below may guide the user to the T-shirt icon by saying or displaying a message such as, "We want to avoid congestion."

[0048] FIG. 6 is a diagram showing an example of a movement mechanism based on decision avoidance. In FIG. 6, it is assumed that a user who has a tendency toward decision avoidance as a cognitive bias is guided to the T-shirt content. In this case, the movement mechanism generation unit 13 (and the field installation unit 19 described below) narrows the user's options and guides the user by generating and arranging a specific route to highlight it. Furthermore, a guidance avatar generated by the navigator generation unit 18 described below may guide the user to the T-shirt icon by saying or displaying a message such as, "It looks like there's something up ahead."

[0049] FIG. 7 is a diagram showing an example of a movement mechanism based on scarcity. In FIG. 7, it is assumed that a user with a cognitive bias toward scarcity is guided to T-shirt content. In this case, the movement mechanism generation unit 13 (and the field installation unit 19 described below) guides the user by generating and placing highly rare items (jewels in the figure) (that attract the user's interest) on the path's guiding line. Instead of items, for example, a path (road) that makes a sound when walked on may also be generated and installed. Furthermore, a guide avatar generated by the navigator generation unit 18 described below may guide the user to the T-shirt icon by saying or displaying a message such as, "There's something lying around."

[0050] FIG. 8 is a diagram showing an example of a movement mechanism based on the mere exposure effect. In FIG. 8, it is assumed that a user who is prone to the mere exposure effect as a cognitive bias is guided to the T-shirt content. In this case, the movement mechanism generation unit 13 (and the field installation unit 19 described below) guides the user by generating and arranging directional music that is familiar to the user to play around the T-shirt content. Furthermore, a guidance avatar generated by the navigator generation unit 18 described below may guide the user to the T-shirt icon by saying or displaying a message such as, "I can hear something over there."

[0051] FIG. 9 is a diagram showing an example of a moving device based on competitive spirit. In FIG. 9, it is assumed that a user with a competitive tendency as a cognitive bias is guided to the T-shirt content. In this case, the moving device generation unit 13 (and the field installation unit 19 described below) generates and arranges scores, such as scores in the game, number of visits, or number of steps, to be visualized, including those of other users, thereby inspiring the user's competitive spirit and guiding the user. Furthermore, a guidance avatar generated by the navigator generation unit 18 described below may guide the user to the T-shirt icon by saying or displaying a message such as, "You'll be in third place when you visit next time."

[0052] The wording optimization unit 14 determines optimal words for the user based on the user's cognitive biases. The optimal words are words that have the greatest effect on the user or words whose effect on the user meets a predetermined standard. The wording optimization 14 may determine one or more words suitable for the user based on one or more cognitive biases of the user. The appropriate words are words whose effect on the user meets a predetermined standard. The wording optimization 14 may determine one or more words suitable for the user based on one or more cognitive biases of the user and one or more attributes of the user. The wording optimization 14 may output wording information regarding the determined words to the navigator generation unit 18 or may store the information in the storage unit 10.

[0053] 10 is a diagram showing an example of a table of cognitive bias information for a specific user. In the example table shown in FIG. 10, a user ID for identifying the user, attribute information regarding the user's attributes (gender, age, etc.), the degree of conformity bias, which is the user's cognitive bias, the degree of time preference, which is the user's cognitive bias, the degree of risk preference, which is the user's cognitive bias, the degree of scarcity, which is the user's cognitive bias, the bandwagon effect, which is the user's cognitive bias, and the degree of other cognitive biases, which is the user's cognitive bias, are associated with each other. The wording optimization 14 may determine optimal wording for the user based on the user's cognitive bias information as shown in FIG. 10.

