JP7763530B2 - Information processing system, information processing method, computer program, and vehicle - Google Patents

Description

REFERENCE TO RELATED APPLICATIONS

This international application claims priority to Japanese Patent Application No. 2014-262907, filed with the Japan Patent Office on December 25, 2014, the entire contents of which are incorporated herein by reference.

The present disclosure relates to an information processing system, an information processing method, and a computer program .

In recent years, robots that can converse with people have been attracting attention. These robots use a microphone to pick up speech spoken by people and use speech recognition to infer the meaning of the speech. They then respond to the inferred meaning with a response that has been pre-associated with the speech (see Patent Document 1).

Patent No. 4015424

It is conceivable that the conversational ability of a robot can be improved by learning using artificial intelligence. One aspect of the present disclosure is to provide a new technology related to conversation using artificial intelligence.

According to one aspect of the present disclosure, there is provided an information processing system including: a voice acquisition unit, an output unit, and a learning control unit.
The voice capturing unit is configured to capture the voice of the user.
The output unit is configured to create a response voice to the user's voice acquired by the voice acquisition unit using artificial intelligence having a learning function for learning the conversation with the user, and to output the response voice to the user.
The learning control unit is configured to limit the learning function based on the user's voice.

According to another aspect of the present disclosure, there may be provided an information processing system including a voice acquisition unit, a readout unit, an output unit, a learning unit, and a learning control unit.
The voice capturing unit is configured to capture the voice of the user.
The reading unit is configured to read from the storage device a dataset relating to the artificial intelligence that has been trained through past conversations with the user.
The output unit is configured to use the retrieved data set to create a response word as a word in response to the voice acquired by the voice acquisition unit by artificial intelligence, and to output the response word to the user.
The learning unit is configured to perform learning operations based on the user's voice and record the learning results in the dataset by updating the dataset stored in the storage device.
The learning control unit is configured to limit learning operations by the learning unit based on the user's voice.

According to another aspect of the present disclosure, there may be provided an information processing system including a voice acquisition unit, a search unit, and an output unit.
The voice capturing unit is configured to capture the voice of the user.
The search unit is configured to search the Internet for relevant information related to the question contained in the user's voice acquired by the voice acquisition unit based on the content of the question.
The output unit is configured to generate an answer to the question based on the relevant information retrieved from the Internet by the search unit, and output the answer to the user in at least the form of voice.

FIG. 2 is a block diagram illustrating an electrical configuration of the robot according to the embodiment. FIG. 2 is a front view showing the configuration of the robot. 10 is a flowchart showing a program setting process executed by a robot. 10 is a flowchart showing a conversation process executed by a robot. 10 is a flowchart showing a learning stop determination process executed by the robot. FIG. 6A is an explanatory diagram showing an example of an operation for instructing to stop learning, and FIG. 6B is an explanatory diagram showing an example of an operation for instructing to restart learning. FIG. 2 is a block diagram showing the electrical configuration of a computer. FIG. 1 is a perspective view illustrating the appearance of a computer. FIG. 2 is a block diagram showing the electrical configuration of the in-vehicle device. 4 is a flowchart showing a vehicle control process executed by an in-vehicle device. FIG. 10 is a block diagram showing the electrical configuration of a robot according to another embodiment. 10 is a flowchart showing a program installation process executed by a robot. 10 is a flowchart showing a conversation process executed by a robot. 10 is a flowchart showing the second artificial intelligence unit learning stop determination process executed by the robot. 10 is a flowchart showing a program upload process executed by a robot. FIG. 10 is a block diagram showing the electrical configuration of a robot according to yet another embodiment. 10 is a flowchart showing a conversation process executed by a robot. FIG. 2 is an explanatory diagram illustrating a configuration for executing call processing. 10 is a flowchart illustrating a call pair setting process executed by a host computer. FIG. 10 is an explanatory diagram showing a display displaying icons.

1, 301...Robot, 3...Control Unit, 5...Microphone, 7...Camera, 9...Touch Panel, 11...Sensors, 13...GPS, 15...Speaker, 17...Motors, 19...Display, 21...Input Unit, 23...Standard Program Storage Unit, 25...Artificial Intelligence Unit, 26...Communication Unit, 27...Calculation Unit, 29...Output Unit, 31...Cloud Network, 33...Terminal, 35...Head, 37...Torso, 39...Right Arm, 41...Left Arm, 43...Right Leg, 45...Left Leg, 47, 49...Wheels, 51...Movement 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

An embodiment of the present disclosure will be described with reference to the drawings.
First Embodiment
1. Configuration of Robot 1 The configuration of the robot 1 will be described with reference to Figures 1 and 2. As shown in Figure 1, the robot 1 includes a control unit 3, a microphone 5, a camera 7, a touch panel 9, a group of sensors 11, a GPS 13, a speaker 15, a group of motors 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, camera 7, touch panel 9, sensor group 11, and GPS 13, and outputs that information to the calculation unit 27 and artificial intelligence unit 25.

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

The artificial intelligence unit 25 can store AI (artificial intelligence) programs for executing various operations of the robot 1. When the artificial intelligence unit 25 stores an AI program, it changes and develops it through learning. The artificial intelligence unit 25 can also install new AI programs and erase existing AI programs.

The communication unit 26 communicates with an external cloud network 31, a terminal 33, etc. The communication may be wireless or wired.
The arithmetic unit 27 uses a standard program or an AI program to perform the calculations required to realize various operations of the robot 1.

The output unit 29 outputs the calculation results 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 appearance and includes a head 35 , a torso 37 , a right arm 39 , a left arm 41 , a right leg 43 , and a left leg 45 .
The microphone 5, camera 7, and speaker 15 are provided on the head 35. The display 19 and touch panel 9 are provided on the front side of the body 37.

