Patent Application
20250162131
This application is based upon and claims the benefit of priority under 35 USC 119 of Japanese Patent Application No. 2023-195474, filed on Nov. 16, 2023, the entire disclosure of which, including the description, claims, drawings, and abstract, is incorporated herein by reference in its entirety.
The present disclosure relates to a robot, a robot control method, and a recording medium.
In the related art, robots are known that simulate living creatures such as pets and humans. For example, Unexamined Japanese Patent Application Publication No. 2003-285286 describes a robot device that can cause a user to feel a sense of pseudo-growth by acting out a scenario corresponding to a value of a growth level to express development of a living creature.
A robot according to an embodiment of the present disclosure includes:
A more complete understanding of this application can be obtained when the following detailed description is considered in conjunction with the following drawings, in which:
Hereinafter, embodiments of the present disclosure are described with reference to the drawings. Note that, in the drawings, identical or corresponding components are denoted with the same reference numerals.
The robot 200 according to Embodiment 1 includes an exterior 201, decorative parts 202, bushy fur 203, head 204, coupler 205, torso 206, housing 207, touch sensor 211, acceleration sensor 212, microphone 213, illuminance sensor 214, and speaker 231 identical to those of the robot 200 disclosed in Unexamined Japanese Patent Application Publication No. 2023-115370 and, as such, description thereof is foregone. Note that the shape of the head 204 may be the shape illustrated in
The robot 200 according to Embodiment 1 includes a twist motor 221 and a vertical motor 222 identical to those of the robot 200 disclosed in Unexamined Japanese Patent Application Publication No. 2023-115370 and, as such, description thereof is foregone. The twist motor 221 and the vertical motor 222 of the robot 200 according to Embodiment 1 operate in the same manner as those of the robot 200 disclosed in Unexamined Japanese Patent Application Publication No. 2023-115370.
The robot 200 includes a gyrosensor 215. With the acceleration sensor 212 and the gyrosensor 215, the robot 200 can detect a change of an attitude of the robot 200 itself, and can detect being picked up, the orientation being changed, being thrown, and the like by the user.
The acceleration sensor 212, the microphone 213, the gyrosensor 215, the illuminance sensor 214, and the speaker 231 are not necessarily provided only on the torso 206, and at least a portion of these elements may be provided on the head 204, or may be provided on both the torso 206 and the head 204.
Next, a functional configuration of the robot 200 is described with reference to
As illustrated in
The control device 100 includes a controller 110, a storage 120, and a communicator 130. The control device 100 controls the actions of the robot 200 by the controller 110 and the storage 120.
The controller 110 includes a central processing unit (CPU). In one example, the CPU is a microprocessor or the like and is a central processing unit that executes a variety of processing and operations. In the controller 110, the CPU reads a control program stored in a read-only memory (ROM) and controls the actions of the entire robot 200 while using a random access memory (RAM) as a working memory. Additionally, although not illustrated in the drawings, the controller 110 is provided with a clock function, a timer function, and the like, and thus can measure the date and time, and the like. The controller 110 may also be called a “processor”.
The storage 120 includes a read-only memory (ROM), a random access memory (RAM), a flash memory, and the like. The storage 120 stores programs and data, including an operating system (OS) and an application program, to be used by the controller 110 to execute various types of processing. Moreover, the storage 120 stores data generated or acquired through execution of the various types of processing by the controller 110.
The sensor unit 210 includes the touch sensor 211, the acceleration sensor 212, the gyrosensor 215, the illuminance sensor 214, and the microphone 213 described above. The sensor unit 210 is an example of detection means for detecting an external stimulus.
The touch sensor 211 includes, for example, a pressure sensor and an electrostatic capacitance sensor, and detects contacting by some sort of object. The controller 110 can detect, based on detection values of the touch sensor 211, that the robot 200 is being petted, is being struck, and the like by the user.
The acceleration sensor 212 detects an acceleration applied to the torso 206 of the robot 200. The acceleration sensor 212 detects an acceleration in each of an X-axis direction, a Y-axis direction, and a Z-axis direction, that is, acceleration on three axes.
In one example, the acceleration sensor 212 detects a gravitational acceleration when the robot 200 is stationary. The controller 110 can detect a current attitude of the robot 200 based on the gravitational acceleration detected by the acceleration sensor 212. In other words, the controller 110 can detect, based on the gravitational acceleration detected by the acceleration sensor 212, whether the housing 207 of the robot 200 is inclined from a horizontal direction. Thus, the acceleration sensor 212 functions as incline detection means for detecting the inclination of the robot 200.
In addition, if a user is lifting or throwing the robot 200, the acceleration sensor 212 detects an acceleration caused by the travel of the robot 200 in addition to the gravitational acceleration. The controller 110 subtracts a component of gravitational acceleration from the detection value detected by the acceleration sensor 212 and can thereby detect the action of the robot 200.
The gyrosensor 215 detects an angular velocity when rotation is applied to the torso 206 of the robot 200. Specifically, the gyrosensor 215 detects the angular velocity on three axes of rotation, namely rotation around the X-axis direction, rotation around the Y-axis direction, and rotation around the Z-axis direction. Combining the detection values detected by the acceleration sensor 212 and the detection values detected by the gyrosensor 215 enables more accurate detection of the movement of the robot 200.
The touch sensor 211, the acceleration sensor 212, and the gyrosensor 215 respectively detect a strength of contact, the acceleration, and the angular velocity, at a synchronized timing, for example, every 0.25 seconds, and output the detection values to the controller 110.
The microphone 213 detects ambient sound of the robot 200. The controller 110 can detect, based on a component of the sound detected by the microphone 213, for example, that the user is speaking to the robot 200, that the user is clapping hands, and the like.
The illuminance sensor 214 detects ambient illuminance of the robot 200. The controller 110 can detect, based on the illuminance detected by the illuminance sensor 214, that the surroundings of the robot 200 have become brighter or darker.
The controller 110 acquires, via the bus line BL and as an external stimulus, detection values detected by the various sensors included in the sensor unit 210. The external stimulus is a stimulus that acts on the robot 200 from outside the robot 200. Examples of the external stimulus include “there is a loud sound”, “spoken to”, “petted”, “lifted up”, “turned upside down”, “became brighter”, “became darker”, and the like.
In one example, the controller 110 acquires the external stimulus of “there is aloud sound” or “spoken to” by the microphone 213, and acquires the external stimulus of “petted” by the touch sensor 211. Additionally, the controller 110 acquires the external stimulus of “lifted up” or “turned upside down” by the acceleration sensor 212 and the gyrosensor 215, and acquires the external stimulus of “became brighter” or “became darker” by the illuminance sensor 214.
