System

Abstract

An object of a system according to an embodiment is to utilize AI in education of kindergarten children and support teachers.SOLUTION: A system according to an embodiment includes a reading unit and a question answering unit. The reading part reads the story to the kindergarten child. The question answering part answers a question from the kindergarten 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, there was a problem in the field of education where teachers' knowledge could not keep up, and educational support using AI was not being provided sufficiently.

[0005] The system of the embodiment aims to utilize AI in the education of kindergarten children and support teachers. [Means for solving the problem]

[0006] The system according to the embodiment includes a storytelling unit and a question and answering unit. The storytelling unit reads a story to the kindergarten children. The question and answering unit answers questions from the kindergarten children. [Effects of the Invention]

[0007] The system of the embodiment can utilize AI in educating kindergarten children and support teachers. [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 educational support system according to the embodiment of the present invention is a system that uses a generative AI to read stories and answer questions to kindergarten children. This enables the educational support system to utilize the generative AI to support children's learning and reduce the burden on teachers.

[0029] An education support system according to an embodiment includes a generation AI, a storytelling unit, and a question-and-answering unit. The generation AI includes a storytelling unit that reads stories to kindergarteners and a question-and-answering unit that answers questions from the kindergarteners. In the storytelling unit, for example, the generation AI reads stories to kindergarteners. For example, when the generation AI is instructed to "read a fairy tale," the generation AI selects an appropriate story and reads it to the kindergarteners. In the storytelling unit, the generation AI can read a story in a natural voice using speech synthesis technology. For example, the generation AI generates a story based on instructions such as the story's title and genre, and reads it to the kindergarteners. In the question-and-answering unit, for example, the generation AI provides an appropriate answer to a question from a kindergartener. For example, if a kindergartener asks, "Why is the sky blue?", the generation AI analyzes the question and provides an easy-to-understand scientific explanation. In the question-and-answering unit, the generation AI generates an answer based on the content of the question from the kindergartener. For example, the generation AI uses natural language generation technology or a machine learning algorithm to provide an appropriate answer to the kindergartener's question. As a result, the education support system according to the embodiment can use the generation AI to read stories to kindergarteners and provide question-and-answer sessions. For example, through storytelling and question-and-answer sessions by the generation AI, kindergarteners can learn in a fun way, and teachers can reduce the burden of preparing and conducting lessons.

[0030] The reading section can analyze the children's reactions in real time and dynamically adjust the progress of the story. For example, the generation AI in the reading section analyzes the children's facial expressions and tone of voice and provides detailed explanations of parts that show interest. For example, it repeatedly emphasizes scenes where the children smile. The generation AI in the reading section also analyzes the children's level of concentration and changes the tempo of the story if their concentration is broken. For example, if the children seem bored, it will speed up the story. The generation AI in the reading section also dynamically changes the content of the story based on the children's reactions. For example, if the children seem scared, it will change the tone of the story to a gentler one. This allows the progress of the story to be adjusted according to the children's reactions, enabling more effective learning.

[0031] The reading section can generate personalized stories based on each child's learning progress and interests. For example, the generation AI analyzes a child's past learning history and selects stories with themes that are likely to interest them. For example, a child who likes animals will be provided with stories featuring animals. The generation AI also generates stories with adjusted difficulty according to the child's learning progress. For example, stories using simple language are provided to match the child's stage of language development. The generation AI also generates stories that incorporate elements that will attract the child's interest. For example, featuring a child's favorite character can increase their interest in the story. This improves learning effectiveness by providing stories that match the child's learning progress and interests.

[0032] The reading section not only reads stories but also generates visual animations, engaging children both visually and aurally. For example, in the reading section, the generation AI generates animations that match the content of the story, providing visual stimulation to children. For example, it displays animations in which characters move according to the scenes in the story. In addition, in the reading section, the generation AI displays changes in the background and characters in real time as the story progresses. For example, it changes the background every time the setting of the story changes. In addition, in the reading section, the generation AI adds special animation effects to emphasize important scenes in the story. For example, it displays an animation of fireworks going off in the climax scene. This engages children both visually and aurally, improving learning effectiveness.