The right leg 43 and left leg 45 are each equipped with wheels 47, 49 for movement. Two wheels 47, 49 are provided on each leg, one in the front-to-rear direction. Therefore, the robot 1 comes into contact with the ground with a total of four wheels. The wheels 47, 49 are driven by a movement motor 51 belonging to the motor group 17. The wheels 47, 49 are rotated by the driving force of the movement motor 51, allowing the robot 1 to move forward and backward. Furthermore, by differentiating the rotation speed of the wheel 47 from the rotation speed of the wheel 49, the robot 1 can turn right or left.

The robot 1 has a neck joint 53, a shoulder joint 55, an elbow joint 57, and a wrist joint 59. The degrees of freedom of each joint can be set appropriately from 1 to 3. Each joint is operated by the driving force of a joint motor 61 belonging to the motor group 17. By driving an appropriately selected one of the above joints with the joint motor 61, the robot 1 can perform a predetermined movement and express a predetermined posture.

The multiple sensors belonging to the sensor group 11 detect the position, speed, acceleration, inclination, joint angles, etc. of each part of the robot 1. The detection results are fed back to the arithmetic unit 27 via the input unit 21 and are used to control the movement of the robot 1.

The standard program storage unit 23, artificial intelligence unit 25, and 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 communication unit 26 are examples of an identification information acquisition unit. The cloud network 31 is an example of an external storage device.

2. Processing Executed by Robot 1 (2-1) Program Setting Processing In order to determine which program to use, either the standard program or the AI program, the control unit 3 executes the program setting processing shown in Fig. 3. This program setting processing is executed repeatedly at predetermined time intervals while the power of the robot 1 is on.

In step 1, it is determined whether or not an AI program is being used at that time. If an AI program is not being used (i.e., the standard program is being used), proceed to step 2; if an AI program is being used, proceed 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, for example, in the following manner.
- The terminal 33 transmits identification information to the communication unit 26.

- Electromagnetic waves (e.g., radio waves, infrared rays, etc.) representing the identification information are transmitted to the communication unit 26. The electromagnetic waves representing the identification information may be transmitted from the terminal 33 or from a fixed device (e.g., a beacon, a wireless LAN access point, etc.). The electromagnetic waves representing the identification information may be transmitted periodically, in response to a user instruction, or triggered by the terminal 33 or a fixed device detecting the robot 1.

The user speaks out the content of the identification information. The microphone 5 picks up the voice and identifies the identification information through voice recognition.
A one-dimensional barcode or two-dimensional barcode representing identification information is photographed by the camera 7.

Use the touch panel 9 to input identification information.
The identification information may consist of numbers or letters, a one-dimensional or two-dimensional image (e.g., a one-dimensional or two-dimensional barcode), the user's biometric information (e.g., a fingerprint, a vein pattern on any part of the body, an iris, a face, etc.), or may consist of voice.

If the identification information is input, the process proceeds to step 3, and if the identification information is not input, the 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 identification information, AI programs, and datasets in association with each other. The datasets are used for using and learning the AI programs, and include dictionary data used in voice recognition and voice synthesis.

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

On the other hand, if the determination in step 1 is affirmative, then in step 5 it is determined whether the conditions for ending use of the robot 1 have been met. The conditions for ending use can be, for example, as follows:

The user does not operate the robot 1 for a predetermined period of time or longer.
- A state in which the user cannot be recognized in images from the camera 7 or audio captured by the microphone 5 continues for more than a predetermined period of time.

The user inputs to the robot 1 that indicates the end of use (for example, the user says out loud "end of use" or inputs information that indicates the end of use into the touch panel 9).

・Robot 1 is powered off.
・The preset time arrives.
If the use end condition is met, the process proceeds to step 6, and if the use end condition is not met, the process ends.

In step 6, the AI program and dataset are uploaded to the cloud network 31. At this time, they are uploaded in association with the user's identification information. Note that if learning, as described below, has been performed, the AI program and dataset uploaded will be those after learning.

In step 7, the AI programs and data sets 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 on, the robot 1 uses the standard program.

(2-2) Conversation Processing The control unit 3 executes the conversation processing shown in Fig. 4. This processing is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold.

In step 11, the microphone 5 is used to capture the voice.
In step 12, the content of the voice acquired in step 11 is recognized using a well-known voice recognition technology. At this time, if a standard program is being used, the content of the voice is recognized using standard dictionary data that is pre-installed in the robot 1. If an AI program is being used, the content of the voice is recognized using dictionary data that is installed from the cloud network 31 together with the AI program and that has been strengthened by past learning.

In step 13, data on the voice that responds to the content of the voice recognized in step 12 (hereinafter referred to as response voice data) is created. At this time, if a standard program is being used, the response voice data is created using standard dictionary data that is pre-installed in the robot 1. Furthermore, if an AI program is being used, the response voice data is created using dictionary data that is installed from the cloud network 31 along with the AI program and has been strengthened by past learning.

The content of the response voice data can be created, for example, by detecting keywords from the content of the voice recognized in step 12 and searching dictionary data for items that are pre-assigned to those keywords. The content of the response voice data may also be inferred using artificial intelligence from the content of the voice recognized in step 12.

In step 14, the speaker 15 produces a voice based on the response voice data created in step 13. In other words, the voice responds to the voice acquired in step 11.

In step 15, it is determined whether the AI program is currently in use. If the AI program is in use, proceed to step 16; if the standard program is in use, end this process.