[0054] FIG. 11 is a diagram showing an example of a table of optimal wording information for a specific user. In the example table shown in FIG. 11, a user ID for identifying a user corresponds to a behavior change rate of a predetermined word (1), a behavior change rate of a predetermined word (2), a behavior change rate of a predetermined word (3), a behavior change rate of a predetermined word (4), and a behavior change rate of other predetermined words. Here, the predetermined words are presets for each type of nudge. For example, word (1) is a preset for nudge (1), word (2) is a preset for nudge (2), word (3) is a preset for nudge (3), and word (4) is a preset for nudge (4). Wording optimization 14 may determine optimal words for a specific user based on optimal wording information for the specific user as shown in FIG. 11.

[0055] The wording optimizer 14 may input at least one of attribute information previously stored by the storage unit 10, nudge information input from the nudge optimizer 12 (or previously stored by the storage unit 10), and scores for each cognitive bias previously stored by the storage unit 10. The wording optimizer 14 may first obtain a preset of wordings associated with the optimal nudge. From the preset wordings (wording (1), wording (2), wording (3), ...), the effectiveness (0% to 100%) of each wording is estimated from the input data, and the wording optimizer 14 determines (selects) which wording is most effective. For example, if it is estimated that wording (1) is 70%, wording (2) is 50%, and wording (3) is 40% for a certain user, wording (1) can be said to be the most effective for the user, and therefore wording (1) is selected. The presets are, for example, (in the case of FIG. 4 ) “There is a crowd of people over there” or “That crowd over there is a shop that is popular on social media.” The wording optimizer 14 may output wording information regarding the determined wording (the most effective wording selected from the wording presets) to the navigator generator 18, or may store it in the storage unit 10.

[0056] The voice optimization unit 15 determines the optimal voice for the user based on the user's cognitive bias. The optimal voice is the voice that has the greatest effect on the user or the effect on the user meets a predetermined criterion. The voice optimization unit 15 may determine one or more voices suitable for the user based on one or more cognitive biases of the user. The appropriate voice is the voice that meets a predetermined criterion for the effect on the user. The voice optimization unit 15 may determine one or more voices suitable for the user based on one or more cognitive biases of the user and one or more attributes of the user. The voice optimization unit 15 may output voice information regarding the determined voice to the navigator generation unit 18 or may store it in the storage unit 10.

[0057] The voice optimization unit 15 may determine the voice that is optimal for the user based on the cognitive bias information of the user as shown in FIG.

[0058] Fig. 12 is a diagram showing an example table of optimal voice information for a specific user. In the example table shown in Fig. 12, a user ID for identifying the user is associated with a behavior change rate for a predetermined voice (voice (1)), a behavior change rate for a predetermined voice (voice (2)), a behavior change rate for a predetermined voice (voice (3)), a behavior change rate for a predetermined voice (voice (4)), and a behavior change rate for other predetermined voices. The voice optimization unit 15 may determine an optimal voice for a specific user based on the optimal voice information for the specific user as shown in Fig. 12.

[0059] The voice optimization unit 15 may input at least one of the attribute information stored in advance by the storage unit 10 and the scores of each cognitive bias stored in advance by the storage unit 10. The voice optimization unit 15 may prepare voice presets (voice (1), voice (2), voice (3), ...), estimate the effectiveness (0% to 100%) of each voice from the input data, and determine (select) which voice is most effective. For example, if it is estimated that voice (1) is 70%, voice (2) is 50%, and voice (3) is 40% for a certain user, the user may determine voice (1) as being most effective. Note that the presets may be, for example, high frequency and fast speech, low frequency and fast speech, high frequency and slow speech, or low frequency and slow speech. The voice optimization unit 15 may output voice information related to the determined voice (the most effective voice selected from the voice presets) to the navigator generation unit 18 or may store the voice information in the storage unit 10.

