System

Abstract

An object of a system according to an embodiment is to enable a parent to serve as a conversation partner or a play partner for a child even when the parent is busy.SOLUTION: A system according to an embodiment includes an avatar generation unit, a conversation response unit, and a play suggestion unit. The avatar generation unit generates a virtual protector avatar on the basis of the personality diagnosis result of the protector. The conversation response unit uses the generated avatar to serve as a conversation partner of the child. The play suggestion section uses the avatar to play as a play partner for the child.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 was difficult for busy parents to talk to or play with their children, which placed a heavy burden on child-rearing.

[0005] The system according to the embodiment aims to provide a conversation partner and play partner for children even when the parents are busy. [Means for solving the problem]

[0006] The system according to the embodiment includes an avatar generation unit, a conversation response unit, and a play suggestion unit. The avatar generation unit generates a virtual guardian avatar based on the results of a personality assessment of the guardian. The conversation response unit uses the generated avatar to talk to the child. The play suggestion unit uses the avatar to play with the child. [Effects of the Invention]

[0007] The system according to the embodiment allows parents to talk and play with their children even when they are busy. [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) The child support system according to an embodiment of the present invention generates a virtual guardian avatar, and the generating AI acts as a conversation partner and play partner for the child. This reduces the burden on guardians and supports the healthy growth of children.

[0029] The child interaction system according to the embodiment includes an avatar generation unit, a conversation response unit, and a play suggestion unit. The avatar generation unit generates a virtual guardian avatar based on the results of a personality assessment of the guardian. For example, the guardian takes a personality assessment test, and the avatar's personality is determined based on the results. The generation AI analyzes the personality assessment results and generates an avatar that closely resembles the guardian's personality. The generation AI receives input from prompts containing instructions from the guardian on what the guardian wants the generation AI to do, and the generation AI generates an avatar based on the prompts. The conversation response unit uses the generated avatar to interact with the child. For example, if a child asks, "What did you do at school today?", the generation AI responds with, "What kind of class did you have today? Was it fun?" The generation AI receives input from the child, and generates an appropriate response based on that input. The play suggestion unit uses the avatar to interact with the child. For example, if a child says, "Let's draw a picture together," the generation AI responds with, "What kind of picture do you want to draw? Let's think about it together," thereby supporting the child in drawing pictures. The AI ​​generator receives input from the child's proposed play content, and the AI ​​then proposes appropriate play based on that content. This allows the child interaction system according to the embodiment to reduce the burden on parents and support the healthy development of children.

[0030] The avatar generation unit analyzes the parent's past SNS posts and message history and can generate an avatar based on more accurate personality assessment results. For example, the avatar generation unit collects the parent's SNS posts and message history and analyzes their content using natural language processing technology. Based on the analysis results, the unit obtains a detailed understanding of the parent's personality and interests and reflects this in the avatar's personality settings. The avatar generation unit also analyzes the parent's past messages and post content and extracts frequently used words and phrases. This allows the avatar to imitate the parent's speaking style and expressions. The avatar generation unit also analyzes the parent's emotional tendencies and stress level based on the parent's SNS posts and message history and reflects this in the avatar's responses. For example, when the parent is feeling stressed, the avatar is set to respond more gently. This allows for more accurate avatar generation by analyzing the parent's past SNS posts and message history.

[0031] The avatar generation unit can analyze voice data to imitate the guardian's tone of voice and speaking style and reflect this in the avatar's voice responses. For example, the avatar generation unit collects the guardian's voice data and extracts characteristics of the guardian's tone of voice and speaking style using voice analysis technology. This allows the avatar to respond in a voice similar to that of the guardian. The avatar generation unit can also analyze voice messages and phone call recordings previously recorded by the guardian and have the avatar learn specific phrases and intonations. This allows the avatar to more naturally imitate the guardian's speaking style. The avatar generation unit can also generate the avatar's voice using voice synthesis technology based on the guardian's voice data. For example, the avatar can reproduce the pitch and rhythm of the guardian's voice to provide a voice response that is familiar to children. This allows the avatar to be generated by imitating the guardian's tone of voice and speaking style.