In step 16, it is determined whether learning is currently stopped. Whether learning is stopped is determined by the learning stop determination process described later. If learning is not currently stopped, the process proceeds to step 17, and if learning is currently stopped, the process ends.

In step 17, the currently set learning restrictions are obtained. Examples of learning restrictions include information about the user's (the user corresponding to the identification information determined to have been entered in step 2) family and acquaintances (such as names, addresses, telephone numbers, email addresses, career history, and images including faces).

In step 18, the content of the speech recognized in step 12 is learned. Learning can be, for example, machine learning. Machine learning can be supervised learning or unsupervised learning. Learning can also be learning using artificial intelligence. In this case, keywords can be extracted from the speech recognized in step 12 and added to a dataset (e.g., dictionary data used for speech recognition). These keywords can be used, for example, in the process of creating response speech data (step 13).

However, even if the content of the voice is recognized in step 12, if the content falls under the learning restriction content acquired in step 17, it is not learned.
In step 19, the AI program and the dataset are updated to reflect the learning results from step 18. The AI program and the dataset updated to reflect the learning results are an example 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 being used. If the AI program is being used, the process proceeds to step 22, and if the standard program is being used, the process ends.

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

In step 25, it is determined whether learning is currently stopped. The learning stopped state begins in step 28, which will be described later, and ends when learning resumes in step 30, which will be described later. If learning is not currently stopped, proceed to step 26; if learning is currently stopped, proceed to step 29.

In step 26, it is determined whether there is anything in the image acquired in step 23 or the audio acquired in step 24 that would trigger learning to stop. Examples of triggers that would cause learning to stop include the following:

- A pre-defined action indicating the end of learning is recognized in the image acquired in step 23. For example, as shown in Figure 6A, one example of such an action is to raise the index finger and place it in front of the mouth. Another example of such an action is a wink.

In the voice acquired in step 24, a voice that is preset as a keyword instructing the robot to stop learning is recognized. Examples of such keywords include "confidential,""off the record," and "private." Other examples of such keywords include the names of the user's family members or acquaintances. The user can register the names of family members or acquaintances in the robot 1 as keywords in advance. Furthermore, the robot 1 may infer which words are the names of the user's family members or acquaintances based on previously recognized voice data, and register the words that it infers are the names of the user's family members or acquaintances as keywords.

- The face of a person registered in advance is recognized in the image acquired in step 23. This person may be, for example, the user's family or acquaintances. The user can also register in advance the facial image of a person that will trigger learning to stop.

If there is a trigger to stop learning, the process proceeds to step 28; if there is no trigger to stop learning, the process proceeds to step 27.
In step 27, it is determined whether the location information acquired in step 22 corresponds to a location registered in advance as a location where learning should be stopped. Examples of locations where learning should be stopped include the user's home, a conference room, etc.

The user can pre-register the location where learning should be stopped. Furthermore, the robot 1 can infer the location where learning should be stopped based on past data and register the inferred location. For example, the robot 1 can infer that the location where learning should be stopped is a location where a trigger for stopping learning was previously recognized.

A learning stop state is initiated in step 28. From this point on, a positive determination is made in step 16 in the conversation process, and learning in step 18 is not performed.
On the other hand, if the determination in step 25 is affirmative, then in step 29 it is determined whether there is anything in the image acquired in step 23 or the audio acquired in step 24 that could be a trigger for restarting learning. Examples of triggers for restarting learning include the following:

- A pre-defined action indicating the resumption of learning is recognized in the image acquired in step 23. For example, such an action may be making a circle with the thumb and index finger (indicating OK), as shown in Figure 6B.

- The voice acquired in step 24 is recognized as a pre-set keyword instructing the user to resume learning. Examples of such keywords include "OK," "resume," and "learn."

If there is a trigger to restart learning, the process proceeds to step 30, and if there is no trigger to restart learning, the process ends.
Learning is resumed in step 30. From this point on, a negative determination is made in step 16 in the conversation process, and learning in step 18 is carried out.

(2-4) Schedule Management Process The robot 1 can execute the schedule management process shown below. The user inputs their 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. The schedule information may also be transmitted from the terminal 33 to the communication unit 26.

The schedule information includes at least a deadline and items to be performed by that deadline. At a predetermined time (e.g., one day, three hours) before the deadline, the robot 1 determines whether the user has performed the items included in the schedule information based on the voice captured by the microphone 5, the image captured by the camera 7, the information captured from the terminal 33, etc., and if the items have not yet been performed, it warns the user by voice from the speaker 15 or by an image displayed on the display 19.

3. Effects of the Robot 1 (1A) The robot 1 can change and develop its AI program through learning. Furthermore, the robot 1 can strengthen the contents of a data set (e.g., dictionary data) through learning. The robot 1 can then upload the AI program and data set after learning to the cloud network 31. Furthermore, the robot 1 can install the AI program and data set uploaded to the cloud network 31.

The robot 1 onto which the learned AI program and dataset are installed may be the same as the robot 1 that previously performed the learning, or it may be different. Furthermore, the robot 1 onto which the learned AI program and dataset are installed may be located at the same location as the previous learning, or it may be located at a different location.

Therefore, the user can install the AI program and data set that have been learned through their own past use from the cloud network 31 onto the robot 1 at the user's current location (for example, at work, in a store, at home, or in any other location), and use the robot 1.

(1B) Robot 1 allows the installation of AI programs and data sets corresponding to a user's identification information, provided that the user's identification information is input. This prevents other people from using a user's AI programs and data sets without permission.

(1C) Robot 1 can recognize human voices and produce speech responses. In other words, robot 1 can converse with people. Furthermore, when using an AI program, robot 1 learns based on the results of voice recognition and uses the learning results to create speech responses, so the more it learns, the more advanced its conversations become.

