JP6994781B2 - On-board equipment and vehicles - Google Patents

Description

Reference of related application

This international application claims priority based on Japanese Patent Application No. 2014-262907 filed with the Japan Patent Office on December 25, 2014, and Japanese Patent Application No. 2014-262907. The entire contents are incorporated in this international application by reference.

This disclosure relates to robots.

In recent years, robots that can talk with people have been attracting attention. This robot acquires the voice produced by a person with a microphone and estimates the meaning and content of the voice by voice recognition. Then, an answer associated in advance is given to the estimated meaning and content (see Patent Document 1).

Japanese Patent No. 4015424

It is conceivable to improve the conversation ability of the robot by learning using artificial intelligence or the like. However, even if the conversation ability of a specific robot is enhanced by learning, for example, when using another robot in a different place, learning must be performed from the beginning. One aspect of the present disclosure is to provide a robot that can reduce the burden on the user regarding learning of the robot.

The in -vehicle device of the present disclosure has a voice acquisition unit configured to acquire the voice of a vehicle occupant, a first learning unit for learning about the recognition result of the voice recognition unit, and a recognition result of the voice recognition unit. , The answer sounding unit configured to pronounce the answer using the learning result of the first learning unit, and the voice acquired by the voice acquisition unit are configured to recognize the instruction to the vehicle. The instruction recognition unit, the control content determination unit configured to determine the control content of the vehicle using a program in response to the instruction recognized by the instruction recognition unit, and the control content determination unit determined by the control content determination unit. A control content output unit configured to output the control content to the vehicle control unit that controls the vehicle, a vehicle state acquisition unit configured to acquire the state of the vehicle using a sensor, and a vehicle state acquisition unit. A second learning unit configured to perform learning based on the control content output by the control content output unit and the state of the vehicle acquired by the vehicle state acquisition unit, and a learning result of the second learning unit. A program update unit configured to update the program to reflect the above .

It is a block diagram which shows the electric structure of the robot of one Embodiment. It is a front view which shows the structure of a robot. It is a flowchart which shows the program setting process which a robot executes. It is a flowchart which shows the conversation process which a robot executes. It is a flowchart which shows the learning stop judgment process executed by a robot. FIG. 6A is an explanatory diagram showing an example of an operation for instructing learning stop, and FIG. 6B is an explanatory diagram showing an example of an operation for instructing learning restart. It is a block diagram which shows the electric structure of a computer. It is a perspective view which shows the appearance of a computer. It is a block diagram which shows the electric structure of an in-vehicle device. It is a flowchart which shows the vehicle control process which the on-board unit executes. It is a block diagram which shows the electric structure of the robot of another embodiment. It is a flowchart which shows the program installation process which a robot executes. It is a flowchart which shows the conversation process which a robot executes. It is a flowchart which shows the 2nd artificial intelligence unit learning stop determination process executed by a robot. It is a flowchart which shows the program upload process executed by a robot. It is a block diagram which shows the electric structure of the robot of still another embodiment. It is a flowchart which shows the conversation process which a robot executes. It is explanatory drawing which shows the structure for executing a call processing. It is a flowchart which shows the call pair setting process executed by a host computer. It is explanatory drawing which shows the display which displays the icon.

1, 301 ... robot, 3 ... control unit, 5 ... microphone, 7 ... camera, 9 ... touch panel, 11 ... sensor group, 13 ... GPS, 15 ... speaker, 17 ... motor group, 19 ... display, 21 ... input unit, 23 ... standard program storage unit, 25 ... artificial intelligence unit, 26 ... communication unit, 27 ... arithmetic unit, 29 ... output unit, 31 ... cloud network, 33 ... terminal, 35 ... head, 37 ... body, 39 ... right arm Part, 41 ... left arm, 43 ... right leg, 45 ... left leg, 47, 49 ... wheel, 51 ... mobile motor, 53 ... neck joint, 55 ... shoulder joint, 57 ... elbow joint, 59 ... wrist joint , 61 ... Joint motor, 63 ... Keyboard, 65 ... Mouse, 67 ... Housing, 69 ... Terminal, 71 ... Vehicle control unit, 101 ... Computer, 201 ... In-vehicle device

The embodiments of the present disclosure will be described with reference to the drawings.
<First Embodiment>
1. 1. Configuration of Robot 1 The configuration of robot 1 will be described with reference to FIGS. 1 and 2. As shown in FIG. 1, the robot 1 includes a control unit 3, a microphone 5, a camera 7, a touch panel 9, a sensor group 11, a GPS 13, a speaker 15, a motor group 17, and a display 19.

The control unit 3 includes a microcomputer. Specifically, the control unit includes an input unit 21, a standard program storage unit 23, an artificial intelligence unit 25, a communication unit 26, an arithmetic unit 27, and an output unit 29.

The input unit 21 acquires information from the microphone 5, the camera 7, the touch panel 9, the sensor group 11, and the GPS 13, and outputs the information to the calculation unit 27 and the artificial intelligence unit 25.

The standard program storage unit 23 constantly stores a standard program for executing various operations of the robot 1. The standard program is different from the AI program described later.
It is a program whose contents do not change.

The artificial intelligence unit 25 can store an AI (artificial intelligence) program for executing various operations of the robot 1. When the artificial intelligence unit 25 stores the program for AI, it changes and develops it by learning. Further, the artificial intelligence unit 25 can newly install the AI program and delete the AI program.

The communication unit 26 communicates with an external cloud network 31, a terminal 33, and the like. The communication may be wireless communication or wired communication.
The arithmetic unit 27 executes operations necessary for realizing various operations of the robot 1 by using a standard program or an AI program.

The output unit 29 outputs the calculation result of the calculation unit 27 to the speaker 15, the motor group 17, and the display 19. The detailed functions of each unit belonging to the control unit 3 will be described later.

As shown in FIG. 2, the robot 1 has a humanoid outer shape, and includes a head portion 35, a body portion 37, a right arm portion 39, a left arm portion 41, a right leg portion 43, and a left leg portion 45.
The microphone 5, the camera 7, and the speaker 15 are provided on the head 35. Further, the display 19 and the touch panel 9 are provided on the front side of the body portion 37.

The right leg portion 43 and the left leg portion 45 are provided with wheels 47 and 49 for movement, respectively. Two wheels 47 and 49 are provided in the front-rear direction, respectively. Therefore, the robot 1 comes into contact with the ground by a total of four wheels. The wheels 47 and 49 are driven by the moving motor 51 belonging to the motor group 17. The robot 1 can move back and forth by rotating the wheels 47 and 49 by the driving force of the moving motor 51. Further, by making the rotation speed of the wheel 47 different from the rotation speed of the wheel 49, the robot 1 can make a right turn or a left turn.

