Information processing system, information processing method, computer program, and vehicle

Abstract

To provide a robot capable of reducing burden of a user regarding learning of the robot.SOLUTION: A robot includes: a voice recognition unit for recognizing voice; a learning unit for learning a recognition result of the voice recognition unit; a processing unit for performing corresponding processing of the recognition result of the voice recognition unit using a learning result of the learning unit; a storage unit for storing the learning result in an external storage device; and a learning result acquisition unit for acquiring the learning result from the storage device. A standard program storage unit 23, an artificial intelligence unit 25 and an operation unit 27 are examples of the voice recognition unit, the processing unit and a learning stop unit.SELECTED DRAWING: Figure 1

Description

REFERENCE TO RELATED APPLICATIONS

[0001] This international application claims priority based on 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. [Technical Field]

[0002] This disclosure , information processing system, information processing method, and computer program - Patents.com Regarding. [Background technology]

[0003] In recent years, robots that can converse with people have been attracting attention. These robots use a microphone to capture speech spoken by people, estimate the meaning of the speech through speech recognition, and then respond with a pre-associated answer to the estimated meaning (see Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 4015424 Summary of the Invention [Problem to be solved by the invention]

[0005] 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. [Means for solving the problem]

[0006] 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.

[0007] 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.

[0008] 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. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 2 is a block diagram illustrating an electrical configuration of the robot according to the embodiment. [Figure 2] FIG. 2 is a front view showing the configuration of the robot. [Figure 3] 10 is a flowchart showing a program setting process executed by a robot. [Figure 4] 10 is a flowchart showing a conversation process executed by a robot. [Figure 5] 10 is a flowchart showing a learning stop determination process executed by the robot. [Figure 6] 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. [Figure 7] FIG. 2 is a block diagram showing the electrical configuration of a computer. [Figure 8] FIG. 1 is a perspective view illustrating the appearance of a computer. [Figure 9] FIG. 2 is a block diagram showing the electrical configuration of the in-vehicle device. [Figure 10] 4 is a flowchart showing a vehicle control process executed by an in-vehicle device. [Figure 11] FIG. 10 is a block diagram showing the electrical configuration of a robot according to another embodiment. [Figure 12] 10 is a flowchart showing a program installation process executed by a robot. [Figure 13] 10 is a flowchart showing a conversation process executed by a robot. [Figure 14]10 is a flowchart showing a second artificial intelligence unit learning stop determination process executed by the robot. [Figure 15] 10 is a flowchart showing a program upload process executed by a robot. [Figure 16] FIG. 10 is a block diagram showing the electrical configuration of a robot according to yet another embodiment. [Figure 17] 10 is a flowchart showing a conversation process executed by a robot. [Figure 18] FIG. 2 is an explanatory diagram illustrating a configuration for executing call processing. [Figure 19] 10 is a flowchart illustrating a call pair setting process executed by a host computer. [Figure 20] FIG. 10 is an explanatory diagram showing a display displaying icons. [Explanation of symbols]

[0010] 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...torso, 39...right arm, 41...left arm, 43...right leg, 45...left leg, 47, 49...wheel, 51...movement motor, 53...neck joint, 55...shoulder joint, 57...elbow joint, 59...wrist joint, 61...joint motor, 63...keyboard, 65...mouse, 67...casing, 69...terminal, 71...vehicle control unit, 101...computer, 201...in-vehicle device DETAILED DESCRIPTION OF THE INVENTION

[0011] An embodiment of the present disclosure will be described with reference to the drawings. First Embodiment 1. Robot 1 Configuration 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.

[0012] The control unit 3 includes a microcomputer, and specifically 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.

[0013] 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 .

[0014] 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.

[0015] 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 the program through learning. Furthermore, the artificial intelligence unit 25 can install a new AI program and erase an AI program.

[0016] 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 execute the calculations required to realize various operations of the robot 1.

[0017] 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.

[0018] 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.

[0019] The right leg 43 and the left leg 45 are each equipped with wheels 47, 49 for movement. Two wheels 47, 49 are provided on each side 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.