(1D) The robot 1 stops learning depending on the behavior of people around it, the results of human identification, etc. This prevents the robot 1 from learning things that are undesirable for the user and later telling others about them.

(1E) Robot 1 stops learning depending on its location. This prevents robot 1 from later telling others about things it learned in a location where it is not desired to learn.

(1F) The robot 1 resumes learning in response to the actions of people around it, etc. This can promote the learning of the robot 1.
(1G) The robot 1 does not learn any items that fall under the learning restriction content. This prevents the robot 1 from learning any items that are undesirable for the user and then telling others about them later.

(1H) The robot 1 can manage the user's schedule.
(1I) When the conditions for ending use of the robot 1 are satisfied, the robot 1 erases the AI program and data set. This allows the user to prevent others from using their AI program and data set later.
Second Embodiment
1. Configuration of Computer 101 The configuration of the computer 101 will be described with reference to Figures 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 of the computer 101 is connected to an external microphone 5, camera 7, keyboard 63, mouse 65, and touch panel 9. In addition, the output unit 29 is connected to an external speaker 15 and display 19.

As shown in Figure 8, the computer 101 has a box-shaped housing 67 that houses various components inside. The computer 101 also has multiple terminals 69 to which external devices (e.g., 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 the broad sense.

2. Processing Executed by Computer 101 The computer 101 executes program setting processing, conversation processing, learning stop determination processing, and schedule management processing, similar to the robot 1 of the first embodiment. The computer 101 also has the same functions as well-known computers.

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

The microphone 5 is installed inside the vehicle's cabin and detects the voices of the vehicle occupants (driver or other passengers). The camera 7 photographs the occupants. The touch panel 9 is installed inside the vehicle's cabin and is operated by the occupants. The sensor group 11 detects the driver's driving operations (steering angle, accelerator depression amount, brake depression amount, shift position, etc.) and the vehicle's status (speed, acceleration, yaw rate, power unit (internal combustion engine, motor, etc.) status, remaining fuel, remaining battery charge, etc.).

The output unit 29 is also connected to the speaker 15, display 19, and vehicle control unit 71. The speaker 15 and display 19 are provided inside the vehicle's cabin. The vehicle control unit 71 performs various controls related to the vehicle (e.g., steering, acceleration, deceleration, gear changes, etc.). The in-vehicle device 201 is a robot in the broad sense.

2. Processing Executed by the Vehicle-Mounted Device 201 (2-1) Processing Similar to That of the Robot 1 The vehicle-mounted device 201 executes program setting processing, conversation processing, learning stop determination processing, and schedule management processing, similar to that of the robot 1 of the first embodiment.

(2-2) Vehicle Control Processing The control unit 3 of the vehicle-mounted device 201 repeatedly executes the vehicle control processing shown in Fig. 10 at predetermined time intervals. This processing is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold.

In step 31, voice is acquired using the microphone 5. In step 32, instructions to the vehicle (e.g., starting, stopping, decelerating, accelerating, turning right or left, changing lanes, shifting gears, etc.) are recognized from the voice acquired in step 31 using well-known voice recognition technology. At this time, if a standard program is being used, the above instructions are recognized using standard dictionary data that is pre-installed in the on-board device 201. Furthermore, if an AI program is being used, the above instructions are recognized using dictionary data that is installed from the cloud network 31 together with the AI program and that has been strengthened by past learning.

In step 33, the vehicle control details are determined in accordance with the vehicle instruction recognized in step 32. For example, if the vehicle instruction is to start, the timing to release the brakes, the amount and speed of increase in engine speed, etc. are determined. If a standard program is being used, the vehicle control details are determined using the standard program. Also, if an AI program is being used, the control details are determined using an 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 in accordance with the control content.
In step 35, it is determined whether or not the AI program is currently being used. If the AI program is currently being used, the process proceeds to step 36, and if the standard program is currently being used, the process ends.

In step 36, the detection results relating to the state of the vehicle are 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 obtained in step 36. This learning corrects the control content so that the sensor output obtained in step 36 falls within a preset optimum range.

For example, if the control content output in step 34 was a start, it is checked whether the sensor output (e.g., sensor output of speed, acceleration, etc.) during the start process was within the optimal range, and if it was outside the optimal range, the start control content is modified so that the sensor output for the next and subsequent starts will be closer to the optimal range.

In step 38, the AI program is updated to reflect the learning results from step 37.
3. Effects of the Vehicle-Mounted Device 201 The vehicle-mounted device 201 has the effects (1A) to (1I) described above. In addition, the vehicle-mounted device 201 also has the following effects.

(3A) The in-vehicle device 201 can recognize human voices and perform corresponding vehicle control. Furthermore, when using an AI program, the in-vehicle device 201 learns based on the vehicle control content output to the vehicle control unit 71 and sensor output indicating the vehicle's state, and the more learning progresses, the more advanced vehicle control it can perform.

(3B) The on-board device 201 can change and develop AI programs related to vehicle control processing through learning. The on-board device 201 can also install AI programs uploaded to the cloud network 31.

The vehicle-mounted device 201 into which the learned AI program is installed may be the same as the vehicle-mounted device 201 that previously performed learning, or it may be different.
Therefore, a user can install and use an AI program that has learned from their own past use in the on-board device 201 of any vehicle from the cloud network 31.
<Fourth embodiment>
1. Configuration of Robot 301 The configuration of the robot 301 of this embodiment is basically the same as that of the robot 1 of the first embodiment. The following description will focus on the differences from the first embodiment. 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 AI programs and datasets for executing various operations of the robot 301. Hereinafter, this program will be referred to as the first AI program, and this dataset will be referred to as the first dataset.