[0033] The reading club can promote multicultural education by reading stories from different cultures and regions. For example, in the reading club, the generating AI collects folk tales and legends from around the world and introduces stories from different cultures to kindergarteners. For example, it might read Japanese folk tales or African folk tales. The reading club also provides multilingual education to kindergarteners by having the generating AI read stories in different languages. For example, it might read stories in English and Spanish. The reading club also generates stories that include the customs and values ​​of different cultures, encouraging multicultural understanding among kindergarteners. For example, it provides stories themed around festivals and events from different cultures. This promotes multicultural education through stories from different cultures and regions.

[0034] The question answering section suggests related experiments and experiences in response to children's questions, allowing them to deepen their understanding by actually trying them out. For example, in the question answering section, the generation AI will suggest simple experiments in response to children's questions. For example, in response to the question, "Why is the sky blue?", it will suggest an experiment that shows the dispersion of light. In addition, in the question answering section, the generation AI will suggest experiential learning activities in response to children's questions. For example, in response to the question, "Why do plants grow?", it will suggest an activity such as planting seeds and observing them. In addition, in the question answering section, the generation AI will introduce experiment kits that can actually be tried out in response to children's questions. For example, in response to the question, "Why does the wind blow?", it will suggest an experiment kit that can create wind. This allows children to deepen their understanding by actually trying it out.

[0035] The question answering unit can analyze the child's past question history and provide answers that are appropriate for each child's learning progress. For example, the generation AI in the question answering unit analyzes the child's past question history and provides answers that are appropriate for the child's learning progress. For example, starting with basic questions and gradually increasing the difficulty level. The generation AI in the question answering unit also suggests related topics based on the child's question history. For example, in response to the question "Why is the sky blue?", the generation AI suggests other questions related to weather and climate. The generation AI in the question answering unit also takes into account the child's learning progress and provides answers at an appropriate level. For example, it explains scientific explanations in simple terms. This improves learning effectiveness by providing answers that are appropriate for the child's learning progress.

[0036] The question answering unit collects feedback from other children and teachers in response to children's questions, enabling it to provide more multifaceted answers. For example, in the question answering unit, the generation AI collects feedback from other children in response to children's questions and reflects it in the answer. For example, in response to the question, "Why is the sky blue?", the opinions of other children are incorporated. The question answering unit also collects feedback from teachers in response to children's questions and reflects this in the answer. For example, in response to the question, "Why do plants grow?", the teacher's explanation is added. In addition, in the question answering unit, the generation AI integrates the opinions of other children and teachers in response to children's questions and provides more multifaceted answers. For example, in response to the question, "Why does the wind blow?", explanations are provided from multiple perspectives. In this way, by collecting feedback from other children and teachers, it is possible to provide more multifaceted answers.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] To foster children's creativity, the educational support system can also be equipped with an interactive function that allows children to create parts of the story themselves. For example, the generation AI could ask the children, "What do you think will happen next?" midway through the story and continue the story based on the children's answers. Visual elements could also be added by incorporating pictures drawn by the children into the story. Furthermore, a function could be provided that allows children to share the stories they have created with other children and have them evaluate each other's stories. This could foster children's creativity and communication skills.

[0039] The educational support system can also provide cooperative learning activities to foster children's social skills. For example, a generative AI could present multiple children with a problem to solve together, leading to a collaborative story creation activity. It could also provide quizzes in which children pair up to answer questions. Furthermore, it could incorporate activities that improve communication skills by having children give each other feedback. This could provide children with opportunities to learn the importance of social skills and cooperation.

[0040] The educational support system can also be equipped with a function to create individual learning plans based on each child's learning history. For example, the generative AI can analyze a child's past learning data to identify their strengths and weaknesses. Based on this, it can suggest the optimal learning content and pace for each child. It can also provide review and additional assignments at the appropriate time depending on the child's learning progress. It can also report the child's learning status to parents and teachers, providing information to enable them to provide appropriate support. This makes it possible to provide effective educational support that meets individual learning needs.