[0060] The avatar optimization unit 16 determines the optimal guide avatar (appearance) for the user based on the user's cognitive bias. The optimal guide avatar is a guide avatar that has the highest effect on the user or whose effect on the user meets a predetermined standard. The avatar optimization unit 16 may determine one or more guide avatars suitable for the user based on one or more cognitive biases of the user. A suitable guide avatar is a guide avatar whose effect on the user meets a predetermined standard. The avatar optimization unit 16 may determine one or more guide avatars suitable for the user based on one or more cognitive biases of the user and one or more attributes of the user. The avatar optimization unit 16 may output guide avatar information regarding the determined guide avatar to the navigator generation unit 18 or may store it in the storage unit 10.

[0061] The avatar optimization unit 16 may determine the optimal guidance avatar for a user based on the cognitive bias information of the user as shown in FIG.

[0062] Fig. 13 is a diagram showing an example of a table of optimal avatar information for a specific user. In the example table shown in Fig. 14, a user ID for identifying a user corresponds to a behavior change rate of avatar (1) which is a predetermined guidance avatar, a behavior change rate of avatar (2) which is a predetermined guidance avatar, a behavior change rate of guidance avatar (3) which is a predetermined avatar, a behavior change rate of guidance avatar (4) which is a predetermined avatar, and a behavior change rate of other predetermined avatars. The avatar optimization unit 16 may determine an optimal guidance avatar for a specific user based on the optimal avatar information for the specific user shown in Fig. 13.

[0063] The avatar optimization unit 16 may input at least one of the attribute information stored in advance by the storage unit 10 and the scores of each cognitive bias stored in advance by the storage unit 10. The avatar optimization unit 16 may prepare preset guidance avatars (avatar (1), avatar (2), avatar (3), ...), estimate the effectiveness (0% to 100%) of each guidance avatar from the input data, and determine (select) which one is most effective. For example, if it is estimated that avatar (1) is 70%, avatar (2) is 50%, and avatar (3) is 40% for a certain user, avatar (1) can be said to be most effective for the user, so the user selects avatar (1). Note that the presets may be, for example, long hair for men, long hair for women, short hair for men, short hair for women, elderly men, elderly women, etc. The avatar optimization unit 16 may output guide avatar information relating to the determined guide avatar (the most effective guide avatar selected from the preset guide avatars) to the navigator generation unit 18, or may cause the storage unit 10 to store it.

[0064] The facial expression optimization unit 17 determines the facial expression (hereinafter simply referred to as "facial expression") of the guidance avatar that is optimal for the user based on the user's cognitive bias. The optimal facial expression is the facial expression that has the greatest effect on the user, or the effect on the user meets a predetermined standard. The facial expression optimization unit 17 may determine one or more facial expressions that are suitable for the user based on one or more cognitive biases of the user. The suitable facial expression is the facial expression that meets a predetermined standard for the effect on the user. The facial expression optimization unit 17 may determine one or more facial expressions that are suitable for the user based on one or more cognitive biases of the user and one or more attributes of the user. The facial expression optimization unit 17 may output facial expression information regarding the determined facial expressions to the navigator generation unit 18, or may store the same in the storage unit 10.

[0065] The facial expression optimization unit 17 may determine the facial expression that is most suitable for the user based on the cognitive bias information of the user as shown in FIG.

[0066] Fig. 14 is a diagram showing an example of a table of optimal facial expression information for a specific user. In the example table shown in Fig. 14, a user ID for identifying a user is associated with a behavior change rate for a predetermined facial expression of joy (happiness), a behavior change rate for a predetermined facial expression of anger (anger), a behavior change rate for a predetermined facial expression of sadness (sorrow), a behavior change rate for a predetermined facial expression of happiness (enjoyment), and a behavior change rate for other predetermined facial expressions. The facial expression optimization unit 17 may determine an optimal facial expression for a specific user based on the optimal facial expression information for the specific user as shown in Fig. 14.