The first artificial intelligence unit 73 changes and develops the first AI program and the first dataset through learning. The learning performed by the first artificial intelligence unit 73 is not affected by the learning stop and learning restrictions described below. Furthermore, the first AI program and the first dataset are not uploaded to the cloud network 31.

The second artificial intelligence unit 75 is capable of storing an AI program and a dataset. Hereinafter, this program will be referred to as the second AI program, and this dataset will be referred to as the second dataset.

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 through learning. The second AI program and the second data set accumulate the learning results and are uploaded to the cloud network 31. This allows the learning results to 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 limit described below. Therefore, the learning results stored on the cloud network 31 by uploading the second AI program and second data set are limited.

The second AI program and second dataset can be downloaded from the cloud network 31 to the second artificial intelligence unit 75. Then, based on the downloaded second AI program and second dataset, learning of the first AI program and first dataset can be performed. Details will be provided below.

2. Processing Executed by the Robot 301 (2-1) Program Installation Processing When the robot 301 is powered on, the control unit 3 executes the program installation processing shown in FIG.

In step 41, it is determined whether the user's identification information has been entered. The identification information can be the same as in the first embodiment. If the identification information has been entered, proceed to step 42; if the identification information has not been entered, end this process.

In step 42, the second AI program and 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 second data set are stored in the second artificial intelligence unit 75. If the second AI program and second data set are already stored in the second artificial intelligence unit 75, they are overwritten.

In step 43, content that is included in the second AI program and second dataset installed in step 42 but is not stored in the first AI program and first dataset is learned.

In step 44, the first AI program and first dataset are updated by adding the content learned in step 43. In other words, the first AI program and first dataset are learned based on the downloaded second AI program and second dataset.

(2-2) Conversation Processing The control unit 3 executes the conversation processing shown in Fig. 13. This processing is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold. The conversation processing is performed using a first AI program and a first data set stored in the first artificial intelligence unit 73.

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

In step 56, the first AI program and the first data set are updated to reflect the learning results of step 55.
In step 57, it is determined whether the second AI unit 75 has stopped learning at that time. Whether the second AI unit 75 has stopped learning is set by the second AI unit learning stop determination process described below. If the second AI unit 75 has not stopped learning, the process proceeds to step 58; if the second AI unit 75 has stopped learning, the process ends.

In step 58, the currently set learning restriction details are obtained. Examples of learning restriction details include information about the user's (the user corresponding to the identification information determined to have been entered in step 41) family and acquaintances (such as names, addresses, telephone numbers, email addresses, career history, and images including faces).

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

In step 60, the second AI program and the second data set are updated to reflect the learning results of step 59.
(2-3) Processing for Determining Whether to Stop Learning by the Second Artificial Intelligence Unit The control unit 3 repeatedly executes the processing for determining whether to stop learning by the second artificial intelligence unit shown in FIG. 14 at predetermined time intervals.

In step 71, the position information of the robot 301 is acquired using the GPS 13.
In step 72, the camera 7 is used to capture 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 the second artificial intelligence unit 75 is currently in a learning pause state. The learning pause state begins in step 77 (to be described later) and ends when learning is resumed in step 79 (to be described later). If learning is not currently paused, the process proceeds to step 75; if learning is currently paused, the process proceeds to step 78.

In step 75, it is determined whether the image acquired in step 72 or the audio acquired in step 73 contains a trigger for stopping learning. The trigger for stopping learning is the same as in the first embodiment. If there is a trigger for stopping learning, proceed to step 77; if there is no trigger for stopping learning, proceed to step 76.

In step 76, it is determined whether the location information obtained in step 71 corresponds to a location previously registered as a location where learning should be stopped. The location where learning should be stopped is the same as in the first embodiment.

In step 77, the second artificial intelligence unit 75 enters a learning suspension state. From this point onwards, a positive judgment is made in step 57 of the conversation process, and learning in step 59 is not performed.

On the other hand, if the determination in step 74 is affirmative, then in step 78 it is determined whether the image acquired in step 72 or the audio acquired in step 73 contains a trigger for restarting learning. The trigger for restarting learning is the same as in the first embodiment. If there is a trigger for restarting learning, the process proceeds to step 79; if there is no trigger for restarting learning, the process ends.

In step 79, learning in the second artificial intelligence unit 75 resumes. From this point on, a negative judgment is made in step 57 in the conversation processing, 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 the usage termination conditions for the robot 301 have been met. The usage termination conditions are the same as those determined in step 5 of the first embodiment. If the usage termination conditions have been met, the process proceeds to step 82; if the usage termination conditions have not been met, the process ends.

In step 82, the second AI program and second dataset are uploaded to the cloud network 31. At this time, they are uploaded in association with the user's identification information. Note that if the learning described above has been performed, the second AI program and second dataset uploaded will be those after learning.

3. Effects of the Robot 301 The robot 301 has the effects (1B), (1C), (1F), and (1H) described above. In addition, 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 through learning. Furthermore, learning in the first AI program and the first data set is not affected by the learning stop process and the learning limit process.

Furthermore, since the first AI program and the first data set are not uploaded to the cloud network 31, their contents can be prevented from being known to others.
(4B) The robot 301 can upload the second AI program and the second data set after learning to the cloud network 31. The robot 301 can also install the second AI program and the second data set from the cloud network 31.

The robot 301 on which the second AI program and second data set after learning are installed may be the same as or different from the robot 301 that previously performed learning. Furthermore, the robot 301 on which the second AI program and second data set after learning are installed may be located in the same location as the previous learning, or in a different location.

