System

Abstract

An object of the system according to the embodiment is to provide a conversation and a video corresponding to the age of a child and to speak to the child at a designated time.SOLUTION: A system according to an embodiment includes a setting unit, a speech generation unit, a projection unit, and a speaking unit. The setting unit acquires age information on the child. The conversation generation unit generates a conversation on the basis of the age information acquired by the setting unit. The projection unit projects the related video on the basis of the conversation generated by the conversation generation unit. The speaking unit speaks at the time designated by the setting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to provide appropriate conversations and images according to the child's age, and there is room for improvement.

[0005] The system according to the embodiment aims to provide conversations and images appropriate for the child's age and to speak to the child at designated times. [Means for solving the problem]

[0006] The system according to the embodiment includes a setting unit, a conversation generation unit, a projection unit, and a calling unit. The setting unit acquires age information of a child. The conversation generation unit generates a conversation based on the age information acquired by the setting unit. The projection unit projects a related video based on the conversation generated by the conversation generation unit. The calling unit calls out at a time specified by the setting unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide conversations and images appropriate for the child's age and speak to the child at a specified time. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention uses a smart speaker to engage in conversations tailored to a child's age. This system acquires a child's age information, generates conversations appropriate to that age, projects related videos, and speaks to the child at a specified time. For example, a user can set the child's age through an app, and AI generates conversations appropriate to that age. The system also has a function to project related videos on a television or mobile phone. For example, if a child says, "Tell me about dinosaurs," the smart speaker projects a dinosaur video on the television or mobile phone. The system also has a function to speak to the child at a time specified by the app. For example, the smart speaker may say "good morning" or "good night" in accordance with the child's wake-up time or bedtime. This allows the system to support communication tailored to the child's development and deepen the bond between parent and child. For example, the system can engage in conversations based on the child's interests and support learning. Speaking to the child at a specified time also helps regulate the child's daily rhythm.

[0029] A smart speaker system according to an embodiment includes a setting unit, a conversation generation unit, a projection unit, and a voice-calling unit. The setting unit acquires a child's age information. For example, the setting unit can acquire the child's age information through an app. The setting unit can also analyze the age information entered by a user and reflect it in the system. The conversation generation unit uses AI to generate age-appropriate conversations based on the acquired age information. For example, the conversation generation unit speaks to a 3-year-old child in simple language and to a 10-year-old child in more complex conversations. The conversation generation unit uses a generative AI to generate conversations using natural language processing technology. The projection unit projects related images onto a television or mobile phone in response to instructions from the conversation generation unit. For example, if a child says, "Tell me about dinosaurs," the projection unit projects an image of a dinosaur onto a television or mobile phone. The projection unit can select and project appropriate images based on image selection criteria. The voice-calling unit receives instructions from the setting unit and calls out at a specified time. For example, the voice-calling unit may say "good morning" or "good night" in accordance with the time the user wakes up in the morning or goes to bed at night. The voice-calling unit may call out at an appropriate timing based on the time set by the user. This allows the smart speaker system according to the embodiment to have conversations, project images, and call out to the child according to their age. For example, the system may acquire the child's age information, generate conversations appropriate for that age, project related images, and call out at a specified time.

[0030] The setting unit can acquire the child's age information through the app. Examples of apps include, but are not limited to, mobile apps and web apps. For example, the setting unit can allow a user to input the child's age information through the app. The setting unit can also reflect the age information acquired through the app in the system. This allows the age information to be easily acquired through the app. For example, the setting unit can provide a simple interface when a user inputs the child's age information through the app, thereby minimizing the input steps. The setting unit can also automatically analyze the age information acquired through the app and reflect it in the system.

[0031] The conversation generation unit allows the AI ​​to generate age-appropriate conversation based on the acquired age information. The conversation generation unit generates age-appropriate conversation using, for example, natural language processing technology. For example, the conversation generation unit speaks in simple words to a 3-year-old child and in a more complex conversation to a 10-year-old child. The conversation generation unit can also generate age-appropriate conversation using a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the conversation generation unit can input a child's age information and instruct the generation AI to generate conversation appropriate to that age. This makes it possible to generate appropriate conversation appropriate to the child's age. For example, the conversation generation unit can use the generation AI to generate conversation appropriate to the child's age and speak to the child through a smart speaker.

[0032] The projection unit can project related video onto a television or a mobile phone upon receiving an instruction from the conversation generation unit. For example, the projection unit projects related video onto a television or a mobile phone upon receiving an instruction from the conversation generation unit. For example, if a child says, "Tell me about dinosaurs," the projection unit projects a video of a dinosaur onto a television or a mobile phone. The projection unit can also select and project appropriate video based on video selection criteria. This allows related video to be projected onto an appropriate device. For example, the projection unit can project related video onto a television or a mobile phone upon receiving an instruction from the conversation generation unit, thereby supporting learning with content that interests the child.

[0033] The voice-calling unit can call out at a specified time upon receiving an instruction from the setting unit. The voice-calling unit, for example, calls out at a specified time upon receiving an instruction from the setting unit. For example, the voice-calling unit calls out "good morning" or "good night" in accordance with the time to wake up in the morning or the time to go to bed at night. The voice-calling unit can also call out at an appropriate timing based on the time set by the user. This makes it possible to call out an appropriate message at a specified time. For example, the voice-calling unit can call out at a specified time upon receiving an instruction from the setting unit, thereby adjusting the rhythm of daily life.

[0034] The setting unit can refer to the child's past age information and automatically update the age information in accordance with the child's growth. For example, the setting unit can automatically update the age information on the child's birthday. For example, the setting unit can refer to the child's growth record and update the age information periodically. The setting unit can also update the age information based on growth events (such as enrollment and graduation) entered by the parent. This allows the age information to be automatically updated in accordance with the child's growth. For example, the setting unit can refer to the child's past age information and automatically update the age information in accordance with the child's growth and reflect it in the system.