The sensor unit 210 may include a sensor other than the touch sensor 211, the acceleration sensor 212, the gyrosensor 215, and the microphone 213. The types of external stimuli acquirable by the controller 110 can be increased by increasing the types of sensors included in the sensor unit 210.
The driver 220 includes the twist motor 221 and the vertical motor 222, and is driven by the controller 110. The twist motor 221 is a servo motor for rotating the head 204, relative to the torso 206, in the right-left direction (the width direction) about the front-rear direction as an axis. The vertical motor 222 is a servo motor for rotating the head 204, relative to the torso 206, in the up-down direction (height direction) about the right-left direction as an axis. The robot 200 can express actions of turning the head 204 sideways by using the twist motor 221, and can express actions of lifting/lowering the head 204 by using the vertical motor 222.
The outputter 230 includes the speaker 231, and the speaker 231 outputs sound as a result of the controller 110 inputting sound data into the outputter 230. For example, the robot 200 emits a pseudo-animal sound as a result of the controller 110 inputting animal sound data of the robot 200 into the outputter 230.
Instead of the speaker 231, or in addition to the speaker 231, a display such as a liquid crystal display, a light emitter such as a light emitting diode (LED), or the like may be provided as the outputter 230, to display emotions such as joy, sadness, and the like on the display, express such emotions by the color and brightness of emitted light, or the like.
The operational unit 240 includes an operation button, a volume knob, or the like. In one example, the operational unit 240 is an interface for receiving user operations such as turning the power ON/OFF, adjusting the volume of the output sound, and the like.
Next, a functional configuration of the controller 110 is described. As illustrated in FIG. 3, the controller 110 functionally includes a parameter setter 113 that is an example of parameter setting means, an action controller 115 that is an example of action control means, and a selection probability adjuster 117 that is an example of selection probability adjustment means. In the controller 110, the CPU performs control by reading the program stored in the ROM into the RAM and executing this program, to thereby function as the components described above.
The storage 120 stores parameter data 121, an action selection table 123, an action content table 124, a motion table 125, and a classification table 127.
The parameter setter 113 sets the parameter data 121. The parameter data 121 is data that defines various types of parameters related to the robot 200. Specifically, the parameter data 121 contains: (1) an emotion parameter, (2) a personality parameter, (3) a growth days count, and (4) a growth level.
The emotion parameter is a parameter that represents a pseudo-emotion of the robot 200. The emotion parameter is expressed by coordinates (X, Y) on an emotion map 300.
As illustrated in
The emotion parameter represents a plurality of mutually different pseudo-emotions. In
Although the emotion map 300 is expressed in the two-dimensional coordinate system in
The parameter setter 113 calculates an emotion change amount that is an amount of change that increases or decreases the X value and the Y value of the emotion parameter. The emotion change amount is expressed by the following four variables. DXP and DXM respectively increase and decrease the X value of the emotion parameter. DYP and DYM respectively increase and decrease the Y value of the emotion parameter.
The parameter setter 113 updates the emotion parameter by adding or subtracting a value, among DXP, DXM, DYP, and DYM as the emotion change amounts, corresponding to the external stimulus to or from the current emotion parameter. For example, when the head 204 is petted, the robot 200 is caused to have a pseudo-emotion of being relaxed, and thus, the parameter setter 113 adds DXP to the X value of the emotion parameter. Conversely, when the head 204 is struck, the robot 200 is caused to have a pseudo-emotion of being worried, and thus, the parameter setter 113 subtracts DXM from the X value of the emotion parameter. Which emotion change amount is associated with the various external stimuli can be set freely. An example is given below.
The sensor unit 210 acquires a plurality of external stimuli of mutually different types by a plurality of sensors. Thus, the parameter setter 113 variously derives the emotion change amounts DXP, DXM, DYP, and DYM in accordance with each individual external stimulus of the plurality of external stimuli, and updates the emotion parameter in accordance with the derived emotion change amounts.
Specifically, when the X value of the emotion parameter is set to the maximum value of the emotion map 300 even once in one day, the parameter setter 113 adds 1 to DXP, and when the Y value of the emotion parameter is set to the maximum value of the emotion map 300 even once in one day, the parameter setter 113 adds 1 to DYP. Additionally, when the X value of the emotion parameter is set to the minimum value of the emotion map 300 even once in one day, the parameter setter 113 adds 1 to DXM, and when the Y value of the emotion parameter is set to the minimum value of the emotion map 300 even once in one day, the parameter setter 113 adds 1 to DYM.
As described above, the parameter setter 113 changes the emotion change amounts, DXP, DXM, DYP, and DYM in accordance with a condition based on whether the value of the emotion parameter reaches the maximum value or the minimum value of the emotion map 300. As an example, assume that all of the initial values of the various variables as the emotion change amounts are set to 10. The parameter setter 113 increases the various variables to a maximum of 20 by updating the emotion change amounts described above. Due to this updating processing, the emotion change amount, that is, the degree of change of emotion, changes.
For example, when only the head 204 is petted multiple times, only DXP as the emotion change amount increases and the other emotion change amounts do not change, and thus, the robot 200 develops a personality of having a tendency to be relaxed. When only the head 204 is struck multiple times, only DXM as the emotion change amount increases and the other emotion change amounts do not change, and thus, the robot 200 develops a personality of having a tendency to be worried. As described above, the parameter setter 113 changes the emotion change amount in accordance with various external stimuli.
The personality parameter is a parameter expressing a pseudo-personality of the robot 200. The personality parameter includes a plurality of personality values that express degrees of mutually different personalities. The parameter setter 113 changes the emotion parameter in accordance with external stimuli detected by the sensor unit 210, to set the personality parameter based on the emotion parameter.
Specifically, the parameter setter 113 calculates four personality values based on (Equation 1) below. Specifically, a value obtained by subtracting 10 from DXP that expresses a tendency to be relaxed is set as a personality value (chipper), a value obtained by subtracting 10 from DXM that expresses a tendency to be worried is set as a personality value (shy), a value obtained by subtracting 10 from DYP that expresses a tendency to be excited is set as a personality value (active), and a value obtained by subtracting 10 from DYM that expresses a tendency to be disinterested is set as a personality value (spoiled).