Therefore, the user can install the second AI program and second dataset, which have been learned through their own past use, from the cloud network 31 onto the robot 301 at the user's current location (e.g., workplace, store, home, or other location). The second AI program and second dataset can then be used to learn, and the first AI program and first dataset can be changed and developed.

(4C) The robot 301 restricts learning of the second AI program and the second data set uploaded to the cloud network 31. That is, the robot 301 stops learning of the second AI program and the second data set depending on the behavior of people around it, the results of person identification, etc. The robot 301 also stops learning of the second AI program and the second data set depending on its location. The robot 301 also does not include any items that fall under the learning restriction content 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 information that the user does not want others to know, from being uploaded to the cloud network 31. <Fifth embodiment>
1. Configuration of Robot 401 The configuration of the robot 401 of this embodiment is basically the same as that of the robot 1 of the first embodiment. The following mainly describes the differences from the first embodiment. As shown in FIG. 16 , the robot 401 can connect to the Internet 77 using a communication unit 26. The Internet 77 is an example of a network. The robot 401 can also be placed inside a vehicle. In this case, the robot 401 can converse with the vehicle occupant using a conversation process, which will be described later.

2. Processing Executed by Robot 401 (2-1) Processing Similar to Robot 1 The robot 401 executes program setting processing, learning stop determination processing, and schedule management processing, similar to 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 processing is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold.

The processes in steps 91 and 92 are similar to the processes in steps 11 and 12 in the first embodiment, respectively.
In step 93, information related to the content of the voice recognized in step 92 is searched and acquired from the Internet 77 using the communication unit 26. For example, keywords are extracted from the content of the voice recognized in step 92, and information including items previously associated with the keywords is searched for. Examples of search targets include SNS (social networking services), blogs, and electronic bulletin boards. Furthermore, if the content of the voice recognized in step 92 is a question, an answer to the question is searched for. As a search method, for example, a known search engine can be used.

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

In step 95, the type of voice to be pronounced in step 96 (described below) is determined based on the content of the answer created in step 94. Examples of voice types include male voice, female voice, adult voice, child's voice, etc.

Specifically, the type of voice is determined as follows: First, the control unit 3 extracts features (e.g., features unique to men, features unique to women, features unique to adults, features unique to children, etc.) from the content of the response created in step 94.

The control unit 3 is pre-installed with a map that associates features in the content of the response with voice types. By inputting the features extracted as described above into the map, the control unit 3 determines the voice type that corresponds to the extracted features.

For example, if features specific to men and features specific to adults are extracted from the content of the answer created in step 94, the control unit 3 determines the voice of an adult male.
In step 96, the speaker 15 produces a sound based on the response voice data created in step 94. At this time, the sound is produced using the type of voice set in step 95.

In step 97, first, the emotion (e.g., 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 is obtained. This emotion is obtained as follows.

The control unit 3 is pre-installed with a map that associates emotions with features that appear in the content of the voice and the content of the response. Features that appear in the content of the voice include, for example, volume, intonation, and pitch. Features that appear in the content of the response include, for example, words and phrases that reflect emotions that are included in the content of the response.

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

The control unit 3 then creates a facial image of a person or character expressing the acquired emotion and displays it on the display 19. For example, if the acquired emotion is joy, a facial image of a smiling person or character is displayed on the display 19. On the other hand, if the acquired emotion is sadness, a facial image of a crying person or character is displayed on the display 19.

The processes in steps 98 to 102 are similar to the processes in steps 15 to 19 in the first embodiment, respectively.
3. Effects of the Robot 401 The robot 401 has the effects (1A) to (1I) described above. In addition, the robot 401 also has the following effects.

(5A) The robot 401 can search the Internet 77 for and obtain information related to the content of the voice. The robot 401 then uses the information obtained in this way to create response voice data. This allows the robot 401 to create more appropriate response voice data.

(5B) The robot 401 can determine the type of voice depending on the content of the answer. Therefore, the user is less likely to feel a discordance between the content of the answer and the type of voice used to pronounce it.

(5C) The robot 401 acquires the emotion corresponding to the content of the recognized voice or the content of the response, and displays a facial image of a person or character expressing the acquired emotion on the display 19. This allows the user to feel as if the robot 401 is a human being.
Sixth Embodiment
1. Configuration of Robot 501 The configuration of the robot 501 of this embodiment is basically the same as that of the robot 1 of the first embodiment.

2. Processing Executed by Robot 501 (2-1) Processing Similar to Robot 1 The robot 501 executes program setting processing, conversation processing, learning stop determination processing, and schedule management processing, similar to the robot 1 of the first embodiment.

18, multiple robots 501 enable a call between a user 79 and a user 81. For example, the robot 501 on the user 79's side (hereinafter referred to as robot 501A) acquires the voice uttered by the user 79 using the microphone 5. Furthermore, the robot 501A receives an input from the user 79 specifying the robot 501 (hereinafter referred to as robot 501B) with which to make a call using the touch panel 9.

The robot 501A transmits to the host computer 83, using the communication unit 26, a signal converted from the voice spoken by the user 79 (hereinafter referred to as the converted voice signal), an identification signal for the robot 501A, and an identification signal for the robot 501B designated by the user 79 as the communication partner.

The host computer 83 determines whether the received identification signal of robot 501A and the identification signal of the other party's robot 501B are set as a call pair. If they are set as a call pair, the voice-converted signal is transferred to the other party's robot 501B. Setting up a call pair will be described later.

Robot 501B receives the transferred voice conversion signal using communication unit 26 and produces a sound using speaker 15 based on the voice conversion signal. Through the above processing, the voice produced by user 79 is transmitted to user 81 on robot 501B's side. In addition to producing a sound, robot 501B also converts the voice into text and displays the text on display 19.