The robot 1 includes a neck joint 53, a shoulder joint 55, an elbow joint 57, and a wrist joint 59. The degree of freedom of each joint can be appropriately set from 1 to 3. Each joint operates by the driving force of the joint motor 61 belonging to the motor group 17. By moving an appropriately selected joint among the above joints with the joint motor 61, the robot 1 performs a predetermined motion and expresses a predetermined posture.

The plurality of sensors belonging to the sensor group 11 detect the position, speed, acceleration, inclination, joint angle, and the like of each part in the robot 1. The detection result is fed back to the calculation unit 27 via the input unit 21 and used for the operation control of the robot 1.

The standard program storage unit 23, the artificial intelligence unit 25, and the calculation unit 27 are examples of a voice recognition unit, a processing unit, and a learning stop unit. The artificial intelligence unit 25 is an example of a learning unit. The communication unit 26 is an example of a storage unit and a learning result acquisition unit. The input unit 21 and the communication unit 26 are examples of the identification information acquisition unit. The cloud network 31 is an example of an external storage device.

2. 2. Process executed by the robot 1 (2-1) Program setting process The control unit 3 executes the program setting process shown in FIG. 3 in order to determine the program to be used among the standard program and the AI program. This program setting process is repeatedly executed at predetermined time intervals when the power of the robot 1 is on.

In step 1, it is determined whether or not the AI program is used at that time. If the AI program is not used (that is, if the standard program is used), the process proceeds to step 2, and if the AI program is used, the process proceeds to step 5.

In step 2, it is determined whether or not the user's identification information has been input. The identification information can be input by, for example, the following method.
-Transmit the identification information from the terminal 33 to the communication unit 26.

-Electromagnetic waves representing identification information (for example, radio waves, infrared rays, etc.) are transmitted to the communication unit 26. The electromagnetic wave representing the identification information may be transmitted from the terminal 33 or may be transmitted from a fixed device (for example, a beacon, a wireless LAN access point, etc.). The electromagnetic wave representing the identification information may be transmitted periodically or in response to a user's instruction, or may be transmitted when the terminal 33, a fixed device, or the like detects the robot 1. You may.

-The user says the content of the identification information aloud. The microphone 5 acquires the voice and identifies the identification information by voice recognition.
-The camera 7 captures a one-dimensional bar code or a two-dimensional bar code representing identification information.

-Enter the identification information using the touch panel 9.
The identification information may be composed of numbers or characters, may be a one-dimensional or two-dimensional image (for example, a one-dimensional bar code or a two-dimensional bar code), or may be user biometric information (for example, a user's biometric information (). For example, it may be a fingerprint, a pattern of a vein in any part of the body, an iris, a face, etc.), or it may be composed of voice.

If the identification information is input, the process proceeds to step 3, and if the identification information is not input, this process ends.
In step 3, the AI program and data set corresponding to the identification information determined to have been input in step 2 are installed from the cloud network 31. The installed AI program and data set are stored in the artificial intelligence unit 25.

The cloud network 31 stores the identification information in association with the AI program and the data set. The data set is a data set used in the use of AI programs, learning, etc., and includes dictionary data used in speech recognition and speech synthesis.

In step 4, the program used by the robot 1 is changed from the standard program to the AI program installed in step 3. From this point onward, the robot 1 uses the AI program.

On the other hand, if the affirmative judgment is made in step 1, it is determined in step 5 whether or not the condition for ending the use of the robot 1 is satisfied. The end-of-use condition can be, for example, as follows.

-The user does not operate the robot 1 for a predetermined time or longer.
-The state in which the user cannot be recognized in the image of the camera 7 or the voice acquired by the microphone 5 continues for a predetermined time or longer.

-The user inputs to the robot 1 corresponding to the end of use (for example, the user aloues "end of use" or inputs the content corresponding to the end of use to the touch panel 9). ..

-The power of the robot 1 is turned off.
・ It will be a preset time.
If the end-of-use condition is satisfied, the process proceeds to step 6, and if the end-of-use condition is not satisfied, this process ends.

In step 6, the AI program and data set are uploaded to the cloud network 31. At this time, uploading is performed in association with the user's identification information. When the learning described later is performed, the AI program and the data set to be uploaded are those after the learning.

In step 7, the AI program and data set are deleted from the artificial intelligence unit 25.
In step 8, the program to be used is changed from the AI program to the standard program. From this point onward, Robot 1 uses the standard program.

(2-2) Conversation processing The control unit 3 executes the conversation processing shown in FIG. This process is executed when the microphone 5 detects a volume equal to or higher than a predetermined threshold value.

In step 11, the microphone 5 is used to acquire voice.
In step 12, the content of the voice acquired in step 11 is recognized by a well-known voice recognition technique. At this time, when the standard program is used, the content of the voice is recognized by using the standard dictionary data provided in advance by the robot 1. When the AI program is used, the content of the voice is recognized by using the dictionary data installed from the cloud network 31 together with the AI program and enhanced by the past learning.

In step 13, voice data (hereinafter referred to as answer voice data) to respond to the content of the voice recognized in step 12 is created. At this time, when the standard program is used, the answer voice data is created using the standard dictionary data provided in advance by the robot 1. If the AI program is used, the answer voice data is created using the dictionary data installed from the cloud network 31 together with the AI program and enhanced by the past learning.

The content of the answer voice data can be created, for example, by detecting a keyword from the content of the voice recognized in step 12 and searching for the item previously targeted for the keyword from the dictionary data. Further, the content of the response voice data may be inferred by using artificial intelligence with respect to the content of the voice recognized in step 12.

In step 14, the speaker 15 is used for pronunciation based on the response voice data created in step 13. That is, the answer to the voice acquired in step 11 is pronounced.

In step 15, it is determined whether or not the AI program is being used at that time. If the AI program is in use, the process proceeds to step 16, and if the standard program is in use, this process ends.

In step 16, it is determined whether or not learning is stopped at that time. The learning stop is set by the learning stop determination process described later. If the learning is not stopped, the process proceeds to step 17, and if the learning is not stopped, the present process is terminated.

In step 17, the learning restriction content set at that time is acquired. The learning restriction content includes, for example, information (name, address, telephone number, e-mail address, biography, image including face) about the family and acquaintances of the user (the user corresponding to the identification information determined to be input in step 2 above). ) Etc.