[0032] The avatar generation unit adds a function that allows customization of the avatar's appearance and clothing, making it possible to create an avatar that is easy for children to relate to. The avatar generation unit, for example, provides an interface that allows customization of the avatar's appearance and clothing, allowing children to set up the avatar to suit their preferences. For example, the child can select hairstyle, clothing, accessories, etc. The avatar generation unit also provides an option to reflect the guardian's characteristics when customizing the avatar's appearance. For example, the guardian's hair color and eye color can be reflected in the avatar. The avatar generation unit also adds a function that allows the avatar's appearance and clothing to be changed according to the season or event. For example, the avatar's clothing can be changed to match events such as Christmas or Halloween. This allows creation of an avatar that is easy for children to relate to, making communication with the child smoother.

[0033] The avatar generation unit can generate avatars that correspond to different cultures and languages, enabling international use. For example, the avatar generation unit develops a multilingual generation AI to generate avatars that correspond to different cultures and languages. For example, it provides avatars that correspond to multiple languages, such as English, French, and Chinese. The avatar generation unit also collects cultural background information and trains the generation AI to generate avatars that reflect the characteristics of different cultures. For example, it reflects greetings and etiquette in a particular culture in the avatar. The avatar generation unit also provides a function that allows customization of the appearance and clothing of avatars that correspond to different cultures and languages, taking international use into consideration. For example, it allows users to select traditional clothing and accessories from each country. This allows for international use by supporting different cultures and languages.

[0034] The conversation response unit can analyze a child's past conversation history and generate conversation patterns optimized for each individual child. For example, the conversation response unit collects a child's past conversation history and analyzes the content using natural language processing technology. This allows the unit to understand the child's interests and generate optimal conversation patterns. The conversation response unit also analyzes what the child has said in the past and extracts frequently used words and phrases. This allows the avatar to imitate the child's speaking style and expressions. The conversation response unit also develops a system that automatically generates conversation topics based on the child's past conversation history in accordance with the child's interests and concerns. For example, it suggests conversations about the child's favorite characters or hobbies. In this way, by analyzing a child's past conversation history, it is possible to generate conversation patterns optimized for each individual child.

[0035] The conversation response unit can periodically collect information about children's interests and concerns in the form of a questionnaire and update the conversation content based on the results. The conversation response unit, for example, builds a system that periodically collects information about children's interests and concerns in the form of a questionnaire and updates the conversation content based on the results. For example, it asks questions about the anime or games that the child likes. The conversation response unit also analyzes the questionnaire results and automatically generates conversation topics based on the children's interests and concerns. For example, it suggests conversations about topics that the child has recently become interested in. The conversation response unit also periodically conducts questionnaires to understand changes in the children's interests and concerns. This allows the avatar to always have a conversation based on the latest information. This allows the avatar to always have a conversation based on the latest information by periodically collecting information about children's interests and concerns and updating the conversation content.

[0036] The conversation response unit can add a function that allows a child to select a character or animal avatar that the child likes, thereby making the conversation more enjoyable. The conversation response unit, for example, provides an interface that allows a child to select a character or animal avatar that the child likes, thereby making the conversation more enjoyable. For example, an anime character or a pet avatar can be selected. The conversation response unit also customizes the content of the conversation based on the character or animal avatar selected by the child. For example, the characteristics and lines of the selected character can be incorporated into the conversation. The conversation response unit also provides an option to reflect the parent's opinion when selecting a character or animal avatar that the child likes. For example, the parent can select a character recommended by the parent. This makes the conversation more enjoyable by allowing the child to select a character or animal avatar that the child likes.

[0037] The conversation response unit can automatically record what the child says in diary format so that parents can check it later. The conversation response unit, for example, creates a system that automatically records what the child says in text format and saves it as a diary. For example, the conversation content is organized and saved by date and time. The conversation response unit also provides a dedicated interface so that parents can check the recorded diary later. For example, parents can view the diary on a smartphone or computer. The conversation response unit also records what the child says in audio format so that parents can play it back as audio data. For example, important conversations and memories are saved as audio. In this way, parents can keep track of their child's growth by recording what the child says and allowing them to check it later.