[0035] The setting unit can acquire additional information taking into consideration the interests and concerns of the child when acquiring age information. The setting unit acquires age information based on, for example, a character or theme that the child likes. For example, the setting unit acquires age information through questions that pique the child's interest. The setting unit can also acquire age information based on the interests and concerns by referring to the child's past behavior history. This makes it possible to acquire information based on the child's interests and concerns. For example, the setting unit can acquire additional information taking into consideration the interests and concerns of the child when acquiring age information and reflect it in the system.

[0036] The setting unit can customize the setting method by reflecting parental feedback when acquiring age information. The setting unit adjusts the method for inputting age information based on, for example, feedback provided by the parent. For example, the setting unit customizes the method for acquiring age information based on specific conditions set by the parent. The setting unit can also optimize the method for acquiring age information by referring to feedback previously provided by the parent. This allows the setting method to be customized based on parental feedback. For example, the setting unit can customize the setting method by reflecting parental feedback when acquiring age information and reflect the customized method in the system.

[0037] The setting unit can perform optimal settings taking into consideration the child's health condition when acquiring age information. The setting unit adjusts the method for acquiring age information based on the child's health condition, for example. For example, the setting unit refers to the child's health records and suggests the optimal method for acquiring age information. The setting unit can also adjust the frequency of acquiring age information depending on the child's health condition. This allows optimal settings to be performed based on the child's health condition. For example, the setting unit can perform optimal settings taking into consideration the child's health condition when acquiring age information and reflect them in the system.

[0038] The setting unit can optimize the settings by referring to the child's learning history when acquiring age information. The setting unit, for example, adjusts the method for acquiring age information based on the child's learning history. For example, the setting unit suggests the optimal method for acquiring age information by referring to the child's learning progress. The setting unit can also adjust the frequency of acquiring age information according to the child's learning history. This allows optimal settings to be made based on the child's learning history. For example, the setting unit can optimize the settings by referring to the child's learning history when acquiring age information and reflect them in the system.

[0039] The setting unit can adjust the setting contents taking into consideration the child's friendships when acquiring age information. The setting unit adjusts the method for acquiring age information based on, for example, the child's friendships. For example, the setting unit suggests an optimal method for acquiring age information by referring to the child's friendships. The setting unit can also adjust the frequency of acquiring age information depending on the child's friendships. This allows settings to be made based on the child's friendships. For example, the setting unit can adjust the setting contents taking into consideration the child's friendships when acquiring age information and reflect the adjustment in the system.

[0040] The conversation generation unit can optimize the content of the conversation by referring to the child's past conversation history when generating the conversation. The conversation generation unit, for example, selects an interesting topic based on the child's past conversation history. For example, the conversation generation unit selects appropriate language by referring to the child's past conversation history. The conversation generation unit can also optimize the flow of the conversation based on the child's past conversation history. This makes it possible to generate an optimal conversation based on the child's past conversation history. For example, the conversation generation unit can optimize the content of the conversation by referring to the child's past conversation history when generating the conversation, and reflect this in the system.

[0041] The conversation generation unit can adjust the difficulty of the conversation according to the child's learning progress when generating the conversation. The conversation generation unit generates a conversation of an appropriate level of difficulty based on, for example, the child's learning progress. For example, the conversation generation unit adjusts the content of the conversation by referring to the child's learning progress. The conversation generation unit can also select a topic of the conversation according to the child's learning progress. This makes it possible to provide a level of difficulty of the conversation according to the child's learning progress. For example, the conversation generation unit can adjust the difficulty of the conversation according to the child's learning progress when generating the conversation and reflect this in the system.

[0042] The conversation generation unit can select a conversation topic based on the child's interests and concerns when generating a conversation. The conversation generation unit, for example, selects a topic that will interest the child and generates a conversation. For example, the conversation generation unit selects a conversation topic based on the child's interests. The conversation generation unit can also select an interesting topic by referring to the child's past behavior history. This makes it possible to provide a conversation topic based on the child's interests and concerns. For example, the conversation generation unit can select a conversation topic based on the child's interests and concerns when generating a conversation and reflect this in the system.

[0043] The conversation generation unit can customize the conversation content based on events in the child's daily life when generating the conversation. The conversation generation unit generates a conversation related to, for example, the child's birthday. For example, the conversation generation unit customizes the conversation based on the child's school events. The conversation generation unit can also adjust the conversation content according to events in the child's daily life. This makes it possible to provide conversation content based on events in the child's daily life. For example, the conversation generation unit can customize the conversation content based on events in the child's daily life when generating the conversation and reflect this in the system.

[0044] The conversation generation unit can set the content of the conversation according to the child's learning goals when generating the conversation. The conversation generation unit generates appropriate conversation based on, for example, the child's learning goals. For example, the conversation generation unit selects a conversation topic by referring to the child's learning goals. The conversation generation unit can also adjust the difficulty of the conversation according to the child's learning goals. This makes it possible to provide conversation content based on the child's learning goals. For example, the conversation generation unit can set the content of the conversation according to the child's learning goals when generating the conversation and reflect this in the system.

[0045] The conversation generation unit can adjust the content of the conversation taking into consideration the child's friendships when generating the conversation. The conversation generation unit generates an appropriate conversation based on, for example, the child's friendships. For example, the conversation generation unit selects a conversation topic by referring to the child's friendships. The conversation generation unit can also adjust the content of the conversation depending on the child's friendships. This makes it possible to provide conversation content based on the child's friendships. For example, the conversation generation unit can adjust the content of the conversation taking into consideration the child's friendships when generating the conversation and reflect it in the system.