In step 18, learning is performed on the content of the voice recognized in step 12. Examples of learning include machine learning. Machine learning may be supervised learning or unsupervised learning. Further, the learning may be learning by artificial incompetence. In this case, a keyword can be extracted from the voice recognized in step 12 and the keyword can be added to a data set (for example, dictionary data used for voice recognition). This keyword can be used, for example, in the process of creating answer voice data (step 13).

However, even if the content of the voice is recognized in step 12, the item corresponding to the learning restriction content acquired in step 17 is not learned.
In step 19, the AI program and the data set are updated so as to reflect the learning result in step 18. The AI program and dataset updated to reflect the learning results are examples of the learning results.

(2-3) Learning stop determination process The control unit 3 repeatedly executes the learning stop determination process shown in FIG. 5 at predetermined time intervals. In step 21 of FIG. 5, it is determined whether or not the AI program is in use. If the AI program is in use, the process proceeds to step 22, and if the standard program is in use, this process ends.

In step 22, the position information of the robot 1 is acquired by using the GPS 13.
In step 23, the camera 7 is used to acquire an image of the surroundings of the robot 1.
In step 24, the microphone 5 is used to acquire voice.

In step 25, it is determined whether or not learning is stopped at that time. The learning stopped state starts in step 28 described later and ends when learning is restarted in step 30 described later. If the learning is not stopped, the process proceeds to step 26, and if the learning is not stopped, the process proceeds to step 29.

In step 26, it is determined whether or not the image acquired in step 23 or the voice acquired in step 24 triggers learning stop. Examples of the trigger for stopping learning include the following.

-In the image acquired in step 23, a preset operation is recognized as an operation for instructing the learning stop. As the operation, for example, as shown in FIG. 6A, the operation of raising the index finger and placing it in front of the mouth can be mentioned. Another operation is a wink.

-In the voice acquired in step 24, the voice preset as a keyword for instructing the learning stop is recognized. Examples of the keywords include "secret", "off record", and "private". Further, as another keyword, for example, the name of a user's family or an acquaintance can be mentioned. The user can register the names of family members and acquaintances in the robot 1 in advance as keywords. Further, the robot 1 infers which word is the name of the user's family or acquaintance based on the voice data recognized in the past, and registers the inferred word as the name of the user's family or acquaintance as a keyword. You may.

-The face of a person registered in advance is recognized in the image acquired in step 23. Examples of this person include the user's family, acquaintances, and the like. In addition, the user can register in advance the face image of the person who triggers the learning stop.

If there is a trigger to stop learning, the process proceeds to step 28, and if there is no trigger to stop learning, the process proceeds to step 27.
In step 27, it is determined whether or not the position information acquired in step 22 corresponds to a position registered in advance as a position where learning should be stopped. Examples of the position where learning should be stopped include the user's home, a conference room, and the like.

The user can register in advance the position where learning should be stopped. Further, the robot 1 can infer a place where learning should be stopped based on past data, and can register the inferred place. For example, the robot 1 can infer a place where the trigger for stopping learning was recognized in the past as a place where learning should be stopped.

In step 28, the learning stopped state is started. After this point, in the step 16 in the conversation process, an affirmative judgment is made, and learning in the step 18 is not performed.
On the other hand, if an affirmative judgment is made in step 25, it is determined in step 29 whether or not the image acquired in step 23 or the voice acquired in step 24 triggers the restart of learning. do. Examples of the trigger for resuming learning include the following.

-In the image acquired in step 23, a preset operation is recognized as an operation for instructing the restart of learning. As the operation, for example, as shown in FIG. 6B, an operation of forming a ring with the thumb and the index finger (indicating so-called OK) can be mentioned.

-In the voice acquired in step 24, the voice preset as a keyword for instructing the restart of learning is recognized. Examples of the keyword include "OK", "restart", "learning" and the like.

If there is a trigger for resuming learning, the process proceeds to step 30, and if there is no trigger for resuming learning, this process ends.
In step 30, learning is resumed. From this point onward, a negative determination is made in step 16 of the conversation process, and learning in step 18 is performed.

(2-4) Schedule management process The robot 1 can execute the schedule management process shown below. The user inputs his / her own schedule information into the robot 1 in advance. The schedule information may be input using, for example, the touch panel 9 or by voice input. Further, the schedule information may be transmitted from the terminal 33 to the communication unit 26.

The schedule information includes at least the due date and the matters to be executed by the due date. The robot 1 is scheduled by the user based on the sound acquired by the microphone 5, the image acquired by the camera 7, the information acquired from the terminal 33, and the like at a predetermined time (for example, one day and three hours) before the due date. It is determined whether or not the matter included in the information has been executed, and if it has not been executed yet, the user is warned by the sound of the speaker 15 or the image displayed on the display 19.

3. 3. Effects of the robot 1 (1A) The robot 1 can change and develop the AI program by learning. Further, the robot 1 can enhance the contents of the data set (for example, dictionary data) by learning. Then, the robot 1 can upload the AI program and the data set after learning to the cloud network 31. In addition, the robot 1 can install the AI program and the data set uploaded to the cloud network 31.

The robot 1 that installs the AI program and the data set after learning may be the same as or different from the robot 1 that has learned in the past. Further, the robot 1 that installs the AI program and the data set after learning may be located in a place where learning has been performed in the past, or may be located in a different place.

Therefore, the user can apply the AI program and data set learned by his / her own use in the past to the robot 1 at the place where the user is at that time (for example, various places such as work, store, and home) from the cloud network 31. You can install and use Robot 1.

(1B) The robot 1 allows the installation of the AI program and the data set corresponding to the identification information on condition that the identification information of the user is input. Therefore, it is possible to prevent another person from using the AI program and data set of a certain user without permission.

(1C) The robot 1 can recognize a human voice and pronounce a voice of an answer to the human voice. That is, the robot 1 can have a conversation with a person. Further, when the robot 1 is using the AI program, it learns based on the recognition result of the voice and creates the voice of the answer using the learning result. Therefore, the more advanced the learning, the more advanced the conversation. It can be carried out.

(1D) The robot 1 stops learning according to the movement of a person around it, the identification result of the person, and the like. Therefore, it is possible to prevent the robot 1 from learning an undesired matter for the user and later talking to another person.

(1E) The robot 1 stops learning according to the place where it exists. Therefore, it is possible to prevent the robot 1 from telling another person later what he / she has learned in a place where he / she does not want to learn.

(1F) The robot 1 resumes learning according to the movements of people around it. Therefore, the learning of the robot 1 can be promoted.
(1G) The robot 1 does not learn the matters corresponding to the learning restriction contents. Therefore, it is possible to prevent the robot 1 from learning an undesired matter for the user and later talking to another person.