[0020] The robot 1 has 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 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.

[0021] 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.

[0022] 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 an identification information acquisition unit. The cloud network 31 is an example of an external storage device.

[0023] 2. Processing performed by Robot 1 (2-1) Program setting process The control unit 3 executes a program setting process shown in Fig. 3 to determine which of the standard program and the AI ​​program to use. is repeatedly executed at predetermined time intervals while the power of the robot 1 is on.

[0024] 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., if a standard program is being used), proceed to step 2; if an AI program is being used, proceed to step 5.

[0025] 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.

[0026] 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 detection of the robot 1 by the terminal 33 or a fixed device, etc.

[0027] 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. The one-dimensional barcode or two-dimensional barcode representing the identification information is photographed by the camera 7.

[0028] · Identification information is input using the touch panel 9. 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, iris, face, etc.), or may consist of voice.

[0029] 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 dataset 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 dataset are stored in the artificial intelligence unit 25.

[0030] Identification information, AI programs, and datasets are associated and stored in the cloud network 31. The datasets are used for using, learning, etc., of the AI ​​programs, and include dictionary data used in voice recognition and voice synthesis.

[0031] 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 onwards, the robot 1 uses the AI ​​program.

[0032] On the other hand, if the determination in step 1 is affirmative, it is determined in step 5 whether or not the usage termination condition for the robot 1 has been met. The usage termination condition may be, for example, as follows.

[0033] - The user does not operate the robot 1 for a specified period of time. - The state in which the user cannot be recognized in the images from the camera 7 or the audio picked up by the microphone 5 continues for a predetermined period of time or longer.

[0034] - The user inputs the robot 1 to end use (for example, (For example, by saying "End of use" out loud or by inputting information corresponding to the end of use on the touch panel 9).

[0035] Robot 1 is powered off. -The preset time arrives. If the use end condition is met, proceed to step 6, and if the use end condition is not met, end this process.

[0036] 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, which will be described later, has been performed, the AI ​​program and dataset to be uploaded will be those after learning.

[0037] In step 7, the AI ​​programs and data sets are erased from the artificial intelligence unit 25. In step 8, the program used is changed from the AI ​​program to the standard program. From this point on, the robot 1 uses the standard program.

[0038] (2-2) Conversation processing The control unit 3 executes the conversation process shown in Fig. 4. This process is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold.

[0039] 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 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. On the other hand, if an AI program is being used, the content of the voice is recognized using dictionary data that is installed together with the AI ​​program from the cloud network 31 and has been strengthened by past learning.

[0040] In step 13, data of 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 used, the response voice data is created using standard dictionary data that is pre-installed in the robot 1. On the other hand, if an AI program is used, the response voice data is created using dictionary data that is installed from the cloud network 31 together with the AI ​​program and that has been strengthened by past learning.

[0041] The content of the response voice data can be created, for example, by detecting a keyword from the content of the voice recognized in step 12 and searching dictionary data for an item that is previously associated with the keyword. The content of the response voice data may also be inferred from the content of the voice recognized in step 12 using artificial intelligence.

[0042] In step 14, a voice is spoken using the speaker 15 based on the answer voice data created in step 13. That is, a voice is spoken in response to the voice acquired in step 11.

[0043] In step 15, 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 16, and if the standard program is currently being used, the process ends.

[0044] In step 16, it is determined whether learning is currently stopped. , which is set by the learning stop determination process described later. If learning is not stopped, the process proceeds to step 17, and if learning is stopped, the process ends.

[0045] In step 17, the currently set learning restriction details are acquired. The learning restriction details include, for example, information about the user's (the user corresponding to the identification information determined to have been entered in step 2) family and acquaintances (names, addresses, telephone numbers, email addresses, career history, images including faces), etc.

[0046] In step 18, the content of the speech recognized in step 12 is learned. Examples of learning include machine learning. Machine learning may be supervised learning or unsupervised learning. Learning may also be learning using artificial intelligence. In this case, keywords may 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).

[0047] 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.

[0048] (2-3) Learning stop decision 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.