[0067] The facial expression optimization unit 17 may input at least one of the attribute information stored in advance by the storage unit 10 and the scores of each cognitive bias stored in advance by the storage unit 10. The facial expression optimization unit 17 may prepare facial expression presets (happiness, anger, sadness, etc.), estimate the effectiveness (0% to 100%) of each facial expression from the input data, and determine (select) which facial expression is most effective. For example, if it is estimated that happiness is 70%, anger is 50%, and sadness is 40% for a certain user, the user may select happiness because it can be said that happiness is most effective. Note that the presets are, for example, happiness, anger, sadness, or happiness. The facial expression optimization unit 17 may output facial expression information regarding the determined facial expression (the most effective facial expression selected from the facial expression presets) to the navigator generation unit 18, or may store it in the storage unit 10.

[0068] The navigator generation unit 18 generates a navigator (device) (in the virtual space) based on the text information determined (input) by the text optimization unit 14, the voice information determined (input) by the voice optimization unit 15, the guide avatar information determined (input) by the avatar optimization unit 16, and the facial expression information determined by the facial expression optimization unit 17. The navigator is a guide who guides the user. The navigator may speak the text indicated by the text information in the voice indicated by the voice information, with the appearance of the guide avatar indicated by the guide avatar information, with the facial expression indicated by the facial expression. In other words, the navigator speaks the text in the voice indicated by the optimal voice, with the optimal avatar (appearance), and with the optimal facial expression for the user. The navigator generation unit 18 may output information related to the generation of the navigator or information related to the generated navigator to the field installation unit 19, or may store the information in the storage unit 10.

[0069] The field installation unit 19 installs the device determined by the device determination unit 11 in the virtual space (field). More specifically, the field installation unit 19 installs the moving device generated (determined) by the moving device generation unit 13 and the navigator generated (determined) by the navigator generation unit 18 in the virtual space. When information indicating that a moving device has been generated is input from the moving device generation unit 13, the field installation unit 19 may install the moving device generated by the moving device generation unit 13 (the moving device indicated by the information regarding the generated moving device input from the moving device generation unit 13) in the virtual space. When information indicating that a navigator has been generated is input from the navigator generation unit 18, the field installation unit 19 may install the navigator generated by the navigator generation unit 18 (the navigator indicated by the information regarding the generated navigator input from the navigator generation unit 18) in the virtual space. The virtual space in which the device has been installed by the field installation unit 19 may be displayed on the user interface of the behavior modification device 1.

[0070] The field installation unit 19 may install a device in the virtual space for each user (individual) according to the generation result of the device determination unit 11. As described above, the device may include a moving device (guidance by the moving device) and a navigator (guidance by the navigator). In the case of a user who can be encouraged to change their behavior without the guidance of a navigator, the field installation unit 19 may not need to install the navigator (or may not need to provide the guidance).

[0071] Next, an example of the processing executed by the behavior modification device 1 will be described with reference to FIGS.

[0072] FIG. 15 is a sequence diagram illustrating an example of processing executed by a behavior modification device according to an embodiment. First, the behavior modification device 1 generates cognitive bias information based on a user (e.g., based on a user's responses to a questionnaire, etc.) and stores it in the storage unit 10. Next, the nudge optimization unit 12 (or the setup determination unit 11) estimates an appropriate nudge based on the cognitive bias information stored in the storage unit 10 (step S1). Next, the movement setup generation unit 13 (or the setup determination unit 11) generates a movement setup based on the estimation result in S1 (step S2). Next, the wording optimization unit 14 (or the setup determination unit 11) estimates appropriate wording based on the estimation result in S1 and the cognitive bias information stored in the storage unit 10 (step S3). Next, the voice optimization unit 15 (or the setup determination unit 11) estimates appropriate voice based on the cognitive bias information stored in the storage unit 10 (step S4). Next, the avatar optimization unit 16 (or the process determination unit 11) estimates an appropriate avatar (guidance avatar) based on the cognitive bias information stored by the storage unit 10 (step S5). Next, the facial expression optimization unit 17 (or the process determination unit 11) estimates an appropriate facial expression based on the cognitive bias information stored by the storage unit 10 (step S6).