(1H) The robot 1 can manage the user's schedule.
(1I) The robot 1 deletes the AI program and the data set when the usage end condition of the robot 1 is satisfied. Therefore, the user can prevent others from using his / her AI program and data set later.
<Second embodiment>
1. 1. Configuration of Computer 101 The configuration of the computer 101 will be described with reference to FIGS. 7 and 8. The electrical configuration of the computer 101 is basically the same as that of the robot 1 in the first embodiment. However, the input unit 21 in the computer 101 is connected to an external microphone 5, a camera 7, a keyboard 63, a mouse 65, and a touch panel 9. Further, the output unit 29 is connected to an external speaker 15 and a display 19.

As shown in FIG. 8, the computer 101 includes a box-shaped housing 67, and each configuration is housed inside the box-shaped housing 67. Further, the computer 101 includes a plurality of terminals 69 to which external devices (for example, microphone 5, camera 7, keyboard 63, mouse 65, touch panel 9, speaker 15, display 19, etc.) can be connected. The computer 101 is a robot in a broad sense.

2. 2. Processing executed by the computer 101 The computer 101 executes a program setting process, a conversation process, a learning stop determination process, and a schedule management process, similarly to the robot 1 of the first embodiment. Further, the computer 101 has the same function as a well-known computer.

3. 3. Effects of the computer 101 The computer 101 exhibits the effects of (1A) to (1I).
<Third embodiment>
1. 1. Configuration of the in-vehicle device 201 The configuration of the in-vehicle device 201 will be described with reference to FIG. The on-board unit 201 is mounted on the vehicle. The electrical configuration of the vehicle-mounted device 201 is basically the same as that of the computer 101 in the second embodiment. However, the input unit 21 in the vehicle-mounted device 201 is connected to the microphone 5, the camera 7, the touch panel 9, the sensor group 11, and the GPS 13 provided in the vehicle.

The microphone 5 is provided in the passenger compartment of the vehicle and detects the voice of the occupant (driver or other occupant) of the vehicle. The camera 7 photographs the occupants. The touch panel 9 is provided in the passenger compartment of the vehicle and is operated by an occupant. The sensor group 11 includes a driver's driving operation (steering angle, accelerator depression amount, brake depression amount, shift position, etc.) and vehicle state (speed, acceleration, yaw rate, power unit (internal combustion engine, motor, etc.) state, etc.). Detects the remaining amount of fuel, the remaining amount of battery, etc.).

Further, the output unit 29 is connected to the speaker 15, the display 19, and the vehicle control unit 71. The speaker 15 and the display 19 are provided in the vehicle interior of the vehicle. The vehicle control unit 71 performs various controls related to the vehicle (for example, steering, acceleration, deceleration, shift change, etc.). The on-board unit 201 is a robot in a broad sense.

2. 2. Process executed by the in-vehicle device 201 (2-1) Processing similar to the robot 1 The in-vehicle device 201 has a program setting process, a conversation process, a learning stop determination process, and a schedule, as in the robot 1 of the first embodiment. Execute management processing.

(2-2) Vehicle Control Process The control unit 3 of the on-board unit 201 repeatedly executes the vehicle control process shown in FIG. 10 at predetermined time intervals. This process is executed when the microphone 5 detects a volume equal to or higher than a predetermined threshold value.

In step 31, voice is acquired using the microphone 5. In step 32, instructions to the vehicle (for example, start, stop, deceleration, acceleration, right / left turn, lane change, shift change, etc.) are recognized from the voice acquired in step 31 by a well-known voice recognition technique. At this time, when the standard program is used, the above instruction is recognized by using the standard dictionary data provided in advance in the vehicle-mounted device 201. If the AI program is used, the above instruction is recognized using the dictionary data installed from the cloud network 31 together with the AI program and enhanced by the past learning.

In step 33, the control content of the vehicle is determined according to the instruction to the vehicle recognized in step 32. For example, when the instruction to the vehicle is to start, the timing of releasing the brake, the amount of increase in engine speed, the speed of increase, and the like are determined. At this time, if a standard program is used, the control content of the vehicle is determined using the standard program. If an AI program is used, the control content is determined using the AI program that has evolved through past learning.

In step 34, the control content determined in step 33 is output to the vehicle control unit 71. The vehicle control unit 71 controls the vehicle according to the control content.
In step 35, it is determined whether or not the AI program is being used at that time. If the AI program is in use, the process proceeds to step 36, and if the standard program is in use, this process ends.

In step 36, the detection result regarding the state of the vehicle is acquired from the sensor group 11.
In step 37, learning is performed based on the control content output in step 34 and the sensor output acquired in step 36. The learning modifies the control content so that the sensor output acquired in step 36 has a preset optimum range.

For example, when the control content output in step 34 is a start, it is confirmed whether or not the sensor output (for example, the sensor output of speed, acceleration, etc.) in the starting process is within the optimum range, and the sensor output is out of the optimum range. If so, the start control content is modified so that the sensor output at the time of starting from the next time onward approaches the optimum range.

In step 38, the AI program is updated so as to reflect the learning result in step 37.
3. 3. Effects of the in-vehicle device 201 The in-vehicle device 201 exhibits the effects of (1A) to (1I). Further, the in-vehicle device 201 also has the following effects.

(3A) The vehicle-mounted device 201 can recognize a human voice and perform vehicle control corresponding to the human voice. Further, when the in-vehicle device 201 is using the AI program, learning is performed based on the content of the vehicle control output to the vehicle control unit 71 and the sensor output indicating the state of the vehicle. More advanced vehicle control can be performed.

(3B) The vehicle-mounted device 201 can change and develop the AI program by learning with respect to the vehicle control process. Further, the in-vehicle device 201 can install the AI program uploaded to the cloud network 31.

The on-board unit 201 on which the AI program after learning is installed may be the same as or different from the on-board unit 201 on which learning has been performed in the past.
Therefore, the user can install and use the AI program learned by his / her own use in the past on the in-vehicle device 201 of any vehicle from the cloud network 31.
<Fourth Embodiment>
1. 1. Configuration of Robot 301 The configuration of the robot 301 of the present embodiment is basically the same as that of the robot 1 of the first embodiment. Hereinafter, the differences from the first embodiment will be mainly described. As shown in FIG. 11, the robot 301 includes a first artificial intelligence unit 73 and a second artificial intelligence unit 75.