[0046] The projection unit can optimize the video content by referring to the child's past viewing history when projecting. The projection unit, for example, selects video that will attract the child's attention based on the child's past viewing history. For example, the projection unit selects appropriate video by referring to the child's past viewing history. The projection unit can also optimize the video content based on the child's past viewing history. This makes it possible to provide optimal video content based on the child's past viewing history. For example, the projection unit can optimize the video content by referring to the child's past viewing history when projecting, and reflect the optimized content in the system.

[0047] The projection unit can adjust the content of the video according to the child's learning progress when projecting. The projection unit, for example, selects appropriate video based on the child's learning progress. For example, the projection unit adjusts the content of the video by referring to the child's learning progress. The projection unit can also select the topic of the video according to the child's learning progress. This makes it possible to provide video content according to the child's learning progress. For example, the projection unit can adjust the content of the video according to the child's learning progress when projecting and reflect it in the system.

[0048] The projection unit can select a video theme based on the child's interests and concerns at the time of projection. For example, the projection unit selects a theme that will interest the child and projects the video. For example, the projection unit selects a video topic based on the child's interests. The projection unit can also select an interesting theme by referring to the child's past behavior history. This makes it possible to provide a video theme based on the child's interests and concerns. For example, the projection unit can select a video theme based on the child's interests and concerns at the time of projection and reflect it in the system.

[0049] The projection unit can customize the video content based on events in the child's daily life at the time of projection. For example, the projection unit projects a video related to the child's birthday. For example, the projection unit customizes the video based on the child's school events. The projection unit can also adjust the video content according to events in the child's daily life. This makes it possible to provide video content based on events in the child's daily life. For example, the projection unit can customize the video content based on events in the child's daily life at the time of projection and reflect it in the system.

[0050] The projection unit can set the content of the video according to the child's learning goals when projecting. The projection unit projects appropriate video based on, for example, the child's learning goals. For example, the projection unit selects a topic for the video by referring to the child's learning goals. The projection unit can also adjust the difficulty level of the video according to the child's learning goals. This makes it possible to provide video content based on the child's learning goals. For example, the projection unit can set the content of the video according to the child's learning goals when projecting and reflect this in the system.

[0051] The projection unit can adjust the video content taking into account the child's friendships when projecting. The projection unit projects an appropriate video based on, for example, the child's friendships. For example, the projection unit selects a topic for the video by referring to the child's friendships. The projection unit can also adjust the video content according to the child's friendships. This makes it possible to provide video content based on the child's friendships. For example, the projection unit can adjust the video content taking into account the child's friendships when projecting and reflect it in the system.

[0052] The speaking unit can optimize the speaking content by referring to the child's past responses when speaking to the child. The speaking unit selects appropriate speaking content based on, for example, the child's past responses. For example, the speaking unit adjusts the tone of the speaking by referring to the child's past responses. The speaking unit can also optimize the timing of speaking to the child based on the child's past responses. This makes it possible to provide optimal speaking content based on the child's past responses. For example, the speaking unit can optimize the speaking content by referring to the child's past responses when speaking to the child and reflect it in the system.

[0053] The calling unit can adjust the timing of calling to the child according to the rhythm of the child's daily life when calling to the child. The calling unit selects an appropriate timing of calling to the child based on, for example, the rhythm of the child's daily life. For example, the calling unit adjusts the content of the calling by referring to the rhythm of the child's daily life. The calling unit can also adjust the frequency of calling to the child according to the rhythm of the child's daily life. This makes it possible to provide timing of calling based on the rhythm of the child's daily life. For example, the calling unit can adjust the timing of calling to the child according to the rhythm of the child's daily life when calling to the child and reflect this in the system.

[0054] The calling unit can select the content of the call based on the child's interests and concerns when calling out to the child. For example, the calling unit calls out to the child with content that will interest the child. For example, the calling unit selects a topic to call out to based on the child's interests. The calling unit can also call out to the child with content that will interest the child by referring to the child's past behavior history. This makes it possible to provide call content based on the child's interests and concerns. For example, the calling unit can select the content of the call based on the child's interests and concerns when calling out to the child and reflect this in the system.

[0055] The calling unit can optimally call out to the child, taking into consideration the child's health condition. The calling unit selects appropriate content to call out to the child, for example, based on the child's health condition. For example, the calling unit can refer to the child's health record to select the optimal timing to call out to the child. The calling unit can also adjust the frequency of calls depending on the child's health condition. This makes it possible to provide optimal calls based on the child's health condition. For example, the calling unit can optimally call out to the child, taking into consideration the child's health condition, and reflect this in the system.

[0056] The calling unit can set the content of the calls according to the child's learning goals when calling out to the child. The calling unit selects appropriate content of the calls based on, for example, the child's learning goals. For example, the calling unit selects a topic for the calls by referring to the child's learning goals. The calling unit can also adjust the frequency of the calls according to the child's learning goals. This makes it possible to provide content of calls based on the child's learning goals. For example, the calling unit can set the content of the calls according to the child's learning goals when calling out to the child and reflect this in the system.

[0057] The calling unit can adjust the call content taking into consideration the child's friendships when calling out to the child. The calling unit selects appropriate call content, for example, based on the child's friendships. For example, the calling unit selects a call topic by referring to the child's friendships. The calling unit can also adjust the frequency of calls depending on the child's friendships. This makes it possible to provide call content based on the child's friendships. For example, the calling unit can adjust the call content taking into consideration the child's friendships when calling out to the child and reflect this in the system.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The conversation generation unit can adjust the style of the conversation depending on the child's learning style. For example, for visual learners, it can use a lot of diagrams and illustrations in the conversation. For auditory learners, it can also emphasize audio explanations and provide detailed audio guides. Furthermore, for experiential learners, it can generate conversations that encourage learning through actual experience. This makes it possible to provide the optimal conversation according to the child's learning style.