[0073] Next, the navigator generation unit 18 (or the device in process determination unit 11) generates a navigator based on the estimation results in S3, S4, S5, and S6 (step S7). Next, the field installation unit 19 forms a virtual space in which the moving devices generated in S2 and the navigator generated in S7 are installed (step S8), and outputs (displays) it to the user.

[0074] In the sequence diagram of FIG. 15, S1 may be performed any time before S2 and S3. S2 may be performed any time after S1 and before S8. S3 may be performed any time after S1 and before S7. S4 to S6 may each be performed any time before S7. S7 may be performed any time after S3 to S6. S8 may be performed any time after S2 and S7.

[0075] 16 is a flowchart showing an example of processing executed by the behavior modification device 1 according to the embodiment. First, the device determination unit 11 determines a device for the user in the virtual space based on the user's cognitive bias (step S10). Next, the field installation unit 19 installs the device determined in S10 in the virtual space (step S11).

[0076] Figure 17 shows an example of selecting an optimal mechanism based on an individual's cognitive bias. As shown in Figure 17, if an individual (user) has a conformity bias, which is a cognitive bias, of 80% and a decision avoidance tendency, which is also a cognitive bias, of 50%, behavior modification device 1 selects a mechanism based on the higher degree of conformity bias.

[0077] Next, the effects of the behavior modification device 1 according to the embodiment will be described.

[0078] According to the behavior modification device 1, the in-progress determination unit 11 determines a trap for the user in the virtual space based on the user's cognitive bias, and the field installation unit 19 installs the trap determined by the in-progress determination unit 11 in the virtual space. With this configuration, a trap for the user is installed in the virtual space, which can encourage behavior modification of the user in the virtual space.

[0079] Furthermore, according to the behavior modification device 1, the setup determination unit 11 may determine a setup using a prediction model that predicts the degree of behavior modification of the user by inputting the degree of cognitive bias of the user. This configuration allows a setup based on the degree of behavior modification predicted by the prediction model, thereby more reliably encouraging behavior modification of the user.

[0080] Furthermore, according to the behavior modification device 1, the mechanism may be a mechanism that guides the user to predetermined content that exists in a virtual space. With this configuration, it is possible to guide the user to predetermined content that exists in a virtual space.

[0081] Furthermore, in the behavior modification device 1, the setup determination unit 11 may determine the setup based further on the user's attributes. This configuration makes it possible to encourage more reliable behavior modification based further on the user's attributes.

[0082] Furthermore, according to the behavior modification device 1, the mechanism may include a mechanism (movement mechanism) related to the virtual movement of the user in the virtual space. This configuration makes it possible to guide the virtual movement of the user in the virtual space, and for example, to guide the user to content that exists in the virtual space.

[0083] Furthermore, according to the behavior modification device 1, the mechanism may include a mechanism for guiding the user by a predetermined avatar (guiding avatar) in the virtual space. With this configuration, the user can be guided by the guiding avatar, which more reliably encourages the user to change their behavior.

[0084] Furthermore, according to the behavior modification device 1, at least one of the words uttered by the avatar when guiding, the voice uttered by the avatar when guiding, the appearance of the avatar (guidance avatar), and the facial expression of the avatar when guiding may be based on the user's cognitive bias. With this configuration, the user can be guided by a guidance avatar that is more suitable for the user based on the user's cognitive bias, thereby more reliably encouraging the user's behavioral change.

[0085] According to the behavior modification device 1, for example, it is possible to realize behavior modification of a user by optimizing guidance in the metaverse.

[0086] The background to this is that the metaverse has been attracting attention amid the COVID-19 pandemic. Because virtual worlds are not restricted by location or time, a wider variety of content can be installed than in the real world. Content includes stores for economic activity or entertainment facilities. In virtual worlds, directing the right users to the right content (behavioral change) can bring benefits to both users and producers. A simple method is messaging guidance. Even with similar guidance content, how it is conveyed and whether it motivates action varies from person to person depending on how it is conveyed, who is delivering it, and the tone of voice (frequency, strength, volume). These are thought to depend on each individual's attributes and cognitive biases.