[0041] The educational support system can also suggest art and craft activities to further stimulate children's creativity. For example, the generative AI can provide drawing assignments and craft ideas based on children's interests and learning content. It can also have a function to digitize the work created by children and share it with other children and parents. It can also provide opportunities for children to give presentations to explain their work, fostering their expressiveness and confidence. This can enhance children's creativity and ability to express themselves.

[0042] The educational support system can also provide virtual field trips to stimulate children's interest in natural science. For example, the generative AI could take children to a virtual zoo or space expedition, providing real-time commentary. It could also provide detailed information about places and creatures that interest children. Furthermore, it could provide an interactive learning experience by allowing children to ask questions during the virtual field trip. This could deepen children's interest in natural science and provide an experience similar to a real field trip.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: In the reading section, the generation AI reads a story to the children. For example, if the generation AI is instructed to "read a fairy tale," it will select an appropriate story and read it to the children. In the reading section, the generation AI can also use speech synthesis technology to read the story in a natural voice. For example, the generation AI can generate a story based on instructions such as the story's title and genre, and then read it to the children. Step 2: In the question-answering section, the generation AI provides appropriate answers to questions from children. For example, if a child asks, "Why is the sky blue?", the generation AI analyzes the question and provides an easy-to-understand scientific explanation. In the question-answering section, the generation AI generates answers based on the questions asked by the children. For example, the generation AI uses natural language generation technology and machine learning algorithms to provide appropriate answers to children's questions.

[0045] (Example 2) The educational support system according to the embodiment of the present invention is a system that uses a generative AI to read stories and answer questions to kindergarten children. This enables the educational support system to utilize the generative AI to support children's learning and reduce the burden on teachers.

[0046] An education support system according to an embodiment includes a generation AI, a storytelling unit, and a question-and-answering unit. The generation AI includes a storytelling unit that reads stories to kindergarteners and a question-and-answering unit that answers questions from the kindergarteners. In the storytelling unit, for example, the generation AI reads stories to kindergarteners. For example, when the generation AI is instructed to "read a fairy tale," the generation AI selects an appropriate story and reads it to the kindergarteners. In the storytelling unit, the generation AI can read a story in a natural voice using speech synthesis technology. For example, the generation AI generates a story based on instructions such as the story's title and genre, and reads it to the kindergarteners. In the question-and-answering unit, for example, the generation AI provides an appropriate answer to a question from a kindergartener. For example, if a kindergartener asks, "Why is the sky blue?", the generation AI analyzes the question and provides an easy-to-understand scientific explanation. In the question-and-answering unit, the generation AI generates an answer based on the content of the question from the kindergartener. For example, the generation AI uses natural language generation technology or a machine learning algorithm to provide an appropriate answer to the kindergartener's question. As a result, the education support system according to the embodiment can use the generation AI to read stories to kindergarteners and provide question-and-answer sessions. For example, through storytelling and question-and-answer sessions by the generation AI, kindergarteners can learn in a fun way, and teachers can reduce the burden of preparing and conducting lessons.

[0047] The reading section can analyze the children's reactions in real time and dynamically adjust the progress of the story. For example, the generation AI in the reading section analyzes the children's facial expressions and tone of voice and provides detailed explanations of parts that show interest. For example, it repeatedly emphasizes scenes where the children smile. The generation AI in the reading section also analyzes the children's level of concentration and changes the tempo of the story if their concentration is broken. For example, if the children seem bored, it will speed up the story. The generation AI in the reading section also dynamically changes the content of the story based on the children's reactions. For example, if the children seem scared, it will change the tone of the story to a gentler one. This allows the progress of the story to be adjusted according to the children's reactions, enabling more effective learning.

[0048] The reading section can generate personalized stories based on each child's learning progress and interests. For example, the generation AI analyzes a child's past learning history and selects stories with themes that are likely to interest them. For example, a child who likes animals will be provided with stories featuring animals. The generation AI also generates stories with adjusted difficulty according to the child's learning progress. For example, stories using simple language are provided to match the child's stage of language development. The generation AI also generates stories that incorporate elements that will attract the child's interest. For example, featuring a child's favorite character can increase their interest in the story. This improves learning effectiveness by providing stories that match the child's learning progress and interests.