Furthermore, by having robot 501B perform the processing of robot 501A described above, and robot 501A perform the processing of robot 501B described above, it is possible to transmit the voice spoken by user 81 to user 79.

On the other hand, if the identification signal of the received robot 501A and the identification signal of the call partner robot 501B are not set as a call pair, the host computer 83 will not transfer the above-mentioned voice conversion signal.

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

In step 111, it is determined whether a new call pair setting request has been received. A call pair setting request is a request sent by robot 501A to host computer 83. The call pair setting request includes an identification signal of robot 501A, which is the sender of the request, characteristics of user 79 on the robot 501A side (e.g., hobbies, preferences in food, clothing, and shelter, interests, etc.), and image data of a facial photograph of user 79.

The call pair setting request may be sent by robot 501A in response to a user instruction, or may be sent automatically by robot 501A. If host computer 83 receives a new call pair setting request, it proceeds to step 112; if it does not, it terminates this process.

In step 112, the characteristics of user 79 included in the call pair setting request (hereinafter referred to as a new setting request) determined to have been received in step 111 are searched for 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) previously received from any robot 501.

In step 113, it is determined whether the search in step 112 has found a previous setting request with characteristics that match the characteristics of user 79 included in the new setting request. If such a previous setting request is found, proceed to step 114; if not, proceed to step 116.

In step 114, robot 501B, the sender of the previous setting request discovered in step 113, and robot 501A, the sender of the new setting request, are set as a call pair.

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

On the other hand, if the determination in step 113 is negative, the new configuration request is added to the configuration waiting list in step 116. From this point on, the new configuration request becomes part of the list of past configuration requests.

The robot 501, which has been notified of the information about the other robot 501, uses the information to create an icon 85 representing the other user in the call pair, and displays it on the display 19, as shown in Figure 20. The icon 85 includes a photograph of the face of the other user in the call pair. The robot 501 can display multiple icons 85 on the display 19.

When a specific icon 85 is touched by a user, the robot 501 recognizes the user corresponding to that icon 85 as the person with whom it is communicating. When the robot 501 transmits the converted voice signal to the host computer 83 as described above, it also transmits to the host computer 83 an identification signal of the robot 501 corresponding to the touched icon 85.

(2-4) Other Processing During the call processing described in (2-2) above, the host computer 83 analyzes the contents of the converted voice signal sent by the robot 501A. If the analysis result corresponds to a predetermined prohibited matter (for example, a matter related to a crime), the host computer 83 may prevent the call processing from starting or terminate the call processing midway. Therefore, the host computer 83 can prevent the call processing from being used for crimes, etc.

Furthermore, during the call processing described above in (2-2), the robot 501B also uses the voice transmitted from the other computer 501A to learn. This allows for even more efficient learning.

Furthermore, in the call processing of (2-2) above, when the user 79 stops speaking for a predetermined period of time or when the user 79 gives an instruction, the robot 501A transmits the voice conversion signal that it created to the host computer 83. In this case, the robot 501A and the user 81 will be in conversation. The computer 501A can create the voice conversion signal using learning results obtained from past call processing. The robot 501A may also store sounds (e.g., backchannels) made by the robot 501A or the robot 501B in past calls, and create a voice conversion signal corresponding to those sounds.

3. Effects of the Robot 501 The robot 501 has the effects (1A) to (1I) described above. In addition, the robot 501 also has the following effects.

(6A) A user 79 on the robot 501A side and a user 81 on the robot 501B side can communicate with each other.
(6B) A call between user 79 and user 81 is premised on the robot 501A and robot 501B being set as a call pair. A call pair is set when the characteristics of user 79 and user 81 match. Therefore, the robot 501 selectively enables calls between users with matching characteristics.

(6C) The robot 501 creates an icon 85 including a facial photograph of the user who is the other party to the call and displays it on the display 19. The user can easily select the other party by touching the icon 85. In addition, since the icon 85 includes a facial photograph 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 in-vehicle device 201 may increase or decrease the learning restriction depending on the situation. For example, if it is recognized that an item that was initially included in the learning restriction has been spoken by multiple people, the item may be excluded from the learning restriction.

(2) In the first to sixth embodiments, when the robot 1, 301, 401, 501, computer 101, and in-vehicle device 201 acquire a voice through the microphone 5, they may judge the tone of the voice and, depending on the judgment result, decide whether or not to learn the content of the voice.

(3) In the first to sixth embodiments, the processing performed in response to the voice recognition results may be something other than creating response voice data (step 13) or determining the vehicle control content (step 33). For example, the robot 1, 301, 401, 501 may be moved or an external device connected to the computer 101 may be operated in response to the voice recognition results. Furthermore, a predetermined action (e.g., opening and closing a box, opening and closing a window, locking or unlocking a door, operating a home appliance, etc.) may be performed in response to the voice recognition results.

(4) In the first to sixth embodiments, the AI program and data set may be stored on something other than the cloud network 31. For example, they may be stored on a well-known server, storage medium, etc.

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

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

(8) In the first to sixth embodiments, the robots 1, 301, 401, 501, computer 101, and in-vehicle device 201 may take the form of home appliances (e.g., televisions, refrigerators, air conditioners, vacuum cleaners, washing machines, etc.), mobile terminals (e.g., mobile phones (including smartphones), eyeglass-type terminals, wristwatch-type terminals), etc.