As a result, as illustrated in
Since the initial value of each of the personality values is 0, the personality at the time of birth of the robot 200 is expressed by the origin of the personality value radar chart 400. Moreover, as the robot 200 grows, the four personality values change, with an upper limit of 10, due to external stimuli and the like (manner in which the user interacts with the robot 200) detected by the sensor unit 210. Therefore, 11 to the power of 4, that is, 14641 types of personalities can be expressed. Thus, the robot 200 is to have various personalities in accordance with the manner in which the user interacts with the robot 200. That is, the personality of each individual robot 200 is formed differently based on the manner in which the user interacts with the robot 200.
These four personality values are fixed when the juvenile period elapses and the pseudo-growth of the robot 200 is complete. In the subsequent adult period, the parameter setter 113 adjusts four personality correction values (chipper correction value, active correction value, shy correction value, and spoiled correction value) in order to correct the personality in accordance with the manner in which the user interacts with the robot 200.
The parameter setter 113 adjusts the four personality correction values in accordance with a condition based on where the area in which the emotion parameter has existed the longest is located on the emotion map 300. Specifically, the four personality correction values are adjusted as in (A) to (E) below.
When setting the four personality correction values, the parameter setter 113 calculates the four personality values in accordance with (Equation 2) below.
The growth days count represents the number of days of pseudo-growth of the robot 200. The robot 200 is pseudo-born at the time of first start up by the user after shipping from the factory, and grows from a juvenile to an adult over a predetermined growth period. The growth days count corresponds to the number of days since the pseudo-birth of the robot 200.
An initial value of the growth days count is 1, and the state parameter acquirer 112 adds 1 to the growth days count for each passing day. In one example, the growth period in which the robot 200 grows from a juvenile to an adult is 50 days, and the 50-day period that is the growth days count since the pseudo-birth is referred to as a “juvenile period”. When the juvenile period elapses, the pseudo-growth of the robot 200 is complete. A period after the completion of the juvenile period is called an “adult period”.
During the juvenile period, each time the pseudo-growth days count of the robot 200 increases one day, the state parameter acquirer 112 increases the maximum value and the minimum value of the emotion map 300 both by 2. Regarding initial values of the size of the emotion map 300, as illustrated by a frame 301 of
The emotion map 300 defines a settable range of the emotion parameter. Thus, as the size of the emotion map 300 expands, the settable range of the emotion parameter expands. Due to the expansion of the settable range of the emotion parameter, richer emotion expression becomes possible, and thus, the pseudo-growth of the robot 200 is expressed by the expansion of the size of the emotion map 300.
The growth level represents the degree of pseudo-growth of the robot 200. The parameter setter 113 sets the growth level based on the personality parameter. Specifically, the growth level is 0 at the pseudo-birth of the robot 200. The parameter setter 113 then increases the growth level by one in one to several days. In this way, the parameter setter 113 increases the growth level to a maximum of 10 during the juvenile period (for example, 50 days from the pseudo-birth.) The parameter setter 113 stops the increase of the growth level when the juvenile period ends.
Specifically, the parameter setter 113 sets the growth level to the largest value among the plurality of personality values (four in the example described above) included in the personality parameter. For example, in the example of
The personality parameter changes depending on the manner in which the user interacts with the robot 200 and, as such, by setting the growth level based on the personality parameter, an effect of the robot 200 pseudo-growing based on the manner in which the user interacts with the robot 200 can be obtained.
Returning to
The action controller 115 determines whether any action trigger among a plurality of predetermined action triggers is satisfied. In a case where any action trigger is satisfied, the action controller 115 causes the robot 200 to execute an action corresponding to the satisfied action trigger. The action trigger is a condition for the robot 200 to act. Examples of the action trigger include triggers based on the external stimuli detected by the sensor unit 210, and triggers not based on the external stimuli.
Examples of the action trigger include “there is a loud sound”, “spoken to”, “petted”, “rocked”, “held”, “struck”, “scolded”, “turned upside down”, “became brighter”, “became darker”, and the like. These action triggers are triggers based on external stimuli and are detected by the sensor unit 210. In one example, the action triggers, “spoken to” and “scolded” are detected by the microphone 213. The action triggers, “petted” and “struck” are detected by the touch sensor 211 provided on the head 204 or the torso 206. The action triggers, “rocked”, “held”, and “turned upside down” are detected by the acceleration sensor 212 or the gyrosensor 215. The action triggers, “became brighter” and “became darker” are detected by the illuminance sensor 214. Alternatively, the action triggers may be action triggers that are not based on external stimuli, such as “a specific time arrived” or “the robot 200 moved to a specific location”.
More specifically, in a case where a relatively small sound is detected by the microphone 213, the action controller 115 determines that the robot 200 is “spoken to”, and in a case where a relatively loud sound is detected by the microphone 213, the action controller 115 determines that the robot 200 is “scolded”. Additionally, in a case where a relatively small value is detected by the touch sensor 211, the action controller 115 determines that the robot 200 is “petted”, and in a case where a relatively large value is detected by the touch sensor 211, the action controller 115 determines that the robot 200 is “struck”. Further, the action controller 115 determines whether the robot 200 is “rocked”, “held”, or “suspended” based on detection values of the acceleration sensor 212 or the gyrosensor 215.
The action controller 115 determines, based on the result of detection performed by the sensor unit 210 and the like, whether any action trigger among the plurality of predetermined action triggers is satisfied. In a case where, as a result of the determination, any action trigger is satisfied, the robot 200 is caused to execute an action corresponding to the satisfied action trigger. The action controller 115 causes the robot 200 to execute various actions in accordance with satisfaction of action trigger. This allows the user and the robot 200 to interact with each other, for example, executing a purring action in response to a call from the user, executing a pleased action when petted by the user, executing an unwilling action when turned upside down by the user, and the like.
In a case where, any action trigger is satisfied, the action controller 115 causes the robot 200 to execute an action selected, from a selection candidate list corresponding to the satisfied action trigger, at a probability dependent on the growth level. The growth level is a degree of pseudo-growth of the robot 200. The action controller 115 references the action selection table 123 that is stored in the storage 120, for selecting an action that the action controller 115 causes the robot 200 to execute.
The action selection table 123 is data that defines, for each of action triggers, options for actions to be executed by the robot 200 in a case where a corresponding action trigger is satisfied, and selection probabilities that the respective actions of the options are selected. Specifically, as illustrated in
As illustrated in
Here, the basic action is dependent on the pseudo-growth of the robot 200, but is non-dependent on the pseudo-personality of the robot 200. In other words, the basic action is an action that does not change by the manner in which the user interacts with (takes care of) the robot 200. In contrast, the personality action is an action that is dependent on both the pseudo-growth and the pseudo-personality of the robot 200. In other words, the personality action is an action that changes by the manner in which the user interacts with (takes care of) the robot 200.