[0060] The conversation generation unit can incorporate quiz and game elements into the conversation to attract children's interest. For example, a simple quiz can be posed during the conversation, and children can answer it to promote learning. Game elements that allow children to earn points can also be incorporated into the conversation, allowing children to learn while having fun. Furthermore, storytelling can be used in the conversation to attract children's interest. This makes it possible to provide conversations that attract children's attention.

[0061] When displaying an image, the projection unit can adjust the image layout according to the user's visual preferences. For example, if the user prefers a simple layout, the image display can be simplified. On the other hand, if the user prefers a complex layout, the image display can be made complex. Furthermore, if the user prefers a specific color or design, the image layout can be customized according to that preference. This makes it possible to provide an image display that suits the user's visual preferences.

[0062] The speaking unit can adjust the content of the speech to suit the rhythm of a child's daily life. For example, it can say encouraging words when it's time to wake up in the morning, or relaxing words when it's time to go to bed at night. It can also provide advice about meals at mealtimes. This makes it possible to provide speech content that suits the rhythm of a child's daily life.

[0063] The setting unit can adjust the method of acquiring age information according to the child's learning progress. For example, if the child's learning progress is fast, the frequency of acquiring age information can be increased. On the other hand, if the child's learning progress is slow, the frequency of acquiring age information can be decreased. Furthermore, the method of acquiring age information can be customized according to the child's learning progress. This makes it possible to provide a method of acquiring age information according to the child's learning progress.

[0064] The conversation generation unit can adjust the content of the conversation based on the child's friendships. For example, if the child is studying with a friend, topics related to the friend can be incorporated. Also, if the child is playing with a friend, topics related to the play can be incorporated. Furthermore, if the child is competing with a friend, topics related to the competition can be incorporated. In this way, it is possible to provide conversation content based on the child's friendships.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The setting unit acquires the child's age information. For example, the setting unit can acquire the child's age information through the app, analyze the age information entered by the user, and reflect it in the system. Step 2: The conversation generation unit uses AI to generate age-appropriate conversations based on the acquired age information. For example, it might speak to a 3-year-old child in simple terms, and engage in more complex conversations with a 10-year-old child. The generation AI uses natural language processing technology to generate conversations. Step 3: The projection unit receives instructions from the conversation generation unit and projects related images onto a television or mobile phone. For example, if a child says, "Tell me about dinosaurs," an image of a dinosaur is projected onto a television or mobile phone. Based on the image selection criteria, an appropriate image is selected and projected. Step 4: The voice-calling unit receives instructions from the setting unit and speaks at the specified time. For example, it may say "good morning" or "good night" to coincide with the time the user wakes up in the morning or goes to bed at night. The voice-calling unit speaks at the appropriate timing based on the time set by the user.

[0067] (Example 2) A system according to an embodiment of the present invention uses a smart speaker to engage in conversations tailored to a child's age. This system acquires a child's age information, generates conversations appropriate to that age, projects related videos, and speaks to the child at a specified time. For example, a user can set the child's age through an app, and AI generates conversations appropriate to that age. The system also has a function to project related videos on a television or mobile phone. For example, if a child says, "Tell me about dinosaurs," the smart speaker projects a dinosaur video on the television or mobile phone. The system also has a function to speak to the child at a time specified by the app. For example, the smart speaker may say "good morning" or "good night" in accordance with the child's wake-up time or bedtime. This allows the system to support communication tailored to the child's development and deepen the bond between parent and child. For example, the system can engage in conversations based on the child's interests and support learning. Speaking to the child at a specified time also helps regulate the child's daily rhythm.

[0068] A smart speaker system according to an embodiment includes a setting unit, a conversation generation unit, a projection unit, and a voice-calling unit. The setting unit acquires a child's age information. For example, the setting unit can acquire the child's age information through an app. The setting unit can also analyze the age information entered by a user and reflect it in the system. The conversation generation unit uses AI to generate age-appropriate conversations based on the acquired age information. For example, the conversation generation unit speaks to a 3-year-old child in simple language and to a 10-year-old child in more complex conversations. The conversation generation unit uses a generative AI to generate conversations using natural language processing technology. The projection unit projects related images onto a television or mobile phone in response to instructions from the conversation generation unit. For example, if a child says, "Tell me about dinosaurs," the projection unit projects an image of a dinosaur onto a television or mobile phone. The projection unit can select and project appropriate images based on image selection criteria. The voice-calling unit receives instructions from the setting unit and calls out at a specified time. For example, the voice-calling unit may say "good morning" or "good night" in accordance with the time the user wakes up in the morning or goes to bed at night. The voice-calling unit may call out at an appropriate timing based on the time set by the user. This allows the smart speaker system according to the embodiment to have conversations, project images, and call out to the child according to their age. For example, the system may acquire the child's age information, generate conversations appropriate for that age, project related images, and call out at a specified time.

[0069] The setting unit can acquire the child's age information through the app. Examples of apps include, but are not limited to, mobile apps and web apps. For example, the setting unit can allow a user to input the child's age information through the app. The setting unit can also reflect the age information acquired through the app in the system. This allows the age information to be easily acquired through the app. For example, the setting unit can provide a simple interface when a user inputs the child's age information through the app, thereby minimizing the input steps. The setting unit can also automatically analyze the age information acquired through the app and reflect it in the system.

[0070] The conversation generation unit allows the AI ​​to generate age-appropriate conversation based on the acquired age information. The conversation generation unit generates age-appropriate conversation using, for example, natural language processing technology. For example, the conversation generation unit speaks in simple words to a 3-year-old child and in a more complex conversation to a 10-year-old child. The conversation generation unit can also generate age-appropriate conversation using a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the conversation generation unit can input a child's age information and instruct the generation AI to generate conversation appropriate to that age. This makes it possible to generate appropriate conversation appropriate to the child's age. For example, the conversation generation unit can use the generation AI to generate conversation appropriate to the child's age and speak to the child through a smart speaker.