[0049] 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.

[0050] In step 25, it is determined whether learning is currently stopped. The learning stopped state begins in step 28 (described later) and ends when learning is resumed in step 30 (described later). If learning is not currently stopped, proceed to step 26; if learning is currently stopped, proceed to step 29.

[0051] In step 26, it is determined whether there is anything that would trigger learning to stop in the image acquired in step 23 or the audio acquired in step 24. Examples of triggers that would trigger learning to stop include the following:

[0052] A predetermined action indicating that learning should be stopped is recognized in the image acquired in step 23. For example, as shown in Fig. 6A, the action of holding the index finger upright and placing it in front of the mouth is one example of such an action. Another example of such an action is a wink.

[0053] In the voice acquired in step 24, a voice that has been set in advance as a keyword instructing the learning to stop is recognized. Examples of such keywords include "confidential," "off the record," and "private." Another example of such a keyword is the name of the user's family or acquaintances. The user may use the name of a family member or acquaintance as a keyword. These can be registered in advance in the robot 1. Alternatively, 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.

[0054] 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 may register in advance the facial image of a person who will trigger the stopping of learning.

[0055] If there is a trigger for stopping learning, proceed to step 28; if there is no trigger for stopping learning, proceed 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.

[0056] The user can register in advance the location where learning should be stopped. Also, 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 recognized in the past.

[0057] 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 trigger the resumption of learning. Examples of triggers for resuming learning include the following:

[0058] A preset action indicating that learning should be resumed is recognized in the image acquired in step 23. For example, as shown in Fig. 6B, the action is to make a circle with the thumb and index finger (a so-called OK sign).

[0059] A predetermined keyword instructing the user to resume learning is recognized from the voice acquired in step 24. Examples of such keywords include "OK," "resume," and "learn."

[0060] 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 onwards, a negative determination is made in step 16 in the conversation processing, and learning in step 18 is carried out.

[0061] (2-4) Schedule management process The robot 1 can execute the following schedule management process. The user inputs their own schedule information into the robot 1 in advance. The schedule information may be input, for example, using the touch panel 9 or by voice input. The schedule information may also be transmitted from the terminal 33 to the communication unit 26.

[0062] The schedule information includes at least a deadline and items to be performed by that 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., at a point in time (for example, one day or three hours) before the deadline. If the items have not yet been performed, the robot 1 notifies the user by voice from the speaker 15 or by an image displayed on the display 19. Warn the user.

[0063] 3. Effects of Robot 1 (1A) The robot 1 can change and develop its AI program through learning. The robot 1 can also 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. The robot 1 can also install the AI ​​program and data set uploaded to the cloud network 31.

[0064] The robot 1 onto which the trained AI program and dataset are installed may be the same as or different from the robot 1 that previously trained. Furthermore, the robot 1 onto which the trained AI program and dataset are installed may be located at the same place where training was previously performed, or may be located at a different place.

[0065] 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 location where the user is at the time (for example, at work, a store, at home, or other locations), and use the robot 1.

[0066] (1B) The robot 1 allows the installation of an AI program and a data set corresponding to the user's identification information, provided that the user's identification information is input. This prevents unauthorized use of a user's AI program and data set by others.

[0067] (1C) The robot 1 can recognize human voices and produce speech responses. In other words, the robot 1 can converse with people. Furthermore, when using an AI program, the robot 1 learns based on the results of voice recognition and uses the learning results to create speech responses. As the robot 1 learns more, it can have more sophisticated conversations.

[0068] (1D) The robot 1 stops learning depending on the behavior of people around it, the results of identifying people, etc. This prevents the robot 1 from learning things that are undesirable for the user and then telling others about them later.

[0069] (1E) The robot 1 stops learning depending on the location it is in. This prevents the robot 1 from later telling others about things it learned in a location where it is not desired to learn.

[0070] (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 items that are undesirable for the user and then telling others about them later.

[0071] (1H) The robot 1 can manage the user's schedule. (1I) When the conditions for ending use of the robot 1 are met, 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. Computer 101 Configuration 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 in 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.