The initial table 131 defines, for each action in the selection candidate list defined for an action trigger, a selection probability to be selected upon satisfaction of the corresponding action trigger, in accordance with the growth level of the robot 200. In the example of
As described above, the initial table 131 defines the selection probability such that the probability of the basic action being selected while the growth level is small is high, and the probability of the personality action being selected increases as the growth level increases. Additionally, the initial table 131 defines the selection probability such that the types of selectable basic actions increase as the growth level increases. This results in having variations in action details executed by the robot 200 as the growth level of the robot 200 increases.
Here, the selection probability of each action defined in the initial table 131 is an initial value (default value) that is a value in a case where the selection probability is not adjusted by the selection probability adjuster 117 described below. That is, the initial table 131 sets, for each action in the selection candidate list corresponding to an action trigger, an initial value of the selection probability in accordance with the growth level. In the case where the selection probability is not adjusted by the selection probability adjuster 117, the action controller 115 selects the action to be executed by the robot 200 in accordance with the selection probability defined in the initial table 131 described above.
A specific example is described in which the microphone 213 detects a loud sound. In this case, the action trigger of “there is a loud sound” is satisfied. In the initial table 131 illustrated in
At the growth level of 1, the selection probability of the basic action 2-0 is 90% and the selection probability of the basic action 2-1 is 10%. Therefore, at the growth level of 1, the action controller 115 selects the basic action 2-0 at a probability of 90% and selects the basic action 2-1 at a probability of 10%. At the growth level of 2, the selection probability of the basic action 2-0 is 80% and the selection probability of the basic action 2-1 is 20%. Therefore, at the growth level of 2, the action controller 115 selects the basic action 2-0 at a probability of 80% and selects the basic action 2-1 at a probability of 20%.
For example, as illustrated in
Upon the basic action or the personality action being selected in this manner, the action controller 115 references the action content table 124 and the motion table 125 and causes the robot 200 to execute the action of the content corresponding to the selected basic action or personality action.
As illustrated in
The action controller 115 calculates, as the selection probability of each personality action, a value obtained by dividing the personality value corresponding to that personality action by the total value of the four personality values. For example, in a case where the personality value (chipper) is 3, the personality value (active) is 8, the personality value (shy) is 5, and the personality value (spoiled) is 4, the total value of these is 3+8+5+4=20. In this case, the action controller 115 selects the personality action of “chipper” at a probability of 3/20=15%, the personality action of “active” at a probability of 8/20=40%, the personality action of “shy” at a probability of 5/20=25%, and the personality action of “spoiled” at a probability of 4/20=20%.
As illustrated in
For example, in a case where the basic action 2-0 is selected, the action controller 115 firstly controls so that, after 100 ms, the angles of the twist motor 221 and the vertical motor 222 are 0 degrees, and controls so that, after 100 ms thereafter, the angle of the vertical motor 222 is −24 degrees. Then, the action controller 115 does not rotate for 700 ms thereafter, and then controls so that, after 500 ms, the angle of the twist motor 221 is 34 degrees and the angle of the vertical motor 222 is −24 degrees. Then, the action controller 115 controls so that, after 400 ms, the angle of the twist motor 221 is −34 degrees and then controls so that, after 500 ms, the angles of the twist motor 221 and the vertical motor 222 are 0 degrees, thereby completing the action of the basic action 2-0. Additionally, in parallel with the driving of the twist motor 221 and the vertical motor 222, the action controller 115 plays an animal sound of an abrupt whistle from the speaker 231 based on sound data of an abrupt whistle sound.
In this way, the action controller 115 causes the robot 200 to execute the action that is dependent on the pseudo-growth of the robot 200. With real living creatures as well, actions such as behaviors, voices, and the like differ for juveniles and adults. For example, with a real living creature, a juvenile acts wildly and speaks with a high-pitched voice, but that wild behavior diminishes and the voice becomes deeper when that real living creature becomes an adult. The action controller 115 expresses differences in the actions in accordance with growth of the living creature.
Returning to
Specifically, the selection probability adjuster 117 determines whether the external stimulus is detected by the sensor unit 210 during a period from when the action controller 115 causes the robot 200 to execute the action until the elapse of the predetermined time. Here, the period from when the action controller 115 causes the robot 200 to execute the action until the elapse of the predetermined time corresponds to a period from a point at which the robot 200 starts the action until an elapse of the predetermined time. In other words, the period from when the action controller 115 causes the robot 200 to execute the action until the elapse of the predetermined time includes not only a point at which the robot 200 ends the action, but also includes a point at which the robot 200 is executing the action. The predetermined time is a time taken for confirmation of a reaction by a user to the action after the robot 200 executes the action. The predetermined time is, for example, the length of time, such as 10 seconds, 30 seconds, 1 minute, or the like.
Specifically, in a case where the external stimulus is detected in the period from when the robot 200 executes the action until the elapse of the predetermined time, the selection probability adjuster 117 determines whether the external stimulus is an external stimulus of first type, an external stimulus of second type, or an external stimulus of other type. To achieve this, the selection probability adjuster 117 references the classification table 127 stored in the storage 120.
As illustrated in
In a case where the external stimulus is detected during a period from when the robot 200 executes the action until the elapse of the predetermined time, the selection probability adjuster 117 references the classification table 127 and determines the type of the detected external stimulus.
Specifically, in a case where a sound is detected by the microphone 213, the selection probability adjuster 117 performs sound recognition of the detected sound and determines whether the robot 200 is praised or scolded. Further, in a case where a contact on the head 204 or the torso 206 is detected by the touch sensor 211, the selection probability adjuster 117 determines whether the robot 200 is petted or struck, based on the strength of the detected contact. Furthermore, in a case where acceleration or angular velocity is detected by the acceleration sensor 212 or the gyrosensor 215, the selection probability adjuster 117 determines whether the robot is rocked gently, rocked forcefully, held, turned upside down, or the like, based on the detected acceleration or angular velocity. In this manner, the selection probability adjuster 117 determines whether the type of the external stimulus detected by the sensor unit 210 is the first type or the second type.
Further, if the type of the external stimulus detected by the sensor unit 210 is neither the first type nor the second type, such as a case where the illuminance sensor 214 detects being brighter or being darker, the selection probability adjuster 117 determines that the type of the detected external stimulus is other type.