[0049] The reading unit can use the emotion estimation function to adjust the tone and content of the story according to the emotional state of the child. For example, in the reading unit, the generation AI analyzes the emotional state of the child and selects a relaxing story. For example, if the child is tired, it will provide a story with a calm tone. In addition, in the reading unit, the generation AI changes the content of the story according to the child's emotions. For example, if the child is sad, it will provide a story with an encouraging message. In addition, in the reading unit, the generation AI adjusts the progress of the story based on the child's emotional state. For example, if the child is excited, it will slow down the tempo of the story to calm them down. This allows for more effective learning by adjusting the tone and content of the story according to the child's emotional state.

[0050] The reading section not only reads stories but also generates visual animations, engaging children both visually and aurally. For example, in the reading section, the generation AI generates animations that match the content of the story, providing visual stimulation to children. For example, it displays animations in which characters move according to the scenes in the story. In addition, in the reading section, the generation AI displays changes in the background and characters in real time as the story progresses. For example, it changes the background every time the setting of the story changes. In addition, in the reading section, the generation AI adds special animation effects to emphasize important scenes in the story. For example, it displays an animation of fireworks going off in the climax scene. This engages children both visually and aurally, improving learning effectiveness.

[0051] The reading club can promote multicultural education by reading stories from different cultures and regions. For example, in the reading club, the generating AI collects folk tales and legends from around the world and introduces stories from different cultures to kindergarteners. For example, it might read Japanese folk tales or African folk tales. The reading club also provides multilingual education to kindergarteners by having the generating AI read stories in different languages. For example, it might read stories in English and Spanish. The reading club also generates stories that include the customs and values ​​of different cultures, encouraging multicultural understanding among kindergarteners. For example, it provides stories themed around festivals and events from different cultures. This promotes multicultural education through stories from different cultures and regions.

[0052] The storytelling unit can use the emotion estimation function to identify the elements of a story that children enjoy most and generate new stories that include those elements. For example, the generation AI in the storytelling unit analyzes children's emotional responses and identifies the characters and scenes that they enjoy most. For example, it may include characters that make children smile in new stories. The generation AI in the storytelling unit also generates new stories that combine elements of popular stories based on children's emotional data. For example, it may provide stories that include elements of adventure and friendship. The generation AI in the storytelling unit also monitors children's emotional responses in real time and generates stories that emphasize the elements that children enjoy. For example, it may repeatedly emphasize scenes that make children excited. This improves learning effectiveness by generating new stories that include elements that children enjoy.

[0053] The question answering section suggests related experiments and experiences in response to children's questions, allowing them to deepen their understanding by actually trying them out. For example, in the question answering section, the generation AI will suggest simple experiments in response to children's questions. For example, in response to the question, "Why is the sky blue?", it will suggest an experiment that shows the dispersion of light. In addition, in the question answering section, the generation AI will suggest experiential learning activities in response to children's questions. For example, in response to the question, "Why do plants grow?", it will suggest an activity such as planting seeds and observing them. In addition, in the question answering section, the generation AI will introduce experiment kits that can actually be tried out in response to children's questions. For example, in response to the question, "Why does the wind blow?", it will suggest an experiment kit that can create wind. This allows children to deepen their understanding by actually trying it out.

[0054] The question answering unit can analyze the child's past question history and provide answers that are appropriate for each child's learning progress. For example, the generation AI in the question answering unit analyzes the child's past question history and provides answers that are appropriate for the child's learning progress. For example, starting with basic questions and gradually increasing the difficulty level. The generation AI in the question answering unit also suggests related topics based on the child's question history. For example, in response to the question "Why is the sky blue?", the generation AI suggests other questions related to weather and climate. The generation AI in the question answering unit also takes into account the child's learning progress and provides answers at an appropriate level. For example, it explains scientific explanations in simple terms. This improves learning effectiveness by providing answers that are appropriate for the child's learning progress.