[0072] 8, the computer 101 has a box-shaped housing 67 that houses various components inside. The computer 101 also has a plurality of terminals 69 to which external devices (for example, a microphone 5, a camera 7, a keyboard 63, a mouse 65, a touch panel 9, a speaker 15, a display 19, etc.) can be connected. The computer 101 is a robot in the broad sense.

[0073] 2. Processing performed 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.

[0074] 3. The Benefits of Computers 101 The computer 101 provides the above-mentioned effects (1A) to (1I). <Third embodiment> 1. Configuration of the in-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 in the on-vehicle device 201 is connected to a microphone 5, a camera 7, a touch panel 9, a group of sensors 11, and a GPS 13 provided in the vehicle.

[0075] The microphone 5 is installed inside the vehicle cabin and detects the voices of the vehicle occupants (driver or other passengers). The camera 7 captures images of the occupants. The touch panel 9 is installed inside the vehicle 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 status (speed, acceleration, yaw rate, power unit (internal combustion engine, motor, etc.) status, remaining fuel, remaining battery, etc.).

[0076] The output unit 29 is also connected to the speaker 15, the display 19, and the vehicle control unit 71. The speaker 15 and the display 19 are provided inside the vehicle cabin. The vehicle control unit 71 performs various controls related to the vehicle (for example, steering, acceleration, deceleration, gear changes, etc.). The in-vehicle device 201 is a robot in a broad sense.

[0077] 2. Processing executed by the in-vehicle device 201 (2-1) Same process as Robot 1 The in-vehicle device 201 executes the program setting process, the conversation process, the learning stop determination process, and the schedule management process, similar to the robot 1 of the first embodiment.

[0078] (2-2) Vehicle control processing The control unit 3 of the in-vehicle device 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 greater than a predetermined threshold.

[0079] In step 31, a voice is acquired using the microphone 5. In step 32, a known voice is acquired. Using recognition technology, vehicle instructions (for example, starting, stopping, decelerating, accelerating, turning right or left, changing lanes, shifting gears, etc.) are recognized from the voice acquired in step 31. 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. Also, 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.

[0080] In step 33, the vehicle control details are determined according to the instruction to the vehicle recognized in step 32. For example, if the instruction to the vehicle is to start, the timing to release the brake, the amount and speed of increase in engine speed, etc. are determined. If a standard program is being used at this time, 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.

[0081] In step 34, the control content determined in step 33 is output to vehicle control unit 71. 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.

[0082] 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 acquired in step 36. This learning corrects the control content so that the sensor output acquired in step 36 falls within a preset optimum range.

[0083] For example, if the control content output in step 34 is a start, it is checked whether the sensor output (e.g., sensor output of speed, acceleration, etc.) during the start process was within the optimum range, and if it was outside the optimum range, the start control content is corrected so that the sensor output at the next and subsequent starts will approach the optimum range.

[0084] In step 38, the AI ​​program is updated to reflect the learning results from step 37. 3. Effects of the in-vehicle device 201 The vehicle-mounted device 201 has the above-mentioned advantages (1A) to (1I). Furthermore, the vehicle-mounted device 201 also has the following advantages.

[0085] (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 the sensor output indicating the vehicle state, so the more the learning progresses, the more advanced the vehicle control can be performed.

[0086] (3B) The on-board device 201 can change and develop the AI ​​program for vehicle control processing through learning. The on-board device 201 can also install the AI ​​program uploaded to the cloud network 31.

[0087] The in-vehicle device 201 into which the AI ​​program after learning is installed may be the same as the in-vehicle device 201 that previously performed learning, or may be different. Therefore, a user can install and use an AI program that has been learned through his or her own past use from the cloud network 31 into the on-board device 201 of any vehicle. <Fourth embodiment> 1. Robot 301 Configuration 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.

[0088] The first artificial intelligence unit 73 stores an AI program and a data set for executing various operations of the robot 301. Hereinafter, this program will be referred to as the first AI program, and this data set will be referred to as the first data set.