In a case where an external stimulus of first type is detected during a period from when the action controller 115 causes the robot 200 to execute the action until the elapse of the predetermined time, the selection probability adjuster 117 increases the selection probability that the action is selected from the selection candidate list in a range less than or equal to the predetermined upper limit. In other words, if the user demonstrates a positive response such as petting, praising, or the like to the action executed by the robot 200, the selection probability adjuster 117 increases the selection probability that the action is selected thereafter from the initial value defined in the initial table 131.
This allows, for example, if the action executed by the robot 200 was a preferable action for the user, the robot 200 to execute that action more frequently by the user demonstrating a positive response to that action. As a result, the preferences of the user can be reflected to the action of the robot 200.
Here, the predetermined upper limit is a limiting value determined so that the selection probability does not deviate significantly from the initial value even if the selection probability adjuster 117 increases the selection probability. The predetermined upper limit is determined based on the initial value of the selection probability set for the action executed by the robot 200 in a case where the growth level is lower than the current growth level.
For example, in the initial table 131 illustrated in
Note that, as the predetermined upper limit, not only the initial value of the selection probability at the growth level that is one level lower than the current growth level, but also the initial value of the selection probability at the growth level that two or more levels lower than the current growth level may be used. In the example of the basic action 0-0 above, in a case where the current value of the growth level is “6”, the predetermined upper limit may be set to “80%” at the growth level of “3”, which is three levels lower than the current growth level. Thus, how the initial value is used as the predetermined upper limit, and how low the growth level is from the current growth level can be determined any way. As one example, a case where the initial value of the selection probability at the growth level that one level lower than the current growth level is used as the predetermined upper limit is described below.
Specifically, in a case where an external stimulus of first type is detected during a period from when the action controller 115 causes the robot 200 to execute the action until the elapse of the predetermined time, the selection probability adjuster 117 compares, in the selection candidate list corresponding to the satisfied action trigger in the initial table 131, the probability set for that action at the current growth level with the selection probability set for that action at a growth level that is one level prior to the current growth level. Then, the selection probability adjuster 117 determines whether the selection probability, that is set in the initial table 131 for the action executed by the robot 200, at the current growth level is less than the selection probability at the growth level that is one level prior to the current growth level. In other words, the selection probability adjuster 117 determines whether the initial value of the selection probability at the current growth level is less than the initial value of the selection probability at the growth level that is one level prior to the current growth level.
As a result of the determination, (i) if the selection probability at the current growth level is less than the selection probability at the growth level that is one level prior to the current growth level, the selection probability adjuster 117 increases the selection probability that the action executed by the robot 200 is selected from the selection candidate list, with the selection probability at the growth level that is one level prior to the current growth level being the predetermined upper limit. Conversely, (ii) if the selection probability at the current growth level is greater than or equal to the selection probability at the growth level that is one level prior to the current growth level, the selection probability adjuster 117 determines that the selection probability at the current growth level is the upper limit, and does not increase the selection probability that the action executed by the robot 200 is selected from the selection candidate list.
A specific example is described in which the action trigger of “there is a loud sound” is satisfied at the current growth level of 8. In this case, the action controller 115 references the initial table 131 illustrated in
(i) A first example is described in which, in a case where the action controller 115 selects the basic action 2-0 to cause the robot 200 to execute the basic action 2-0, 20% that is the selection probability of the basic action 2-0 at the growth level of 8 is less than 30% that is the selection probability of the basic action 2-0 at the growth level of 7 in the initial table 131. In this case, if the external stimulus of first type is detected in response to the execution of the basic action 2-0, the selection probability adjuster 117 sets the upper limit of the selection probability of the basic action 2-0 as 30% that is the selection probability at the growth level of 7. Thus, the selection probability adjuster 117 increases the selection probability of the basic action 2-0 at the growth level of 8 from 20% with the upper limit of 30%.
With reference to the adjustment table 132 illustrated in
When the external stimulus of first type is detected by the sensor unit 210 upon execution of the basic action 2-0, the selection probability adjuster 117 increases the selection probability of the basic action 2-0 at the growth level of 8 by a predetermined increase value ΔP. The increase value ΔP may be any value such as 0.1%, 0.5%, 1%, or the like. In the example below, the increase value ΔP is 0.3%. As in the adjustment table 132 illustrated in
The selection probability adjuster 117 increases the selection probability of the action executed by the robot 200 as above and also decreases the selection probabilities that actions other than the action executed by the robot 200 are selected from the selection candidate list. Specifically, the selection probability adjuster 117 decreases, with respect to the action trigger of “there is a loud sound”, the selection probabilities of the basic action 2-1, the basic action 2-2, and the personality action 2-0, which are the actions other than the basic action 2-0, in the selection probability list.
More specifically, the selection probability adjuster 117 determines the decrease value of the selection probability of each of the basic action 2-1, the basic action 2-2, and the personality action 2-0 so that the sum of the adjusted selection probabilities of the actions is 100%. The selection probability adjuster 117 assigns the increase value ΔP of the selection probability of the basic action 2-0 equally to the basic action 2-1, the basic action 2-2, and the personality action 2-0 to determine the decrease value of the selection probability of each action as ΔP/3. Then, the selection probability adjuster 117 decreases the selection probabilities of the basic action 2-1, the basic action 2-2, and the personality action 2-0 by the determined decrease value ΔP/3. In the example of the adjustment table 132 illustrated in
As described above, each time the external stimulus of first type to the basic action 2-0 is detected, the selection probability adjuster 117 increases the selection probability of the basic action 2-0 by the increase value ΔP=0.3% and decreases the selection probabilities of the basic action 2-1, the basic action 2-2, and the personality action 2-0 by the decrease value ΔP/3=0.1%. As a result, it can increase the probability that the robot 200 executes the basic action 2-0 in a case where the action trigger of “there is a loud sound” is satisfied in future while maintaining the sum of the selection probabilities of the actions included in a selection candidate list corresponding to one action trigger at 100%.
Note that if there is an action of which selection probability is 0% among actions of which selection probabilities are to be decreased, the selection probability of that action cannot be decreased. In this case, the selection probability adjuster 117 determines the decrease value by equally assigning the increase value ΔP to at least one action other than the action of which selection probability is 0%, and decreases the selection probability of the at least one action other than the action of which selection probability is 0%. Further, if any of the selection probabilities of actions becomes a negative value after equally assigning the increase value ΔP, the selection probability adjuster 117 adjusts the decrease value so that none of the selection probabilities becomes a negative value. Thus, within a constraint that the sum of selection probabilities of actions included in a selection candidate list corresponding to one action trigger is maintained at 100% and none of the selection probabilities of the actions is a negative value, the selection probability adjuster 117 decreases the selection probability that each of actions other than the action executed by the robot 200 is selected from the selection candidate list.