[0038] The play suggestion unit can analyze a child's play history and suggest new games based on the games that the child enjoyed most. For example, the play suggestion unit collects a child's past play history and analyzes the content using natural language processing technology. This identifies the games that the child enjoyed most and suggests new games. The play suggestion unit also analyzes patterns of games that the child enjoyed in the past and extracts games that were played frequently or that the child particularly enjoyed. This allows the avatar to suggest new games that suit the child. The play suggestion unit will also develop a system that automatically generates new games based on the child's play history and matches the child's interests. For example, it will suggest games based on the child's favorite themes. This makes it possible to provide more enjoyable games for children by analyzing a child's play history and suggesting new games based on the games that the child enjoyed most.

[0039] The play suggestion unit can add a function to automatically adjust the difficulty of games according to the child's age and developmental stage. The play suggestion unit, for example, builds a system that automatically adjusts the difficulty of games based on the child's age and developmental stage. For example, it suggests games appropriate for the child's age. The play suggestion unit also analyzes the child's developmental stage and adjusts the difficulty of games based on the results. For example, it gradually increases the difficulty of games as the child grows. The play suggestion unit also develops an algorithm that automatically adjusts the difficulty of games according to the child's age and developmental stage. For example, it customizes the content of games to match the child's skill level. This makes it possible to provide appropriate games by automatically adjusting the difficulty of games according to the child's age and developmental stage.

[0040] The play suggestion unit adds a function that allows children to play with other children online, thereby fostering social skills. The play suggestion unit fosters social skills, for example, by providing a platform where children can play with other children online. For example, children can interact with other children through online games or collaborative activities. The play suggestion unit also builds a system for obtaining parental permission when playing together online. For example, it allows parents to check in advance who their child will be playing with. The play suggestion unit also adds a function in which an avatar acts as an intermediary when a child plays with other children online. For example, the avatar can explain the rules of the game or mediate if a dispute arises. This allows children to develop social skills by playing together online with other children.

[0041] The play suggestion unit can add educational content that allows children to learn while playing, thereby fusing learning and play. For example, the play suggestion unit provides educational content that allows children to learn while playing, fusing learning and play. For example, it provides games that allow children to learn arithmetic or science knowledge. The play suggestion unit also allows children to learn naturally by incorporating educational content into play. For example, children can acquire knowledge through puzzle or quiz-style play. The play suggestion unit also builds a system that adjusts the difficulty level of the educational content according to the child's learning progress. For example, it provides questions at a level that is easy for children to understand. In this way, learning and play can be fusing by providing educational content that allows children to learn while playing.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The child-focused system can also be equipped with a health management unit that monitors the child's health. For example, while the child is playing, the health management unit can measure the child's heart rate and body temperature and notify the parent if any abnormalities are detected. The health management unit can also manage the child's diet and exercise records and provide advice to support healthy lifestyle habits. For example, if a child is not getting enough exercise, the avatar will suggest games that will encourage exercise. This allows the system to monitor a child's health in real time and support healthy lifestyle habits.

[0044] The child-oriented system can further include a learning management unit that manages a child's learning progress. For example, the learning management unit records what a child has learned and their progress, and reports this to parents and teachers. The learning management unit can also suggest optimal learning content based on a child's learning style and interests. For example, if a child is interested in mathematics, it can suggest mathematics-related games and quizzes. This makes it possible to manage a child's learning progress and support effective learning.

[0045] The child interaction system may further include a creative support unit to foster children's creativity. For example, the creative support unit may support children in activities such as drawing pictures and writing stories. The creative support unit may also digitally save the works created by children and share them with parents and friends. For example, children may upload their drawings to an online gallery and interact with other children. This may foster children's creativity and provide opportunities for self-expression.

[0046] The child interaction system can further include a social interaction section to foster children's social skills. For example, the social interaction section provides opportunities for children to interact with other children online. The social interaction section can also plan online events and workshops that children can participate in and support activities that foster social skills. For example, children can play games with other children or work on projects together. This can foster children's social skills and improve their ability to communicate with others.