[0087] One issue is the potential for content saturation, which could lead to a mismatch between consumers and producers. Simple messaging alone is not sufficient to change behavior, and combining various technologies is expected to maximize the effect. While technologies for automatically generating avatars (images) and voice using GANs (Generative Adversarial Networks) and estimation technologies using machine learning have been established, there are no examples of their application for behavioral change. Combining these technologies with messaging based on behavioral economics could be expected to produce more effective behavioral change, but the technology and mechanisms for doing so have not yet been established.

[0088] The behavior modification device 1 sets up mechanisms linked to cognitive biases in the virtual world to guide users to specific content. The behavior modification device 1 selects the optimal mechanism based on the individual's cognitive bias. The behavior modification device 1 individually optimizes each element of navigation (estimating it from attributes and cognitive biases). Specifically, this includes message content (way of delivery), tone of voice (frequency, strength, volume), avatar (speaker's appearance), and facial expressions. The behavior modification device 1 prepares presets for the above and selects the optimal one. The tone of voice and avatar may be automatically generated using GAN instead of presets (variant example). The behavior modification device 1 does not necessarily need to include all elements of voice guidance.

[0089] The behavior modification device 1 is a system that prepares mechanisms linked to cognitive biases, such as the installation of groups of NPCs and the highlighting of specific routes in a virtual world, and delivers messages to each individual according to the user's individual cognitive bias. The behavior modification device 1 optimizes the message content, speaker (avatar) appearance, speaker voice, and speaker facial expression when navigating the virtual world to best encourage behavioral change for each individual based on cognitive bias, and further guides each individual to specific content through navigation using the most optimal means. The behavior modification device 1 may not require navigation depending on the user.

[0090] The behavior modification device 1 of the present disclosure has the following configuration.

[0091] [1] a determination unit that determines a mechanism for a user in a virtual space based on the cognitive bias of the user; an installation unit that installs the device determined by the determination unit in the virtual space; A behavior modification device comprising:

[0092] [2] the determination unit determines the mechanism using a prediction model that predicts a degree of behavioral change of the user due to the mechanism by inputting a degree of the cognitive bias of the user. [1] A behavior modification device as described in [1].

[0093] [3] The mechanism is a mechanism for guiding the user to predetermined content present in the virtual space. A behavior modification device according to [1] or [2].

[0094] [4] the determination unit determines the mechanism further based on an attribute of the user. A behavior modification device according to any one of [1] to [3].

[0095] [5] The mechanism includes a mechanism related to virtual movement of the user in the virtual space. A behavior modification device according to any one of [1] to [4].

[0096] [6] The mechanism includes a mechanism for guiding the user by a predetermined avatar in the virtual space. A behavior modification device according to any one of [1] to [5].

[0097] [7] At least one of the words uttered by the avatar during the guidance, the voice uttered by the avatar during the guidance, the appearance of the avatar, or the facial expression of the avatar during the guidance is based on the cognitive bias of the user. [6] A behavior modification device as described in [6].

[0098] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining the single device or multiple devices with software.

[0099] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocation, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0100] For example, a behavior modification device 1 according to an embodiment of the present disclosure may function as a computer that performs processing of the behavior modification method of the present disclosure. Figure 18 is a diagram showing an example of the hardware configuration of a behavior modification device 1 according to an embodiment of the present disclosure. The behavior modification device 1 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0101] In the following explanation, the term "apparatus" can be interpreted as a circuit, device, unit, etc. The hardware configuration of behavior modification apparatus 1 may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices.

[0102] Each function of the behavior modification device 1 is realized by loading specified software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and storage 1003.

[0103] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, the above-mentioned process determination unit 11, nudge optimization unit 12, movement process generation unit 13, wording optimization unit 14, voice optimization unit 15, avatar optimization unit 16, facial expression optimization unit 17, navigator generation unit 18, field installation unit 19, etc. may be realized by the processor 1001.