(ii) A second example is described in which, in a case where the action controller 115 selects the basic action 2-2 to cause the robot 200 to execute the basic action 2-2, 40% that is the selection probability of the basic action 2-2 at the growth level of 8 is greater than 20% that is the selection probability of the basic action 2-2 at the growth level of 7 in the initial table 131. In this case, if the external stimulus of first type is detected in response to the execution of the basic action 2-2, the selection probability adjuster 117 sets the upper limit of the selection probability of the basic action 2-2 as 40% that is the selection probability at the current growth level of 8. As such, the selection probability adjuster 117 maintains the selection probability of the basic action 2-2 at the growth level of 8 at 40% and does not increase the selection probability.
As above, with respect to the selection probability of the action executed by the robot 200, the selection probability adjuster 117, while increasing the selection probability of the action in a case where the selection probability at the current growth level is less than the selection probability at the previous growth level, does not increase the selection probability of the action in a case where the selection probability at the current growth level is greater than the selection probability at the previous growth level. In this way, among the actions frequently executed by the robot 200 when the growth level is low, that is, in younger days, the probability of action that the user likes can be maintained even after the robot 200 is grown up.
Returning to
For example, in a case where the action trigger of “petted” is satisfied, upon increase of the growth level from 1 to 2, the selection probability of the basic action 0-0 decreases from 100% to 80% and the selection probability of the basic action 0-1 increases from 0% to 20%. Thus, the growth index table 133 defines, at the growth level of 1 with respect to the action trigger of “petted”, the growth index of the basic action 0-0 as −20% and the growth index of the basic action 0-1 as +20%.
As the growth level is increased by the parameter setter 113, the selection probability adjuster 117 updates the adjustment table 132 based on the growth index of each action defined in the growth index table 133. Specifically, in a case where the growth level increases from n to n+1, the selection probability adjuster 117 adds, to the selection probability of each action of which the growth level is defined as n in the adjustment table 132, the growth index of the corresponding action of which the growth level is defined as n in the growth index table 133.
For example, at the growth level of 1 in the growth index table 133, the growth index of the basic action 0-0 is defined as −20% and the growth index of the basic action 0-1 is defined as +20%. Thus, in a case where the growth level increases from 1 to 2, the selection probability adjuster 117 subtracts 20% from 100% that is the selection probability of the basic action 0-0 at the growth level of 1 to calculate the selection probability of the basic action 0-0 at the growth level of 2 as 80% Further, the selection probability adjuster 117 adds 20% to 0% that is the selection probability of the basic action 0-1 at the growth level of 1 to calculate the selection probability of the basic action 0-1 at the growth level of 2 as 20%
In a case where the growth level increases from n to n+1, the selection probability adjuster 117 calculates the selection probability of each action at the growth level of n+1 as above. Then, the selection probability adjuster 117 updates the adjustment table 132 by inputting the value of the calculated selection probability to the column of the selection probability of each action at the growth level of n+1 in the adjustment table 132.
Specifically, even after the selection probability is changed from the initial value defined in the initial table 131, the selection probability adjuster 117 adds, to the selection probability of each action, the growth index defined in the growth index table 133 when the growth level increases. For example, the adjustment table 132 illustrated in
In a case where the growth level increases after increase or decrease of selection probability as above, the selection probability adjuster 117 adds the growth index defined for each action in the growth index table 133 to the selection probability that is after the increase or the decrease. In the example of
In this way, even after the selection probability of an action is increased or decreased due to the external stimulus of first type, the selection probability adjuster 117 changes, along with the increase of growth level, the selection probability of each action based on the initially-set growth index. This enables, in a case where the selection probability of an action changes due to the external stimulus of first type, the change of the selection probability to be taken over even after the pseudo-growth of the robot 200. Thus, even after the pseudo-growth of the robot 200, individuality can be imparted to the action to be executed by the robot 200.
Next, the flow of the robot control processing is described with reference to
Upon starting the robot control processing, the controller 110 functions as the parameter setter 113 and sets the parameter data 121 (step S101). When the robot 200 is started up for the first time (the time of the first start up by the user after shipping from the factory), the controller 110 sets the various parameters, namely the emotion parameter, the personality parameter, the growth days count, and the growth level to initial values (for example, 0). Meanwhile, at the time of starting up for the second and subsequent times, the controller 110 reads the values of the various parameters stored in step S105, described later, of the robot control processing to set the parameter data 121. However, a configuration may be employed in which the values of the emotion parameter are all initialized to 0 each time the power is turned on.
Upon setting the parameter data 121, the controller 110 determines whether an action trigger of the plurality of action triggers is satisfied (step S102). In a case where the action trigger is satisfied (Yes in step S102), the controller 110 causes the robot 200 to execute the action corresponding to the satisfied action trigger (step S103). Details of the action control processing in step S103 are described with reference to the flowchart of
Upon starting the action control processing illustrated in
Upon updating the parameter data 121, the controller 110 determines whether the growth level updated in step S201 is increased from the growth level before the update (step S202). In a case where the growth level has increased (Yes in step S202), the controller 110 updates the selection probability in the adjustment table 132 (step S203). Specifically, the controller 110 adds, to the selection probability of each action at the growth level before increase in the adjustment table 132, the corresponding growth index defined in the growth index table 133. By doing so, the controller 110 updates the selection probability of each action at the current growth level in the adjustment table 132. In contrast, in a case where the growth level has not increased (No in step S202), the controller 110 skips the processing in step S203 and does not update the selection probability.
Next, the controller 110 references the adjustment table 132 and reads the selection probability that corresponds to the action trigger determined as being satisfied in step S102 and the current growth level action (step S204). Then, the controller 110 selects, based on the read selection probability, an action to be executed by the robot 200 using random numbers (step S205). For example, in the adjustment table 132 illustrated in
Then, upon selecting the action to be executed by the robot 200, the controller 110 causes the robot 200 to execute the selected action (step S206). Specifically, the controller 110 performs the motion and the sound output defined in the motion table 125 to cause the robot 200 to execute the action of the action content defined in the action content table 124.