[0047] The child interaction system can further include a relaxation section to stabilize the child's emotions. For example, the relaxation section can provide music and images that help the child relax. If the child is feeling stressed, the relaxation section can also suggest games or activities that will help them relax. For example, if the child is tired, the avatar can suggest yoga or meditation to help them relax. This can stabilize the child's emotions and provide an environment where they can relax.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The avatar generation unit generates a virtual guardian avatar based on the guardian's personality assessment results. For example, the guardian takes a personality assessment test, and the avatar's personality is determined based on the results. The generation AI analyzes the personality assessment results and generates an avatar that closely resembles the guardian's personality. The input to the generation AI is a prompt containing instructions on what the guardian wants the generation AI to do, and the generation AI generates an avatar based on that prompt. Step 2: The conversation response unit uses the generated avatar to act as a conversation partner for the child. For example, if a child asks, "What did you do at school today?", the generation AI will respond with something like, "What kind of lessons did you have today? Was it fun?" The input to the generation AI is what the child said, and the generation AI generates an appropriate response based on that content. Step 3: The play suggestion unit uses an avatar to act as a playmate for the child. For example, if a child says, "Let's draw a picture together," the generation AI responds by saying, "What kind of picture do you want to draw? Let's think about it together," supporting the child in the activity of drawing a picture together. The input to the generation AI is the content of the play proposed by the child, and the generation AI suggests appropriate games based on that content.

[0050] (Example 2) The child support system according to an embodiment of the present invention generates a virtual guardian avatar, and the generating AI acts as a conversation partner and play partner for the child. This reduces the burden on guardians and supports the healthy growth of children.

[0051] The child interaction system according to the embodiment includes an avatar generation unit, a conversation response unit, and a play suggestion unit. The avatar generation unit generates a virtual guardian avatar based on the results of a personality assessment of the guardian. For example, the guardian takes a personality assessment test, and the avatar's personality is determined based on the results. The generation AI analyzes the personality assessment results and generates an avatar that closely resembles the guardian's personality. The generation AI receives input from prompts containing instructions from the guardian on what the guardian wants the generation AI to do, and the generation AI generates an avatar based on the prompts. The conversation response unit uses the generated avatar to interact with the child. For example, if a child asks, "What did you do at school today?", the generation AI responds with, "What kind of class did you have today? Was it fun?" The generation AI receives input from the child, and generates an appropriate response based on that input. The play suggestion unit uses the avatar to interact with the child. For example, if a child says, "Let's draw a picture together," the generation AI responds with, "What kind of picture do you want to draw? Let's think about it together," thereby supporting the child in drawing pictures. The AI ​​generator receives input from the child's proposed play content, and the AI ​​then proposes appropriate play based on that content. This allows the child interaction system according to the embodiment to reduce the burden on parents and support the healthy development of children.

[0052] The avatar generation unit analyzes the parent's past SNS posts and message history and can generate an avatar based on more accurate personality assessment results. For example, the avatar generation unit collects the parent's SNS posts and message history and analyzes their content using natural language processing technology. Based on the analysis results, the unit obtains a detailed understanding of the parent's personality and interests and reflects this in the avatar's personality settings. The avatar generation unit also analyzes the parent's past messages and post content and extracts frequently used words and phrases. This allows the avatar to imitate the parent's speaking style and expressions. The avatar generation unit also analyzes the parent's emotional tendencies and stress level based on the parent's SNS posts and message history and reflects this in the avatar's responses. For example, when the parent is feeling stressed, the avatar is set to respond more gently. This allows for more accurate avatar generation by analyzing the parent's past SNS posts and message history.

[0053] The avatar generation unit can analyze voice data to imitate the guardian's tone of voice and speaking style and reflect this in the avatar's voice responses. For example, the avatar generation unit collects the guardian's voice data and extracts characteristics of the guardian's tone of voice and speaking style using voice analysis technology. This allows the avatar to respond in a voice similar to that of the guardian. The avatar generation unit can also analyze voice messages and phone call recordings previously recorded by the guardian and have the avatar learn specific phrases and intonations. This allows the avatar to more naturally imitate the guardian's speaking style. The avatar generation unit can also generate the avatar's voice using voice synthesis technology based on the guardian's voice data. For example, the avatar can reproduce the pitch and rhythm of the guardian's voice to provide a voice response that is familiar to children. This allows the avatar to be generated by imitating the guardian's tone of voice and speaking style.