The first artificial intelligence unit 73 stores an AI program and a data set for executing various operations of the robot 301. In the following, this program will be referred to as a first AI program, and this data set will be referred to as a first data set.

The first artificial intelligence unit 73 changes and develops the first AI program and the first data set by learning. The learning performed by the first artificial intelligence unit 73 is not affected by the learning stop and learning restriction described later. Further, the first AI program and the first data set are not uploaded to the cloud network 31.

The second artificial intelligence unit 75 can store programs and data sets for AI. In the following, this program will be referred to as a second AI program, and this data set will be referred to as a second data set.

The second AI program is basically the same as the first AI program, but is not used to execute various operations of the robot 301. The second artificial intelligence unit 75 changes and develops the second AI program and the second data set by learning. The second AI program and the second data set accumulate learning results and are uploaded to the cloud network 31. As a result, the learning result can be stored in the cloud network 31.

However, the learning performed by the second artificial intelligence unit 75 is limited by the learning stop and learning restriction described later. Therefore, the learning result stored in the cloud network 31 is limited by the upload of the second AI program and the second data set.

The second AI program and the second data set can be downloaded from the cloud network 31 to the second artificial intelligence unit 75. Then, based on the downloaded second AI program and the second data set, the first AI program and the first data set can be learned. Details will be described later.

2. 2. Process executed by the robot 301 (2-1) Program installation process The control unit 3 executes the program installation process shown in FIG. 12 when the power of the robot 301 is turned on.

In step 41, it is determined whether or not the user's identification information has been input. The identification information can be the same as that of the first embodiment. If the identification information is input, the process proceeds to step 42, and if the identification information is not input, this process ends.

In step 42, the second AI program and the second data set corresponding to the identification information determined to have been input in step 41 are installed from the cloud network 31. The installed second AI program and the second data set are stored in the second artificial intelligence unit 75. If the second AI program and the second data set are already stored in the second artificial intelligence unit 75, they are overwritten.

In step 43, the contents included in the second AI program and the second data set installed in the step 42 but not stored in the first AI program and the first data set. learn.

In step 44, the content learned in step 43 is added to and updated with the first AI program and the first data set. That is, learning of the first AI program and the first data set is performed based on the downloaded second AI program and the second data set.

(2-2) Conversation processing The control unit 3 executes the conversation processing shown in FIG. This process is executed when the microphone 5 detects a volume equal to or higher than a predetermined threshold value. The conversation processing is performed using the first AI program and the first data set stored in the first artificial intelligence unit 73.

The processing of steps 51 to 54 is the same as the processing of steps 11 to 14 in the first embodiment.
In step 55, the first artificial intelligence unit 73 learns about the content of the voice recognized in step 52. The content of learning is the same as that of the first embodiment.

In step 56, the first AI program and the first data set are updated so as to reflect the learning result in step 55.
In step 57, it is determined whether or not the second artificial intelligence unit 75 is learning stopped at that time. The learning stop of the second artificial intelligence unit 75 is set by the second artificial intelligence unit learning stop determination process, which will be described later. If the second artificial intelligence unit 75 is not stopped learning, the process proceeds to step 58, and if the second artificial intelligence unit 75 is not stopped learning, this process is terminated.

In step 58, the learning restriction content set at that time is acquired. The learning restriction content includes, for example, information (name, address, telephone number, e-mail address, biography, face) related to the family and acquaintances of the user (the user corresponding to the identification information determined to be input in step 41). ) Etc.

In step 59, the second artificial intelligence unit 75 learns about the content of the voice recognized in step 52. The content of learning is the same as that of the first embodiment. However, even if the content of the voice is recognized in step 52, the item corresponding to the learning restriction content acquired in step 58 is not learned.

In step 60, the second AI program and the second data set are updated so as to reflect the learning result in step 59.
(2-3) Second artificial intelligence unit learning stop determination process The control unit 3 repeatedly executes the second artificial intelligence unit learning stop determination process shown in FIG. 14 at predetermined time intervals.

In step 71, the position information of the robot 301 is acquired by using the GPS 13.
In step 72, the camera 7 is used to acquire an image of the surroundings of the robot 301.

In step 73, the microphone 5 is used to acquire voice.
In step 74, it is determined whether or not the second artificial intelligence unit 75 is learning stopped at that time. The learning stopped state starts in step 77 described later and ends when learning is restarted in step 79 described later. If the learning is not stopped, the process proceeds to step 75, and if the learning is not stopped, the process proceeds to step 78.

In step 75, it is determined whether or not the image acquired in step 72 or the voice acquired in step 73 triggers learning stop. The trigger for stopping learning is the same as that of the first embodiment. If there is a trigger to stop learning, the process proceeds to step 77, and if there is no trigger to stop learning, the process proceeds to step 76.

In step 76, it is determined whether or not the position information acquired in step 71 corresponds to a position registered in advance as a position where learning should be stopped. The position where learning should be stopped is the same as that of the first embodiment.

In step 77, the learning stop state of the second artificial intelligence unit 75 is started. After this point, in the step 57 in the conversation process, an affirmative judgment is made, and learning in the step 59 is not performed.

On the other hand, if an affirmative judgment is made in step 74, it is determined in step 78 whether or not the image acquired in step 72 or the voice acquired in step 73 triggers the restart of learning. do. The trigger for resuming learning is the same as that of the first embodiment. If there is a trigger for resuming learning, the process proceeds to step 79, and if there is no trigger for resuming learning, this process ends.

In step 79, learning in the second artificial intelligence unit 75 is resumed. From this point onward, a negative determination is made in step 57 of the conversation process, and learning in step 59 is performed.

(2-4) Program upload process The control unit 3 repeatedly executes the program upload process shown in FIG. 15 at predetermined time intervals.

In step 81, it is determined whether or not the condition for ending the use of the robot 301 is satisfied. The conditions for ending use are the same as those determined in step 5 of the first embodiment. If the end-of-use condition is satisfied, the process proceeds to step 82, and if the end-of-use condition is not satisfied, this process ends.

In step 82, the second AI program and the second data set are uploaded to the cloud network 31. At this time, uploading is performed in association with the user's identification information. When the above-mentioned learning is performed, the second AI program and the second data set to be uploaded are those after learning.

3. 3. Effects of the robot 301 The robot 301 exhibits the effects of (1B), (1C), (1F), and (1H). Further, the robot 301 also has the following effects.

(4A) The robot 301 can change and develop the first AI program and the first data set by learning. Further, the learning in the first AI program and the first data set is not affected by the learning stop processing and the learning restriction processing.