[0071] The projection unit can project related video onto a television or a mobile phone upon receiving an instruction from the conversation generation unit. For example, the projection unit projects related video onto a television or a mobile phone upon receiving an instruction from the conversation generation unit. For example, if a child says, "Tell me about dinosaurs," the projection unit projects a video of a dinosaur onto a television or a mobile phone. The projection unit can also select and project appropriate video based on video selection criteria. This allows related video to be projected onto an appropriate device. For example, the projection unit can project related video onto a television or a mobile phone upon receiving an instruction from the conversation generation unit, thereby supporting learning with content that interests the child.

[0072] The voice-calling unit can call out at a specified time upon receiving an instruction from the setting unit. The voice-calling unit, for example, calls out at a specified time upon receiving an instruction from the setting unit. For example, the voice-calling unit calls out "good morning" or "good night" in accordance with the time to wake up in the morning or the time to go to bed at night. The voice-calling unit can also call out at an appropriate timing based on the time set by the user. This makes it possible to call out an appropriate message at a specified time. For example, the voice-calling unit can call out at a specified time upon receiving an instruction from the setting unit, thereby adjusting the rhythm of daily life.

[0073] The setting unit can estimate the user's emotions and adjust the input method for age information based on the estimated user emotions. For example, the setting unit can estimate the user's emotions and adjust the input method for age information based on the estimated user emotions. For example, if the user is feeling stressed, the setting unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the setting unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the setting unit can prioritize voice input to enable the user to quickly input age information. This makes it possible to provide an input method that suits the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the setting unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0074] The setting unit can refer to the child's past age information and automatically update the age information in accordance with the child's growth. For example, the setting unit can automatically update the age information on the child's birthday. For example, the setting unit can refer to the child's growth record and update the age information periodically. The setting unit can also update the age information based on growth events (such as enrollment and graduation) entered by the parent. This allows the age information to be automatically updated in accordance with the child's growth. For example, the setting unit can refer to the child's past age information and automatically update the age information in accordance with the child's growth and reflect it in the system.

[0075] The setting unit can acquire additional information taking into consideration the interests and concerns of the child when acquiring age information. The setting unit acquires age information based on, for example, a character or theme that the child likes. For example, the setting unit acquires age information through questions that pique the child's interest. The setting unit can also acquire age information based on the interests and concerns by referring to the child's past behavior history. This makes it possible to acquire information based on the child's interests and concerns. For example, the setting unit can acquire additional information taking into consideration the interests and concerns of the child when acquiring age information and reflect it in the system.

[0076] The setting unit can customize the setting method by reflecting parental feedback when acquiring age information. The setting unit adjusts the method for inputting age information based on, for example, feedback provided by the parent. For example, the setting unit customizes the method for acquiring age information based on specific conditions set by the parent. The setting unit can also optimize the method for acquiring age information by referring to feedback previously provided by the parent. This allows the setting method to be customized based on parental feedback. For example, the setting unit can customize the setting method by reflecting parental feedback when acquiring age information and reflect the customized method in the system.

[0077] The setting unit can estimate the user's emotions and adjust the frequency of age information acquisition based on the estimated user emotions. For example, the setting unit can reduce the frequency of age information acquisition when the user is feeling stressed. For example, the setting unit can increase the frequency of age information acquisition when the user is relaxed. The setting unit can also temporarily reduce the frequency of age information acquisition when the user is in a hurry. This makes it possible to provide an acquisition frequency that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the setting unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0078] The setting unit can perform optimal settings taking into consideration the child's health condition when acquiring age information. The setting unit adjusts the method for acquiring age information based on the child's health condition, for example. For example, the setting unit refers to the child's health records and suggests the optimal method for acquiring age information. The setting unit can also adjust the frequency of acquiring age information depending on the child's health condition. This allows optimal settings to be performed based on the child's health condition. For example, the setting unit can perform optimal settings taking into consideration the child's health condition when acquiring age information and reflect them in the system.

[0079] The setting unit can optimize the settings by referring to the child's learning history when acquiring age information. The setting unit, for example, adjusts the method for acquiring age information based on the child's learning history. For example, the setting unit suggests the optimal method for acquiring age information by referring to the child's learning progress. The setting unit can also adjust the frequency of acquiring age information according to the child's learning history. This allows optimal settings to be made based on the child's learning history. For example, the setting unit can optimize the settings by referring to the child's learning history when acquiring age information and reflect them in the system.

[0080] The setting unit can adjust the setting contents taking into consideration the child's friendships when acquiring age information. The setting unit adjusts the method for acquiring age information based on, for example, the child's friendships. For example, the setting unit suggests an optimal method for acquiring age information by referring to the child's friendships. The setting unit can also adjust the frequency of acquiring age information depending on the child's friendships. This allows settings to be made based on the child's friendships. For example, the setting unit can adjust the setting contents taking into consideration the child's friendships when acquiring age information and reflect the adjustment in the system.

[0081] The conversation generation unit can estimate the user's emotions and adjust the tone of the conversation based on the estimated user's emotions. For example, if the user is relaxed, the conversation generation unit generates conversation in a calm tone. For example, if the user is excited, the conversation generation unit can generate conversation in a lively tone. Furthermore, if the user is sad, the conversation generation unit can also generate conversation in a comforting tone. This makes it possible to provide a conversation tone that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the conversation generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0082] The conversation generation unit can optimize the content of the conversation by referring to the child's past conversation history when generating the conversation. The conversation generation unit, for example, selects an interesting topic based on the child's past conversation history. For example, the conversation generation unit selects appropriate language by referring to the child's past conversation history. The conversation generation unit can also optimize the flow of the conversation based on the child's past conversation history. This makes it possible to generate an optimal conversation based on the child's past conversation history. For example, the conversation generation unit can optimize the content of the conversation by referring to the child's past conversation history when generating the conversation, and reflect this in the system.