(9) In the first to sixth embodiments, the robots 1, 301, 401, 501, computer 101, and in-vehicle device 201 may have the function of automatically acquiring images using the camera 7 and transmitting the acquired images over a network. Examples of destinations include a server, a terminal, a vehicle, and another robot. The image to be transmitted may be the image itself acquired using the camera 7, or a portion extracted from the image acquired using the camera 7 (for example, an image of a person's face, a person's entire body, a vehicle, a vehicle's license plate, etc.).

Robots 1, 301, 401, 501, computers 101, and vehicle-mounted devices 201 with the above functions can be used for crime prevention purposes, monitoring other robots, etc. Robots 1, 301, 401, 501, computers 101, and vehicle-mounted devices 201 with the above functions can be installed, for example, indoors, on the street, at the entrance to a specific facility (e.g., a home, apartment building, office, parking lot, etc.), near other robots, etc.

Furthermore, robots 1, 301, 401, 501, computers 101, and on-board devices 201 with the above-mentioned functions have the ability to fly in the air or can be mounted on flying objects. In this case, images of the ground taken from an aerial viewpoint can be transmitted over a network. In this case, robots 1, 301, 401, 501, computers 101, and on-board devices 201 can recognize white lines on the road in the captured images and move along those lines. Furthermore, robots 1, 301, 401, 501, computers 101, and on-board devices 201 can recognize traffic lights in images of the ground taken from an aerial viewpoint and transmit the displayed information (red light, green light, etc.) to vehicles on the ground.

(10) In the first to sixth embodiments, the robots 1, 301, 401, 501, computer 101, and vehicle-mounted device 201 may use the camera 7 to recognize human behavior and output a sound that is pre-associated with the recognition result.

For example, if the system recognizes that a person is leaving the kitchen with the gas still on, it can output a warning sound to that person. Also, if the system recognizes that a person is searching for a specific object, it can search for the object's location and notify the user of its location through a voice message. Also, if the system recognizes that a person is entering a house through the entrance, it can output a voice message saying "Welcome home." Human actions and the voices associated with them can be increased through learning.

When emitting a voice as described above, the type of voice can be associated with the situation at the time and the content of the voice. For example, if a father is recognized in an image, the type of voice saying "Welcome home" can be determined to be the voice of his child.

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

Claims (13)

a voice capturing unit configured to capture a voice of a user;
an output unit configured to create a response voice in response to the user's voice acquired by the voice acquisition unit using artificial intelligence having a learning function of learning a conversation with the user, and to output the response voice to the user;
a learning control unit configured to restrict the learning function when the user's voice includes voice indicating that the conversation content is confidential or related to privacy;
An information processing system comprising: a voice capturing unit configured to capture a voice of a user;
a reading unit configured to read from a storage device a dataset related to artificial intelligence learned from past conversations with the user;
an output unit configured to generate a response word as a word responsive to the voice acquired by the voice acquisition unit by the artificial intelligence using the read data set, and output the response word to the user;
a learning unit configured to perform a learning operation based on the user's voice and record a learning result in the dataset stored in the storage device by updating the dataset;
a learning control unit configured to restrict a learning operation by the learning unit when the user's voice includes voice indicating that the conversation content is confidential or related to privacy;
An information processing system comprising: a search unit configured to search the Internet for relevant information related to the question included in the user's voice acquired by the voice acquisition unit based on the content of the question;
The information processing system according to claim 1 , wherein the output unit is configured to create an answer to the question based on the related information acquired by the search unit from the Internet, and to output the answer to the user as the response voice. A location acquisition unit that acquires user location information
Further provided with
4. The information processing system according to claim 1, wherein the learning control unit is further configured to restrict the learning function when the user's location indicated by the location information satisfies a predetermined condition. a usage restriction unit that analyzes the voice acquired by the voice acquisition unit, and restricts the user from using the information processing system if the voice includes content that corresponds to a prohibited matter;
5. The information processing system according to claim 1, further comprising: The learning control unit is configured to restrict the learning function related to the voice when the voice acquired by the voice acquisition unit belongs to a learning restriction content, but not restrict the learning function when the voice is a voice under a specific speaking situation.
5. An information processing system according to claim 1, claim 3 or claim 4. A vehicle equipped with the information processing system according to any one of claims 1 to 6 . The vehicle according to claim 7 , wherein the voice acquisition unit is configured to execute vehicle control according to the acquired voice. an image transmission unit that acquires a surrounding image captured by a camera and transmits the acquired surrounding image to an external device via a communication network;
9. The vehicle according to claim 7 or claim 8, further comprising: 1. A computer-implemented information processing method, comprising:
Acquiring a user's voice;
creating a response voice in response to the acquired user voice using artificial intelligence having a learning function for learning a conversation with the user, and outputting the response voice to the user;
restricting a learning function of the artificial intelligence when the acquired voice of the user includes voice indicating that the content of the conversation is confidential or related to privacy;
An information processing method including: 1. A computer-implemented information processing method, comprising:
Acquiring a user's voice;
Reading from a storage device a dataset related to artificial intelligence that has been trained through past conversations with the user;
Using the retrieved data set, the artificial intelligence creates a response word as a word to respond to the acquired user's voice, and outputs the response word to the user;
performing a learning operation based on the user's voice and updating the data set stored in the storage device, thereby recording a learning result in the data set;
restricting the learning operation when the user's voice includes voice indicating that the conversation content is confidential or related to privacy;
An information processing method including: 11. The information processing method according to claim 10 ,
Searching the Internet for relevant information related to the question contained in the user's voice based on the content of the question.
Further comprising :
The information processing method includes creating an answer to the question based on the related information obtained from the Internet, and outputting the answer to the user as the response voice . A computer program for causing a computer to implement the information processing method according to any one of claims 10 to 12 .