[0055] The question answering unit uses the emotion estimation function to adjust the tone and difficulty of answers to children's questions and provide them in an easy-to-understand format. For example, the generation AI in the question answering unit analyzes the emotional state of a child and provides answers in a relaxed tone. For example, if a child is nervous, it will explain in a gentle tone. The generation AI in the question answering unit also adjusts the difficulty of the answers according to the child's emotions. For example, if a child is confused, it will explain in simple terms. The generation AI in the question answering unit also adjusts the tone of the answers based on the child's emotional response. For example, if a child is excited, it will answer in a cheerful tone. In this way, the tone and difficulty of the answers can be adjusted according to the child's emotional state, making it possible to provide answers in an easy-to-understand format.

[0056] The question answering unit collects feedback from other children and teachers in response to children's questions, enabling it to provide more multifaceted answers. For example, in the question answering unit, the generation AI collects feedback from other children in response to children's questions and reflects it in the answer. For example, in response to the question, "Why is the sky blue?", the opinions of other children are incorporated. The question answering unit also collects feedback from teachers in response to children's questions and reflects this in the answer. For example, in response to the question, "Why do plants grow?", the teacher's explanation is added. In addition, in the question answering unit, the generation AI integrates the opinions of other children and teachers in response to children's questions and provides more multifaceted answers. For example, in response to the question, "Why does the wind blow?", explanations are provided from multiple perspectives. In this way, by collecting feedback from other children and teachers, it is possible to provide more multifaceted answers.

[0057] The question-answering unit can use the emotion estimation function to identify topics that interest children most and suggest additional information or activities related to those topics. In the question-answering unit, for example, the generation AI analyzes children's emotional responses and identifies the topics that interest them most. For example, it provides information related to topics that make children smile. In addition, the generation AI in the question-answering unit suggests activities related to topics of interest based on children's emotional data. For example, it suggests experiments or observation activities based on themes that children show interest in. In addition, the generation AI in the question-answering unit monitors children's emotional responses in real time and provides additional information related to topics of interest. For example, it provides detailed explanations about topics that children are excited about. This improves learning effectiveness by suggesting information and activities related to topics that interest children most.

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

[0059] To foster children's creativity, the educational support system can also be equipped with an interactive function that allows children to create parts of the story themselves. For example, the generation AI could ask the children, "What do you think will happen next?" midway through the story and continue the story based on the children's answers. Visual elements could also be added by incorporating pictures drawn by the children into the story. Furthermore, a function could be provided that allows children to share the stories they have created with other children and have them evaluate each other's stories. This could foster children's creativity and communication skills.

[0060] The educational support system can also be equipped with a function to monitor the health of children and suggest breaks at appropriate times. For example, the generative AI can analyze children's facial expressions and movements to detect signs of fatigue or stress. If it determines that a child is tired, the system can suggest relaxing music or a short break. It can also provide instructions for appropriate exercise and stretching depending on the child's health condition. This can support effective learning while maintaining children's health.

[0061] The educational support system can also provide cooperative learning activities to foster children's social skills. For example, a generative AI could present multiple children with a problem to solve together, leading to a collaborative story creation activity. It could also provide quizzes in which children pair up to answer questions. Furthermore, it could incorporate activities that improve communication skills by having children give each other feedback. This could provide children with opportunities to learn the importance of social skills and cooperation.

[0062] The educational support system can also use the child's emotion estimation function to implement a reward system based on the child's learning progress. For example, the generative AI can analyze the child's emotional state and award badges and points according to the child's learning achievement. When a child achieves a specific goal, the system can display praise and animations to motivate the child. It can also provide encouraging messages when the child is working on a difficult task. This helps to maintain the child's motivation to learn and gives them a sense of accomplishment.