[0054] The avatar generation unit can analyze the parent's emotional state in real time using an emotion estimation function and adjust the avatar's responses based on the emotions. The avatar generation unit, for example, analyzes the parent's facial expressions and voice in real time and identifies the parent's emotional state using emotion estimation technology. This allows the avatar to respond according to the parent's emotions. The avatar generation unit also analyzes text messages and voice messages entered by the parent and calculates an emotion score using emotion estimation technology. This allows the avatar to generate an appropriate response based on the parent's emotions. The avatar generation unit also monitors the parent's emotional state in real time and dynamically adjusts the avatar's responses based on the emotion estimation data. For example, if the parent is feeling stressed, the avatar is set to offer more encouraging words. This allows for a more natural conversation by responding according to the parent's emotional state.

[0055] The avatar generation unit adds a function that allows customization of the avatar's appearance and clothing, making it possible to create an avatar that is easy for children to relate to. The avatar generation unit, for example, provides an interface that allows customization of the avatar's appearance and clothing, allowing children to set up the avatar to suit their preferences. For example, the child can select hairstyle, clothing, accessories, etc. The avatar generation unit also provides an option to reflect the guardian's characteristics when customizing the avatar's appearance. For example, the guardian's hair color and eye color can be reflected in the avatar. The avatar generation unit also adds a function that allows the avatar's appearance and clothing to be changed according to the season or event. For example, the avatar's clothing can be changed to match events such as Christmas or Halloween. This allows creation of an avatar that is easy for children to relate to, making communication with the child smoother.

[0056] The avatar generation unit can generate avatars that correspond to different cultures and languages, enabling international use. For example, the avatar generation unit develops a multilingual generation AI to generate avatars that correspond to different cultures and languages. For example, it provides avatars that correspond to multiple languages, such as English, French, and Chinese. The avatar generation unit also collects cultural background information and trains the generation AI to generate avatars that reflect the characteristics of different cultures. For example, it reflects greetings and etiquette in a particular culture in the avatar. The avatar generation unit also provides a function that allows customization of the appearance and clothing of avatars that correspond to different cultures and languages, taking international use into consideration. For example, it allows users to select traditional clothing and accessories from each country. This allows for international use by supporting different cultures and languages.

[0057] The avatar generation unit uses an emotion estimation function to change the avatar's facial expressions and movements in real time according to the child's emotions. For example, the avatar generation unit analyzes the child's facial expressions and voice in real time and identifies the child's emotional state using emotion estimation technology. This allows the avatar to make expressions and movements that correspond to the child's emotions. The avatar generation unit also monitors the child's emotional state in real time and dynamically adjusts the avatar's facial expressions and movements based on the emotion estimation data. For example, if the child is happy, the avatar will smile. The avatar generation unit will also use the emotion estimation function to develop a system that automatically generates the avatar's facial expressions and movements according to the child's emotions. For example, if the child is sad, the avatar will make a comforting movement. This allows for more natural communication by making expressions and movements that correspond to the child's emotions.

[0058] The conversation response unit can analyze a child's past conversation history and generate conversation patterns optimized for each individual child. For example, the conversation response unit collects a child's past conversation history and analyzes the content using natural language processing technology. This allows the unit to understand the child's interests and generate optimal conversation patterns. The conversation response unit also analyzes what the child has said in the past and extracts frequently used words and phrases. This allows the avatar to imitate the child's speaking style and expressions. The conversation response unit also develops a system that automatically generates conversation topics based on the child's past conversation history in accordance with the child's interests and concerns. For example, it suggests conversations about the child's favorite characters or hobbies. In this way, by analyzing a child's past conversation history, it is possible to generate conversation patterns optimized for each individual child.

[0059] The conversation response unit can periodically collect information about children's interests and concerns in the form of a questionnaire and update the conversation content based on the results. The conversation response unit, for example, builds a system that periodically collects information about children's interests and concerns in the form of a questionnaire and updates the conversation content based on the results. For example, it asks questions about the anime or games that the child likes. The conversation response unit also analyzes the questionnaire results and automatically generates conversation topics based on the children's interests and concerns. For example, it suggests conversations about topics that the child has recently become interested in. The conversation response unit also periodically conducts questionnaires to understand changes in the children's interests and concerns. This allows the avatar to always have a conversation based on the latest information. This allows the avatar to always have a conversation based on the latest information by periodically collecting information about children's interests and concerns and updating the conversation content.