[0089] The first artificial intelligence unit 73 changes and develops the first AI program and the first data set through learning. The learning performed by the first artificial intelligence unit 73 is not affected by the learning stop and learning limit described below. Furthermore, the first AI program and the first data set are not uploaded to the cloud network 31.

[0090] The second artificial intelligence unit 75 can store 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.

[0091] 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.

[0092] 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 in the cloud network 31 by uploading the second AI program and the second data set are limited.

[0093] 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 second data set, learning of the first AI program and the first data set can be performed. This will be described in more detail below.

[0094] 2. Processing performed by robot 301 (2-1) Program installation process When the power supply of the robot 301 is turned on, the control unit 3 executes the program installation process shown in FIG.

[0095] In step 41, it is determined whether or not the user's identification information has been input. The identification information may be the same as that in the first embodiment. If the identification information has been input, the process proceeds to step 42; if the identification information has not been input, the process ends.

[0096] 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.

[0097] 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.

[0098] In step 44, the first AI program and the first data set are updated by adding the content learned in step 43. In other words, the first AI program and the first data set are learned based on the downloaded second AI program and second data set.

[0099] (2-2) Conversation processing The control unit 3 executes the conversation process shown in Fig. 13. This process is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold. The conversation process is performed using a first AI program and a first data set stored in the first artificial intelligence unit 73.

[0100] 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.

[0101] In step 56, the first AI program and the first data set are updated to reflect the learning results from step 55. In step 57, it is determined whether the second artificial intelligence unit 75 has stopped learning at that time. The stopping of learning by the second artificial intelligence unit 75 is set by the second artificial intelligence unit learning stop determination process described below. If the second artificial intelligence unit 75 has not stopped learning, the process proceeds to step 58; if learning has stopped, the process ends.

[0102] In step 58, the currently set learning restriction details are acquired, such as information about the user's (the user corresponding to the identification information determined to have been entered in step 41) family and acquaintances (names, addresses, telephone numbers, email addresses, career history, images including faces), etc.

[0103] 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 is included in the learning restriction content acquired in step 58, it is not learned.

[0104] In step 60, the second AI program and the second data set are updated to reflect the learning results from step 59. (2-3) Second AI unit learning stop decision process The control unit 3 repeatedly executes the second artificial intelligence unit learning stop determination process shown in FIG. 14 at predetermined time intervals.

[0105] 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.

[0106] In step 73, the microphone 5 is used to capture the voice. In step 74, it is determined whether the second artificial intelligence unit 75 is currently in a learning suspension state. The learning suspension state is initiated in step 77, which will be described later. The process ends when learning is resumed in step 79. If learning is not stopped, the process proceeds to step 75, and if learning is stopped, the process proceeds to step 78.

[0107] In step 75, it is determined whether there is a trigger for stopping learning in the image acquired in step 72 or the audio acquired in step 73. The trigger for stopping learning is the same as in the first embodiment. If there is a trigger for stopping learning, the process proceeds to step 77, and if there is no trigger for stopping learning, the process proceeds to step 76.

[0108] In step 76, it is determined whether 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 in the first embodiment.

[0109] In step 77, a learning stop state is initiated for the second artificial intelligence unit 75. From this point on, a positive determination is made in step 57 in the conversation processing, and learning in step 59 is not performed.

[0110] On the other hand, if the determination in step 74 is affirmative, then in step 78 it is determined whether or not there is a trigger for restarting learning in the image acquired in step 72 or the audio acquired in step 73. 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.

[0111] In step 79, learning is resumed in the second artificial intelligence unit 75. From this point onwards, a negative determination is made in step 57 in the conversation processing, and learning in step 59 is carried out.

[0112] (2-4) Program upload process The control unit 3 repeatedly executes the program upload process shown in FIG. 15 at predetermined time intervals.

[0113] In step 81, it is determined whether or not the usage termination condition for the robot 301 has been satisfied. The usage termination condition is the same as that determined in step 5 of the first embodiment. If the usage termination condition has been satisfied, the process proceeds to step 82, and if the usage termination condition has not been satisfied, the process ends.