Upon causing the robot 200 to execute the selected action, the controller 110 determines whether an external stimulus is detected by the sensor unit 210 within a predetermined time from the execution of the action (step S207). That is, the controller 110 determines whether a user response to the action executed by the robot 200 is detected during a period from when the robot 200 is caused to execute the action until the elapse of the predetermined time.
In a case where the external stimulus is detected within the predetermined time from the execution of the action (Yes in step S207), the controller 110 determines whether the type of the detected external stimulus is the first type (step S208). Specifically, the controller 110 references the classification table 127 illustrated in
In a case where the type of the detected external stimulus is the first type (Yes in step S208), the controller 110 adjusts, based on the user response, the selection probabilities of the actions including the executed action (step S209). Details of the selection probability adjustment processing in step S209 are described with reference to the flowchart of
Upon starting the selection probability adjustment processing illustrated in
In a case where the selection probability at the current growth level is less than the selection probability at the growth level that is one level prior to the current growth level (Yes in step S301), the controller 110 increases, by the predetermined increase value ΔP, the selection probability of the action executed by the robot 200, in the selection candidate list corresponding to the action trigger satisfied in step S102, in the adjustment table 132 (step S302).
Then, the controller 110 decreases the selection probability of at least one action other than the action executed by the robot 200, in the selection candidate list corresponding to the action trigger satisfied in step S102, in the adjustment table 132 (step S303). Specifically, within a constraint that the sum of selection probabilities of actions included in a selection candidate list corresponding to one action trigger is maintained at 100% and none of the selection probabilities of the actions is a negative value, the controller 110 determines the decrease value and decreases the selection probability of at least one action of which selection probability is to be decreased.
In contrast, in a case where the selection probability at the current growth level is greater than or equal to the selection probability at the growth level that is one level prior to the current growth level (No in step S301), the controller 110 skips the processing in steps S302 and S303, and does not change the selection probability of each action. Thus, the selection probability adjustment processing illustrated in
Returning to
Returning to
In a case where the processing does not end (No in step S104), the controller 110 uses the clock function to determine whether a date has changed (step S106). In a case where the date has not changed (No in step S106), the controller 110 returns the processing to the processing in step S102.
In contrast, in a case where the date has changed (Yes in step S106), the controller 110 updates the parameter data 121 (step S107). Specifically, in a case where it is during the juvenile period (for example, 50 days from birth), the controller 110 changes the values of the emotion change amounts DXP, DXM, DYP, and DYM in accordance with whether the emotion parameter has reached the maximum value or the minimum value of the emotion map 300. Additionally, in a case where it is during the juvenile period, the controller 110 increases both the maximum value and the minimum value of the emotion map 300 by a predetermined increase amount (for example, 2). In contrast, in a case where it is during the adult period, the controller 110 adjusts the personality correction values.
When the parameter data 121 is updated, the controller 110 adds 1 to the growth days count (step S108), and returns the processing to the processing in step S102. Then, as long as the robot 200 is operating normally, the controller 110 repeats the processing in steps S102 to S108.
As described above, the robot 200 according to Embodiment 1 executes, in a case where the predetermined action trigger is satisfied, the action selected, from the selection candidate list corresponding to the action trigger, at the selection probability that is dependent on the growth level of the robot 200, and changes, in a case where an external stimulus is detected during a period from the execution of the action until the elapse of the predetermined time, the selection probability that the action is selected from the selection candidate list. As such, the probability that the action executed by the robot 200 is selected in future changes due to the external stimulus such as the relationship with the user, and the like. Thus, the manner of pseudo-growth of the robot 200 is not uniform and individuality can be imparted to the manner of pseudo-growth of the robot 200. Therefore, the robot 200 according to Embodiment 1 can realistically simulate a living creature and can enhance lifelikeness.
In particular, in a case where an external stimulus of first type is detected during a period from the execution of the action until the elapse of the predetermined time, the robot 200 according to Embodiment 1 increases the selection probability that the action is selected from the selection candidate list in a range less than or equal to the predetermined upper limit. This allows, if the action executed by the robot 200 was a preferable action for the user, the probability that that action is selected in future can be increased by the user demonstrating a positive response such as praising, petting, or the like. Thus, in the robot 200 that executes the action in accordance with the growth level, the preferences of the user can be reflected to the action of the robot 200.
Next, Embodiment 2 is described. In Embodiment 2, as appropriate, descriptions of configurations and functions that are the same as those described in Embodiment 1 are forgone.
In Embodiment 1, in a case where the selection probabilities that actions other than the action executed by the robot 200 are selected from the selection candidate list are decreased, the selection probability adjuster 117 equally assigns the increase value ΔP to determine the decrease value of the selection probabilities that the actions are selected. In contrast, in Embodiment 2, the selection probability adjuster 117 determines, based on priorities preassigned to respective actions, the decrease value of the selection probabilities that the actions are selected.
In a case where the sensor unit 210 detects an external stimulus of first type during a period from when the action controller 115 causes the robot 200 to execute the action until the elapse of the predetermined time, the selection probability adjuster 117 increases the selection probability that the action is selected from the selection candidate list. In addition, the selection probability adjuster 117 determines, in accordance with the priorities set in the priority table 128, the decrease value for decreasing the selection probabilities that actions other than the action executed by the robot 200 are selected and decreases the selection probabilities of the actions.
Specifically, within a constraint that the sum of selection probabilities of actions included in in a selection candidate list corresponding to one action trigger is maintained at 100% and none of the selection probabilities of the actions is a negative value, the selection probability adjuster 117 determines the decrease value of the selection probability of each action so that the higher the priority set in the priority table 128, the greater the decrease value. By setting the priority as above, the decrease value of the selection probability can be designed more flexibly than equally assigning the decrease value as in Embodiment 1.
Specifically, the priority table 128 illustrated in
As above, the newly exhibited action is assigned with a higher priority, and thus, the selection probability thereof is preferentially decreased. If the user desires to increase the selection probability of the newly exhibited action, the robot 200 may be grown. Conversely, if the user does not desire to increase the selection probability of the newly exhibited action, the user can decrease the selection probability of the action executed by the robot 200 preferentially by demonstrating a positive response to that action.
Next, Embodiment 3 is described. In Embodiment 3, as appropriate, descriptions of configurations and functions that are the same as those described in Embodiments 1 and 2 are forgone.