[0060] The conversation response unit can use the emotion estimation function to analyze a child's emotional state in real time and generate a response that corresponds to that emotion. For example, the conversation response unit analyzes a child's facial expressions and voice in real time and identifies the child's emotional state using emotion estimation technology. This allows the avatar to respond appropriately according to the child's emotions. The conversation response unit also monitors the child's emotional state in real time and dynamically adjusts the avatar's response based on the emotion estimation data. For example, if a child is sad, the avatar will say comforting words. The conversation response unit will also use the emotion estimation function to develop a system that automatically generates a response that corresponds to a child's emotions. For example, if a child is excited, the avatar will say empathetic words. This allows for responses that correspond to the child's emotional state, enabling more natural conversations.

[0061] The conversation response unit can add a function that allows a child to select a character or animal avatar that the child likes, thereby making the conversation more enjoyable. The conversation response unit, for example, provides an interface that allows a child to select a character or animal avatar that the child likes, thereby making the conversation more enjoyable. For example, an anime character or a pet avatar can be selected. The conversation response unit also customizes the content of the conversation based on the character or animal avatar selected by the child. For example, the characteristics and lines of the selected character can be incorporated into the conversation. The conversation response unit also provides an option to reflect the parent's opinion when selecting a character or animal avatar that the child likes. For example, the parent can select a character recommended by the parent. This makes the conversation more enjoyable by allowing the child to select a character or animal avatar that the child likes.

[0062] The conversation response unit can automatically record what the child says in diary format so that parents can check it later. The conversation response unit, for example, creates a system that automatically records what the child says in text format and saves it as a diary. For example, the conversation content is organized and saved by date and time. The conversation response unit also provides a dedicated interface so that parents can check the recorded diary later. For example, parents can view the diary on a smartphone or computer. The conversation response unit also records what the child says in audio format so that parents can play it back as audio data. For example, important conversations and memories are saved as audio. In this way, parents can keep track of their child's growth by recording what the child says and allowing them to check it later.

[0063] The conversation response unit can add a function that uses an emotion estimation function to predict topics the child wants to talk about and lead the conversation. For example, the conversation response unit analyzes the child's facial expressions and voice in real time and uses emotion estimation technology to predict topics the child wants to talk about. This allows the avatar to lead the conversation. The conversation response unit also analyzes the child's past conversation history and suggests topics that the child is likely to be interested in based on the emotion estimation data. For example, it leads the conversation on topics the child likes. The conversation response unit also uses the emotion estimation function to predict topics the child wants to talk about and develops a system in which the avatar asks appropriate questions. For example, it provides topics that the child is likely to be interested in. This allows the avatar to predict topics the child wants to talk about and lead the conversation, making for more natural conversations.

[0064] The play suggestion unit can analyze a child's play history and suggest new games based on the games that the child enjoyed most. For example, the play suggestion unit collects a child's past play history and analyzes the content using natural language processing technology. This identifies the games that the child enjoyed most and suggests new games. The play suggestion unit also analyzes patterns of games that the child enjoyed in the past and extracts games that were played frequently or that the child particularly enjoyed. This allows the avatar to suggest new games that suit the child. The play suggestion unit will also develop a system that automatically generates new games based on the child's play history and matches the child's interests. For example, it will suggest games based on the child's favorite themes. This makes it possible to provide more enjoyable games for children by analyzing a child's play history and suggesting new games based on the games that the child enjoyed most.

[0065] The play suggestion unit can add a function to automatically adjust the difficulty of games according to the child's age and developmental stage. The play suggestion unit, for example, builds a system that automatically adjusts the difficulty of games based on the child's age and developmental stage. For example, it suggests games appropriate for the child's age. The play suggestion unit also analyzes the child's developmental stage and adjusts the difficulty of games based on the results. For example, it gradually increases the difficulty of games as the child grows. The play suggestion unit also develops an algorithm that automatically adjusts the difficulty of games according to the child's age and developmental stage. For example, it customizes the content of games to match the child's skill level. This makes it possible to provide appropriate games by automatically adjusting the difficulty of games according to the child's age and developmental stage.