Further, since the first AI program and the first data set are not uploaded to the cloud network 31, it is possible to prevent others from knowing their contents.
(4B) The robot 301 can upload the second AI program and the second data set after learning to the cloud network 31. In addition, the robot 301 can install the second AI program and the second data set from the cloud network 31.

The robot 301 that installs the second AI program and the second data set after learning may be the same as or different from the robot 301 that has been trained in the past. Further, the robot 301 that installs the second AI program and the second data set after learning may be in a place where learning has been performed in the past, or may be in a different place. good.

Therefore, the user can use the second AI program and the second data set learned by his / her own use in the past at the robot 301 at the place where the user is at that time (for example, various places such as work, store, and home). It can be installed from the cloud network 31. Then, learning can be performed using the second AI program and the second data set, and the first AI program and the first data set can be changed and developed.

(4C) The robot 301 limits learning about the second AI program and the second data set uploaded to the cloud network 31. That is, the robot 301 stops learning about the second AI program and the second data set according to the movements of people around it, the identification result of the person, and the like. Also, the robot 301 stops learning in the second AI program and the second dataset, depending on where it resides. Further, the robot 301 does not include the items corresponding to the learning restriction contents in the second AI program and the second data set.

Therefore, it is possible to prevent the second AI program and the second data set, which include matters that the user does not want others to know, from being uploaded to the cloud network 31. <Fifth Embodiment>
1. 1. Configuration of Robot 401 The configuration of the robot 401 of the present embodiment is basically the same as that of the robot 1 of the first embodiment. Hereinafter, the differences from the first embodiment will be mainly described. As shown in FIG. 16, the robot 401 can be connected to the Internet 77 by using the communication unit 26. The Internet 77 is an example of a network. Further, the robot 401 can be placed in the vehicle. In this case, the robot 401 can talk with the occupant of the vehicle by the conversation processing described later.

2. 2. Process executed by the robot 401 (2-1) Process similar to the robot 1 The robot 401 executes a program setting process, a learning stop determination process, and a schedule management process in the same manner as the robot 1 of the first embodiment. ..

(2-2) Conversation processing The control unit 3 of the robot 401 repeatedly executes the conversation processing shown in FIG. 17 at predetermined time intervals. This process is executed when the microphone 5 detects a volume equal to or higher than a predetermined threshold value.

The processes of steps 91 and 92 are the same as the processes of steps 11 and 12, respectively, in the first embodiment.
In step 93, information related to the content of the voice recognized in step 92 is searched for and acquired on the Internet 77 using the communication unit 26. For example, a keyword is extracted from the content of the voice recognized in step 92, and information including items associated with the keyword in advance is searched for. Examples of the search target include SNS (social networking service), blogs, electronic bulletin boards, and the like. If the content of the voice recognized in step 92 is a question, the answer to the question is searched. As a search method, for example, a known search engine can be used.

In step 94, response voice data is basically created for the content of the voice recognized in step 92, as in step 13 in the first embodiment. However, in this step, the answer voice data is created by using the information acquired in step 93. Using the information acquired in step 93 means, for example, including a voiced version of the information in the answer voice data.

In step 95, the type of voice to be pronounced in step 96, which will be described later, is determined according to the content of the answer created in step 94. Examples of the type of voice include male voice, female voice, adult voice, and child voice.

Specifically, the type of voice is determined as follows. First, the control unit 3 extracts characteristics (for example, characteristics peculiar to men, characteristics peculiar to women, characteristics peculiar to adults, characteristics peculiar to children, etc.) from the contents of the answer created in step 94. ..

The control unit 3 is provided with a map in which the features in the content of the answer and the type of voice are associated with each other in advance. The control unit 3 inputs the features extracted as described above into the map, and determines the type of voice corresponding to the extracted features.

For example, if the characteristics peculiar to a man and the characteristics peculiar to an adult are extracted from the contents of the answer created in step 94, the control unit 3 determines the voice of an adult male.
In step 96, the speaker 15 is used for pronunciation based on the response voice data created in step 94. At this time, the voice of the type set in step 95 is used for pronunciation.

In step 97, first, emotions (for example, joy, anger, sadness, calmness, etc.) corresponding to the content of the voice recognized in step 92 or the content of the answer created in step 94 are acquired. The acquisition of this emotion is performed as follows.

The control unit 3 is provided with a map in which emotions are associated with features appearing in the contents of voice and answers. Features that appear in the content of the voice include, for example, volume, intonation of the voice, pitch, and the like. In addition, as features that appear in the content of the answer, for example, words and phrases that reflect emotions, phrases that reflect emotions, and the like included in the content of the answer can be mentioned.

The control unit 3 acquires the corresponding emotion by inputting the content of the voice recognized in the step 92 or the feature extracted from the answer created in the step 94 into the map.

Next, the control unit 3 creates a face image of a person or a character expressing the acquired emotion and displays it on the display 19. For example, when the acquired emotion is joy, a face image of a smiling person or character is displayed on the display 19. When the acquired emotion is sadness, a face image of a person or a character with a crying face is displayed on the display 19.

The processes of steps 98 to 102 are the same as the processes of steps 15 to 19 in the first embodiment, respectively.
3. 3. Effects of the robot 401 The robot 401 exhibits the effects of (1A) to (1I). Further, the robot 401 also has the following effects.

(5A) The robot 401 can search and acquire information related to the content of the voice on the Internet 77. Then, the robot 401 also creates the response voice data by using the information acquired in this way. As a result, more appropriate answer voice data can be created.

(5B) The robot 401 can determine the type of voice according to the content of the answer. Therefore, the user is less likely to feel a discrepancy between the content of the answer and the type of voice that pronounces it.

(5C) The robot 401 acquires an emotion corresponding to the recognized voice content or the response content, and displays a face image of a person or a character expressing the acquired emotion on the display 19. As a result, the user can feel the robot 401 as if it were a human being.
<Sixth Embodiment>
1. 1. Configuration of Robot 501 The configuration of the robot 501 of the present embodiment is basically the same as that of the robot 1 of the first embodiment.

2. 2. Processing executed by the robot 501 (2-1) Processing similar to the robot 1 The robot 501 performs program setting processing, conversation processing, learning stop determination processing, and schedule management processing, as in the robot 1 of the first embodiment. To execute.

(2-2) Call Processing As shown in FIG. 18, the plurality of robots 501 enable a call between the user 79 and the user 81. For example, the robot 501 on the user 79 side (hereinafter referred to as the robot 501A) acquires the voice pronounced by the user 79 with the microphone 5. Further, the robot 501A receives an input by the user 79 for designating the robot 501 of the other party (hereinafter referred to as the robot 501B) by using the touch panel 9.