[0104] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with the programs. The programs used are programs that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the process determining unit 11, the nudge optimization unit 12, the movement process generating unit 13, the wording optimization unit 14, the voice optimization unit 15, the avatar optimization unit 16, the facial expression optimization unit 17, the navigator generating unit 18, and the field installation unit 19 may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and the other functional blocks may be implemented in a similar manner. While the above-described various processes have been described as being executed by one processor 1001, they may also be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may also be transmitted from a network via a telecommunications line.

[0105] The memory 1002 is a computer-readable recording medium and may be configured, for example, by at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.

[0106] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray disc), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of memory 1002 and storage 1003.

[0107] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned setup determination unit 11, nudge optimization unit 12, movement setup generation unit 13, wording optimization unit 14, voice optimization unit 15, avatar optimization unit 16, facial expression optimization unit 17, navigator generation unit 18, and field installation unit 19 may be realized by the communication device 1004.

[0108] The input device 1005 is an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that outputs to the outside. The input device 1005 and the output device 1006 may be integrated into one device (for example, a touch panel).

[0109] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0110] Behavior modification device 1 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, processor 1001 may be implemented using at least one of these pieces of hardware.

[0111] Notification of information is not limited to the aspects / embodiments described in this disclosure, and may be performed using other methods.

[0112] Each aspect / embodiment described in the present disclosure may be applied to at least one of systems using LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (New Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark), IEEE 802.20, UWB (Ultra-Wideband), Bluetooth (registered trademark), or other appropriate systems, and next-generation systems extended based on these. Furthermore, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G, etc.) may also be applied.

[0113] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0114] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0115] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0116] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, notification of predetermined information (e.g., notification that "X is true") is not limited to being done explicitly, but may be done implicitly (e.g., by not notifying the predetermined information).

[0117] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0118] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0119] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0120] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0121] In addition, terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0122] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0123] Furthermore, the information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information.

[0124] The names used for the above parameters are not limiting in any way, and furthermore, the mathematical formulas etc. using these parameters may differ from those explicitly disclosed in this disclosure.

[0125] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0126] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using one or more wires, cables, and / or printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0127] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0128] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0129] The "means" in the configuration of each of the above devices may be replaced with "part," "circuit," "device," etc.

[0130] When used in this disclosure, the terms "include," "including," and variations thereof are intended to be inclusive, similar to the term "comprising." Furthermore, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0131] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0132] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0133] 1...behavior modification device, 10...storage unit, 11...machine determination unit, 12...nudge optimization unit, 13...movement machine generation unit, 14...text optimization unit, 15...voice optimization unit, 16...avatar optimization unit, 17...facial expression optimization unit, 18...navigator generation unit, 19...field installation unit, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device, 1007...bus.

Claims

1. a determination unit that determines a mechanism for a user in a virtual space based on the cognitive bias of the user; an installation unit that installs the device determined by the determination unit in the virtual space; Equipped with the determination unit determines the mechanism using a prediction model that predicts a degree of behavioral change of the user due to the mechanism by inputting a degree of the cognitive bias of the user; Behavior modification devices.

2. The mechanism is a mechanism for guiding the user to predetermined content present in the virtual space. The behavior modification device according to claim 1 .

3. the determination unit determines the mechanism further based on an attribute of the user. The behavior modification device according to claim 1 .

4. The mechanism includes a mechanism related to virtual movement of the user in the virtual space. The behavior modification device according to claim 1 .

5. The mechanism includes a mechanism for guiding the user by a predetermined avatar in the virtual space. The behavior modification device according to claim 1 or 4.

6. At least one of the words uttered by the avatar during the guidance, the voice uttered by the avatar during the guidance, the appearance of the avatar, or the facial expression of the avatar during the guidance is based on the cognitive bias of the user. The behavior modification device according to claim 5.

Images