[0066] The play suggestion unit can use the emotion estimation function to analyze a child's emotional state in real time and suggest games that correspond to those emotions. For example, the play suggestion unit analyzes a child's facial expressions and voice in real time and identifies the child's emotional state using emotion estimation technology. This allows the avatar to suggest appropriate games that correspond to the child's emotions. The play suggestion unit also monitors the child's emotional state in real time and dynamically adjusts the avatar's game suggestions based on the emotion estimation data. For example, if a child is tired, it suggests games that will help them relax. The play suggestion unit also uses the emotion estimation function to develop a system that automatically generates games that correspond to a child's emotions. For example, if a child is excited, it suggests games that will help them release energy. This makes it possible to suggest games that correspond to a child's emotional state and provide games that children can enjoy more.

[0067] The play suggestion unit adds a function that allows children to play with other children online, thereby fostering social skills. The play suggestion unit fosters social skills, for example, by providing a platform where children can play with other children online. For example, children can interact with other children through online games or collaborative activities. The play suggestion unit also builds a system for obtaining parental permission when playing together online. For example, it allows parents to check in advance who their child will be playing with. The play suggestion unit also adds a function in which an avatar acts as an intermediary when a child plays with other children online. For example, the avatar can explain the rules of the game or mediate if a dispute arises. This allows children to develop social skills by playing together online with other children.

[0068] The play suggestion unit can add educational content that allows children to learn while playing, thereby fusing learning and play. For example, the play suggestion unit provides educational content that allows children to learn while playing, fusing learning and play. For example, it provides games that allow children to learn arithmetic or science knowledge. The play suggestion unit also allows children to learn naturally by incorporating educational content into play. For example, children can acquire knowledge through puzzle or quiz-style play. The play suggestion unit also builds a system that adjusts the difficulty level of the educational content according to the child's learning progress. For example, it provides questions at a level that is easy for children to understand. In this way, learning and play can be fusing by providing educational content that allows children to learn while playing.

[0069] The play suggestion unit can use the emotion estimation function to predict the play that a child will enjoy most and optimize the play suggestions. For example, the play suggestion unit analyzes the child's facial expressions and voice in real time and predicts the play that a child will enjoy most using emotion estimation technology. This allows the avatar to suggest the most suitable play. The play suggestion unit also analyzes the child's past play history and identifies the play that a child will enjoy based on the emotion estimation data. For example, it suggests new games based on the games that a child particularly enjoyed. The play suggestion unit also uses the emotion estimation function to develop a system that automatically generates the play that a child will enjoy most. For example, if a child is excited, it suggests games that allow them to release their energy. This makes it possible to predict the play that a child will enjoy most and suggest the most suitable games, thereby providing more enjoyable play for children.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The child-focused system can also be equipped with a health management unit that monitors the child's health. For example, while the child is playing, the health management unit can measure the child's heart rate and body temperature and notify the parent if any abnormalities are detected. The health management unit can also manage the child's diet and exercise records and provide advice to support healthy lifestyle habits. For example, if a child is not getting enough exercise, the avatar will suggest games that will encourage exercise. This allows the system to monitor a child's health in real time and support healthy lifestyle habits.

[0072] The child-oriented system can further include a learning management unit that manages a child's learning progress. For example, the learning management unit records what a child has learned and their progress, and reports this to parents and teachers. The learning management unit can also suggest optimal learning content based on a child's learning style and interests. For example, if a child is interested in mathematics, it can suggest mathematics-related games and quizzes. This makes it possible to manage a child's learning progress and support effective learning.

[0073] The child interaction system may further include a creative support unit to foster children's creativity. For example, the creative support unit may support children in activities such as drawing pictures and writing stories. The creative support unit may also digitally save the works created by children and share them with parents and friends. For example, children may upload their drawings to an online gallery and interact with other children. This may foster children's creativity and provide opportunities for self-expression.

[0074] The child interaction system can further include a social interaction section to foster children's social skills. For example, the social interaction section provides opportunities for children to interact with other children online. The social interaction section can also plan online events and workshops that children can participate in and support activities that foster social skills. For example, children can play games with other children or work on projects together. This can foster children's social skills and improve their ability to communicate with others.