[0114] In step 82, the second AI program and the second data set 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 above-mentioned learning has been performed, the second AI program and the second data set to be uploaded are those after learning.

[0115] 3. Effects of Robot 301 The robot 301 has the above-mentioned advantages (1B), (1C), (1F), and (1H). In addition, the robot 301 also has the following advantages.

[0116] (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.

[0117] Furthermore, since the first AI program and the first data set are not uploaded to the cloud network 31, it is possible to prevent their contents 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.

[0118] The robot 301 on which the learned second AI program and second data set are installed may be the same as or different from the robot 301 that previously performed learning. Furthermore, the robot 301 on which the learned second AI program and second data set are installed may be located at the same location as the previous learning, or at a different location.

[0119] Therefore, the user can install the second AI program and the second dataset that have been learned through their own past use in the robot 301 at the location where the user is currently located (for example, various locations such as the workplace, store, or home) from the cloud network 31. Then, the robot can learn using the second AI program and the second dataset, and change and develop the first AI program and the first dataset.

[0120] (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 identifying people, 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.

[0121] 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. Robot 401 Configuration 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 by a conversation process, which will be described later.

[0122] 2. Processing performed by robot 401 (2-1) Same process as Robot 1 The robot 401 executes the program setting process, the learning stop determination process, and the schedule management process, similar to the robot 1 of the first embodiment.

[0123] (2-2) Conversation processing The control unit 3 of the robot 401 repeatedly executes the conversation process shown in Fig. 17 at predetermined time intervals. This process is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold.

[0124] 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 containing 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 that question is searched for. As a search method, for example, a known search engine can be used.

[0125] In step 94, response voice data is created for the content of the voice recognized in step 92, basically in the same manner as in step 13 in the first embodiment. However, in this step, the response voice data is created also 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.

[0126] In step 95, the type of voice to be pronounced in step 96, which will be described later, is determined based on the content of the answer created in step 94. Examples of the type of voice include a male voice, a female voice, an adult voice, a child's voice, etc.

[0127] 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 answer created in step 94.

[0128] The control unit 3 is provided with a map in which the characteristics of the content of the answer are associated with the type of voice. The control unit 3 inputs the extracted characteristics as described above into the map, and determines the type of voice corresponding to the extracted characteristics.

[0129] 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.

[0130] In step 97, first, an emotion (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 is acquired. This emotion is acquired as follows.

[0131] The control unit 3 is provided with a map in which characteristics appearing in the content of the voice and the content of the answer are associated with emotions. Examples of characteristics appearing in the content of the voice include volume, intonation, and high and low pitch. Examples of characteristics appearing in the content of the answer include words and phrases that reflect emotions and are included in the content of the answer.

[0132] The control unit 3 inputs the features extracted from the speech content recognized in step 92 or the response generated in step 94 into the map to obtain the corresponding emotion.

[0133] Next, the control unit 3 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, the control unit 3 displays a facial image of a smiling person or character on the display 19. If the acquired emotion is sadness, the control unit 3 displays a facial image of a crying person or character on the display 19.

[0134] The processing in steps 98 to 102 is the same as the processing in steps 15 to 19 in the first embodiment, respectively. 3. Effects of Robot 401 The robot 401 has the above-mentioned advantages (1A) to (1I). Furthermore, the robot 401 also has the following advantages.

[0135] (5A) The robot 401 can search and acquire information related to the content of the voice from the Internet 77. The robot 401 then uses the information acquired in this way to create answer voice data. This allows the robot 401 to create more appropriate answer voice data.

[0136] (5B) The robot 401 can determine the type of voice depending on the content of the answer, so the user is less likely to feel a mismatch between the content of the answer and the type of voice used to pronounce it.

[0137] (5C) The robot 401 acquires the emotion corresponding to the content of the recognized voice or the content of the reply, 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.

[0138] 2. Processing performed by robot 501 (2-1) Same process as 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.

[0139] (2-2) Call processing 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 with a microphone 5. Furthermore, the robot 501A receives an input from the user 79, using the touch panel 9, specifying the robot 501 (hereinafter referred to as robot 501B) with which to make a call.