[0083] The conversation generation unit can adjust the difficulty of the conversation according to the child's learning progress when generating the conversation. The conversation generation unit generates a conversation of an appropriate level of difficulty based on, for example, the child's learning progress. For example, the conversation generation unit adjusts the content of the conversation by referring to the child's learning progress. The conversation generation unit can also select a topic of the conversation according to the child's learning progress. This makes it possible to provide a level of difficulty of the conversation according to the child's learning progress. For example, the conversation generation unit can adjust the difficulty of the conversation according to the child's learning progress when generating the conversation and reflect this in the system.

[0084] The conversation generation unit can select a conversation topic based on the child's interests and concerns when generating a conversation. The conversation generation unit, for example, selects a topic that will interest the child and generates a conversation. For example, the conversation generation unit selects a conversation topic based on the child's interests. The conversation generation unit can also select an interesting topic by referring to the child's past behavior history. This makes it possible to provide a conversation topic based on the child's interests and concerns. For example, the conversation generation unit can select a conversation topic based on the child's interests and concerns when generating a conversation and reflect this in the system.

[0085] The conversation generation unit can estimate the user's emotions and adjust the length of the conversation based on the estimated user emotions. For example, the conversation generation unit generates a longer conversation when the user is relaxed. For example, the conversation generation unit generates a short, to-the-point conversation when the user is in a hurry. The conversation generation unit can also generate a lively, short conversation when the user is excited. This makes it possible to provide a conversation length that matches the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the conversation generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0086] The conversation generation unit can customize the conversation content based on events in the child's daily life when generating the conversation. The conversation generation unit generates a conversation related to, for example, the child's birthday. For example, the conversation generation unit customizes the conversation based on the child's school events. The conversation generation unit can also adjust the conversation content according to events in the child's daily life. This makes it possible to provide conversation content based on events in the child's daily life. For example, the conversation generation unit can customize the conversation content based on events in the child's daily life when generating the conversation and reflect this in the system.

[0087] The conversation generation unit can set the content of the conversation according to the child's learning goals when generating the conversation. The conversation generation unit generates appropriate conversation based on, for example, the child's learning goals. For example, the conversation generation unit selects a conversation topic by referring to the child's learning goals. The conversation generation unit can also adjust the difficulty of the conversation according to the child's learning goals. This makes it possible to provide conversation content based on the child's learning goals. For example, the conversation generation unit can set the content of the conversation according to the child's learning goals when generating the conversation and reflect this in the system.

[0088] The conversation generation unit can adjust the content of the conversation taking into consideration the child's friendships when generating the conversation. The conversation generation unit generates an appropriate conversation based on, for example, the child's friendships. For example, the conversation generation unit selects a conversation topic by referring to the child's friendships. The conversation generation unit can also adjust the content of the conversation depending on the child's friendships. This makes it possible to provide conversation content based on the child's friendships. For example, the conversation generation unit can adjust the content of the conversation taking into consideration the child's friendships when generating the conversation and reflect it in the system.

[0089] The projection unit can estimate the user's emotion and adjust the image display method based on the estimated user emotion. For example, if the user is relaxed, the projection unit displays a calm image. For example, if the user is excited, the projection unit can display a visually stimulating image. Furthermore, if the user is sad, the projection unit can also display a comforting image. This makes it possible to provide an image display method that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the projection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0090] The projection unit can optimize the video content by referring to the child's past viewing history when projecting. The projection unit, for example, selects video that will attract the child's attention based on the child's past viewing history. For example, the projection unit selects appropriate video by referring to the child's past viewing history. The projection unit can also optimize the video content based on the child's past viewing history. This makes it possible to provide optimal video content based on the child's past viewing history. For example, the projection unit can optimize the video content by referring to the child's past viewing history when projecting, and reflect the optimized content in the system.

[0091] The projection unit can adjust the content of the video according to the child's learning progress when projecting. The projection unit, for example, selects appropriate video based on the child's learning progress. For example, the projection unit adjusts the content of the video by referring to the child's learning progress. The projection unit can also select the topic of the video according to the child's learning progress. This makes it possible to provide video content according to the child's learning progress. For example, the projection unit can adjust the content of the video according to the child's learning progress when projecting and reflect it in the system.

[0092] The projection unit can select a video theme based on the child's interests and concerns at the time of projection. For example, the projection unit selects a theme that will interest the child and projects the video. For example, the projection unit selects a video topic based on the child's interests. The projection unit can also select an interesting theme by referring to the child's past behavior history. This makes it possible to provide a video theme based on the child's interests and concerns. For example, the projection unit can select a video theme based on the child's interests and concerns at the time of projection and reflect it in the system.

[0093] The projection unit can estimate the user's emotion and adjust the length of the video based on the estimated user emotion. For example, if the user is relaxed, the projection unit projects a longer video. For example, if the user is in a hurry, the projection unit can project a short, to-the-point video. The projection unit can also project a short, visually stimulating video if the user is excited. This allows the video length to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the projection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0094] The projection unit can customize the video content based on events in the child's daily life at the time of projection. For example, the projection unit projects a video related to the child's birthday. For example, the projection unit customizes the video based on the child's school events. The projection unit can also adjust the video content according to events in the child's daily life. This makes it possible to provide video content based on events in the child's daily life. For example, the projection unit can customize the video content based on events in the child's daily life at the time of projection and reflect it in the system.

[0095] The projection unit can set the content of the video according to the child's learning goals when projecting. The projection unit projects appropriate video based on, for example, the child's learning goals. For example, the projection unit selects a topic for the video by referring to the child's learning goals. The projection unit can also adjust the difficulty level of the video according to the child's learning goals. This makes it possible to provide video content based on the child's learning goals. For example, the projection unit can set the content of the video according to the child's learning goals when projecting and reflect this in the system.