In Embodiments 1 and 2, in a case where an external stimulus of first type is detected during a period from when the action controller 115 causes the robot 200 to execute the action until the elapse of the predetermined time, the selection probability adjuster 117 increases the selection probability that the action is selected from the selection candidate list. Instead of or in addition to the above, in Embodiment 3, in a case where an external stimulus of second type is detected during a period from when the action controller 115 causes the robot 200 to execute the action until the elapse of the predetermined time, the selection probability adjuster 117 decrease the selection probability that the action is selected from the selection candidate list.
As described in Embodiment 1, the external stimulus of second type is a stimulus detected when the user demonstrates a negative response such as getting angry, striking, or the like to an action executed by the robot 200. In a case where the external stimulus is detected in the period from when the robot 200 executes the action until the elapse of the predetermined time, the selection probability adjuster 117 references the classification table 127 and determines whether the external stimulus is an external stimulus of first type, an external stimulus of second type, or an external stimulus of other type.
In a case where an external stimulus of second type is detected during a period from when the action controller 115 causes the robot 200 to execute the action until the elapse of the predetermined time, the selection probability adjuster 117 decrease the selection probability that the action is selected from the selection candidate list. In addition, the selection probability adjuster 117 increases, within a range less than or equal to the predetermined upper limit, the selection probability that at least one action other than the action is selected from the selection candidate list. As in Embodiment 1, the predetermined upper limit is set based on the initial value of the selection probability that is to the action executed by the robot 200 in a case where the growth level is one level lower than the current growth level.
Details of the processing by the selection probability adjuster 117 in Embodiment 3 can be explained in the same way as in Embodiment 1 by replacing “in a case where an external stimulus of first type is detected” described in Embodiment 1 with “in a case where an external stimulus of second type is detected” and by interchanging “increase” and “decrease” regarding the adjustment of selection probability described in Embodiment 1 with each other in Embodiment 3. However, the predetermined upper limit in Embodiment 3 is used when the selection probability of at least one action other than the action executed by the robot 200 increases, and not when the selection probability of the action executed by the robot 200 increases.
As above, with respect to the robot 200 according to Embodiment 3, the selection probability that the action is selected from the selection candidate list is decreased in a case where an external stimulus of second type is detected during a period from the execution of the action until the elapse of the predetermined time. This allows, if the action executed by the robot 200 was not a preferable action for the user, the robot 200 to execute that action less often in future by the user demonstrating a negative response to that action. As a result, the probability that the robot 200 executes the action preferred by the user increases, and thus, the preferences of the user can be reflected to the action of the robot 200.
Embodiments of the present disclosure are described above, but these embodiments are merely examples and do not limit the scope of application of the present disclosure. That is, the embodiment of the present disclosure may be variously modified, and any modified embodiments are included in the scope of the present disclosure.
For example, in the embodiments described above, the parameter setter 113 sets the emotion parameter and the personality parameter, and sets, as a growth level, the maximum value among the plurality of personality values included in the personality parameter. The growth level, however, is not limited to this, and may be set based on any criteria. For example, the growth level is not limited to be based on the personality parameter, and may be based directly on the growth days count.
If the growth level is set not based on the personality parameter, the parameter setter 113 may not necessarily set the personality parameter. Further, the parameter setter 113 may not necessarily set the emotion parameter for setting the personality parameter. Further, the emotion parameter and the personality parameter described in the embodiments above are merely an example. Even if the emotion parameter and the personality parameter are set, the emotion parameter and the personality parameter may be set using another method.
In the embodiments described above, the action selection table 123 defines actions for each of the action triggers as a selection candidate list. The actions are basic actions and/or personality actions. However, the actions to be executed by the robot 200 are not limited to the basic actions or the personality actions, and may be defined any way. Note that, in the embodiments described above, the personality action selected for each action trigger is one but, as with the basic actions, the type of personality action may be increased in accordance with increase of the personality values.
In the embodiments described above, the exterior 201 is formed in a barrel shape from the head 204 to the torso 206, and the robot 200 has a shape as if lying on its belly. However, the robot 200 is not limited to resembling a living creature that has a shape as if lying on its belly. For example, a configuration may be employed in which the robot 200 has a shape provided with arms and legs, and resembles a living creature that walks on four legs or two legs.
Although the above embodiments describe a configuration in which the control device 100 is installed in the robot 200, a configuration may be employed in which the control device 100 is not installed in the robot 200 but, rather, is a separated device (for example, a server). When the control device 100 is provided outside the robot 200, the control device 100 communicates with the robot 200 via the communicator 130, the control device 100 and the robot 200 send and receive data to and from each other, and the control device 100 controls the robot 200 as described in the embodiments described above.
In the embodiments described above, in the controller 110, the CPU executes the program stored in the ROM to function as the various components, namely the parameter setter 113, the action controller 115, and the selection probability adjuster 117. However, in the present disclosure, the controller 110 may include, for example, dedicated hardware such as an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), various control circuitry, or the like instead of the CPU, and this dedicated hardware may function as the various components, namely the parameter setter 113, the action controller 115, and the selection probability adjuster 117. In this case, the functions of each of the components may be achieved by individual pieces of hardware, or the functions of each of the components may be collectively achieved by a single piece of hardware. Furthermore, a part of the functions of the components may be implemented by dedicated hardware and another part thereof may be implemented by software or firmware.
It is possible to provide a robot provided in advance with the configurations for achieving the functions according to the present disclosure, but it is also possible to apply a program to cause an existing information processing device or the like to function as the robot according to the present disclosure. That is, applying a program for achieving each functional configuration of the robot 200 of the above embodiments so as to be executable by a CPU or the like that controls an existing information processing device or the like enables causing the existing information processing device or the like to function as the robot according to the present disclosure.
Additionally, any method may be used to apply the program. For example, the program can be applied by storing the program on a non-transitory computer-readable recording medium such as a flexible disc, a compact disc (CD) ROM, a digital versatile disc (DVD) ROM, and a memory card. Furthermore, the programs can be superimposed on a carrier wave and applied via a communication medium such as the Internet. For example, the program may be posted to and distributed via a bulletin board system (BBS) on a communication network. Moreover, a configuration is possible in which the processing described above is executed by starting the program and, under the control of the operating system (OS), executing the program in the same manner as other applications/programs.
The foregoing describes some example embodiments for explanatory purposes. Although the foregoing discussion has presented specific embodiments, persons skilled in the art will recognize that changes may be made in form and detail without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. This detailed description, therefore, is not to be taken in a limiting sense, and the scope of the invention is defined only by the included claims, along with the full range of equivalents to which such claims are entitled.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2023-195474 | Nov 2023 | JP | national |