[0140] 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 voice conversion signal), an identification signal for the robot 501A, and an identification signal for the robot 501B, the other party designated by the user 79.

[0141] The host computer 83 determines whether the received identification signal of the robot 501A and the identification signal of the communication partner robot 501B are set as a call pair. If they are set as a call pair, the host computer 83 transfers the voice-converted signal to the communication partner robot 501B. The setting of the call pair will be described later.

[0142] The robot 501B receives the transferred voice conversion signal using the communication unit 26, and produces a sound using the speaker 15 based on the voice conversion signal. Through the above process, the voice produced by the user 79 is transmitted to the user 81 on the robot 501B side. In addition to producing a sound, the robot 501B also converts the voice into characters and displays the characters on the display 19.

[0143] Furthermore, 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 uttered by the user 81 can be transmitted to the user 79.

[0144] On the other hand, if the received identification signal of the robot 501A and the identification signal of the other party's robot 501B are not set as a call pair, the host computer 83 does not transfer the voice-converted signal.

[0145] (2-3) Call pair setting process Next, the process by which the host computer 83 sets up the above-mentioned call pair will be described with reference to FIG.

[0146] 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 the robot 501A to the host computer 83. The call pair setting request includes an identification signal of the robot 501A that sent the request, characteristics of the user 79 of the robot 501A (for example, hobbies, preferences in food, clothing, and shelter, interests, etc.), and image data of a facial photograph of the user 79.

[0147] The call pair setting request may be sent by the robot 501A in response to a user instruction, or may be sent automatically by the robot 501A. If the host computer 83 newly receives a call pair setting request, the process proceeds to step 112; if not, the process ends.

[0148] In step 112, the setup waiting list is searched for the characteristics of the user 79 included in the call pair setup request (hereinafter referred to as a new setup request) determined to have been received in step 111. Here, the setup waiting list is a list of call pair setup requests (hereinafter referred to as past setup requests) received from any robot 501 in the past.

[0149] In step 113, it is determined whether a past setting request having characteristics matching those of user 79 included in the new setting request has been found as a result of the search in step 112. If such a past setting request has been found, the process proceeds to step 114; if not, the process proceeds to step 116.

[0150] In step 114, the robot 501B, which was the sender of the previous setting request and which was found in step 113, and the robot 501A, which is the sender of the new setting request, are set as a call pair.

[0151] 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 an identification signal of the other robot 501, image data of a face photo of the corresponding user, etc.

[0152] 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.

[0153] 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 of the call pair, and displays it on the display 19 as shown in FIG. 20. The icon 85 includes a photograph of the face of the user who is the other user of the call pair. The widget 501 can display a plurality of icons 85 on the display 19 .

[0154] When a specific icon 85 is touched by a user, the robot 501 recognizes the user corresponding to the icon 85 as the other party. When transmitting the converted voice signal to the host computer 83 as described above, the robot 501 transmits to the host computer 83 an identification signal of the robot 501 corresponding to the touched icon 85.

[0155] (2-4) Other processing During the call processing of (2-2) above, the host computer 83 analyzes the contents of the converted voice signal transmitted 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 will not start the call processing or will terminate the call processing midway. Therefore, the host computer 83 can prevent the call processing from being used for crimes, etc.

[0156] Furthermore, during the call processing of (2-2) above, the robot 501B also uses the voice transmitted from the other computer 501A to learn, which allows for more efficient learning.

[0157] 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 converted voice signal that it has 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 converted voice signal using learning results obtained from past call processing. Furthermore, the robot 501A may store voices (for example, backchannels) that the robot 501A or the robot 501B has made in past calls, and create a converted voice signal corresponding to the voices.

[0158] 3. Effects of Robot 501 The robot 501 has the above-mentioned advantages (1A) to (1I). Furthermore, the robot 501 also has the following advantages.

[0159] (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.

[0160] (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 content depending on the situation. For example, if it is recognized that an item that was initially included in the learning restriction content has been spoken by multiple people, the item may be excluded from the learning restriction content.