[0075] The child interaction system can further include a relaxation section to stabilize the child's emotions. For example, the relaxation section can provide music and images that help the child relax. If the child is feeling stressed, the relaxation section can also suggest games or activities that will help them relax. For example, if the child is tired, the avatar can suggest yoga or meditation to help them relax. This can stabilize the child's emotions and provide an environment where they can relax.

[0076] The child-focused system can also use its emotion estimation function to provide learning content based on a child's emotions. For example, if a child is excited, it can suggest active learning content that will help them release their energy. On the other hand, if a child is calm, it can provide quiet learning content to improve their concentration. For example, if a child is relaxing, it can suggest reading or puzzles. This allows it to provide learning content that suits a child's emotions and support effective learning.

[0077] The child interaction system can also use its emotion estimation function to suggest games based on the child's emotions. For example, if a child is sad, it can suggest fun games to brighten their mood. Also, if a child is angry, it can provide games that will help them release stress. For example, if a child is angry, the avatar can suggest playing sports together. This makes it possible to suggest games that correspond to the child's emotions and support emotional control.

[0078] The child interaction system can also use an emotion estimation function to communicate based on the child's emotions. For example, if a child feels lonely, the avatar will speak kind words. Also, if a child is happy, the avatar can use empathetic words. For example, when a child talks about something that makes them happy, the avatar will respond with "That's wonderful!" This allows communication to be tailored to the child's emotions, building a relationship of trust with the child.

[0079] The child-focused system can also use its emotion estimation function to suggest relaxation methods based on the child's emotions. For example, if a child is feeling stressed, it can provide relaxing music or videos. If a child is feeling anxious, it can also suggest meditation or deep breathing techniques to give a sense of security. For example, if a child is anxious, the avatar can suggest taking deep breaths with the child. This allows the system to suggest relaxation methods that correspond to the child's emotions and support emotional stability.

[0080] The child-focused system can also use its emotion estimation function to adjust the child's learning pace based on their emotions. For example, if a child is tired, the learning pace can be slowed down. On the other hand, if a child is concentrating, the learning pace can be increased. For example, when a child is concentrating, the avatar can suggest the next task. This allows the learning pace to be adjusted according to the child's emotions, supporting effective learning.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The avatar generation unit generates a virtual guardian avatar based on the guardian's personality assessment results. For example, the guardian takes a personality assessment test, and the avatar's personality is determined based on the results. The generation AI analyzes the personality assessment results and generates an avatar that closely resembles the guardian's personality. The input to the generation AI is a prompt containing instructions on what the guardian wants the generation AI to do, and the generation AI generates an avatar based on that prompt. Step 2: The conversation response unit uses the generated avatar to act as a conversation partner for the child. For example, if a child asks, "What did you do at school today?", the generation AI will respond with something like, "What kind of lessons did you have today? Was it fun?" The input to the generation AI is what the child said, and the generation AI generates an appropriate response based on that content. Step 3: The play suggestion unit uses an avatar to act as a playmate for the child. For example, if a child says, "Let's draw a picture together," the generation AI responds by saying, "What kind of picture do you want to draw? Let's think about it together," supporting the child in the activity of drawing a picture together. The input to the generation AI is the content of the play proposed by the child, and the generation AI suggests appropriate games based on that content.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, 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 robot 414 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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. an avatar generation unit that generates a virtual guardian avatar based on the result of the guardian's personality diagnosis; a conversation response unit that uses the generated avatar to talk to the child; a play suggestion unit that uses the avatar to play with the child. A system characterized by:

2. The avatar generation unit The avatar is generated based on the more accurate personality assessment results by analyzing the parent's past SNS posts and message history.

2. The system of claim 1.

3. The avatar generation unit Analyzing the voice data and adapting the avatar's voice responses to mimic the parent's tone of voice and speaking style.

2. The system of claim 1.

4. The avatar generation unit Analyzing the parent's emotional state in real time and adjusting the avatar's response based on the emotion.

2. The system of claim 1.

5. The avatar generation unit Adding a function to customize the appearance and clothing of the avatar, and creating an avatar that is friendly to the child.

2. The system of claim 1.

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