The robot 501A communicates a signal obtained by converting the voice produced by the user 79 (hereinafter referred to as a voice conversion signal), the identification signal of the robot 501A, and the identification signal of the robot 501B of the other party designated by the user 79. The unit 26 is used to transmit to the host computer 83.

The host computer 83 determines whether or not the received identification signal of the robot 501A and the identification signal of the robot 501B of the other party are set as a call pair. If it is set as a call pair, the voice conversion signal is transferred to the robot 501B of the other party. The setting of the call pair will be described later.

The robot 501B receives the transferred voice conversion signal using the communication unit 26, and pronounces it using the speaker 15 based on the voice conversion signal. By the above processing, the voice pronounced by the user 79 is transmitted to the user 81 on the robot 501B side. In addition to pronouncing the voice, the robot 501B converts the voice into characters and displays the characters on the display 19.

Further, the robot 501B performs the processing of the robot 501A described above, and the robot 501A performs the processing of the robot 501B described above, so that the voice pronounced by the user 81 can be transmitted to the user 79.

On the other hand, the host computer 83 does not transfer the voice conversion signal unless the received identification signal of the robot 501A and the identification signal of the robot 501B of the other party are set as a call pair.

(2-3) Call Pair Setting Process Next, a process in which the host computer 83 sets the above call pair will be described with reference to FIG.

In step 111, it is determined whether or not a new call pair setting request has been received. The call pair setting request is a request transmitted by the robot 501A to the host computer 83. The call pair setting request includes the identification signal of the robot 501A that is the source of the request, the characteristics of the user 79 on the robot 501A side (for example, hobbies, tastes of food, clothing, and housing, matters of interest, etc.), and the user 79. Includes image data of facial photographs.

The call pair setting request may be transmitted by the robot 501A according to a user's instruction, or may be automatically transmitted by the robot 501A. If the host computer 83 newly receives the call pair setting request, the process proceeds to step 112, and if the host computer 83 does not receive the call pair setting request, the process ends.

In step 112, the feature of the user 79 included in the setting request (hereinafter referred to as a new setting request) of the call pair determined to have been received in step 111 is searched in the setting waiting list. Here, the setting waiting list is a list of call pair setting requests (hereinafter referred to as past setting requests) received from any robot 501 in the past.

In step 113, as a result of the search in step 112, it is determined whether or not a past setting request having characteristics matching the characteristics of the user 79 included in the new setting request has been found. If such a past setting request is found, the process proceeds to step 114, and if not found, the process proceeds to step 116.

In step 114, the robot 501B, which is the source of the past setting request, and the robot 501A, which is the source of the new setting request, found in step 113 are set as a call pair.

In step 115, each of the robots 501A and 501B set as a call pair is notified of information about the other robot 501. That is, the robot 501A is notified of the information about the robot 501B, and the robot 501B is notified of the information about the robot 501A. The information to be notified includes the identification signal of the robot 501 of the other party, the image data of the corresponding user's facial photograph, and the like.

On the other hand, if a negative determination is made in step 113, a new setting request is added to the setting waiting list in step 116. From then on, new configuration requests will be part of the list of past configuration requests.

The robot 501 notified of the information about the robot 501 on the other side creates an icon 85 representing the other party of the call pair using the information, and displays it on the display 19 as shown in FIG. 20. Icon 85 includes a face photo of the user on the other side of the call pair. The robot 501 can display a plurality of icons 85 on the display 19.

When a specific icon 85 is touched by the user, the robot 501 recognizes the user corresponding to the icon 85 as a call partner. Then, when the robot 501 transmits the voice conversion signal to the host computer 83 as described above, the robot 501 transmits the identification signal of the robot 501 corresponding to the touched icon 85 to the host computer 83.

(2-4) Other Processing The host computer 83 analyzes the content of the voice conversion signal transmitted by the robot 501A during the call processing of (2-2). If the analysis result corresponds to a preset prohibited matter (for example, a matter related to a crime), the call processing is prevented from being started or the call processing is terminated in the middle. Therefore, the host computer 83 can prevent the call processing from being used for crimes and the like.

Further, at the time of the call processing of (2-2), the robot 501B also learns by using the voice transmitted from the other computer 501A. Therefore, learning can be performed more efficiently.

Further, in the call processing of (2-2), when the pronunciation of the user 79 is interrupted for a predetermined time or more, or when the user 79 instructs, the robot 501A transmits the voice conversion signal created by the robot 501 to the host computer 83. Send. In this case, the robot 501A and the user 81 will talk to each other. The computer 501A can create a voice conversion signal using the learning result obtained by the past call processing. Further, the robot 501A may store a voice (for example, an aizuchi) produced by the robot 501A or the robot 501B in a past call, and create a voice conversion signal corresponding to the voice.

3. 3. Effects of Robot 501 Robot 501 exhibits the effects of (1A) to (1I). Further, the robot 501 also has the following effects.

(6A) The user 79 on the robot 501A side and the user 81 on the robot 501B side can make a call.
(6B) The call between the user 79 and the user 81 is premised on the fact that the robot 501A and the robot 501B are set as a call pair. The call pair is set when the characteristics of the user 79 and the user 81 match. Therefore, the robot 501 selectively enables a call between users having the same characteristics.

(6C) The robot 501 creates an icon 85 including a face photograph of the user of the other party and displays it on the display 19. The user can easily select the other party by touching the icon 85. Further, since the icon 85 includes a photograph of the face of the other party, the user can easily understand which icon 85 corresponds to which other party.
<Other embodiments>
(1) In the first to sixth embodiments, the robots 1, 301, 401, 501, the computer 101, and the on-board unit 201 may increase or decrease the learning restriction content depending on the situation. For example, when it is recognized that a matter that originally belonged to the learning restriction content was spoken by a plurality of persons, the matter may be excluded from the learning restriction content.

(2) In the first to sixth embodiments, the robots 1, 301, 401, 501, the computer 101, and the vehicle-mounted device 201 determine the voice color of the voice when the voice is acquired by the microphone 5, and the determination is made. Depending on the result, it may be decided whether or not to learn the content of the voice.

(3) In the first to sixth embodiments, the processing performed according to the voice recognition result is other than the creation of the response voice data (step 13) and the determination of the control content of the vehicle (step 33). There may be. For example, the robots 1, 301, 401, and 501 may be moved or an external device connected to the computer 101 may be operated according to the voice recognition result. Further, depending on the voice recognition result, for example, a predetermined operation (for example, opening / closing a box, opening / closing a window, locking / unlocking a key, operating a home electric appliance, etc.) may be performed.