[0161] (2) In the first to sixth embodiments, when the robot 1, 301, 401, 501, the computer 101, and the vehicle-mounted device 201 acquire a voice by the microphone 5, the voice of the voice is The color may be judged, and depending on the judgment result, it may be decided whether or not to learn the content of the sound.

[0162] (3) In the first to sixth embodiments, the processing performed in response to the voice recognition result may be something other than the creation of answer voice data (step 13) or the determination of 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 result. 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 result.

[0163] (4) In the first to sixth embodiments, the AI ​​program and data set may be stored in something other than the cloud network 31. For example, they may be stored in a well-known server, storage medium, or the like.

[0164] (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.

[0165] (7) In the first to third, fifth, and sixth embodiments, the robot 1, 401, 501, the computer 101, and the in-vehicle device 201 may use the standard program and the 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.

[0166] (8) In the first to sixth embodiments, the robots 1, 301, 401, 501, the computer 101, and the in-vehicle device 201 may be in 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.

[0167] (9) In the first to sixth embodiments, the robots 1, 301, 401, 501, the computer 101, and the in-vehicle device 201 may have a 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 may be an image of 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, or a vehicle license plate).

[0168] The robots 1, 301, 401, 501, computer 101, and in-vehicle device 201 having the above functions can be used for crime prevention purposes, monitoring other robots, etc. The robots 1, 301, 401, 501, computer 101, and in-vehicle device 201 having the above functions can be installed, for example, indoors, on the road, at the entrance to a specific facility (e.g., a house, an apartment building, an office, a parking lot, etc.), near other robots, etc.

[0169] Furthermore, the robots 1, 301, 401, 501, computer 101, and on-board device 201 having the above-described functions can fly in the air or be mounted on an aircraft. In this case, images of the ground captured from an aerial viewpoint can be transmitted over a network. In this case, the robots 1, 301, 401, 501, computer 101, and on-board device 201 can recognize white lines on the road in the captured images and move along those lines. Furthermore, the robots 1, 301, 401, 501, computer 101, and on-board device 201 can recognize traffic lights in images of the ground captured from an aerial viewpoint and transmit the displayed information (red light, green light, etc.) to vehicles on the ground.

[0170] (10) In the first to sixth embodiments, the robot 1, 301, 401, 501, computer 101, and vehicle-mounted device 201 may recognize human behavior using the camera 7 and output a sound associated with the recognition result in advance.

[0171] 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 by voice. Also, if the system recognizes that a person is entering a house through the entrance, it can output a voice saying "Welcome home." Human actions and the voices associated with them can be increased through learning.

[0172] When emitting a voice as described above, the type of voice can be associated with the situation 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.

[0173] (11) In the first to sixth embodiments, the control unit includes a microcomputer, but it 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

1. 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:

2. 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:

3. 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; Further provided with The output unit is configured to create an answer to the question based on the related information acquired from the Internet by the search unit, and output the answer to the user as the response voice.

2. The information processing system according to claim 1.

4. A location acquisition unit for acquiring 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.

5. A usage restriction unit that analyzes the voice acquired by the voice acquisition unit, and restricts the use of the information processing system by the user if the voice includes content that corresponds to a prohibited matter.

5. The information processing system according to claim 1, further comprising:

6. The learning control unit is configured to restrict the learning function related to the voice acquired by the voice acquisition unit when the voice belongs to a learning restriction content, but not to 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.

7. A vehicle equipped with the information processing system according to any one of claims 1 to 6.

8. A vehicle as described in claim 7, configured to perform vehicle control in accordance with the voice acquired by the voice acquisition unit.

9. An image transmission unit that acquires a surrounding image photographed by a camera and transmits the acquired surrounding image to an external device through a communication network.

9. The vehicle according to claim 7 or claim 8, further comprising:

10. 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:

11. 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 content of the conversation is confidential or involves privacy; An information processing method including:

12. An 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.

13. A computer program for causing a computer to implement the information processing method according to any one of claims 10 to 12.

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