[0096] The projection unit can adjust the video content taking into account the child's friendships when projecting. The projection unit projects an appropriate video based on, for example, the child's friendships. For example, the projection unit selects a topic for the video by referring to the child's friendships. The projection unit can also adjust the video content according to the child's friendships. This makes it possible to provide video content based on the child's friendships. For example, the projection unit can adjust the video content taking into account the child's friendships when projecting and reflect it in the system.

[0097] The voice-calling unit can estimate the user's emotions and adjust the tone of the voice-call based on the estimated user's emotions. For example, if the user is relaxed, the voice-calling unit can use a calm tone to voice the user. For example, if the user is excited, the voice-calling unit can use a lively tone to voice the user. Furthermore, if the user is sad, the voice-calling unit can use a comforting tone to voice the user. This allows the voice-calling unit to provide a tone of voice that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the voice-calling unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0098] The speaking unit can optimize the speaking content by referring to the child's past responses when speaking to the child. The speaking unit selects appropriate speaking content based on, for example, the child's past responses. For example, the speaking unit adjusts the tone of the speaking by referring to the child's past responses. The speaking unit can also optimize the timing of speaking to the child based on the child's past responses. This makes it possible to provide optimal speaking content based on the child's past responses. For example, the speaking unit can optimize the speaking content by referring to the child's past responses when speaking to the child and reflect it in the system.

[0099] The calling unit can adjust the timing of calling to the child according to the rhythm of the child's daily life when calling to the child. The calling unit selects an appropriate timing of calling to the child based on, for example, the rhythm of the child's daily life. For example, the calling unit adjusts the content of the calling by referring to the rhythm of the child's daily life. The calling unit can also adjust the frequency of calling to the child according to the rhythm of the child's daily life. This makes it possible to provide timing of calling based on the rhythm of the child's daily life. For example, the calling unit can adjust the timing of calling to the child according to the rhythm of the child's daily life when calling to the child and reflect this in the system.

[0100] The calling unit can select the content of the call based on the child's interests and concerns when calling out to the child. For example, the calling unit calls out to the child with content that will interest the child. For example, the calling unit selects a topic to call out to based on the child's interests. The calling unit can also call out to the child with content that will interest the child by referring to the child's past behavior history. This makes it possible to provide call content based on the child's interests and concerns. For example, the calling unit can select the content of the call based on the child's interests and concerns when calling out to the child and reflect this in the system.

[0101] The speaking unit can estimate the user's emotions and adjust the frequency of speaking to the user based on the estimated user emotions. For example, the speaking unit can increase the frequency of speaking to the user when the user is relaxed. For example, the speaking unit can decrease the frequency of speaking to the user when the user is in a hurry. The speaking unit can also adjust the frequency of speaking to the user when the user is excited. This makes it possible to provide a frequency of speaking to the user according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the speaking unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0102] The calling unit can optimally call out to the child, taking into consideration the child's health condition. The calling unit selects appropriate content to call out to the child, for example, based on the child's health condition. For example, the calling unit can refer to the child's health record to select the optimal timing to call out to the child. The calling unit can also adjust the frequency of calls depending on the child's health condition. This makes it possible to provide optimal calls based on the child's health condition. For example, the calling unit can optimally call out to the child, taking into consideration the child's health condition, and reflect this in the system.

[0103] The calling unit can set the content of the calls according to the child's learning goals when calling out to the child. The calling unit selects appropriate content of the calls based on, for example, the child's learning goals. For example, the calling unit selects a topic for the calls by referring to the child's learning goals. The calling unit can also adjust the frequency of the calls according to the child's learning goals. This makes it possible to provide content of calls based on the child's learning goals. For example, the calling unit can set the content of the calls according to the child's learning goals when calling out to the child and reflect this in the system.

[0104] The calling unit can adjust the call content taking into consideration the child's friendships when calling out to the child. The calling unit selects appropriate call content, for example, based on the child's friendships. For example, the calling unit selects a call topic by referring to the child's friendships. The calling unit can also adjust the frequency of calls depending on the child's friendships. This makes it possible to provide call content based on the child's friendships. For example, the calling unit can adjust the call content taking into consideration the child's friendships when calling out to the child and reflect this in the system. === Hard Collateral 1-1 === Each of the multiple elements including the setting unit, conversation generation unit, projection unit, and voice-calling unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart device 14 and acquires the child's age information through an app. The conversation generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates conversation appropriate to the child's age based on the acquired age information. The projection unit is realized, for example, by the control unit 46A of the smart device 14 and projects related video onto a television or mobile phone. The voice-calling unit is realized, for example, by the control unit 46A of the smart device 14 and makes a call at a specified time. === Hard Collateral 1-2 === Each of the multiple elements including the setting unit, conversation generation unit, projection unit, and speaking unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart glasses 214 and acquires the child's age information through an app. The conversation generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates conversation appropriate to the child's age based on the acquired age information. The projection unit is realized, for example, by the control unit 46A of the smart glasses 214 and projects related video onto a television or mobile phone. The speaking unit is realized, for example, by the control unit 46A of the smart glasses 214 and makes a speech at a specified time. === Hard Collateral 1-3 === Each of the multiple elements including the setting unit, conversation generation unit, projection unit, and voice-calling unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the headset-type terminal 314 and acquires the child's age information through an app. The conversation generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates conversation appropriate to the child's age based on the acquired age information. The projection unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and projects related video onto a television or mobile phone. The voice-calling unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and makes a call at a specified time. === Hard Collateral 1-4 === Each of the multiple elements including the setting unit, conversation generation unit, projection unit, and voice-calling unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the robot 414 and acquires the child's age information through an app. The conversation generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates conversation appropriate to the child's age based on the acquired age information. The projection unit is realized, for example, by the control unit 46A of the robot 414 and projects related video onto a television or mobile phone. The voice-calling unit is realized, for example, by the control unit 46A of the robot 414 and makes a call at a specified time.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The setting unit can analyze the user's voice tone and speaking speed to estimate the user's stress level. For example, if the user speaks quickly, the setting unit can estimate that the user is stressed and simplify the interface. If the user speaks slowly, the setting unit can estimate that the user is relaxed and provide more detailed input options. Furthermore, if the user's voice tone is high-pitched, the setting unit can estimate that the user is excited and adjust the interface. This makes it possible to optimize the method for inputting age information based on the characteristics of the user's voice.