(4) In the first to sixth embodiments, the one that stores the AI program and the data set may be something other than the cloud network 31. For example, it may be stored in a well-known server, storage medium, or the like.

(5) In the third embodiment, the on-board unit 201 may be mounted on a moving body other than a vehicle (for example, a railroad vehicle, an aircraft, a ship, etc.) and control them.
(6) In the first, fourth to sixth embodiments, the robots 1, 301, 401 and 501 do not have to be humanoid. For example, it may be in the form of an animal, a fish, an imaginary character, or the like.

(7) In the first to third, fifth, and sixth embodiments, the robots 1, 401, 501, the computer 101, and the vehicle-mounted device 201 may use the standard program and the AI program at the same time. .. In this case, the standard program can execute the basic processing, and the AI program can execute the additional processing obtained as a result of the learning.

(8) In the first to sixth embodiments, the robots 1, 301, 401, 501, the computer 101, and the in-vehicle device 201 are home appliances (for example, a television, a refrigerator, an air conditioner, a vacuum cleaner, a washing machine, etc.). ), A mobile terminal (for example, a mobile phone (including a smartphone), a glasses-type terminal, a wristwatch-type terminal) and the like.

(9) In the first to sixth embodiments, the robots 1, 301, 401, 501, the computer 101, and the on-board unit 201 automatically acquire images using the camera 7, and the acquired images are obtained on the network. It may have a function of transmitting to. Examples of the destination include servers, terminals, vehicles, and other robots. The image to be transmitted may be the image itself acquired by using the camera 7, or a part (for example, a person's face, a person's whole body, a vehicle, a vehicle number) extracted from the image acquired by using the camera 7. It may be an image of a plate or the like).

The robots 1, 301, 401, 501, the computer 101, and the on-board unit 201 having the above functions can be used for crime prevention, monitoring of other robots, and the like. The robots 1, 301, 401, 501, the computer 101, and the in-vehicle device 201 having the above functions are, for example, indoors, on the street, at the entrance of a specific facility (for example, a residence, an apartment, an office, a parking lot, etc.), and other robots. It can be installed in the vicinity of.

Further, the robots 1, 301, 401, 501, the computer 101, and the on-board unit 201 having the above functions have a function of flying in the air or can be mounted on a flying object. In that case, the image on the ground taken from the viewpoint in the sky can be transmitted on the network. In this case, the robots 1, 301, 401, 501, the computer 101, and the on-board unit 201 can recognize the white line of the road in the captured image and move along the white line. Further, the robots 1, 301, 401, 501, the computer 101, and the on-board unit 201 recognize the traffic light in the image of the ground taken from the viewpoint in the sky, and display the display contents (red light, green light, etc.) on the vehicle on the ground. Can be sent.

(10) In the first to sixth embodiments, the robots 1, 301, 401, 501, the computer 101, and the vehicle-mounted device 201 recognize the human behavior by using the camera 7, and are associated with the recognition result in advance. You may output the voice.

For example, if a person recognizes the behavior of leaving the kitchen with the gas lit, a warning voice for the person can be output. In addition, when a person recognizes an action of searching for a predetermined object, he / she can search for the location of the object and notify the location of the object by voice. In addition, when a person recognizes the behavior of entering the room from the entrance of the house, the voice "Welcome back" can be output. Human behavior and the voice associated with it can be increased by learning.

When the voice is emitted as described above, the type of the voice can be associated with the situation at that time and the content of the voice. For example, if the father is recognized in the image, the type of voice "Welcome back" can be the voice of the child.

(11) In the first to sixth embodiments, the control unit includes a microcomputer, but it may be a combination of individual electronic circuits or an AISIC (Programmable Integrated Circuit). It may be a programmable logic device such as FPGA (Field Programmable Gate Array) or a combination thereof.
(12) A part or all of the configurations of the first to sixth embodiments may be combined as appropriate. For example, the configuration of the fourth to sixth embodiments may be applied to the second and third embodiments.

Claims (7)

With a voice acquisition unit configured to acquire the voice of the occupants of the vehicle,
A voice recognition unit configured to recognize the voice acquired by the voice acquisition unit, and a voice recognition unit.
A first learning unit that learns about the recognition result of the voice recognition unit, and
An answer pronunciation unit configured to pronounce an answer using the learning result of the first learning unit with respect to the recognition result of the voice recognition unit.
An instruction recognition unit configured to recognize an instruction to the vehicle from the voice acquired by the voice acquisition unit.
A control content determination unit configured to determine the control content of the vehicle using a program in response to the instruction recognized by the instruction recognition unit.
A control content output unit configured to output the control content determined by the control content determination unit to a vehicle control unit that controls the vehicle, and a control content output unit.
A vehicle state acquisition unit configured to acquire the state of the vehicle using sensors, and
A second learning unit configured to perform learning based on the control content output by the control content output unit and the state of the vehicle acquired by the vehicle state acquisition unit.
A program update unit configured to update the program so as to reflect the learning result of the second learning unit, and a program update unit.
Equipped with
The instructions are instructions for starting, stopping, decelerating, accelerating, turning left or right, lane change, or shift change.
In-vehicle device. The in-vehicle device according to claim 1.
An identification information identification unit configured to identify the identification information by voice recognition from the identification information that the occupant said aloud.
An installation unit configured to install the program corresponding to the identification information specified by the identification information identification unit on the in-vehicle device, and an installation unit.
In-vehicle device equipped with. The in-vehicle device according to claim 1 or 2.
It further comprises an installation unit configured to install the program from the cloud onto the in-vehicle device.
In-vehicle device. The in-vehicle device according to any one of claims 1 to 3.
The program updated in any of the in-vehicle devices can be installed in any of the in-vehicle devices.
In-vehicle device. The in-vehicle device according to any one of claims 1 to 4.
A learning stop unit configured to stop learning by the first learning unit according to the movement of a person around the in-vehicle device, the identification result of the person, or the place where the in-vehicle device exists.
A learning restart unit configured to restart learning by the first learning unit that has been stopped according to the movement of the person or the recognition result of the voice.
Further prepare,
In-vehicle device. The in-vehicle device according to any one of claims 1 to 5.
The learning performed by the second learning unit modifies the control content of the vehicle determined by the control content determination unit so that the state of the vehicle acquired by the vehicle state acquisition unit is within a preset optimum range. Is a thing,
In-vehicle device. A vehicle including the in-vehicle device according to any one of claims 1 to 6.

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