[0063] The educational support system can also be equipped with a function to create individual learning plans based on each child's learning history. For example, the generative AI can analyze a child's past learning data to identify their strengths and weaknesses. Based on this, it can suggest the optimal learning content and pace for each child. It can also provide review and additional assignments at the appropriate time depending on the child's learning progress. It can also report the child's learning status to parents and teachers, providing information to enable them to provide appropriate support. This makes it possible to provide effective educational support that meets individual learning needs.

[0064] The educational support system can also use the child's emotion estimation function to suggest relaxation activities for stress management. For example, the generative AI can analyze a child's emotional state and, if they are feeling stressed, instruct them on deep breathing or meditation. It can also calm them by playing relaxing music or sounds of nature. It can also suggest activities or games that the child likes to play to help them change their mood. This can reduce stress for children and provide a healthy learning environment.

[0065] The educational support system can also suggest art and craft activities to further stimulate children's creativity. For example, the generative AI can provide drawing assignments and craft ideas based on children's interests and learning content. It can also have a function to digitize the work created by children and share it with other children and parents. It can also provide opportunities for children to give presentations to explain their work, fostering their expressiveness and confidence. This can enhance children's creativity and ability to express themselves.

[0066] The educational support system can also use the child's emotion estimation function to provide feedback according to their learning progress. For example, the generative AI can analyze a child's emotional state and suggest specific advice and areas for improvement according to their learning progress. If a child is working on a difficult task, it can provide encouraging messages to maintain their motivation. If a child succeeds, it can also provide praise and feedback that makes them feel a sense of accomplishment. This can increase children's motivation to learn and support effective learning.

[0067] The educational support system can also provide virtual field trips to stimulate children's interest in natural science. For example, the generative AI could take children to a virtual zoo or space expedition, providing real-time commentary. It could also provide detailed information about places and creatures that interest children. Furthermore, it could provide an interactive learning experience by allowing children to ask questions during the virtual field trip. This could deepen children's interest in natural science and provide an experience similar to a real field trip.

[0068] The educational support system can also use the child's emotion estimation function to provide a customized learning plan based on the child's learning progress. For example, the generative AI can analyze the child's emotional state and suggest the optimal learning content and pace based on the child's learning progress. If a child is working on a difficult task, the system can adjust the difficulty level or provide supplementary learning materials. If a child is successful, the system can also provide specific advice on how to move on to the next step. This increases children's motivation to learn and enables effective educational support that meets their individual learning needs.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: In the reading section, the generation AI reads a story to the children. For example, if the generation AI is instructed to "read a fairy tale," it will select an appropriate story and read it to the children. In the reading section, the generation AI can also use speech synthesis technology to read the story in a natural voice. For example, the generation AI can generate a story based on instructions such as the story's title and genre, and then read it to the children. Step 2: In the question-answering section, the generation AI provides appropriate answers to questions from children. For example, if a child asks, "Why is the sky blue?", the generation AI analyzes the question and provides an easy-to-understand scientific explanation. In the question-answering section, the generation AI generates answers based on the questions asked by the children. For example, the generation AI uses natural language generation technology and machine learning algorithms to provide appropriate answers to children's questions.

[0071] 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.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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 AI 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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 AI 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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 AI 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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]

[0138] 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. Equipped with generative AI, The generated AI is A reading club that reads stories to kindergarteners, A question answering unit that answers questions from children A system characterized by:

2. The reading section The reactions of the children are analyzed in real time, and the progress of the story is dynamically adjusted.

2. The system of claim 1.

3. The reading section Generate personalized stories based on the individual learning progress and interests of the children 2. The system of claim 1.

4. The reading section Adjust the tone and content of the story according to the child's emotional state 2. The system of claim 1.

5. The reading section In addition to reading the story, it also generates visual animations to engage the children both visually and aurally.

2. The system of claim 1.

6. The reading section Promote multicultural education by reading these stories from different cultures and regions 2. The system of claim 1.

7. The reading section Identifying the elements of the story that the child enjoys most and generating a new story that includes those elements 2. The system of claim 1.

8. The question answering unit In response to the questions of the children, suggest related experiments and experiences and deepen their understanding by actually trying them out.

2. The system of claim 1.

Images