[0107] The conversation generation unit can adjust the style of the conversation depending on the child's learning style. For example, for visual learners, it can use a lot of diagrams and illustrations in the conversation. For auditory learners, it can also emphasize audio explanations and provide detailed audio guides. Furthermore, for experiential learners, it can generate conversations that encourage learning through actual experience. This makes it possible to provide the optimal conversation according to the child's learning style.

[0108] The projection unit can estimate the user's emotion and adjust the color tone of the image based on the estimated user's emotion. For example, if the user is relaxed, it can display an image in warm colors. If the user is excited, it can display an image in vivid colors. Furthermore, if the user is sad, it can display an image in subdued colors. In this way, it is possible to provide an image color tone that corresponds to the user's emotion.

[0109] The voice-calling unit can estimate the user's emotions and adjust the voice content based on the estimated user's emotions. For example, if the user is relaxed, the voice-calling unit can offer encouraging words. If the user is excited, the voice-calling unit can offer calming words. Furthermore, if the user is sad, the voice-calling unit can offer comforting words. This makes it possible to provide voice content that matches the user's emotions.

[0110] The setting unit can estimate the user's emotions and adjust the input method for age information based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, the setting unit can prioritize voice input to enable the user to quickly input age information. In this way, an input method can be provided according to the user's emotions.

[0111] The conversation generation unit can incorporate quiz and game elements into the conversation to attract children's interest. For example, a simple quiz can be posed during the conversation, and children can answer it to promote learning. Game elements that allow children to earn points can also be incorporated into the conversation, allowing children to learn while having fun. Furthermore, storytelling can be used in the conversation to attract children's interest. This makes it possible to provide conversations that attract children's attention.

[0112] When displaying an image, the projection unit can adjust the image layout according to the user's visual preferences. For example, if the user prefers a simple layout, the image display can be simplified. On the other hand, if the user prefers a complex layout, the image display can be made complex. Furthermore, if the user prefers a specific color or design, the image layout can be customized according to that preference. This makes it possible to provide an image display that suits the user's visual preferences.

[0113] The speaking unit can adjust the content of the speech to suit the rhythm of a child's daily life. For example, it can say encouraging words when it's time to wake up in the morning, or relaxing words when it's time to go to bed at night. It can also provide advice about meals at mealtimes. This makes it possible to provide speech content that suits the rhythm of a child's daily life.

[0114] The setting unit can adjust the method of acquiring age information according to the child's learning progress. For example, if the child's learning progress is fast, the frequency of acquiring age information can be increased. On the other hand, if the child's learning progress is slow, the frequency of acquiring age information can be decreased. Furthermore, the method of acquiring age information can be customized according to the child's learning progress. This makes it possible to provide a method of acquiring age information according to the child's learning progress.

[0115] The conversation generation unit can adjust the content of the conversation based on the child's friendships. For example, if the child is studying with a friend, topics related to the friend can be incorporated. Also, if the child is playing with a friend, topics related to the play can be incorporated. Furthermore, if the child is competing with a friend, topics related to the competition can be incorporated. In this way, it is possible to provide conversation content based on the child's friendships.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The setting unit acquires the child's age information. For example, the setting unit can acquire the child's age information through the app, analyze the age information entered by the user, and reflect it in the system. Step 2: The conversation generation unit uses AI to generate age-appropriate conversations based on the acquired age information. For example, it might speak to a 3-year-old child in simple terms, and engage in more complex conversations with a 10-year-old child. The generation AI uses natural language processing technology to generate conversations. Step 3: The projection unit receives instructions from the conversation generation unit and projects related images onto a television or mobile phone. For example, if a child says, "Tell me about dinosaurs," an image of a dinosaur is projected onto a television or mobile phone. Based on the image selection criteria, an appropriate image is selected and projected. Step 4: The voice-calling unit receives instructions from the setting unit and speaks at the specified time. For example, it may say "good morning" or "good night" to coincide with the time the user wakes up in the morning or goes to bed at night. The voice-calling unit speaks at the appropriate timing based on the time set by the user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a setting unit that acquires a child's age information; a conversation generation unit that generates a conversation based on the age information acquired by the setting unit; a projection unit that projects related video based on the conversation generated by the conversation generation unit; and a calling unit that calls out to the child at a time specified by the setting unit.

2. The setting unit Obtaining children's age information through the app 2. The system of claim 1.

3. The conversation generation unit Based on the acquired age information, AI generates age-appropriate conversations.

2. The system of claim 1.

4. 2. The system according to claim 1, wherein the projection unit projects the related video onto a television or a mobile phone in response to an instruction from the conversation generation unit.

5. The calling unit Receiving instructions from the setting unit, the call is made at a specified time.

2. The system of claim 1.

6. The system according to claim 1 , wherein the setting unit estimates a user's emotion and adjusts a method for inputting age information based on the estimated user's emotion.

7. The setting unit Refer to the child's past age information and automatically update the age information as the child grows.

2. The system of claim 1.

8. The setting unit When acquiring age information, acquire additional information taking into account the child's interests and concerns.

2. The system of claim 1.

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